{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "

Contents

\n", "\n", "[Overview](#Overview) \n", "[Working environment](#WE)\n", "- [Python setup](#pyset)\n", "\n", "[Making maps out of flower photos](#digitize)\n", " - [Getting images out of Doug's raster pipeline](#dougOut)\n", " - [Digitizing petal outlines](#skimmage)\n", " \n", " - [Calling center, edge and throat zones](#callZones)\n", " - [Side note: Geojson files equal freedom!](#geoJFree)\n", " - [Manual curation of petal maps](#manCor) \n", "\n", "[Measuring raw data](#zones)\n", " - [Traits overview](#TraitOverview)\n", " \n", "[Notes on updating and maintaining the package](#updatePyPi)\n", " " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "

Overview

\n", "\n", "In general, converting photographic images to vector images in order to answer biological questions requires at least three steps: \n", "\n", "1. [Making maps](#digitize): process the image into a schematic of the image that recognizes and retains the parts of the photo that are informational.\n", "\n", "2. [Measuring raw data from from this map](#zones): calling zones, counting spots, etc.\n", "\n", "3. Statistical analyses on these data\n", "\n", "All three of these steps require a lot of decision-making and coding. What follows is a review of the path we've taken to get to statistical analysis for our images. \n", "\n", "If the progamming code is confusing to read at any point, skip it. Read the human-language stuff. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "

Working environment

\n", "\n", "Due to the history of the project, the first bit of code below is for matlab/octave. After that, we'll use mostly BASH and python(>3.6). The custom modules used here, contained in the makeFlowerPolygons package, are written for unix-like environments, so windows won't work at this point, but MacOS and linux systems should be compatible.\n", "\n", "To start, clone the github repo at . If you don't already have git on your computer, I would recommend it for this project.\n", "\n", "`git clone https://github.com/danchurch/mimulusSpeckling.git`\n", "\n", "I will try to remember to make all paths used below relative to the main directory of the is repo, which is called \"mimulusSpeckling\".\n", "\n", "After this install the custom modules we use to phenotype our flowers, in the package \"makeFlowerPolygons\". It is available in pypi, install it using pip, something like:\n", "\n", "`pip3 install makeFlowerPolygons-dcthom`\n", "\n", "You may want superuser privileges for that:\n", "\n", "`sudo pip3 install makeFlowerPolygons-dcthom`\n", "\n", "You may find that you are missing some dependencies for the modules, you'll have to install them as they come up. Apologies, in the future if I have time I'll define the requirements in the setup files." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "You may find it useful to make a shortcut to your repo directory. I put this in my `~/.bashrc` (linux) or `~/.bash_profile` for mac OS." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "alias speck=\"cd /home/daniel/Documents/cooley_lab/mimulusSpeckling\"" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "

Python setup

\n", "\n", "We do a lot of this work in python. Here is what you need when in the python environment:" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import os, pickle, sys, pathlib\n", "## have to do makeFlower import before our matplotlib import, \n", "## because makeFlowerPolygons sets the proper backend for matplotlib:\n", "from makeFlowerPolygons import flowerPetal, geojsonIO\n", "## but if we want to use tkagg inside the notebook, we have to do this?:\n", "%matplotlib notebook\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import shapely.geometry as sg\n", "import shapely.affinity as sa\n", "import matplotlib.image as mpimg\n", "from skimage import measure\n", "from descartes import PolygonPatch\n", "from scipy.spatial import distance" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Make sure that you're in the home directory of the repo. This will vary depending on where you put it, of course, but mine looks like this:" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "os.chdir(\"/home/daniel/Documents/cooley_lab/mimulusSpeckling\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "

Making maps out of flower photos

" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "

Getting images out of Doug's raster pipeline

\n", "\n", "Flower photos were broken into their various petals of interest, and color differences were categorized, by forcing all pixels into three color poles using kmeans clustering. Doug coded this pipeline, and Melia implemented it, also doing manual corrections where needed. A \"bottom\" petal, for instance looks like this, after Doug's pipeline (converted to grayscale):" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "## stay in python for a sec, before we go to matlab/octave\n", "exFlowerImage=\"dougRaster/Rotated_and_Cropped/plate1/P431F1.JPG\"\n", "\n", "plt.rcParams['figure.figsize'] = [15, 5]\n", "\n", "img=mpimg.imread(exFlowerImage)\n", "plt.imshow(img)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In matlab or octave, we can see how Doug's pipeline sees these petals:" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [], "source": [ "octave --no-gui" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "scrolled": false }, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "load(\"dougRaster/Rotated_and_Cropped/plate1/P431F1.mat\")\n", "pkg load image\n", "aa = mat2gray(Petals.Clusters.right);\n", "imshow(aa) %% that works." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We want to make polygons of spots and their petals. We can begin by \"peeling\" these two apart into separate, solid-black images:" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "scrolled": false }, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "bb = aa < 1;\n", "imshow(bb);\n" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "scrolled": false }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "cc = aa == 0;\n", "imshow(cc);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can generalize the above process, to get all of the available photos from Doug's efforts into a form ready to digitized. All of our image processing was originally done in chunks, named by the plates in which flower DNA was placed and thus data was outputted from the sequencers. This is code for plate 4 for instance: " ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "wd='/home/daniel/Documents/cooley_lab/mimulusSpeckling/make_polygons/polygons/plate4'\n", "\n", "dougRasterDir='/home/daniel/Documents/cooley_lab/mimulusSpeckling/dougRaster/Rotated_and_Cropped/plate4'\n", "\n", "cd(dougRasterDir)\n", "\n", "files = dir('P*.mat');\n", "\n", "for file = files';\n", " im = file.name;\n", " imName = regexprep(im,'\\.mat', ''); \n", " %% go get our file, come back\n", " cd(dougRasterDir);\n", " rast=load(im);\n", " cd(wd);\n", " %% make a spot for our image, go to it:\n", " mkdir(imName);\n", " cd (imName);\n", " %% get our petal names (left, right mid)\n", " petNames = fieldnames(rast.Petals.Clusters);\n", " %% split images into petal and spot, export, for each of the three petals:\n", " for i = 1:length(petNames);\n", " pet = rast.Petals.(petNames{i}).data; %petal at hand\n", " mkdir(petNames{i});\n", " cd(petNames{i});\n", " %% for octave\n", " fileNamePetal = [imName \"_\" char(petNames(i)) \"_\" 'melted.csv'];\n", " %% for matlab, this may work better?\n", " %%fileNamePetal = imName + \"_\" + petNames(i) + \"_\" + 'melted.csv';\n", " csvwrite(fileNamePetal,pet);\n", " cd ..;\n", " end;\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "(This is brittle code, if the file architectures or Doug's matlab structures changes, it will break. But for the moment it works. And it gets us out of matlab real quick-like.)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "It is important to note what is going on here, because this may be one of the more universal entry points in to the pipeline. At this step, we are going into the matlab objects that are created by Doug's color simplification process. These are stored as matlab-type \".mat\" files, in the following directory of our repo, in doug's folder, by plate. The jpg images in this folder are also stored here:" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\u001b[0m\u001b[01;34mplate1\u001b[0m \u001b[01;34mplate2\u001b[0m \u001b[01;34mplate3\u001b[0m \u001b[01;34mplate3Additional\u001b[0m \u001b[01;34mplate4\u001b[0m \u001b[01;34mplate4Additional\u001b[0m\n" ] } ], "source": [ "ls dougRaster/Rotated_and_Cropped" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "P773F1.JPG P788F2.JPG P814F3.JPG P828F1.JPG P842F1.JPG P861F1.JPG\n", "P773F1.mat P788F2.mat P814F3.mat P828F1.mat P842F1.mat P861F1.mat\n", "P773F2.JPG P789F1.JPG P815F2.JPG P828F2.JPG P842F2.JPG P862F1.JPG\n", "P773F2.mat P789F1.mat P815F2.mat P828F2.mat P842F2.mat P862F2.JPG\n", "P774F2.JPG P789F2.JPG P815F3.JPG P829F1.JPG P843F1.JPG P863F1.JPG\n", "P774F2.mat P789F2.mat P815F3.mat P829F1.mat P843F1.mat P863F1.mat\n", "P774F3.JPG P791F1.JPG P816F1.JPG P829F2.JPG P843F2.JPG P863F2.JPG\n", "P774F3.mat P791F1.mat P816F1.mat P829F2.mat P843F2.mat P863F2.mat\n", "P775F2.JPG P791F2.JPG P816F2.JPG P830F1.JPG P844F1.JPG P864F1.JPG\n", "P775F2.mat P791F2.mat P816F2.mat P830F1.mat P844F1.mat P864F2.JPG\n", "P775F3.JPG P792F4.JPG P817F1.JPG P830F2.JPG P844F2.JPG P865F1.JPG\n", "P775F3.mat P792F4.mat P817F1.mat P830F2.mat P844F2.mat P865F1.mat\n", "P776F1.JPG P793F1.JPG P817F2.JPG P831F1.JPG P845F1.JPG P865F2.JPG\n", "P776F1.mat P793F1.mat P817F2.mat P831F1.mat P845F1.mat P865F2.mat\n", "P776F2.JPG P793F2.JPG P818F1.JPG P831F2.JPG P845F2.JPG P866F1.JPG\n", "P776F2.mat P793F2.mat P818F1.mat P831F2.mat P845F2.mat P866F1.mat\n", "P777F3.JPG P804F1.JPG P818F2.JPG P832F1.JPG P846F1.JPG P866F2.JPG\n", "P777F3.mat P804F1.mat P818F2.mat P832F1.mat P846F1.mat P866F2.mat\n", "P777F4.JPG P804F2.JPG P819F1.JPG P832F2.JPG P846F2.JPG P867F1.JPG\n", "P777F4.mat P804F2.mat P819F1.mat P832F2.mat P846F2.mat P867F1.mat\n", "P778F1.JPG P805F1.JPG P819F2.JPG P833F1.JPG P847F1.JPG P867F2.JPG\n", "P778F1.mat P805F1.mat P819F2.mat P833F1.mat P847F1.mat P867F2.mat\n", "P778F2.JPG P805F3.JPG P820F1.JPG P833F2.JPG P847F2.JPG P868F1.JPG\n", "P778F2.mat P805F3.mat P820F1.mat P833F2.mat P847F2.mat P868F1.mat\n", "P779F2.JPG P806F1.JPG P820F2.JPG P834F1.JPG P848F1.JPG P868F2.JPG\n", "P779F2.mat P806F1.mat P820F2.mat P834F1.mat P848F1.mat P868F2.mat\n", "P779F3.JPG P806F2.JPG P821F1.JPG P834F2.JPG P848F2.JPG P869F1.JPG\n", "P779F3.mat P806F2.mat P821F1.mat P834F2.mat P848F2.mat P869F1.mat\n", "P782F1.JPG P807F1.JPG P821F2.JPG P835F1.JPG P849F1.JPG P869F2.JPG\n", "P782F1.mat P807F1.mat P821F2.mat P835F1.mat P849F1.mat P869F2.mat\n", "P782F2.JPG P807F2.JPG P822F1.JPG P835F2.JPG P849F2.JPG P870F1.JPG\n", "P782F2.mat P807F2.mat P822F1.mat P835F2.mat P849F2.mat P870F1.mat\n", "P783F1.JPG P808F1.JPG P822F2.JPG P836F1.JPG P850F1.JPG P870F2.JPG\n", "P783F1.mat P808F1.mat P822F2.mat P836F1.mat P850F1.mat P870F2.mat\n", "P783F2.JPG P808F2.JPG P823F1.JPG P836F2.JPG P850F2.JPG P871F1.JPG\n", "P783F2.mat P808F2.mat P823F1.mat P836F2.mat P850F2.mat P871F1.mat\n", "P784F1.JPG P810F1.JPG P823F2.JPG P837F1.JPG P852F1.JPG P871F2.JPG\n", "P784F1.mat P810F1.mat P823F2.mat P837F1.mat P852F1.mat P871F2.mat\n", "P784F2.JPG P810F2.JPG P824F1.JPG P837F2.JPG P852F2.JPG P872F1.JPG\n", "P784F2.mat P810F2.mat P824F1.mat P837F2.mat P852F2.mat P872F2.JPG\n", "P785F1.JPG P811F1.JPG P824F2.JPG P838F1.JPG P853F1.JPG P873F1.JPG\n", "P785F1.mat P811F1.mat P824F2.mat P838F1.mat P853F1.mat P873F1.mat\n", "P785F2.JPG P811F2.JPG P825F1.JPG P838F2.JPG P855F1.JPG P873F2.JPG\n", "P785F2.mat P811F2.mat P825F1.mat P838F2.mat P855F2.JPG P873F2.mat\n", "P786F1.JPG P812F1.JPG P825F2.JPG P839F1.JPG P856F1.JPG P875F1.JPG\n", "P786F1.mat P812F1.mat P825F2.mat P839F1.mat P857F1.JPG P875F1.mat\n", "P786F2.JPG P812F2.JPG P826F1.JPG P839F2.JPG P859F1.JPG P875F2.JPG\n", "P786F2.mat P812F2.mat P826F1.mat P839F2.mat P859F1.mat P875F2.mat\n", "P787F2.JPG P813F1.JPG P826F2.JPG P840F1.JPG P859F2.JPG P876F2.JPG\n", "P787F2.mat P813F1.mat P826F2.mat P840F1.mat P859F2.mat P876F3.JPG\n", "P787F3.JPG P813F2.JPG P827F1.JPG P841F1.JPG P860F1.JPG P877F1.JPG\n", "P787F3.mat P813F2.mat P827F1.mat P841F1.mat P860F1.mat P877F1.mat\n", "P788F1.JPG P814F2.JPG P827F2.JPG P841F2.JPG P860F2.JPG P877F2.JPG\n", "P788F1.mat P814F2.mat P827F2.mat P841F2.mat P860F2.mat P877F2.mat\n" ] } ], "source": [ "ls dougRaster/Rotated_and_Cropped/plate3" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In the matlab/octave script above we grab a particular component of these matlab object files, a matrix that contains the pixel color and location info, and export it as a csv. Each petal is labeled by Doug by the order they appear on the photos: \"left\", \"mid\", and \"right\". These names have nothing to do with the position of the petals on the flowers.\n", "\n", "The script above then makes a directory for each flower, then subdirectories for each petal, where the CSV files are placed. Later, we will also store our geojson files here, see [below](#geoJFree). The file names for these new CSVs are:\n", "\n", "`[Plant-number] + [Flower-number] + \"_\" + [left/mid/right] + \"_melted.csv\"`\n", "\n", "For instance: `P773F1_left_melted.csv`\n", "\n", "Each of these matrix-turned-CSVs have four columns, and a row for each pixel. There is no header for these files, so here is an explanation:\n", "\n", "
    \n", "
  • The first and second columns are X and Y coordinates.
  • \n", "
  • The third column cells should all contain the same value, either all 1, 2, or 3. 1 is a 'left' petal, 2 is a 'mid' petal, and 3 is a 'right' petal.
  • \n", "
  • The fourth column contains the color information. Doug's formatting only retains non-background (non-white-tape) pixels. 2 indicates petal color, 3 is spot color. Thus this column should contain only 2's and 3's.
  • \n", "
\n", "\n", "Looking at a few lines of one of these files:" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "120,631,1,2\n", "120,632,1,2\n", "120,633,1,2\n", "120,634,1,2\n", "120,635,1,2\n", "120,636,1,2\n", "120,637,1,3\n", "120,638,1,3\n", "120,639,1,3\n", "120,640,1,3\n", "120,641,1,3\n" ] } ], "source": [ "sed -n '4310,4320'p make_polygons/polygons/plate3/P773F1/left/P773F1_left_melted.csv" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This is showing pixels with an X of 120, a y-positions from 631 to 641. In the third column, we can see this is a 1='left' petal, and in the fourth column we see some pixels are petal ('2') and some are spots ('3'). " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "

Some notes for future development

\n", "\n", "These pixel position data get inverted often because coordinate systems vary in their origins when dealing with pixels and rasters. Raster/graphical coordinate systems often have the origin in the upperleft corner and increase in number as they go down the screen, we'll see that this flips the images when we go from viewing our petals as a raster to viewing their polygons in pyplot. I tried not to worry about these inversions too much, and did not mess with the original x, y positions at each step, just to try to retain the integrity of the data as much as possible. This means, however, that we end up looking at and working with upsidedown images quite often.\n", "\n", "Perhaps more important here is that if this place is to be used as a starting point for image inputs to our pipeline, we may want to simplify here for others' ease of use. This pipeline is written specifically to deal with the quirks of the outputs from Doug's matlab scripts (2 and 3 for colors, no 1's, an unnecessary third column, etc etc.). If other labs were to use this, they would probably find these requirements annoying, " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "

Digitizing petal outlines

\n", "\n", "Now we create map-like features out of our images, a process in GIS known as digitizing. We'll be using our custom python package for this project, `flowerPetal`, which can be found at https://pypi.org/project/makeFlowerPolygons-dcthom/. The github repo for this package is currently here . `flowerPetal` is built mostly using tools from [scikit-image](https://scikit-image.org/), and [shapely](toblerity.org/shapely/manual.html). These are among the standard packages for manipulating images and polygons in python. We'll also import the other packages as necessary for this analysis. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "
Calling spots and petals from images
\n", "\n", "Above we dug into Doug's matlab pipeline, and extracted the data we needed from his matlab data objects into a more universal format, a CSV. As our next step, we need find our petals and spots in these CSVs, and put their information into a format that is useful to us. The module `get_spots` of our `makeFlowerPolygons` package tries to do this. \n", "\n", "As an example, we'll assemble a toy directory with a few flowers images in it, and run our modules as scripts on these:" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "toy=$PWD\"/make_polygons/toy\"\n", "mkdir $toy\n", "newSpots=$toy\"/newSpots\"\n", "mkdir $newSpots" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Get some sample CSVs to play with:" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "cp -r ./make_polygons/polygons/plate3/P77{3,4}* $toy" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Just for continuity, I'm also going to bring in this CSV, which is from one of the petals that we looked at [above](#dougOut). It's an image from plate 1. Plate 1 and 2 geojsons and CSVs are in our google drive of finished images, to keep the repo from being so bloated. I've put a copy back in the repo:" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [], "source": [ "mkdir -p $toy/P431F1/right\n", "cp ./make_polygons/notebooks/P431F1_right_melted.csv $toy/P431F1/right" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Our modules are available to be used as scripts in the repo itself. The `get_spots` module is here:" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [], "source": [ "get_spots='make_polygons/package/makeFlowerPolygons/get_spots.py'" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The `get_spots` module acts on a single CSV file at a time, in the format mention above from Doug's pipeline. For one petal csv file at a time, the syntax for the `get_spots` script is:" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "usage: get_spots.py [-h] [--destination DESTINATION] [--skipFinalClean] file\n", "\n", "positional arguments:\n", " file Name of file that contains CSV versions of doug's\n", " \"melted\" matrix, the result of manually choosing color\n", " centers from his matlab color-categorization scripts.\n", "\n", "optional arguments:\n", " -h, --help show this help message and exit\n", " --destination DESTINATION, -d DESTINATION\n", " Folder where you would like the outputted geojson.\n", " Default is same directory.\n", " --skipFinalClean, -s Use this flag to skip the clean step that occasionally\n", " disappears polygons, if it can't fix them any other\n", " way.\n" ] } ], "source": [ "$get_spots -h" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The last optional argument, `--skipFinalClean`, is necessary sometimes because our spot \"cleaning\" process, which attempts to resolve invalidities in the polygons, also sometimes deletes entire spots when it cannot resolve the errors in them any other way. When this happens, it's sometimes useful to retain the invalid polygon and fix it manually, with QGIS or with a text editor. \n", "\n", "Another developer issue here is that error logging for polygon calls from images is really bad right now. There should be better tracking of which polygons worked, which ones needed fixing, which ones were thrown out, etc. \n", "\n", "We'll get the list of the CSVs and run the script on each:" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "/home/daniel/Documents/cooley_lab/mimulusSpeckling/make_polygons/toy/P774F2/mid/P774F2_mid_melted.csv\n", "P774F2_mid_polys\n", "P774F2_mid_polys petal is invalid! Opening bag of tricks\n", "Trying buffering\n", "Buffering complete. P774F2_mid_polys petal seems better.\n", "This spot multipolygon seems okay without buffering.\n", "/home/daniel/Documents/cooley_lab/mimulusSpeckling/make_polygons/toy/P774F2/left/P774F2_left_melted.csv\n", "P774F2_left_polys\n", "P774F2_left_polys petal is invalid! Opening bag of tricks\n", "Trying buffering\n", "Buffering complete. P774F2_left_polys petal seems better.\n", "This spot multipolygon seems okay without buffering.\n", "/home/daniel/Documents/cooley_lab/mimulusSpeckling/make_polygons/toy/P774F2/right/P774F2_right_melted.csv\n", "P774F2_right_polys\n", "This spot multipolygon seems okay without buffering.\n", "/home/daniel/Documents/cooley_lab/mimulusSpeckling/make_polygons/toy/P774F3/mid/P774F3_mid_melted.csv\n", "P774F3_mid_polys\n", "This spot multipolygon seems okay without buffering.\n", "/home/daniel/Documents/cooley_lab/mimulusSpeckling/make_polygons/toy/P774F3/left/P774F3_left_melted.csv\n", "P774F3_left_polys\n", "This spot multipolygon seems okay without buffering.\n", "/home/daniel/Documents/cooley_lab/mimulusSpeckling/make_polygons/toy/P774F3/right/P774F3_right_melted.csv\n", "P774F3_right_polys\n", "P774F3_right_polys petal is invalid! Opening bag of tricks\n", "Trying buffering\n", "multiple polygons detected where there should only be one\n", "Buffering complete. P774F3_right_polys petal seems better.\n", "P774F3_right_polys spots is invalid! Opening bag of tricks\n", "Trying buffering\n", "multiple polygons detected where there should only be one\n", "Buffering complete. P774F3_right_polys spots seems better.\n", "This spot multipolygon seems okay without buffering.\n", "/home/daniel/Documents/cooley_lab/mimulusSpeckling/make_polygons/toy/P773F2/mid/P773F2_mid_melted.csv\n", "P773F2_mid_polys\n", "P773F2_mid_polys petal is invalid! Opening bag of tricks\n", "Trying buffering\n", "multiple polygons detected where there should only be one\n", "Buffering complete. P773F2_mid_polys petal seems better.\n", "P773F2_mid_polys spots is invalid! Opening bag of tricks\n", "Trying buffering\n", "multiple polygons detected where there should only be one\n", "Buffering complete. P773F2_mid_polys spots seems better.\n", "Buffering spot multipolygon.\n", "/home/daniel/Documents/cooley_lab/mimulusSpeckling/make_polygons/toy/P773F2/left/P773F2_left_melted.csv\n", "P773F2_left_polys\n", "Buffering spot multipolygon.\n", "/home/daniel/Documents/cooley_lab/mimulusSpeckling/make_polygons/toy/P773F2/right/P773F2_right_melted.csv\n", "P773F2_right_polys\n", "This spot multipolygon seems okay without buffering.\n", "/home/daniel/Documents/cooley_lab/mimulusSpeckling/make_polygons/toy/P431F1/right/P431F1_right_melted.csv\n", "P431F1_right_polys\n", "P431F1_right_polys spots is invalid! Opening bag of tricks\n", "Trying buffering\n", "multiple polygons detected where there should only be one\n", "Buffering complete. P431F1_right_polys spots seems better.\n", "P431F1_right_polys spots is invalid! Opening bag of tricks\n", "Trying buffering\n", "Buffering complete. P431F1_right_polys spots seems better.\n", "This spot multipolygon seems okay without buffering.\n", "/home/daniel/Documents/cooley_lab/mimulusSpeckling/make_polygons/toy/P773F1/mid/P773F1_mid_melted.csv\n", "P773F1_mid_polys\n", "P773F1_mid_polys spots is invalid! Opening bag of tricks\n", "Trying buffering\n", "Buffering complete. P773F1_mid_polys spots seems better.\n", "P773F1_mid_polys spots is invalid! Opening bag of tricks\n", "Trying buffering\n", "Buffering complete. P773F1_mid_polys spots seems better.\n", "This spot multipolygon seems okay without buffering.\n", "/home/daniel/Documents/cooley_lab/mimulusSpeckling/make_polygons/toy/P773F1/left/P773F1_left_melted.csv\n", "P773F1_left_polys\n", "P773F1_left_polys petal is invalid! Opening bag of tricks\n", "Trying buffering\n", "Buffering complete. P773F1_left_polys petal seems better.\n", "P773F1_left_polys spots is invalid! Opening bag of tricks\n", "Trying buffering\n", "multiple polygons detected where there should only be one\n", "Buffering complete. P773F1_left_polys spots seems better.\n", "Buffering spot multipolygon.\n", "/home/daniel/Documents/cooley_lab/mimulusSpeckling/make_polygons/toy/P773F1/right/P773F1_right_melted.csv\n", "P773F1_right_polys\n", "P773F1_right_polys petal is invalid! Opening bag of tricks\n", "Trying buffering\n", "Buffering complete. P773F1_right_polys petal seems better.\n", "P773F1_right_polys spots is invalid! Opening bag of tricks\n", "Trying buffering\n", "Buffering complete. P773F1_right_polys spots seems better.\n", "This spot multipolygon seems okay without buffering.\n" ] } ], "source": [ "csvs=$(find $toy -name \"*_melted.csv\") \n", "\n", "for i in ${csvs[@]}; do\n", " echo $i\n", " $get_spots $i -d $newSpots \n", "done" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Using matplotlib (so back to python), we can see the original image:" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "exFlowerImage=\"./dougRaster/Rotated_and_Cropped/plate1/P431F1.JPG\"" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "scrolled": false }, "outputs": [ { "data": { "application/javascript": [ "/* Put everything inside the global mpl namespace */\n", "window.mpl = {};\n", "\n", "\n", "mpl.get_websocket_type = function() {\n", " if (typeof(WebSocket) !== 'undefined') {\n", " return WebSocket;\n", " } else if (typeof(MozWebSocket) !== 'undefined') {\n", " return MozWebSocket;\n", " } else {\n", " alert('Your browser does not have WebSocket support.' +\n", " 'Please try Chrome, Safari or Firefox ≥ 6. 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Calling center, edge and throat zones

\n", "\n", "Later in our analysis, we're going to focus on spotting in only certain regions of the petal, and make comparisons among these regions. We'll call these zones. We define three zones of interest - center, margin and petal. The tools for trying to do this in an automated fashion are in another module, \"[get_zones.py](https://github.com/danchurch/mimulusSpeckling/blob/master/make_polygons/package/makeFlowerPolygons/get_zones.py)\". As with our other modules, it is both a set of functions that are tools to handle zone calling, and a script that can be run from the terminal.\n", "\n", "We define the proportion ('percent') of petal we would like to remain as \"center\", and the rest is given to the margin and throat. \n", "\n", "To check out the `get_zones` module, we go back in BASH kernel..." ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "get_zones='./make_polygons/package/makeFlowerPolygons/get_zones.py'" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "usage: get_zones.py [-h] [-o OUT] geojson centerSize\n", "\n", "positional arguments:\n", " geojson Name of the geojson file to which you want to assign\n", " zones.\n", " centerSize give the proportion of the middle of the flower you would\n", " like to call Center Zone, from 0.01 to 0.99.\n", "\n", "optional arguments:\n", " -h, --help show this help message and exit\n", " -o OUT, --out OUT Name for outfile. If none given, modified in place.\n" ] } ], "source": [ "$get_zones -h" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "There is a lot going on in that script, and it usually fails in some way or another. In brief:\n", "\n", "
    \n", "\n", "
  1. \n", "A margin is created by start with a polygon the same size and shape as the petal, that is iteratively shrunk in small until it is approximately the size given by the user in the \"centerSize\" argument. For our research purposes, we set the centerSize to 50% for all calculations. This becomes our center zone, and this call usually suceeds quite well. \n", "
  2. \n", "\n", "
  3. \n", "Following this, separation of margin into edge and throat zones are attempted. The script simplifies the lines of the petal and center polygons. This results in fewer and \"harder\" vertices, and we pick the top two of these vertices in our petal outline and in our margin. These four points delineate the four corners of the throat, and the upper portion of the margin polygon is clipped using this rectangle (with some buffering to allow for the shape of the petal). This portion of the margin is called as the throat zone. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This second step sometimes works but fails more often for finding the throat/edge. So downstream we have built in a manual correction/sanity check into this pipeline. \n", "\n", "Anyway, let's try it on the files in our toy directory, using a center zone of 50% area:" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "./make_polygons/toy/newSpots/P431F1_right_polys.geojson\n", "./make_polygons/toy/newSpots/P773F1_left_polys.geojson\n", "./make_polygons/toy/newSpots/P773F1_mid_polys.geojson\n", "./make_polygons/toy/newSpots/P773F1_right_polys.geojson\n", "./make_polygons/toy/newSpots/P773F2_left_polys.geojson\n", "./make_polygons/toy/newSpots/P773F2_mid_polys.geojson\n", "./make_polygons/toy/newSpots/P773F2_right_polys.geojson\n", "./make_polygons/toy/newSpots/P774F2_left_polys.geojson\n", "./make_polygons/toy/newSpots/P774F2_mid_polys.geojson\n", "./make_polygons/toy/newSpots/P774F2_right_polys.geojson\n", "./make_polygons/toy/newSpots/P774F3_left_polys.geojson\n", "./make_polygons/toy/newSpots/P774F3_mid_polys.geojson\n", "./make_polygons/toy/newSpots/P774F3_right_polys.geojson\n" ] } ], "source": [ "for inF in ./make_polygons/toy/newSpots/*; do\n", " echo $inF\n", " $get_zones $inF 0.5\n", "done\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can view these with a plotter that is in our geojsonIO model, in the python kernel:" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "(petal,spots,\n", "center, edge,throat,\n", " _,_,_) = geojsonIO.parseGeoJson(\"make_polygons/toy/newSpots/P431F1_right_polys.geojson\")" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "scrolled": false }, "outputs": [ { "data": { "application/javascript": [ "/* Put everything inside the global mpl namespace */\n", "window.mpl = {};\n", "\n", "\n", "mpl.get_websocket_type = function() {\n", " if (typeof(WebSocket) !== 'undefined') {\n", " return WebSocket;\n", " } else if (typeof(MozWebSocket) !== 'undefined') {\n", " return MozWebSocket;\n", " } else {\n", " alert('Your browser does not have WebSocket support.' +\n", " 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n", " 'Firefox 4 and 5 are also supported but you ' +\n", " 'have to enable WebSockets in about:config.');\n", " };\n", "}\n", "\n", "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n", " this.id = figure_id;\n", "\n", " this.ws = websocket;\n", "\n", " this.supports_binary = (this.ws.binaryType != undefined);\n", "\n", " if (!this.supports_binary) {\n", " var warnings = document.getElementById(\"mpl-warnings\");\n", " if (warnings) {\n", " warnings.style.display = 'block';\n", " warnings.textContent = (\n", " \"This browser does not support binary websocket messages. \" +\n", " \"Performance may be slow.\");\n", " }\n", " }\n", "\n", " this.imageObj = new Image();\n", "\n", " this.context = undefined;\n", " this.message = undefined;\n", " this.canvas = undefined;\n", " this.rubberband_canvas = undefined;\n", " this.rubberband_context = undefined;\n", " this.format_dropdown = undefined;\n", "\n", " this.image_mode = 'full';\n", "\n", " this.root = $('
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    ');\n", " var titletext = $(\n", " '
    ');\n", " titlebar.append(titletext)\n", " this.root.append(titlebar);\n", " this.header = titletext[0];\n", "}\n", "\n", "\n", "\n", "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n", "\n", "}\n", "\n", "\n", "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n", "\n", "}\n", "\n", "mpl.figure.prototype._init_canvas = function() {\n", " var fig = this;\n", "\n", " var canvas_div = $('
    ');\n", "\n", " canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n", "\n", " function canvas_keyboard_event(event) {\n", " return fig.key_event(event, event['data']);\n", " }\n", "\n", " canvas_div.keydown('key_press', canvas_keyboard_event);\n", " canvas_div.keyup('key_release', canvas_keyboard_event);\n", " this.canvas_div = canvas_div\n", " this._canvas_extra_style(canvas_div)\n", " this.root.append(canvas_div);\n", "\n", " var canvas = $('');\n", " canvas.addClass('mpl-canvas');\n", " canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n", "\n", " this.canvas = canvas[0];\n", " this.context = canvas[0].getContext(\"2d\");\n", "\n", " var backingStore = this.context.backingStorePixelRatio ||\n", "\tthis.context.webkitBackingStorePixelRatio ||\n", "\tthis.context.mozBackingStorePixelRatio ||\n", "\tthis.context.msBackingStorePixelRatio ||\n", "\tthis.context.oBackingStorePixelRatio ||\n", "\tthis.context.backingStorePixelRatio || 1;\n", "\n", " mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n", "\n", " var rubberband = $('');\n", " rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n", "\n", " var pass_mouse_events = true;\n", "\n", " canvas_div.resizable({\n", " start: function(event, ui) {\n", " pass_mouse_events = false;\n", " },\n", " resize: function(event, ui) {\n", " fig.request_resize(ui.size.width, ui.size.height);\n", " },\n", " stop: function(event, ui) {\n", " pass_mouse_events = true;\n", " fig.request_resize(ui.size.width, ui.size.height);\n", " },\n", " });\n", "\n", " function mouse_event_fn(event) {\n", " if (pass_mouse_events)\n", " return fig.mouse_event(event, event['data']);\n", " }\n", "\n", " rubberband.mousedown('button_press', mouse_event_fn);\n", " rubberband.mouseup('button_release', mouse_event_fn);\n", " // Throttle sequential mouse events to 1 every 20ms.\n", " rubberband.mousemove('motion_notify', mouse_event_fn);\n", "\n", " rubberband.mouseenter('figure_enter', mouse_event_fn);\n", " rubberband.mouseleave('figure_leave', mouse_event_fn);\n", "\n", " canvas_div.on(\"wheel\", function (event) {\n", " event = event.originalEvent;\n", " event['data'] = 'scroll'\n", " if (event.deltaY < 0) {\n", " event.step = 1;\n", " } else {\n", " event.step = -1;\n", " }\n", " mouse_event_fn(event);\n", " });\n", "\n", " canvas_div.append(canvas);\n", " canvas_div.append(rubberband);\n", "\n", " this.rubberband = rubberband;\n", " this.rubberband_canvas = rubberband[0];\n", " this.rubberband_context = rubberband[0].getContext(\"2d\");\n", " this.rubberband_context.strokeStyle = \"#000000\";\n", "\n", " this._resize_canvas = function(width, height) {\n", " // Keep the size of the canvas, canvas container, and rubber band\n", " // canvas in synch.\n", " canvas_div.css('width', width)\n", " canvas_div.css('height', height)\n", "\n", " canvas.attr('width', width * mpl.ratio);\n", " canvas.attr('height', height * mpl.ratio);\n", " canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n", "\n", " rubberband.attr('width', width);\n", " rubberband.attr('height', height);\n", " }\n", "\n", " // Set the figure to an initial 600x600px, this will subsequently be updated\n", " // upon first draw.\n", " this._resize_canvas(600, 600);\n", "\n", " // Disable right mouse context menu.\n", " $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n", " return false;\n", " });\n", "\n", " function set_focus () {\n", " canvas.focus();\n", " canvas_div.focus();\n", " }\n", "\n", " window.setTimeout(set_focus, 100);\n", "}\n", "\n", "mpl.figure.prototype._init_toolbar = function() {\n", " var fig = this;\n", "\n", " var nav_element = $('
    ')\n", " nav_element.attr('style', 'width: 100%');\n", " this.root.append(nav_element);\n", "\n", " // Define a callback function for later on.\n", " function toolbar_event(event) {\n", " return fig.toolbar_button_onclick(event['data']);\n", " }\n", " function toolbar_mouse_event(event) {\n", " return fig.toolbar_button_onmouseover(event['data']);\n", " }\n", "\n", " for(var toolbar_ind in mpl.toolbar_items) {\n", " var name = mpl.toolbar_items[toolbar_ind][0];\n", " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", " var image = mpl.toolbar_items[toolbar_ind][2];\n", " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", "\n", " if (!name) {\n", " // put a spacer in here.\n", " continue;\n", " }\n", " var button = $('');\n", " button.click(method_name, toolbar_event);\n", " button.mouseover(tooltip, toolbar_mouse_event);\n", " nav_element.append(button);\n", " }\n", "\n", " // Add the status bar.\n", " var status_bar = $('');\n", " nav_element.append(status_bar);\n", " this.message = status_bar[0];\n", "\n", " // Add the close button to the window.\n", " var buttongrp = $('
    ');\n", " var button = $('');\n", " button.click(function (evt) { fig.handle_close(fig, {}); } );\n", " button.mouseover('Stop Interaction', toolbar_mouse_event);\n", " buttongrp.append(button);\n", " var titlebar = this.root.find($('.ui-dialog-titlebar'));\n", " titlebar.prepend(buttongrp);\n", "}\n", "\n", "mpl.figure.prototype._root_extra_style = function(el){\n", " var fig = this\n", " el.on(\"remove\", function(){\n", "\tfig.close_ws(fig, {});\n", " });\n", "}\n", "\n", "mpl.figure.prototype._canvas_extra_style = function(el){\n", " // this is important to make the div 'focusable\n", " el.attr('tabindex', 0)\n", " // reach out to IPython and tell the keyboard manager to turn it's self\n", " // off when our div gets focus\n", "\n", " // location in version 3\n", " if (IPython.notebook.keyboard_manager) {\n", " IPython.notebook.keyboard_manager.register_events(el);\n", " }\n", " else {\n", " // location in version 2\n", " IPython.keyboard_manager.register_events(el);\n", " }\n", "\n", "}\n", "\n", "mpl.figure.prototype._key_event_extra = function(event, name) {\n", " var manager = IPython.notebook.keyboard_manager;\n", " if (!manager)\n", " manager = IPython.keyboard_manager;\n", "\n", " // Check for shift+enter\n", " if (event.shiftKey && event.which == 13) {\n", " this.canvas_div.blur();\n", " event.shiftKey = false;\n", " // Send a \"J\" for go to next cell\n", " event.which = 74;\n", " event.keyCode = 74;\n", " manager.command_mode();\n", " manager.handle_keydown(event);\n", " }\n", "}\n", "\n", "mpl.figure.prototype.handle_save = function(fig, msg) {\n", " fig.ondownload(fig, null);\n", "}\n", "\n", "\n", "mpl.find_output_cell = function(html_output) {\n", " // Return the cell and output element which can be found *uniquely* in the notebook.\n", " // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n", " // IPython event is triggered only after the cells have been serialised, which for\n", " // our purposes (turning an active figure into a static one), is too late.\n", " var cells = IPython.notebook.get_cells();\n", " var ncells = cells.length;\n", " for (var i=0; i= 3 moved mimebundle to data attribute of output\n", " data = data.data;\n", " }\n", " if (data['text/html'] == html_output) {\n", " return [cell, data, j];\n", " }\n", " }\n", " }\n", " }\n", "}\n", "\n", "// Register the function which deals with the matplotlib target/channel.\n", "// The kernel may be null if the page has been refreshed.\n", "if (IPython.notebook.kernel != null) {\n", " IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n", "}\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "([], None)" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "(petal,spots,\n", "center, edge,throat,\n", " _,_,_) = geojsonIO.parseGeoJson(\"make_polygons/toy/\"\n", " \"newSpots/P774F3_mid_polys.geojson\")\n", "color='white'\n", "alpha=0.3\n", "geojsonIO.plotOne(petal)\n", "geojsonIO.addOne(spots)\n", "geojsonIO.addOne(center, col=color, a=alpha)\n", "geojsonIO.addOne(edge, col=color, a=alpha)\n", "geojsonIO.addOne(throat, col=color, a=alpha)" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "scrolled": false }, "outputs": [ { "data": { "application/javascript": [ "/* Put everything inside the global mpl namespace */\n", "window.mpl = {};\n", "\n", "\n", "mpl.get_websocket_type = function() {\n", " if (typeof(WebSocket) !== 'undefined') {\n", " return WebSocket;\n", " } else if (typeof(MozWebSocket) !== 'undefined') {\n", " return MozWebSocket;\n", " } else {\n", " alert('Your browser does not have WebSocket support.' +\n", " 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n", " 'Firefox 4 and 5 are also supported but you ' +\n", " 'have to enable WebSockets in about:config.');\n", " };\n", "}\n", "\n", "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n", " this.id = figure_id;\n", "\n", " this.ws = websocket;\n", "\n", " this.supports_binary = (this.ws.binaryType != undefined);\n", "\n", " if (!this.supports_binary) {\n", " var warnings = document.getElementById(\"mpl-warnings\");\n", " if (warnings) {\n", " warnings.style.display = 'block';\n", " warnings.textContent = (\n", " \"This browser does not support binary websocket messages. \" +\n", " \"Performance may be slow.\");\n", " }\n", " }\n", "\n", " this.imageObj = new Image();\n", "\n", " this.context = undefined;\n", " this.message = undefined;\n", " this.canvas = undefined;\n", " this.rubberband_canvas = undefined;\n", " this.rubberband_context = undefined;\n", " this.format_dropdown = undefined;\n", "\n", " this.image_mode = 'full';\n", "\n", " this.root = $('
    ');\n", " this._root_extra_style(this.root)\n", " this.root.attr('style', 'display: inline-block');\n", "\n", " $(parent_element).append(this.root);\n", "\n", " this._init_header(this);\n", " this._init_canvas(this);\n", " this._init_toolbar(this);\n", "\n", " var fig = this;\n", "\n", " this.waiting = false;\n", "\n", " this.ws.onopen = function () {\n", " fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n", " fig.send_message(\"send_image_mode\", {});\n", " if (mpl.ratio != 1) {\n", " fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n", " }\n", " fig.send_message(\"refresh\", {});\n", " }\n", "\n", " this.imageObj.onload = function() {\n", " if (fig.image_mode == 'full') {\n", " // Full images could contain transparency (where diff images\n", " // almost always do), so we need to clear the canvas so that\n", " // there is no ghosting.\n", " fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n", " }\n", " fig.context.drawImage(fig.imageObj, 0, 0);\n", " };\n", "\n", " this.imageObj.onunload = function() {\n", " fig.ws.close();\n", " }\n", "\n", " this.ws.onmessage = this._make_on_message_function(this);\n", "\n", " this.ondownload = ondownload;\n", "}\n", "\n", "mpl.figure.prototype._init_header = function() {\n", " var titlebar = $(\n", " '
    ');\n", " var titletext = $(\n", " '
    ');\n", " titlebar.append(titletext)\n", " this.root.append(titlebar);\n", " this.header = titletext[0];\n", "}\n", "\n", "\n", "\n", "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n", "\n", "}\n", "\n", "\n", "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n", "\n", "}\n", "\n", "mpl.figure.prototype._init_canvas = function() {\n", " var fig = this;\n", "\n", " var canvas_div = $('
    ');\n", "\n", " canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n", "\n", " function canvas_keyboard_event(event) {\n", " return fig.key_event(event, event['data']);\n", " }\n", "\n", " canvas_div.keydown('key_press', canvas_keyboard_event);\n", " canvas_div.keyup('key_release', canvas_keyboard_event);\n", " this.canvas_div = canvas_div\n", " this._canvas_extra_style(canvas_div)\n", " this.root.append(canvas_div);\n", "\n", " var canvas = $('');\n", " canvas.addClass('mpl-canvas');\n", " canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n", "\n", " this.canvas = canvas[0];\n", " this.context = canvas[0].getContext(\"2d\");\n", "\n", " var backingStore = this.context.backingStorePixelRatio ||\n", "\tthis.context.webkitBackingStorePixelRatio ||\n", "\tthis.context.mozBackingStorePixelRatio ||\n", "\tthis.context.msBackingStorePixelRatio ||\n", "\tthis.context.oBackingStorePixelRatio ||\n", "\tthis.context.backingStorePixelRatio || 1;\n", "\n", " mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n", "\n", " var rubberband = $('');\n", " rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n", "\n", " var pass_mouse_events = true;\n", "\n", " canvas_div.resizable({\n", " start: function(event, ui) {\n", " pass_mouse_events = false;\n", " },\n", " resize: function(event, ui) {\n", " fig.request_resize(ui.size.width, ui.size.height);\n", " },\n", " stop: function(event, ui) {\n", " pass_mouse_events = true;\n", " fig.request_resize(ui.size.width, ui.size.height);\n", " },\n", " });\n", "\n", " function mouse_event_fn(event) {\n", " if (pass_mouse_events)\n", " return fig.mouse_event(event, event['data']);\n", " }\n", "\n", " rubberband.mousedown('button_press', mouse_event_fn);\n", " rubberband.mouseup('button_release', mouse_event_fn);\n", " // Throttle sequential mouse events to 1 every 20ms.\n", " rubberband.mousemove('motion_notify', mouse_event_fn);\n", "\n", " rubberband.mouseenter('figure_enter', mouse_event_fn);\n", " rubberband.mouseleave('figure_leave', mouse_event_fn);\n", "\n", " canvas_div.on(\"wheel\", function (event) {\n", " event = event.originalEvent;\n", " event['data'] = 'scroll'\n", " if (event.deltaY < 0) {\n", " event.step = 1;\n", " } else {\n", " event.step = -1;\n", " }\n", " mouse_event_fn(event);\n", " });\n", "\n", " canvas_div.append(canvas);\n", " canvas_div.append(rubberband);\n", "\n", " this.rubberband = rubberband;\n", " this.rubberband_canvas = rubberband[0];\n", " this.rubberband_context = rubberband[0].getContext(\"2d\");\n", " this.rubberband_context.strokeStyle = \"#000000\";\n", "\n", " this._resize_canvas = function(width, height) {\n", " // Keep the size of the canvas, canvas container, and rubber band\n", " // canvas in synch.\n", " canvas_div.css('width', width)\n", " canvas_div.css('height', height)\n", "\n", " canvas.attr('width', width * mpl.ratio);\n", " canvas.attr('height', height * mpl.ratio);\n", " canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n", "\n", " rubberband.attr('width', width);\n", " rubberband.attr('height', height);\n", " }\n", "\n", " // Set the figure to an initial 600x600px, this will subsequently be updated\n", " // upon first draw.\n", " this._resize_canvas(600, 600);\n", "\n", " // Disable right mouse context menu.\n", " $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n", " return false;\n", " });\n", "\n", " function set_focus () {\n", " canvas.focus();\n", " canvas_div.focus();\n", " }\n", "\n", " window.setTimeout(set_focus, 100);\n", "}\n", "\n", "mpl.figure.prototype._init_toolbar = function() {\n", " var fig = this;\n", "\n", " var nav_element = $('
    ')\n", " nav_element.attr('style', 'width: 100%');\n", " this.root.append(nav_element);\n", "\n", " // Define a callback function for later on.\n", " function toolbar_event(event) {\n", " return fig.toolbar_button_onclick(event['data']);\n", " }\n", " function toolbar_mouse_event(event) {\n", " return fig.toolbar_button_onmouseover(event['data']);\n", " }\n", "\n", " for(var toolbar_ind in mpl.toolbar_items) {\n", " var name = mpl.toolbar_items[toolbar_ind][0];\n", " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", " var image = mpl.toolbar_items[toolbar_ind][2];\n", " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", "\n", " if (!name) {\n", " // put a spacer in here.\n", " continue;\n", " }\n", " var button = $('');\n", " button.click(method_name, toolbar_event);\n", " button.mouseover(tooltip, toolbar_mouse_event);\n", " nav_element.append(button);\n", " }\n", "\n", " // Add the status bar.\n", " var status_bar = $('');\n", " nav_element.append(status_bar);\n", " this.message = status_bar[0];\n", "\n", " // Add the close button to the window.\n", " var buttongrp = $('
    ');\n", " var button = $('');\n", " button.click(function (evt) { fig.handle_close(fig, {}); } );\n", " button.mouseover('Stop Interaction', toolbar_mouse_event);\n", " buttongrp.append(button);\n", " var titlebar = this.root.find($('.ui-dialog-titlebar'));\n", " titlebar.prepend(buttongrp);\n", "}\n", "\n", "mpl.figure.prototype._root_extra_style = function(el){\n", " var fig = this\n", " el.on(\"remove\", function(){\n", "\tfig.close_ws(fig, {});\n", " });\n", "}\n", "\n", "mpl.figure.prototype._canvas_extra_style = function(el){\n", " // this is important to make the div 'focusable\n", " el.attr('tabindex', 0)\n", " // reach out to IPython and tell the keyboard manager to turn it's self\n", " // off when our div gets focus\n", "\n", " // location in version 3\n", " if (IPython.notebook.keyboard_manager) {\n", " IPython.notebook.keyboard_manager.register_events(el);\n", " }\n", " else {\n", " // location in version 2\n", " IPython.keyboard_manager.register_events(el);\n", " }\n", "\n", "}\n", "\n", "mpl.figure.prototype._key_event_extra = function(event, name) {\n", " var manager = IPython.notebook.keyboard_manager;\n", " if (!manager)\n", " manager = IPython.keyboard_manager;\n", "\n", " // Check for shift+enter\n", " if (event.shiftKey && event.which == 13) {\n", " this.canvas_div.blur();\n", " event.shiftKey = false;\n", " // Send a \"J\" for go to next cell\n", " event.which = 74;\n", " event.keyCode = 74;\n", " manager.command_mode();\n", " manager.handle_keydown(event);\n", " }\n", "}\n", "\n", "mpl.figure.prototype.handle_save = function(fig, msg) {\n", " fig.ondownload(fig, null);\n", "}\n", "\n", "\n", "mpl.find_output_cell = function(html_output) {\n", " // Return the cell and output element which can be found *uniquely* in the notebook.\n", " // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n", " // IPython event is triggered only after the cells have been serialised, which for\n", " // our purposes (turning an active figure into a static one), is too late.\n", " var cells = IPython.notebook.get_cells();\n", " var ncells = cells.length;\n", " for (var i=0; i= 3 moved mimebundle to data attribute of output\n", " data = data.data;\n", " }\n", " if (data['text/html'] == html_output) {\n", " return [cell, data, j];\n", " }\n", " }\n", " }\n", " }\n", "}\n", "\n", "// Register the function which deals with the matplotlib target/channel.\n", "// The kernel may be null if the page has been refreshed.\n", "if (IPython.notebook.kernel != null) {\n", " IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n", "}\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "plt.rcParams['figure.figsize'] = [20, 20]\n", "img=mpimg.imread('./make_polygons/notebooks/spotBreakScreenshot.png')\n", "plt.imshow(img)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "
    Manual correction of zones
    \n", "\n", "We've coded a similar program for adjusting the zones. Use it in a similar way:" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "usage: manZoneCaller.py [-h] [-j JPEG] [-o OUT] geojson\n", "\n", "positional arguments:\n", " geojson Name of the geojson file to which you want to assign\n", " zones.\n", "\n", "optional arguments:\n", " -h, --help show this help message and exit\n", " -j JPEG, --jpeg JPEG The jpeg associated with this flower.\n", " -o OUT, --out OUT Name for outfile. If none given, modified in place.\n" ] } ], "source": [ "manZoneCaller=\"./make_polygons/package/makeFlowerPolygons/manZoneCaller.py\"\n", "$manZoneCaller -h" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For one file, looks like this:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "manZoneCaller=\"./make_polygons/package/makeFlowerPolygons/manZoneCaller.py\"\n", "geoj=\"./make_polygons/toy/newSpots/P773F2_right_polys.geojson\"\n", "jpeg=\"./dougRaster/Rotated_and_Cropped/plate3/P773F2.JPG\"\n", "$manZoneCaller $geoj -j $jpeg " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "You can then draw your own corners. To do this, draw a polygon that incorporates all the area of the petal margin that you would like to be included in the throat region. It's okay to go outside the margin, the excess will be removed:" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "scrolled": false }, "outputs": [ { "data": { "application/javascript": [ "/* Put everything inside the global mpl namespace */\n", "window.mpl = {};\n", "\n", "\n", "mpl.get_websocket_type = function() {\n", " if (typeof(WebSocket) !== 'undefined') {\n", " return WebSocket;\n", " } else if (typeof(MozWebSocket) !== 'undefined') {\n", " return MozWebSocket;\n", " } else {\n", " alert('Your browser does not have WebSocket support.' +\n", " 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n", " 'Firefox 4 and 5 are also supported but you ' +\n", " 'have to enable WebSockets in about:config.');\n", " };\n", "}\n", "\n", "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n", " this.id = figure_id;\n", "\n", " this.ws = websocket;\n", "\n", " this.supports_binary = (this.ws.binaryType != undefined);\n", "\n", " if (!this.supports_binary) {\n", " var warnings = document.getElementById(\"mpl-warnings\");\n", " if (warnings) {\n", " warnings.style.display = 'block';\n", " warnings.textContent = (\n", " \"This browser does not support binary websocket messages. \" +\n", " \"Performance may be slow.\");\n", " }\n", " }\n", "\n", " this.imageObj = new Image();\n", "\n", " this.context = undefined;\n", " this.message = undefined;\n", " this.canvas = undefined;\n", " this.rubberband_canvas = undefined;\n", " this.rubberband_context = undefined;\n", " this.format_dropdown = undefined;\n", "\n", " this.image_mode = 'full';\n", "\n", " this.root = $('
    ');\n", " this._root_extra_style(this.root)\n", " this.root.attr('style', 'display: inline-block');\n", "\n", " $(parent_element).append(this.root);\n", "\n", " this._init_header(this);\n", " this._init_canvas(this);\n", " this._init_toolbar(this);\n", "\n", " var fig = this;\n", "\n", " this.waiting = false;\n", "\n", " this.ws.onopen = function () {\n", " fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n", " fig.send_message(\"send_image_mode\", {});\n", " if (mpl.ratio != 1) {\n", " fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n", " }\n", " fig.send_message(\"refresh\", {});\n", " }\n", "\n", " this.imageObj.onload = function() {\n", " if (fig.image_mode == 'full') {\n", " // Full images could contain transparency (where diff images\n", " // almost always do), so we need to clear the canvas so that\n", " // there is no ghosting.\n", " fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n", " }\n", " fig.context.drawImage(fig.imageObj, 0, 0);\n", " };\n", "\n", " this.imageObj.onunload = function() {\n", " fig.ws.close();\n", " }\n", "\n", " this.ws.onmessage = this._make_on_message_function(this);\n", "\n", " this.ondownload = ondownload;\n", "}\n", "\n", "mpl.figure.prototype._init_header = function() {\n", " var titlebar = $(\n", " '
    ');\n", " var titletext = $(\n", " '
    ');\n", " titlebar.append(titletext)\n", " this.root.append(titlebar);\n", " this.header = titletext[0];\n", "}\n", "\n", "\n", "\n", "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n", "\n", "}\n", "\n", "\n", "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n", "\n", "}\n", "\n", "mpl.figure.prototype._init_canvas = function() {\n", " var fig = this;\n", "\n", " var canvas_div = $('
    ');\n", "\n", " canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n", "\n", " function canvas_keyboard_event(event) {\n", " return fig.key_event(event, event['data']);\n", " }\n", "\n", " canvas_div.keydown('key_press', canvas_keyboard_event);\n", " canvas_div.keyup('key_release', canvas_keyboard_event);\n", " this.canvas_div = canvas_div\n", " this._canvas_extra_style(canvas_div)\n", " this.root.append(canvas_div);\n", "\n", " var canvas = $('');\n", " canvas.addClass('mpl-canvas');\n", " canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n", "\n", " this.canvas = canvas[0];\n", " this.context = canvas[0].getContext(\"2d\");\n", "\n", " var backingStore = this.context.backingStorePixelRatio ||\n", "\tthis.context.webkitBackingStorePixelRatio ||\n", "\tthis.context.mozBackingStorePixelRatio ||\n", "\tthis.context.msBackingStorePixelRatio ||\n", "\tthis.context.oBackingStorePixelRatio ||\n", "\tthis.context.backingStorePixelRatio || 1;\n", "\n", " mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n", "\n", " var rubberband = $('');\n", " rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n", "\n", " var pass_mouse_events = true;\n", "\n", " canvas_div.resizable({\n", " start: function(event, ui) {\n", " pass_mouse_events = false;\n", " },\n", " resize: function(event, ui) {\n", " fig.request_resize(ui.size.width, ui.size.height);\n", " },\n", " stop: function(event, ui) {\n", " pass_mouse_events = true;\n", " fig.request_resize(ui.size.width, ui.size.height);\n", " },\n", " });\n", "\n", " function mouse_event_fn(event) {\n", " if (pass_mouse_events)\n", " return fig.mouse_event(event, event['data']);\n", " }\n", "\n", " rubberband.mousedown('button_press', mouse_event_fn);\n", " rubberband.mouseup('button_release', mouse_event_fn);\n", " // Throttle sequential mouse events to 1 every 20ms.\n", " rubberband.mousemove('motion_notify', mouse_event_fn);\n", "\n", " rubberband.mouseenter('figure_enter', mouse_event_fn);\n", " rubberband.mouseleave('figure_leave', mouse_event_fn);\n", "\n", " canvas_div.on(\"wheel\", function (event) {\n", " event = event.originalEvent;\n", " event['data'] = 'scroll'\n", " if (event.deltaY < 0) {\n", " event.step = 1;\n", " } else {\n", " event.step = -1;\n", " }\n", " mouse_event_fn(event);\n", " });\n", "\n", " canvas_div.append(canvas);\n", " canvas_div.append(rubberband);\n", "\n", " this.rubberband = rubberband;\n", " this.rubberband_canvas = rubberband[0];\n", " this.rubberband_context = rubberband[0].getContext(\"2d\");\n", " this.rubberband_context.strokeStyle = \"#000000\";\n", "\n", " this._resize_canvas = function(width, height) {\n", " // Keep the size of the canvas, canvas container, and rubber band\n", " // canvas in synch.\n", " canvas_div.css('width', width)\n", " canvas_div.css('height', height)\n", "\n", " canvas.attr('width', width * mpl.ratio);\n", " canvas.attr('height', height * mpl.ratio);\n", " canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n", "\n", " rubberband.attr('width', width);\n", " rubberband.attr('height', height);\n", " }\n", "\n", " // Set the figure to an initial 600x600px, this will subsequently be updated\n", " // upon first draw.\n", " this._resize_canvas(600, 600);\n", "\n", " // Disable right mouse context menu.\n", " $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n", " return false;\n", " });\n", "\n", " function set_focus () {\n", " canvas.focus();\n", " canvas_div.focus();\n", " }\n", "\n", " window.setTimeout(set_focus, 100);\n", "}\n", "\n", "mpl.figure.prototype._init_toolbar = function() {\n", " var fig = this;\n", "\n", " var nav_element = $('
    ')\n", " nav_element.attr('style', 'width: 100%');\n", " this.root.append(nav_element);\n", "\n", " // Define a callback function for later on.\n", " function toolbar_event(event) {\n", " return fig.toolbar_button_onclick(event['data']);\n", " }\n", " function toolbar_mouse_event(event) {\n", " return fig.toolbar_button_onmouseover(event['data']);\n", " }\n", "\n", " for(var toolbar_ind in mpl.toolbar_items) {\n", " var name = mpl.toolbar_items[toolbar_ind][0];\n", " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", " var image = mpl.toolbar_items[toolbar_ind][2];\n", " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", "\n", " if (!name) {\n", " // put a spacer in here.\n", " continue;\n", " }\n", " var button = $('');\n", " button.click(method_name, toolbar_event);\n", " button.mouseover(tooltip, toolbar_mouse_event);\n", " nav_element.append(button);\n", " }\n", "\n", " // Add the status bar.\n", " var status_bar = $('');\n", " nav_element.append(status_bar);\n", " this.message = status_bar[0];\n", "\n", " // Add the close button to the window.\n", " var buttongrp = $('
    ');\n", " var button = $('');\n", " button.click(function (evt) { fig.handle_close(fig, {}); } );\n", " button.mouseover('Stop Interaction', toolbar_mouse_event);\n", " buttongrp.append(button);\n", " var titlebar = this.root.find($('.ui-dialog-titlebar'));\n", " titlebar.prepend(buttongrp);\n", "}\n", "\n", "mpl.figure.prototype._root_extra_style = function(el){\n", " var fig = this\n", " el.on(\"remove\", function(){\n", "\tfig.close_ws(fig, {});\n", " });\n", "}\n", "\n", "mpl.figure.prototype._canvas_extra_style = function(el){\n", " // this is important to make the div 'focusable\n", " el.attr('tabindex', 0)\n", " // reach out to IPython and tell the keyboard manager to turn it's self\n", " // off when our div gets focus\n", "\n", " // location in version 3\n", " if (IPython.notebook.keyboard_manager) {\n", " IPython.notebook.keyboard_manager.register_events(el);\n", " }\n", " else {\n", " // location in version 2\n", " IPython.keyboard_manager.register_events(el);\n", " }\n", "\n", "}\n", "\n", "mpl.figure.prototype._key_event_extra = function(event, name) {\n", " var manager = IPython.notebook.keyboard_manager;\n", " if (!manager)\n", " manager = IPython.keyboard_manager;\n", "\n", " // Check for shift+enter\n", " if (event.shiftKey && event.which == 13) {\n", " this.canvas_div.blur();\n", " event.shiftKey = false;\n", " // Send a \"J\" for go to next cell\n", " event.which = 74;\n", " event.keyCode = 74;\n", " manager.command_mode();\n", " manager.handle_keydown(event);\n", " }\n", "}\n", "\n", "mpl.figure.prototype.handle_save = function(fig, msg) {\n", " fig.ondownload(fig, null);\n", "}\n", "\n", "\n", "mpl.find_output_cell = function(html_output) {\n", " // Return the cell and output element which can be found *uniquely* in the notebook.\n", " // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n", " // IPython event is triggered only after the cells have been serialised, which for\n", " // our purposes (turning an active figure into a static one), is too late.\n", " var cells = IPython.notebook.get_cells();\n", " var ncells = cells.length;\n", " for (var i=0; i= 3 moved mimebundle to data attribute of output\n", " data = data.data;\n", " }\n", " if (data['text/html'] == html_output) {\n", " return [cell, data, j];\n", " }\n", " }\n", " }\n", " }\n", "}\n", "\n", "// Register the function which deals with the matplotlib target/channel.\n", "// The kernel may be null if the page has been refreshed.\n", "if (IPython.notebook.kernel != null) {\n", " IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n", "}\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "([], None)" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "plt.rcParams['figure.figsize'] = [5, 5]\n", "color='white'\n", "alpha=0.3\n", "geojsonIO.plotOne(petal)\n", "geojsonIO.addOne(spots)\n", "geojsonIO.addOne(center, col=color, a=alpha)\n", "geojsonIO.addOne(edge, col=color, a=alpha)\n", "geojsonIO.addOne(throat, col=color, a=alpha)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "

    Calling spotting events

    \n", "\n", "Our hypotheses for how these flowers make their spots are based on the concept of the initiation singular \"spotting events,\" where a cell reaches a threshold concentration in a transcription factor and anthocyanin production begins. These events are hard to identify, because spots merge and form big blobs with meaningless centers. \n", "\n", "So, as a first estimate of where these spotting events may have occurred on a petal, we have a program for manually calling spots as circles. Assuming anthcyanin production spreads outward (or anthocyanins diffuse? seems unlikely, vacuolar pigments) in a relatively even rate in all directions, the centroids of these circles are potentially sites where the spotting began.\n" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "usage: spotMarker.py [-h] [-o OUT] geojson jpg\n", "\n", "positional arguments:\n", " geojson Name of the geojson file to which you want to add\n", " estimated spots.\n", " jpg Name of the jpeg image that corresponds to the geojson of\n", " petal.\n", "\n", "optional arguments:\n", " -h, --help show this help message and exit\n", " -o OUT, --out OUT Name for outfile. If none given, modified in place.\n" ] } ], "source": [ "spotMarker=\"./make_polygons/package/makeFlowerPolygons/spotMarker.py\"\n", "$spotMarker -h" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To use spotMarker on one petal, looks like this:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "spotMarker=\"./make_polygons/package/makeFlowerPolygons/spotMarker.py\"\n", "geoj=\"./make_polygons/toy/newSpots/P773F2_right_polys.geojson\"\n", "jpeg=\"./dougRaster/Rotated_and_Cropped/plate3/P773F2.JPG\"\n", "\n", "$spotMarker $geoj $jpeg" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "scrolled": false }, "outputs": [ { "data": { "application/javascript": [ "/* Put everything inside the global mpl namespace */\n", "window.mpl = {};\n", "\n", "\n", "mpl.get_websocket_type = function() {\n", " if (typeof(WebSocket) !== 'undefined') {\n", " return WebSocket;\n", " } else if (typeof(MozWebSocket) !== 'undefined') {\n", " return MozWebSocket;\n", " } else {\n", " alert('Your browser does not have WebSocket support.' +\n", " 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n", " 'Firefox 4 and 5 are also supported but you ' +\n", " 'have to enable WebSockets in about:config.');\n", " };\n", "}\n", "\n", "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n", " this.id = figure_id;\n", "\n", " this.ws = websocket;\n", "\n", " this.supports_binary = (this.ws.binaryType != undefined);\n", "\n", " if (!this.supports_binary) {\n", " var warnings = document.getElementById(\"mpl-warnings\");\n", " if (warnings) {\n", " warnings.style.display = 'block';\n", " warnings.textContent = (\n", " \"This browser does not support binary websocket messages. \" +\n", " \"Performance may be slow.\");\n", " }\n", " }\n", "\n", " this.imageObj = new Image();\n", "\n", " this.context = undefined;\n", " this.message = undefined;\n", " this.canvas = undefined;\n", " this.rubberband_canvas = undefined;\n", " this.rubberband_context = undefined;\n", " this.format_dropdown = undefined;\n", "\n", " this.image_mode = 'full';\n", "\n", " this.root = $('
    ');\n", " this._root_extra_style(this.root)\n", " this.root.attr('style', 'display: inline-block');\n", "\n", " $(parent_element).append(this.root);\n", "\n", " this._init_header(this);\n", " this._init_canvas(this);\n", " this._init_toolbar(this);\n", "\n", " var fig = this;\n", "\n", " this.waiting = false;\n", "\n", " this.ws.onopen = function () {\n", " fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n", " fig.send_message(\"send_image_mode\", {});\n", " if (mpl.ratio != 1) {\n", " fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n", " }\n", " fig.send_message(\"refresh\", {});\n", " }\n", "\n", " this.imageObj.onload = function() {\n", " if (fig.image_mode == 'full') {\n", " // Full images could contain transparency (where diff images\n", " // almost always do), so we need to clear the canvas so that\n", " // there is no ghosting.\n", " fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n", " }\n", " fig.context.drawImage(fig.imageObj, 0, 0);\n", " };\n", "\n", " this.imageObj.onunload = function() {\n", " fig.ws.close();\n", " }\n", "\n", " this.ws.onmessage = this._make_on_message_function(this);\n", "\n", " this.ondownload = ondownload;\n", "}\n", "\n", "mpl.figure.prototype._init_header = function() {\n", " var titlebar = $(\n", " '
    ');\n", " var titletext = $(\n", " '
    ');\n", " titlebar.append(titletext)\n", " this.root.append(titlebar);\n", " this.header = titletext[0];\n", "}\n", "\n", "\n", "\n", "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n", "\n", "}\n", "\n", "\n", "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n", "\n", "}\n", "\n", "mpl.figure.prototype._init_canvas = function() {\n", " var fig = this;\n", "\n", " var canvas_div = $('
    ');\n", "\n", " canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n", "\n", " function canvas_keyboard_event(event) {\n", " return fig.key_event(event, event['data']);\n", " }\n", "\n", " canvas_div.keydown('key_press', canvas_keyboard_event);\n", " canvas_div.keyup('key_release', canvas_keyboard_event);\n", " this.canvas_div = canvas_div\n", " this._canvas_extra_style(canvas_div)\n", " this.root.append(canvas_div);\n", "\n", " var canvas = $('');\n", " canvas.addClass('mpl-canvas');\n", " canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n", "\n", " this.canvas = canvas[0];\n", " this.context = canvas[0].getContext(\"2d\");\n", "\n", " var backingStore = this.context.backingStorePixelRatio ||\n", "\tthis.context.webkitBackingStorePixelRatio ||\n", "\tthis.context.mozBackingStorePixelRatio ||\n", "\tthis.context.msBackingStorePixelRatio ||\n", "\tthis.context.oBackingStorePixelRatio ||\n", "\tthis.context.backingStorePixelRatio || 1;\n", "\n", " mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n", "\n", " var rubberband = $('');\n", " rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n", "\n", " var pass_mouse_events = true;\n", "\n", " canvas_div.resizable({\n", " start: function(event, ui) {\n", " pass_mouse_events = false;\n", " },\n", " resize: function(event, ui) {\n", " fig.request_resize(ui.size.width, ui.size.height);\n", " },\n", " stop: function(event, ui) {\n", " pass_mouse_events = true;\n", " fig.request_resize(ui.size.width, ui.size.height);\n", " },\n", " });\n", "\n", " function mouse_event_fn(event) {\n", " if (pass_mouse_events)\n", " return fig.mouse_event(event, event['data']);\n", " }\n", "\n", " rubberband.mousedown('button_press', mouse_event_fn);\n", " rubberband.mouseup('button_release', mouse_event_fn);\n", " // Throttle sequential mouse events to 1 every 20ms.\n", " rubberband.mousemove('motion_notify', mouse_event_fn);\n", "\n", " rubberband.mouseenter('figure_enter', mouse_event_fn);\n", " rubberband.mouseleave('figure_leave', mouse_event_fn);\n", "\n", " canvas_div.on(\"wheel\", function (event) {\n", " event = event.originalEvent;\n", " event['data'] = 'scroll'\n", " if (event.deltaY < 0) {\n", " event.step = 1;\n", " } else {\n", " event.step = -1;\n", " }\n", " mouse_event_fn(event);\n", " });\n", "\n", " canvas_div.append(canvas);\n", " canvas_div.append(rubberband);\n", "\n", " this.rubberband = rubberband;\n", " this.rubberband_canvas = rubberband[0];\n", " this.rubberband_context = rubberband[0].getContext(\"2d\");\n", " this.rubberband_context.strokeStyle = \"#000000\";\n", "\n", " this._resize_canvas = function(width, height) {\n", " // Keep the size of the canvas, canvas container, and rubber band\n", " // canvas in synch.\n", " canvas_div.css('width', width)\n", " canvas_div.css('height', height)\n", "\n", " canvas.attr('width', width * mpl.ratio);\n", " canvas.attr('height', height * mpl.ratio);\n", " canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n", "\n", " rubberband.attr('width', width);\n", " rubberband.attr('height', height);\n", " }\n", "\n", " // Set the figure to an initial 600x600px, this will subsequently be updated\n", " // upon first draw.\n", " this._resize_canvas(600, 600);\n", "\n", " // Disable right mouse context menu.\n", " $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n", " return false;\n", " });\n", "\n", " function set_focus () {\n", " canvas.focus();\n", " canvas_div.focus();\n", " }\n", "\n", " window.setTimeout(set_focus, 100);\n", "}\n", "\n", "mpl.figure.prototype._init_toolbar = function() {\n", " var fig = this;\n", "\n", " var nav_element = $('
    ')\n", " nav_element.attr('style', 'width: 100%');\n", " this.root.append(nav_element);\n", "\n", " // Define a callback function for later on.\n", " function toolbar_event(event) {\n", " return fig.toolbar_button_onclick(event['data']);\n", " }\n", " function toolbar_mouse_event(event) {\n", " return fig.toolbar_button_onmouseover(event['data']);\n", " }\n", "\n", " for(var toolbar_ind in mpl.toolbar_items) {\n", " var name = mpl.toolbar_items[toolbar_ind][0];\n", " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", " var image = mpl.toolbar_items[toolbar_ind][2];\n", " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", "\n", " if (!name) {\n", " // put a spacer in here.\n", " continue;\n", " }\n", " var button = $('');\n", " button.click(method_name, toolbar_event);\n", " button.mouseover(tooltip, toolbar_mouse_event);\n", " nav_element.append(button);\n", " }\n", "\n", " // Add the status bar.\n", " var status_bar = $('');\n", " nav_element.append(status_bar);\n", " this.message = status_bar[0];\n", "\n", " // Add the close button to the window.\n", " var buttongrp = $('
    ');\n", " var button = $('');\n", " button.click(function (evt) { fig.handle_close(fig, {}); } );\n", " button.mouseover('Stop Interaction', toolbar_mouse_event);\n", " buttongrp.append(button);\n", " var titlebar = this.root.find($('.ui-dialog-titlebar'));\n", " titlebar.prepend(buttongrp);\n", "}\n", "\n", "mpl.figure.prototype._root_extra_style = function(el){\n", " var fig = this\n", " el.on(\"remove\", function(){\n", "\tfig.close_ws(fig, {});\n", " });\n", "}\n", "\n", "mpl.figure.prototype._canvas_extra_style = function(el){\n", " // this is important to make the div 'focusable\n", " el.attr('tabindex', 0)\n", " // reach out to IPython and tell the keyboard manager to turn it's self\n", " // off when our div gets focus\n", "\n", " // location in version 3\n", " if (IPython.notebook.keyboard_manager) {\n", " IPython.notebook.keyboard_manager.register_events(el);\n", " }\n", " else {\n", " // location in version 2\n", " IPython.keyboard_manager.register_events(el);\n", " }\n", "\n", "}\n", "\n", "mpl.figure.prototype._key_event_extra = function(event, name) {\n", " var manager = IPython.notebook.keyboard_manager;\n", " if (!manager)\n", " manager = IPython.keyboard_manager;\n", "\n", " // Check for shift+enter\n", " if (event.shiftKey && event.which == 13) {\n", " this.canvas_div.blur();\n", " event.shiftKey = false;\n", " // Send a \"J\" for go to next cell\n", " event.which = 74;\n", " event.keyCode = 74;\n", " manager.command_mode();\n", " manager.handle_keydown(event);\n", " }\n", "}\n", "\n", "mpl.figure.prototype.handle_save = function(fig, msg) {\n", " fig.ondownload(fig, null);\n", "}\n", "\n", "\n", "mpl.find_output_cell = function(html_output) {\n", " // Return the cell and output element which can be found *uniquely* in the notebook.\n", " // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n", " // IPython event is triggered only after the cells have been serialised, which for\n", " // our purposes (turning an active figure into a static one), is too late.\n", " var cells = IPython.notebook.get_cells();\n", " var ncells = cells.length;\n", " for (var i=0; i= 3 moved mimebundle to data attribute of output\n", " data = data.data;\n", " }\n", " if (data['text/html'] == html_output) {\n", " return [cell, data, j];\n", " }\n", " }\n", " }\n", " }\n", "}\n", "\n", "// Register the function which deals with the matplotlib target/channel.\n", "// The kernel may be null if the page has been refreshed.\n", "if (IPython.notebook.kernel != null) {\n", " IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n", "}\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "(petal,spots,\n", "center, edge,throat,\n", " _,_,_) = geojsonIO.parseGeoJson(\"./make_polygons/toy/\"\n", " \"/newSpots/P297F2_right_polys.geojson\")\n", "## use our custom plotting functions\n", "geojsonIO.plotOne(petal)\n", "_=geojsonIO.addOne(spots)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This petal has the following measurements (\"traits\"):" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": true }, "outputs": [ { "data": { "text/plain": [ "{'plantName': None,\n", " 'flowerName': 'P297F2',\n", " 'petalName': 'right',\n", " 'geojson': '/home/daniel/Documents/cooley_lab/mimulusSpeckling/make_polygons/polygons/P297F2/right/P297F2_right_polys.geojson',\n", " 'petal': ,\n", " 'spots': ,\n", " 'center': ,\n", " 'edge': ,\n", " 'throat': ,\n", " 'biggestSpotArea': 0.11177826873338739,\n", " 'smallestSpotArea': 0.016721179916812177,\n", " 'avgSpotSize': 0.043707264626960035,\n", " 'medSpotSize': 0.031442029551284681,\n", " 'nuSpots': 6,\n", " 'nuSpotsContainedInCenter': 0,\n", " 'nuSpotsTouchCenter': 5,\n", " 'nuSpotsMostlyInCenter': 1,\n", " 'nuSpotCentroidsInCenter': 1,\n", " 'avgDist2CenterAllSpots': 0.37681071000591493,\n", " 'avgDist2CenterCenterSpots': 0.17899763180246953,\n", " 'propSpotsInCenter': 0.3202816260213142,\n", " 'centerCoveredbySpots': 0.1681477151460129,\n", " 'spotOnCentroid': False,\n", " 'nuSpotsContainedInEdge': 1,\n", " 'nuSpotsTouchEdge': 2,\n", " 'propSpotsInEdge': 0.408151835595078,\n", " 'nuSpotsMostlyInEdge': 2,\n", " 'edgeCoveredbySpots': 0.29705388680544165,\n", " 'nuSpotsTouchActualEdge': 2,\n", " 'realEdgeSpotted': 0.22912916987734203,\n", " 'avgDistSpotEdge2Edge': 0.17621697063871114,\n", " 'avgDistSpotCentroid2Edge': 0.26412825783179705,\n", " 'throatCoveredbySpots': 0.5080893047095963,\n", " 'propSpotsInThroat': 0.2715665383836074,\n", " 'nuSpotsTouchThroat': 4,\n", " 'nuSpotsMostlyInThroat': 3,\n", " 'nuSpotsTouchCut': 2,\n", " 'nuProxSpots': 4,\n", " 'propSpotsInProx': 0.47527832275487414,\n", " 'proxCoveredbySpots': 0.24963940866251919,\n", " 'nuDistSpots': 2,\n", " 'propSpotsInDist': 0.5247216772451259,\n", " 'distCoveredbySpots': 0.2748112629904847,\n", " 'nuQuadISpots': 2,\n", " 'propSpotsInQuadI': 0.2318585808247405,\n", " 'quadICoveredbySpots': 0.2292068403072505,\n", " 'nuQuadIISpots': 2,\n", " 'propSpotsInQuadII': 0.2434197419301336,\n", " 'quadIICoveredbySpots': 0.27280335026237895,\n", " 'nuQuadIIISpots': 1,\n", " 'propSpotsInQuadIII': 0.284570590244836,\n", " 'quadIIICoveredbySpots': 0.2838281063607832,\n", " 'nuQuadIVSpots': 1,\n", " 'propSpotsInQuadIV': 0.2401510870002894,\n", " 'quadIVCoveredbySpots': 0.26484137192947865}" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "vars(flP)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "So what are all these numbers?...\n", "***" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "

    Traits overview

    " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "[

    General polygon:

    ](#GP)\n", "\n", "explanation | name of trait in code/data\n", "------------|---------------------------\n", "Biggest spot | [biggestSpotArea](#biggestSpotArea)\n", "Smallest spot (is this useful at all?) | [smallestSpotArea](#smallestSpotArea)\n", "Avg size | [avgSpotsize](#avgSpotsize)\n", "Median size | [medSpotsize](#medSpotsize)\n", "Number of spots | [nuSpots](#nuSpots)\n", "\n", "[

    Centeredness:

    ](#centerStats)\n", "\n", "explanation | name of trait in code/data\n", "------------|---------------------------\n", "Number of spots entirely contained in center zone | [nuSpotsContainedInCenter](#nuSpotsContainedInCenter)\n", "Number of spots touching center zone | [nuSpotsTouchCenter](#nuSpotsTouchCenter)\n", "Number of spots mostly in center zone | [nuSpotsMostlyInCenter](#nuSpotsMostlyInCenter)\n", "Number of spot centroids in center zone | [nuSpotCentroidsInCenter](#nuSpotCentroidsInCenter)\n", "avg spot centroid distance to center of all spots | [avgDist2CenterAllSpots](#avgDist2CenterAllSpots)\n", "avg spot centroid distance to center of spots in center zone (excludes edge spots) | [avgDist2CenterCenterSpots](#avgDist2CenterCenterSpots)\n", "Percent of total spot area in center | [propSpotsInCenter](#propSpotsInCenter)\n", "Percent of center zone covered by spots | [centerCoveredbySpots](#centerCoveredbySpots)\n", "Spot on centroid | [spotOnCentroid](#spotOnCentroid)\n", "\n", "[

    Edgeness:

    ](#edgeStats)\n", "\n", "explanation | name of trait in code/data\n", "------------|---------------------------\n", "Number of spots entirely contained in edge zone | [nuSpotsContainedInEdge](#nuSpotsContainedInEdge)\n", "Number of spots touching edge zone in some way | [nuSpotsTouchEdge](#nuSpotsTouchEdge)\n", "Percent of total spot area in edge | [propSpotsInEdge](#propSpotsInEdge)\n", "Number of spots mostly in edge zone (>Percent of) | [nuSpotsMostlyInEdge](#nuSpotsMostlyInEdge)\n", "Percent of edge zone covered | [edgeCoveredbySpots](#edgeCoveredbySpots)\n", "Number of spots touching actual edge (not zone) | [nuSpotsTouchActualEdge](#nuSpotsTouchActualEdge)\n", "Percent of length of real petal edge (not zone) covered | [realEdgeSpotted](#realEdgeSpotted)\n", "avg dist of outline of spots to edge | [avgDistSpotEdge2Edge](#avgDistSpotEdge2Edge)\n", "avg dist of centroid of spots to edge | [avgDistSpotCentroid2Edge](#avgDistSpotCentroid2Edge)\n", "\n", "These last two exclude spots that contact the basal cut on the petal, if possible and measure the distance to the actual edge of the petal, not the zone.\n", "\n", "[

    Throat:

    ](#throatStats)\n", "\n", "explanation | name of trait in code/data\n", "------------|---------------------------\n", "Percent of throat zone covered | [throatCoveredbySpots](#throatCoveredbySpots)\n", "Percent of total spot area in throat | [propSpotsInthroat](#propSpotsInthroat)\n", "Number of spots touching throat zone | [nuSpotsTouchThroat](#nuSpotsTouchThroat)\n", "Number of spots mostly in throat zone | [nuSpotsMostlyInThroat](#nuSpotsMostlyInThroat)\n", "Number of spots touching actual cut/base | [nuSpotsTouchCut](#nuSpotsTouchCut)\n", "\n", "[

    Regions of petal, by distal/proximal half, or quadrant.

    ](#regions)\n", "\n", "This is a pretty crude division of the petals into quadrants. The quadrants of the petal are designated based on the centroid and the bounding box sides as othogonal bases. The quadrants are not necesarily equal in size. Probably very rarely, actually.\n", "\n", "explanation | name of trait in code/data\n", "------------|---------------------------\n", "Spots with centroids in proximal half (quadrants I and II) | [nuProxSpots](#nuProxSpots)\n", "Percent of total spot area in proximal half (quadrants I and II) | [propSpotsInProx](#propSpotsInProx)\n", "Percent of proximal half covered by spots (quadrants I and II) | [proxCoveredbySpots](#proxCoveredbySpots)\n", "\n", "explanation | name of trait in code/data\n", "------------|---------------------------\n", "Spots with centroids in Distal half (quadrants III and IV) | [nuDistSpots](#nuDistSpots)\n", "Percent of total spot area in Distal half (quadrants III and IV) | [propSpotsInDist](#propSpotsInDist)\n", "Percent of Distal half covered by spots (quadrants III and IV) | [distCoveredbySpots](#distCoveredbySpots)\n", "\n", "explanation | name of trait in code/data\n", "------------|---------------------------\n", "Spots with centroids in quadrant I | [nuQuadISpots](#nuQuadSpots)\n", "Percent of total spot area in quadrant I | [propSpotsInQuadI](#propSpotsInQuad)\n", "Percent of quadrant I covered by spots | [quadICoveredbySpots](#quadCoveredbySpots)\n", "\n", "explanation | name of trait in code/data\n", "------------|---------------------------\n", "Spots with centroids in quadrant II | [nuQuadIISpots](#nuQuadSpots)\n", "Percent of total spot area in quadrant II | [propSpotsInQuadII](#propSpotsInQuad)\n", "Percent of quadrant II covered by spots | [quadIICoveredbySpots](#quadCoveredbySpots)\n", "\n", "explanation | name of trait in code/data\n", "------------|---------------------------\n", "Spots with centroids in quadrant III | [nuQuadIIISpots](#nuQuadSpots)\n", "Percent of total spot area in quadrant III | [quadIIISpotsInQuadIII](#propSpotsInQuad)\n", "Percent of quadrant III covered by spots | [quadIIICoveredbySpots](#quadCoveredbySpots)\n", "\n", "explanation | name of trait in code/data\n", "------------|---------------------------\n", "Spots with centroids in quadrant IV | [nuQuadIVSpots](#nuQuadSpots)\n", "Percent of total spot area in quadrant IV | [propSpotsInQuadIV](#propSpotsInQuad)\n", "Percent of quadrant IV covered by spots | [quadIVCoveredbySpots](#quadCoveredbySpots)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "

    General polygon measurements:

    " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "These are pretty much self-explanatory. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "*** \n", "biggestSpotArea\n", "\n", "We take the area of largest spot polygon detected:\n", "\n", "`biggestSpotArea = max([ i.area for i in self.spots ])`" ] }, { "cell_type": "code", "execution_count": 87, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.11177826873338739" ] }, "execution_count": 87, "metadata": {}, "output_type": "execute_result" } ], "source": [ "flP.biggestSpotArea" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "***\n", "smallestSpotArea\n", "\n", "We take the area of smallest spot polygon detected. Seems probably useless, but it's easy to do:\n", "\n", "`smallestSpotArea = min([ i.area for i in self.spots ])`" ] }, { "cell_type": "code", "execution_count": 88, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.016721179916812177" ] }, "execution_count": 88, "metadata": {}, "output_type": "execute_result" } ], "source": [ "flP.smallestSpotArea" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "***\n", "avgSpotsize\n", "\n", "We take the mean area of all spot polygons detected:\n", "\n", "`avgSpotSize = mean([ i.area for i in self.spots ])`" ] }, { "cell_type": "code", "execution_count": 89, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.043707264626960035" ] }, "execution_count": 89, "metadata": {}, "output_type": "execute_result" } ], "source": [ "flP.avgSpotSize" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "***\n", "medSpotsize\n", "\n", "We take the median value of all spot polygon areas detected:\n", "\n", "`medSpotSize = np.median([ i.area for i in self.spots ])`" ] }, { "cell_type": "code", "execution_count": 90, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.031442029551284681" ] }, "execution_count": 90, "metadata": {}, "output_type": "execute_result" } ], "source": [ "flP.medSpotSize" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "***\n", "nuSpots\n", "\n", "This is the total number of distinct spots detected:\n", "\n", "` nuSpots = len([ i.area for i in self.spots ])`" ] }, { "cell_type": "code", "execution_count": 91, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "6" ] }, "execution_count": 91, "metadata": {}, "output_type": "execute_result" } ], "source": [ "flP.nuSpots" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "[back to Traits overview](#TraitOverview)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "

    Centeredness:

    \n", "\n", "In our digitizing pipeline we define a center zone that occupies the center 50% of the petal (translucent white zone) and a centroid of the petal that we consider the true center of the petal (blue plus sign): " ] }, { "cell_type": "code", "execution_count": 84, "metadata": { "scrolled": false }, "outputs": [ { "data": { "text/plain": [ "[]" ] }, "execution_count": 84, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
    " ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "geojsonIO.plotOne(flP.petal); geojsonIO.addOne(flP.spots)\n", "geojsonIO.addOne(flP.center, col='white', a=0.5)\n", "plt.plot(flP.petal.centroid.x,flP.petal.centroid.y, 'b+', markersize=15, mew = 3)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "***\n", "nuSpotsContainedInCenter\n", "\n", "This reports the number of spots contained entirely within the center zone, with no part of any of the candidate spots containing pixels outside of the center zone. This is a picky statistic, in this example petal no spots meet the criteria. \n", "\n", "`nuSpotsContainedInCenter = sum([ i.within(self.center) for i in self.spots ])`\n" ] }, { "cell_type": "code", "execution_count": 86, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0" ] }, "execution_count": 86, "metadata": {}, "output_type": "execute_result" } ], "source": [ "flP.nuSpotsContainedInCenter" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "***\n", "nuSpotsTouchCenter\n", "\n", "Reports the number of spot polygons that have any contact at all with the center zone. This is the least picky center statistic.\n", "\n", "`nuSpotsTouchCenter = sum([ i.intersects(self.center) for i in self.spots ])`" ] }, { "cell_type": "code", "execution_count": 92, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "5" ] }, "execution_count": 92, "metadata": {}, "output_type": "execute_result" } ], "source": [ "flP.nuSpotsTouchCenter" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "***\n", "nuSpotsMostlyInCenter\n", "\n", "This reports the number of spot polygons that have at least 50% of their area in the center zone:\n", "\n", "`spotsMostlyInCenter = [ i for i in self.spots if (i.intersection(self.center).area / i.area > 0.5) ]`\n", "\n", "`nuSpotsMostlyInCenter = len(spotsMostlyInCenter)`" ] }, { "cell_type": "code", "execution_count": 93, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "1" ] }, "execution_count": 93, "metadata": {}, "output_type": "execute_result" } ], "source": [ "flP.nuSpotsMostlyInCenter" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "nuSpotCentroidsInCenter\n", "\n", "In addition the general petal centroid, the spot polygons each have their own centroid. This statistic reports if this centroid is within the center zone.\n", "\n", "`nuSpotCentroidsInCenter = sum([ i.centroid.intersects(self.center) for i in self.spots ] )`" ] }, { "cell_type": "code", "execution_count": 95, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "1" ] }, "execution_count": 95, "metadata": {}, "output_type": "execute_result" } ], "source": [ "flP.nuSpotCentroidsInCenter" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "avgDist2CenterAllSpots\n", "\n", "This calculates the average distance between centroids of our spots to our petal centroid. This includes all of our spots, whereas the next statistic [avgDist2CenterCenterSpots](#avgDist2CenterCenterSpots) excludes spots not in the center zone. \n", "\n", "`avgDist2CenterAllSpots = mean([ i.centroid.distance(self.center.centroid) for i in self.spots ])`" ] }, { "cell_type": "code", "execution_count": 96, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.37681071000591493" ] }, "execution_count": 96, "metadata": {}, "output_type": "execute_result" } ], "source": [ "flP.avgDist2CenterAllSpots" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "***\n", "avgDist2CenterCenterSpots\n", "\n", "This is the same as [avgDist2CenterAllSpots](#avgDist2CenterAllSpots), but only considers spots within the center zone. This was done to make possible the search for highly centered petal spots even when numerous spots exist on the edge of a petal.\n", "\n", "`spotsMostlyInCenter = [ i for i in self.spots if (i.intersection(self.center).area / i.area > 0.5) ]`\n", "`avgDist2CenterCenterSpots = mean([ i.centroid.distance(self.center.centroid) for i in spotsMostlyInCenter ])`" ] }, { "cell_type": "code", "execution_count": 97, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.17899763180246953" ] }, "execution_count": 97, "metadata": {}, "output_type": "execute_result" } ], "source": [ "flP.avgDist2CenterCenterSpots" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "***\n", "propSpotsInCenter\n", "\n", "This reports the percentage of total spotted area to be found in the center zone. This is meant to be more robust than simple counts of polygons, against merging errors in the spots. This should be equivalent to asking how many of our red pixels are found in the center zone of the petal. \n", "\n", "`partSpotsInCenter = [ i.intersection(self.center) for i in self.spots if i.intersects(self.center) ]`\n", "\n", "`propSpotsInCenter = partSpotsInCenter.area / self.spots.area`" ] }, { "cell_type": "code", "execution_count": 99, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.3202816260213142" ] }, "execution_count": 99, "metadata": {}, "output_type": "execute_result" } ], "source": [ "flP.propSpotsInCenter" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "***\n", "centerCoveredbySpots\n", "\n", "This is similar to [propSpotsInCenter](#propSpotsInCenter), but reports the percentage of the center zone occupied by red pixels.\n", "\n", "`partSpotsInCenter = [ i.intersection(self.center) for i in self.spots if i.intersects(self.center) ]`\n", "\n", "`centerCoveredbySpots = partSpotsInCenter.area / self.center.area`" ] }, { "cell_type": "code", "execution_count": 100, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.1681477151460129" ] }, "execution_count": 100, "metadata": {}, "output_type": "execute_result" } ], "source": [ "flP.centerCoveredbySpots" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "***\n", "spotOnCentroid\n", "\n", "This simply reports whether any spot polygons are sitting on our petal centroid. True/False.\n", "\n", "`spotOnCentroid = any([ i.intersects(self.petal.centroid) for i in self.spots ])`" ] }, { "cell_type": "code", "execution_count": 102, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "False" ] }, "execution_count": 102, "metadata": {}, "output_type": "execute_result" } ], "source": [ "flP.spotOnCentroid" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "[back to Traits overview](#TraitOverview)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "

    Edgeness:

    \n", "\n", "In our digitizing pipeline we define an center zone that occupies the center 50% of the petal. The remaining inner margin that contains the other 50% is then divided into an outer \"edge\" zone that buffers the natural edge of the petal, and an inner \"throat\" zone, that buffers the line of separation (\"cut\") of the petal from its flower. \n", "\n", "Here the edge is the translucent white zone: " ] }, { "cell_type": "code", "execution_count": 104, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
    " ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "geojsonIO.plotOne(flP.petal); geojsonIO.addOne(flP.spots)\n", "geojsonIO.addOne(flP.edge, col='white', a=0.5)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "***\n", "nuSpotsContainedInEdge\n", "\n", "Number of spots entirely within the edge zone. A picky statistic, very sensitive to spot-merging.\n", "\n", "`nuSpotsContainedInEdge = sum([ i.within(self.edge) for i in self.spots ])`" ] }, { "cell_type": "code", "execution_count": 107, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/plain": [ "1" ] }, "execution_count": 107, "metadata": {}, "output_type": "execute_result" } ], "source": [ "flP.nuSpotsContainedInEdge" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "***\n", "nuSpotsTouchEdge\n", "\n", "Number of spots that have any part of their area in the edge zone.\n", "\n", "`nuSpotsTouchEdge = sum([ i.intersects(self.edge) for i in self.spots ]`" ] }, { "cell_type": "code", "execution_count": 108, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "2" ] }, "execution_count": 108, "metadata": {}, "output_type": "execute_result" } ], "source": [ "flP.nuSpotsTouchEdge" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "***\n", "propSpotsInEdge\n", "\n", "Proportion of the total spotted (red) area of the petal that's in the edge zone. Code note shown, it's complicated, so see the FlowerPetal.py source code if you're curious." ] }, { "cell_type": "code", "execution_count": 111, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/plain": [ "0.408151835595078" ] }, "execution_count": 111, "metadata": {}, "output_type": "execute_result" } ], "source": [ "flP.propSpotsInEdge" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "***\n", "nuSpotsMostlyInEdge\n", "\n", "Number of spots that have 50% or more area within the edge zone.\n", "\n", "`nuSpotsMostlyInEdge = len([ i for i in self.spots if (i.intersection(self.edge).area / i.area > 0.5) ])`" ] }, { "cell_type": "code", "execution_count": 112, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/plain": [ "2" ] }, "execution_count": 112, "metadata": {}, "output_type": "execute_result" } ], "source": [ "flP.nuSpotsMostlyInEdge" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "***\n", "edgeCoveredbySpots\n", "\n", "The proportion of the edge zone that is spotted (red). \n", "\n", "` partSpotsInEdge = [ i.intersection(self.edge) for i in self.spots if i.intersects(self.edge) ]\n", "edgeCoveredbySpots = partSpotsInEdge.area / self.edge.area `\n" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.29705388680544165" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "flP.edgeCoveredbySpots" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "***\n", "nuSpotsTouchActualEdge\n", "\n", "The number of spots that run to the physical edge (\"rim\") of the petal. This rim does not include the throat.\n", "\n", "`nuSpotsTouchActualEdge = sum([ i.intersects(self.petal.exterior) for i in partSpotsInEdge ])`" ] }, { "cell_type": "code", "execution_count": 115, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "2" ] }, "execution_count": 115, "metadata": {}, "output_type": "execute_result" } ], "source": [ "flP.nuSpotsTouchActualEdge" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "***\n", "realEdgeSpotted\n", "\n", "The proportion of the length of physical edge of the petal (\"rim\") that is spotted (red). \n", "\n", "`spotEdges = sum([ i.intersection(self.petal.exterior).length for i in partSpotsInEdge ])\n", "realEdgeSpotted = spotEdges / self.petal.exterior.length`\n" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.22912916987734203" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "flP.realEdgeSpotted" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "***\n", "avgDistSpotEdge2Edge\n", "\n", "The average distance between the closest edge of all spots and the petal rim. \n", "\n", "`avgDistSpotEdge2Edge = mean([ petalRim.distance(i) for i in self.spots ])`" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.17621697063871114" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "flP.avgDistSpotEdge2Edge" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "***\n", "avgDistSpotCentroid2Edge\n", "\n", "The average distance between all spot centroids and the petal rim. \n", " \n", "`avgDistSpotCentroid2Edge = mean([ petalRim.distance(i.centroid) for i in self.spots ])`" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "scrolled": false }, "outputs": [ { "data": { "text/plain": [ "0.26412825783179705" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "flP.avgDistSpotCentroid2Edge" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "[back to Traits overview](#TraitOverview)" ] }, { "cell_type": "markdown", "metadata": { "scrolled": true }, "source": [ "

    Throat:

    \n", "\n", "These are measurements pertaining to spots that interact with the basal zone of a petal, closest to where it was separated from the rest of the flower. The throat is the translucent white zone in the map:" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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    " ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "geojsonIO.plotOne(flP.petal); geojsonIO.addOne(flP.spots)\n", "geojsonIO.addOne(flP.throat, col='white', a=0.5)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "***\n", "throatCoveredbySpots\n", "\n", "Proportion of the throat area that is spotted (red). \n", "
    \n", "`\n", " partSpotsInThroat = [ i.intersection(self.throat) for i in self.spots if i.intersects(self.throat) ]\n", "throatCoveredbySpots = partSpotsInThroat.area / self.throat.area\n", "`" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.5080893047095963" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "flP.throatCoveredbySpots" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "***\n", "propSpotsInthroat \n", "
    \n", "Proportion of the total spotted (red) area of petal that is within the throat. \n", "
    \n", "`\n", "propSpotsInThroat = partSpotsInThroat.area / self.spots.area\n", "`" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.2715665383836074" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "flP.propSpotsInThroat" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "***\n", "nuSpotsTouchThroat\n", "\n", "The number of spots that touch the throat zone in any way. \n", "\n", "`\n", "nuSpotsTouchThroat = sum([ i.intersects(self.throat) for i in self.spots ])\n", "`" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "4" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "flP.nuSpotsTouchThroat" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "***\n", "nuSpotsMostlyInThroat \n", "\n", "Number of spots with more than 50% of their area in the throat zone. \n", "
    \n", "`\n", "nuSpotsMostlyInThroat = len([ i for i in self.spots if (i.intersection(self.throat).area / i.area > 0.5) ])\n", "`" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "3" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "flP.nuSpotsMostlyInThroat" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "***\n", "nuSpotsTouchCut\n", "\n", "Number of spots that touch the basal cut of the petal. \n", "\n", "`\n", "nuSpotsTouchCut = len([ i for i in partSpotsInThroat if i.intersects(self.petal.exterior) ])\n", "`\n" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/plain": [ "2" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "flP.nuSpotsTouchCut" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "[back to Traits overview](#TraitOverview)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "

    Regions of petal, by distal/proximal half, or quadrant:

    " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "

    Spots in proximal half

    \n", "\n", "The translucent-shaded half of the petal is examined." ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
    " ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "geojsonIO.plotOne(flP.petal); geojsonIO.addOne(flP.spots)\n", "proxBox=sg.box(-1,0,1,1)\n", "geojsonIO.addOne(proxBox, col='white', a=0.5)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "***\n", "nuProxSpots\n", "\n", "Number of spots that touch the proximal half of the petal.\n", "\n", "`\n", "nuProxSpots = sum([ i.intersects(petalProx) for i in self.spots ])\n", "`" ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "4" ] }, "execution_count": 28, "metadata": {}, "output_type": "execute_result" } ], "source": [ "flP.nuProxSpots" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "***\n", "\n", "propSpotsInProx \n", "\n", "Percentage of total spot (red) area of petal that is found in the proximal half of petal. \n", "
    \n", "`\n", "propSpotsInProx = spottedSurfaceProx.area / self.spots.area\n", "`" ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.47527832275487414" ] }, "execution_count": 30, "metadata": {}, "output_type": "execute_result" } ], "source": [ "flP.propSpotsInProx" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "***\n", "proxCoveredbySpots \n", "
    \n", "Proportion of area of proximal half of petal covered by spots. \n", "
    \n", "`\n", "proxCoveredbySpots = spottedSurfaceProx.area / petalProx.area\n", "`" ] }, { "cell_type": "code", "execution_count": 32, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.24963940866251919" ] }, "execution_count": 32, "metadata": {}, "output_type": "execute_result" } ], "source": [ "flP.proxCoveredbySpots" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "[back to Traits overview](#TraitOverview)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "

    Spots in distal half

    \n", "\n", "The translucent-shaded half of the petal is examined." ] }, { "cell_type": "code", "execution_count": 34, "metadata": { "scrolled": true }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
    " ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "geojsonIO.plotOne(flP.petal); geojsonIO.addOne(flP.spots)\n", "distBox=sg.box(-1,-1,1,0)\n", "geojsonIO.addOne(distBox, col='white', a=0.5)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "***\n", "nuDistSpots\n", "\n", "Number of spots that touch the distal half of the petal.\n", "\n", "`\n", "nuDistSpots = sum([ i.intersects(petalDist) for i in self.spots ])\n", "`" ] }, { "cell_type": "code", "execution_count": 36, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "2" ] }, "execution_count": 36, "metadata": {}, "output_type": "execute_result" } ], "source": [ "flP.nuDistSpots" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "***\n", "propSpotsInDist\n", "\n", "Percentage of total spot (red) area of petal that is found in the distal half of petal. \n", "
    \n", "`\n", "propSpotsInDist = spottedSurfaceDist.area / self.spots.area\n", "`" ] }, { "cell_type": "code", "execution_count": 37, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.5247216772451259" ] }, "execution_count": 37, "metadata": {}, "output_type": "execute_result" } ], "source": [ "flP.propSpotsInDist" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "***\n", "distCoveredbySpots\n", "\n", "Proportion of area of distal half of petal covered by spots.\n", "\n", "`\n", "distCoveredbySpots = spottedSurfaceDist.area / petalDist.area\n", "`" ] }, { "cell_type": "code", "execution_count": 38, "metadata": { "scrolled": false }, "outputs": [ { "data": { "text/plain": [ "0.2748112629904847" ] }, "execution_count": 38, "metadata": {}, "output_type": "execute_result" } ], "source": [ "flP.distCoveredbySpots" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "[back to Traits overview](#TraitOverview)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "

    Spots in quadrants

    " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We also check the number of spots in a quadrant, the proportion of all spots found in each quadrant, and the percent coverage of a quadrant by spots.\n", "\n", "This is a pretty crude division of the petals into quadrants. The quadrants of the petal are designated based on the centroid and the bounding box sides as othogonal bases. The quadrants are not necesarily equal in size. Probably very rarely, actually. \n", "\n", "This was also the beginning of analysis of symmetry, but a whole lot of work needs to happen there..." ] }, { "cell_type": "code", "execution_count": 53, "metadata": { "scrolled": false }, "outputs": [ { "data": { "text/plain": [ "Text(0.25, -0.25, 'IV')" ] }, "execution_count": 53, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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jj7FixYpAXp0WKqiv7xYvXny+s9GgQZpIg8Uvv0CDBnDTTa5E+izwCXAD0APvEml2ztJwH38s4+K0o7b5UlJkHUFga+2aydk5dfNmcN5G+4Ug6CkZJAzguNVBmGjaNGmV3LBhAwcOHLA6nByCNpkuXryYG2+8kfT0dO65B+6/3+qI1NSp8unw+uthu2Pg2p3A30jxczOHJNwPOAfPON8oNaGay9kzuZK1YfiksWO9eTM0aQJVqsBhII++b0XSWGSS9agoKFvW6mh8V6MG1K9vdRR5C9pkumbNGlIcH5179dJ7pMFgyxbYt0+2bwU2Ap8is3iYPbYvApiIdDLpi1S4ad9eOprVqyfNeco3S5fKel7BuwU1ZzLdskU6azmL4xQ+2rloWOJYv/xy8CahcBG0Kerpp5+mX79+gPRCU9ZZtkzK4b3+unx9HzAFuV/lzwHyEUgnk2+R5A1SYGD3bujcGdav9+PJi5AQHccPuGLfulXuATt79Ocxf3mRFsq9ePOjzbweaJl9+gwVcH/8IcMmOnWCkyflsQeACQT2DycSmRj6MPAvMln06dPQpYteoRZ15ZDhVcnJ8O+/MosMwDIguOcY8b8s5P8l3FxyiazvvvtuEhISrA0mm6BOptu2bbM6hCLrzz/l3uiZM/L1rcBp4EOs+aOJRN40awM/AL2QhKpXqMpZp2XlSqhWDZo2hRSkGlJRNhLYD8TFyb3GcPHVV9IZ9eDBg8THx1sdznlBm0wnTJjAtGnTAOlYoPwvKwuOHpWyXd26QWIi3AIkA1ORq4BgqHsaA8xEZjVxXqFqQi26rnWsly2TtfO+aVFv6t3qWL/6qvQzCBelSgXnh4OgTKbz5s3joYceAuD992XqKuV/hw7JJ/uePSWRDkKKh8cSHEk0uxhgBjkTqjb5um//fkhPtzoKc1zrWOdOpkW5E1IaMlYbwiuR5rZp0yarQzgvKJOp88Zy9+7wn/9YHEwRcfSoVBtyug2ZcDmYq3rkTqja5Ouew4dlSiuAYgT379gdzZHylHv2yIeEdu0gJgbWAp7NCRQe0oD+wAGgfHm47DKLA/KDWrVkfdttt7EhSD5FB2UydU6lFqzzP4aTv/6CL7+UqZlGjJDHbkeadUPhTdaZUJ33UPUKtXAVKrgmiLY7llAWCbRzbK9aJXPKduggXxfFuaRGIXOYlisnc4DWrWt1ROY7e1bWtWvXpmaQJIqgS6Zz5849f6808BOqFz3t28PQoRAfL5/u70IqGYUS5z3UnkhC1QkPChYTAzNmyHYWoZ9MAZyTCx05Iuuiet80BfjRsf3sswXPvRvKnP2OJk+eTPny5QveOUCCLpmePn0agB49Aj93ZFGzdi0459q9BWkS+5TQuCLNLQb4xrHt+BNSBYiJgehoq6MwTxXH+qhjQlZnMp0HhMmt4UKlADcBu5GZdHr3tjigAFi3bp3VIZwXdMnUKZjnmAwHa9e6xuTdjEx9Furvrc5pt7KytPSgJ5ZaHYAJqjrWx47JukkTmf/yGPCZVUEF2Dik01X58lLd6tJLrY7IfwYNkvX9998fNMNjgi6Zeji/qvKCM5HGx8sn2W8Iwj8EL0QiPZAB+ui0IYW6/XZZh0MDkPPK1JlMbTZ4/nnZfpnwvzpNQvoOgPR9aBTKZa3c8Oqr0rqSkZFBRkaG1eEAQfYempWVxVdffWV1GGFr2zap0dm5syTSm5FSfaF+RepkAz5ybJ88CYsXWxlN8PvwQ1mnWBuGKXInU5DbRI0aSa/WcL46TUL6C+wELrpI+kCEO5tN5rANJkGVTOfMmcP8+fOJiIDhw62OJnzs3AljxkDbtjL/aEKCJNJvCJ9E6lQaGObYHjasoD1VOE0e4Wzmdd4zBXl9L7wg2+F6depMpMuRRLp0qavcngqsoOlrkpmZyYQJEwAZX9q+vcUBhYnt22VMofNNpgEycfKLhF8iBfl0+BoytCcz0+JgVMCsdqyPHwfDkCsXcF2dbtkiV6f3WRWgidKQ+tipwDtIx8Fy5cL/PmmwC5rPposWLWLhwoVERMBzz1kdTXjInkgvQybwXge8QngmUqeg+aNWfvcTUmBkoOPrO2+XDmhO4XZ16izI8BgyntRZlGLoUE2kVguaK9O0NJnjoVMnuOoqi4MJA9kTaSfkTaeExTEFWpD0Swh6oTrO9CtkAgZn/DffDNddLxNhZ1fJMfv5AaACUgC/aaCCNJEzkf4ElCwJd94pRexBqj4VVUHTadUwDE8Wv0hJSTHatWtnAMYNN3gaki65l23bMKpVwwCMTmCck5avIrMcR147YHz6qfW/D18WyLmYeezMTNdx3wiC35sny5dgRDhi79MH44sv8n+dSxbn/BnGgXEnGFuC4HW4u6SC0csRf/nyGOvXm/u3EIpLlSry83jooYcMu91u+JFbAXn6Cvxi6dKlBmBERmJs2mT9LymUl23bMKpWdSXSpCB4Iwj0kpntjbNkSYzjx63/vXi7+DOZGgbGJ5+EZkKt44h5xIjCX+O5cxgPPIDx+usYV1yRM6neCsbOIHg9+S3pYDwFRgM0keZefvjB9bs8e/as4UduBeTpK/CLuXPnGoDRvr31v6BQXjSRyrIM1z/ZjTda/3vxZfF3MjUMSag2mxz/zSD4/RW2TM7289i+3bPXmpqK8dFHGK1a5Uyqg8DYGwSvLfuSAUb/bK+1fHmMdev88zcQqkuZMvKzGT58uD+vTt0KxtPoTZeYmGi0bNnSAIxu3az/5YTqsmOHJtLsyxuON6Data3/3fiyBCKZGgbGW2853rCD4HdX0DI128/i8ce9f73nzsmHiObNXcerTfAk1HQw+mV7ra++irFli/9+/6G6zJ+PERMjP6MHH3zQ8BO3gvE0etOtWrXKAIyoKIw9e6z/5YTq8tJL8gdVH02kBhh70GTqyXL4MEZEhJzjxSD4/eW1fA6GLdvPYvVq3193YiLGlCkYTZrIMWORD6OdwegLxqkAv8ZMMB4Bo2a211mtWuD+3kJxmTNHfk7R0dGGn7gVSNCMImjeHOrUKXw/daFNm+Ddd2V7ABBnaTQqFFWrBlOnylCSF4FLgXctjim3D5Hs0rs3zJ5tzlCQkiWluMdvv0GbNlINaimwBPgOqAVcnW25A+lVayYDeNpx/JrA+0jP49Kl4YMPYMoUk08YZoJllijLh8acOnUKcA2yVp7ZtEmGE8XHQ3dgtNUBqZA1dKj8H952G+yyw6NIveP/WB0YUqRgjWP7mWfMHz5Xpgz88ovM7wsy89DTT8uE439m2+9PZIqzWoUc7z/A3Xk8/inwQa7HjgNHsn0dFwdvvAHXX6/VjEKJzTAMT/b3aOfCnDlzhjZt2rBz50569ICffzbz6OFv9255Uzl5UhLp90Bxq4MKEv8CdYHateHffy0Oxge5P2R69u/qnS1b4PXX5Uq1KWDlXOuTgeeBQ46vP/gAHn44MOc+eVJKcTrt3i1F5N2d4q8mOf8fU5ErzrxER8Nbb0Hr1lC1qrbSeSItDYoXh+jo6PP1Ckzm1qWepcl006ZNNGnShOhoOHQIKlY08+jh77vvoF8/qA1sRRNpdppMfbN2LVxxBdjtUAZp8XgiMKc+7zPk6s75kgOZSPNz5EjOYvp5+fpr+TCSn6eeck0h5lS2rPytKs85kynAuXPnKFHC9PI0biVTS5t5Dx48CMi9D02k3muBJlJlrpYt5cp02DBIsMNIx+P+TqjfA8OR+5KJjseeflomvgiGq7Vq1WQpSPPmEm9etaGjoqBuXf/EVlRFRsrV/NGj0LdvX+bPn29JHJYl0zNnzjDcMTVMMPyThKI9e6yOQIWzoUOlc8f06fD4466E+jhuflT3gIEk0luA7DnotdfkSi7UaMIMnKgo+PFHuPJKWLVqlWVxWNab9/Tp0xw4cICoKPjyS6uiCF1bt7oKeNe3NpQiIzMTzp0rfLGHarHbPFSuDI89Bp98Ik3OI5E3jXeAZJPO8TPSstIPSaQjRsCJE3DqVGgmUhV4wVDk3/KhMTVrQqlSVkcRen7+GZKT4RJkNgzlP2lpMnFA69YylKKwpW9fubd28qQsgbrP6U933y0J1ekxZAjWB8DJbMtZD455Fpmcvg+u2VyefBLeeUdu+5Qvb0LgSgWIZcl08+bNVp065G3fDs8+K9sDkeELylxpabB/P2zcKDNyNGgAGxzdWksUsIA0OVWvLrOVVKoE994rxwr1pHrXXfIa/vc/18wsjwCVsi1VkEnn9wN5dXo97fjeN8iE3rcgifThh+WK/vXXdZicClEelpswxZkzZ4yyZcsagDF4sPUVNEJtWbZMKn7UASMrCCrTBOPiTQWk9HSMrVsx1q7F6NjxwupDzcA4VMh5Vzuq11QAo1yu5993n+e1ZHPHYOXfXe7ljTcwKlRwLZGROWMtDcYMMLY6lplglMn1eiIjMR55BMNut/716BK6y+nT8vdUtmxZww/cCsLTqE1x+PBhA6R8WVqa9b+IUFtefVX+cDoEQdIK1sXTZJqejtG374XJqyYYzfG+ZuvPYNTIdcyHH5aEfepU4XG5m0ztdozp0zGysvz3d1fYkpWF8eCDGDVrYkRHXxi7c4mOln0eeMDaeHUJnyUYkqkl40y///57+vbtS9Wqcm9JuW/HDmjUCLKyYBzwrNUBBal/cW+caVYWrFghvUadPerrI/cDvwAamhTPD8h9xuyhTJ8uPWYLkrvJc+NGSEjI+ZhhwIQJ8NVXMu54xAgpCxgdLfd5rWg2zciQ5u0//8z5+JVXyr3XYsUCH5MKX/Hxrnvs27dvp359U7tlBmfRhnPnzlG5cmWSk5O59174v//z9YhFy5o1UkO0NrAH84coeCIR2A0kkXM4g6eigdJIEos2IS5wL5lmZck4Smdv8jLAQuAKk2LIy9fAYMd2t27SI7tt2/z3z50IbTZJnu665x4YMCDncSpVkrGQRV1mplQ4SkiA1NSC942NlcIK9erJuEYVXDIyoHNn+WDcsGFDtmzZYubhgzOZJiQkULZsWUDezCIs708cWpzJtDWuWqWBlIkUAv+tdBzbSsVRu0ZlSpcsQVQx74YsG4ZBenoGZ86e4/Ch47Q6e45Oyak+J7TCkmnuRHo18B7+TaROM5GON84RNK++KoUJstu0ScrX3Xzzhc+/DKiQx3E7Ar86tlOAdfmcPyJCXvctt3gceljYvBkWLYzmz5UlqVCuIhXLlyImJhpbPpfwhmGQmprOydNnOXP2JFe3S+K66zK47LIAB64KtHMnOC9IN27cyOWXX27WoYOzAtK8efMAmRFBE2loyQTeio3hRIvLuKFdCx6rXZ3iXibRvCQkp7Jqz0EmLlrF7m17GZTl+4DN3J8V7Xb44QdpEVmwAEoC84FrfD6T+/oDXwLjgdVI4fZ//5UJC0C2x46FlJQLnzseKUDvjhlA7oafI8BmOwwZIl8XtYS6aBFM+bQGN3W/mjdfuJiKFTwrPXf0eBJ/rN7HS8//wUOPHOGaQP7hqALVqiVXp0uWwODBg9m4cWNAzx/QK9PMzExKlChBRkYGTzwBb77py9GKJquuTA3g9dgYzl3dlJEDriM6yn9tXafPpfDC5B/ptnk3A7xMqM4rU5CZQFq1kkR6//0waZI8bkUize1z4C7y/sdqAGzL9ZivTUMGMsXaGOTD7GOPQcOG0Lix3M8MZ0uXwuT/q8kLI/tSo3ppn4717754xo7/jkcePWz6DDbKe5s2QZMmcMkll7Br1y6zDuvWlWlArw0NwyAjIwOQKYaU9wJdZGcHsLvORX5PpADl42J58c7ezKhcnjwuztxSI9v22rU5E2kEcCPWJ1KQ+TGnIc2+2ZdnAX+MxLYhyfR55Gfy1ltSkKFjR1cHrHBkGPD55LI8/UhvnxMpQJ1a5Rhxb0+mfFbGhOiUWYpbWKQ8oM2806dPl5NG6cBsX611LC0DdL7foiK5pk1jvydSpwolS9Cgfi3WHDlJBy+efwxJHNEx0sPVmUiLA3OArqZG65uhjiVQnAm1ArAeube6Pg1uv73wGVFC1bZtEFe8EpfUMa+sUtPGVUj+rDT79ydw8cWmHVaFqIBemd5zzz0AvPhiIM8aXpo1c80sPyKA511ZthRXN6gdwDPC1a0asqJkrFfP/RKvmhBAAAAgAElEQVRp0uzVSzr3BGsitYoNqV40GblCBzh+XKo3haMVv0VxdetGph7TZrNxVatGrFihVwYqgMl04sSJ2B0VwLV4tfeio10/vxXAHwE4ZwZwulgUF5cPbJNW3UrlOBQb4/HzDGCqYzszUxNpYSrjmlqtXz+44w5YvNjCgPzg4IHi1Kll/jyPdWtV5tBBnQBRBSiZTpw4kfvvvx+QItZRls6iGvo6doQePWT7pQCcLxmIjSmW79ABf4mLiSbJi3OuR+43lisHs2dL7WJNpPmzAW8gCTUrC6ZMgZ49w+seanJyJHGx5leKiIuL5lySDjwNNunp6YXvZLKAJNMXHHOFjRsH//1vIM4Y3qKi4KGHZLuQseamyAIiLRjHFBFhw+5FWYppjnXp0tLxZDiaSAvjTKiTgSuRQv/PPWdtTGay2yEy0vwPgxE2G3a7NvMGC2dBjQMHDpzvoxMoAXmHTE6WmQ/vvjsQZysaYhytn78CCyyNJLhkIvdLAfbtg1JIZxtVOBtwJzJXKciQonC9h6rCU5068KhjIPYbAR4y4vdk+vrrr5OYmIjNps27ZurSRe5vAbxtbSg5jJv9K7H3jiX23rEXfM/5+LjZv+bxTHO8jPTkdX7YeAa5J6jcdxWue6j9+0uRi6Jk3Fu/ElttLLHVLvwbVsHvjjusOa/fk+nEiRMBePttmfBXmcNmc13pLwTmWhpNcJiFXIXabNJMWRPQuwqey34PNTNTavsWtYSqlKf8mkwNwyA+Ph6Am27y55mKpu7dYeBA2Z5gbSiWWw8Mc2yXKiXrVwDvBtaovBLqXP3EpkKIM/cEil+T6ahRozhz5gzFismsC8pcNhvceqts/4xM81UUHQN6I72OW7SAs2elmMUQa8MKec6EOgBJqN9/b3FASrkhLk5KZR48eJBXXnklYOf1azKdPXs2AO+/D9Wq+fNMRVevXjDYMafXZ9aGYok0oB9wAJm7c/t2efxtAlyRJEzZgG5WB6GUBy65BJw51JmDAsFv7zd2u52jR48C0L69v86ibDZXU+9sXMUKigIDeBD4HahRQwq2JyfLVeq1lkYWng4ftjoCpdzTsaOsjxw5goeTuXjNb8n0v//9L6dPnz4/qa7yn5tvljG8IIXTi0pz73vIuMjYWBg/Hr74Qgo0vG5xXOGmumM9b578jJUKdhUqSLW4/fv3M3r06ICc02/J9I8/pNDd++/DRRf56yzK6dln5QrVAL62OpgAWAA87tj+/HP47TcZmH8vMnWZMs8NyDRxAH/+aWUkSrnn0kvhtddke+XKlQE5p1+SaUZGBnv37gWkMLsKjP79Zf0N8osN1ybf7cg0ZXakSs/AgfD77/K9IjbXdcA0tzoApTzU3PFHu2fPHrKysvx+PtOTaXp6OrfccgsnT56kbFmoWtXsM6j8DBggzZ0gV6h3EH4JNR65J5oA9OkjMxAlJcGGDdLEe4Wl0SmlgkXNmlCypDT1jhw50u/nMz2Z/ve//+X777+nbFlYuFBekAqcRx+VYQzjxoVfQs0EBiMTlTdtClOnShf4NWukQHtzIM7aEJVSQaJePXj5Zdne7uzm70emJtPU1FTmzJkDwKuvylAFFXiRkXIP9eWXwyuhPoncK61YUWrGliwpjztvibS1KrAiZO1auTetVCi45JLAncu0ZJqVlUXfvn05ePAgANdfb9aRlbdGjQqfK9TJSAH2YsXgu++gdm3X9zSZ+l8rx3rd3+DoDqGUysa0ZBoZGUmnTp3Of33ypFlHVr549tnQT6i/A/c7tidMyDlu2W4HR8dxTaZ+1BboAaSkgaPctlIhY+3atSQlJfn1HKY28957773nt0+dMvPIyheh3OSbCvQFMoBHHoF77sn5/e3bIT4eSgMXBz68IqWj1QEo5aGmTaX63tGjR3nsscf8ei5Tk+mIESMAuPhiHRITbEaNCr2EmgVsAI4DXbvKzEO57dgh67OAfn5TSmVXsya88IJs+7vwvanJ9OzZs4AEr7V4g0+oJFQDOAKsBpKQXnnffJP3fLi9e0OJErKdFrgQlVIhonx5WS9fvpzTp0/77TymJdOjR4/y668y6bOWDwxewZ5QDwN/An8jV5sxxWXqL+c/RG42G5QpE7j4lFKhpV07qFULjh8/ztNPP+238+TxWd9zR48epXPnzsTHx3P55dChgxlHVf4yapSsn31WEiq45gL11ejeHRndO++7aymTnsv3eQcdi7PfWoQNqlSG5s2gfn2TglPKDaOf6MjoJ/QOcbioVg2eegoefFCGb/qLKcl0zJgxbN26lUsugSVLZBygCm7+TKie+gfY79iOsEGVqtK0m2WHknMKf76Oe1RKFSTOUc3F7sc3C1OSaWZmJgAjRkClSmYcUQVCMCRUZyK12aB6dRk/Wq6cfO+0G/0F5s6FY8f8GKA6z//VTZXyr59++om9e/dSO/tAdZOY2gEpJsbMo6lAcPceqt1u/pyAa3FdkV51FbRo4UqkAIZhYLMVfAxHB3LuBrTPm//EE/qTz/vjb1jmygzMfJnKezfdBI0bQ0JCAq+/7p9JGv02BZsKHdkT6u3IzCvZW1dLAGkZGaae8x+ksxFIB6IKFS7cJzklg9gSBb9ROef9fRooJO8qH0wHdiEtT//5j9XReC421k5Kaqbpx01JyaBEnN5nCHZlysD9jsov/pos3JRmXn/e1FWBkb3J91tgBtAHqAj0ByIyMjmTnErZEsW9Psce4Bwy48sZpEh9rVpQunTe+x87cY7y5d17A9RE6l/OYUf9+0ONGpaG4pXyFdI5dsL8CjjHTiRSrly66cdV5straJ2ZfL4yXbduHTNnzgT8H6zyr1Gj4Ntv5Q3TAL4D/g/oDvybmMwXuw7gybDnA8B6x/I7sAXYhyTSyEi48kppeslvZqHVf++mzVWJ3r8gZYojwP8c26F6K+eqq9NYvW6b6cddtW4rV15l/hWvCj0+p7/PP/+clJQUrroKbr3VjJCUlQYMkGQ6a5aUhJw7F+bMgb9T0ij51xZqN72UyhT+KSwNyD082maDunWlZ12ZMgWPD83MtLN6/XZuG+7b61G+m4t8CKpWTVouQlGrVvDe+AMknE2lTGnvW1eyO3o8iVPxx2jSxJTDqRDnczJNTJQrh1tugehon+NRQcBmk4QKcO+9kljPnoW33viX3//aTNvWjd0+Vu3ariIecXE5Oxjlx243+PDT32jcJF57hweRHj1Cd9hbTAx07prIexOX8NQj3YiJ8e2tLzk5g/cmLqJHz0QiI00KUoU0n/6iVq5cyRdffAG45pZU4SUiQq5WAXr2TOTuOxdSs0YGXa9tSMm4gtv8YmLyr1yUn+Mnz/HFjDUkZfzF8y+4fy/ev/NBKKDQntXB7t7h6Yx/eyOvvQd339qOGtXzuVlfAMMw2H8wgY8/X8aljbYx9FZt4lXCp2Q6Z84c0tPTufZauPNOkyJSQatqVZgyLZFJ/7eIse/8Qf26F9P4srqULhVDsWLe3X43DEhLyyQhMZW//9nG8ZPHaNchkcfuyXDr/pyz89KDwBIgRG/pBTX/VTMNrIgIeOzxdKZP28CY8XsoGVuR1s0aUL5cHLHFi+X7YcEwICU1g5OnElm9bisZWafpfkMiA2/JCvkPGMo8pnQZ6tYNbeooIipWhGdGpZCamsKaNSfZtnUDRw9FkZnpfV+2mJgsSpfJ5PZ77DRp4llHthkz4NJLYSUya0x1r6NQedkBOEfludNEH+wiImDY7ZncNiyebdviWbNmFwdPFyMlpeA3sNjYLMqWz+CJZwwuvTT0r9KV+bT/rfJK8eIySXf79llYWRunXj3pGHPkiGUhhLW/kCvTevVg7FirozGPzQYNG0LDhgagQ1uU73waGnPw4EGz4lDKZyesDiAMOf/Dr7hCPkApFer8lbe8TqZz58493/no4otNi0cpjzn//oagc5qaaSvgvBjV/3EV6pzj2efOncuXX35p+vG9TqZr167FMAxuugmGDjUzJKU8M3u2rLcAKZZGEl5+RnpJX3opjBtndTRK+aZnT7jjDtn+66+/TD++zxWQdMCyslrlyjpBuNmWAc87tvv21epmKjw489X27dtNP7YWuldK5bAM6AkkI0PeXnnF2niUMkujRtL57Oeff+bzzz839diaTJVS5y0jZyL95BMZTqJUOOje3VUTYdeuXaYeW/9NVFjZZHUAIWwZmkhV+KtTR9Zr1qwxdTo2r/5VDMPg999/Ny0IpXzVqZOsbwa0wJvnlqGJVBUNbdtKH4BffvmFTz75xLTjevXv8tlnn7FgwQKiouDaa02LRSmvff21rE8BOlWzZ5ahiVQVHZ07u3r1Hj9+3LTjevUvc/q0VOscOhS6dDEtFqW8FhMDxYrJ9kprQwkpy9BEqoqeypXNP6ZP/zahOh2TCk8DB8r6JmvDCBnL0ESqlFn0X0eFjU8/lbVOx+aem5FEevvtmkhV0RQUHZCUCjbOmYvsSPUelb8FQIJj+6OPNJGqomnWrFmkpro/b3JBPP4XSklJ4auvvjLl5EqZKTISHn5YtvsA8yyNJngtwNUU/sADWsBeFT1DhkCpUrB+/XomTZpkyjE9TqZTp05l3bp1lCoFd91lSgxKmcJmg/ffh3vvlUm17rA6oCDkTKRpSCL98EOdm1MVPY0bu2rKZ2aaM5jO42SalSVzVw4cKKWZlAomNhuMGSPbx4HvLY0muGgiVcrF2SJjWTJ1io425fxKma5KFXjySdkeiCbUFcBdQC80kSrl5Pz7nzRpEvHx8T4fT7sdqLBjs8Frr8F990k1pCesDshCy4Drgc+Qn4UmUqXEo49ChQqwc+dOvv32W5+Pp8lUhSWbTf5ZAPYARbHL3DJc40i7dIFJkzSRKuVUsyb06WPe8TSZqrB12WXwwguyfStFL6G+gSTSXr3gl1/gnns0kSqVnXNI2Llz53w/ls9HUCqIvfiiJFQ7RSehLgOuwzU06N57dRypUnmJiZH1+PHjOXjwoE/H0n8xFfaKUkJdhjTtLnJ8/cADcOON1sWjVDAbMwYuuggOHTrEkiVLfDqWJlNVJBSFhLoM1z3S3r3h55/1HqlSBSlb1jV9o6+izDmMUsHvxRdl/dJLklBtwCAL4zHLH8CDwHrH11q0Xin3mfVhU//dVJGS/Qp1MFAHaEDolh78Axn6oolUKWvpv5wqcpwJFWAvsB24AbgYWGxVUF5YiSTSRKRZd80aTaRKWUX/7VSR9OKLsHMnbN0Kd9whjx0AugLVkapBwepvJPFfgyTSwYNh1ixo3VoTqVLe2r9/v0/P1389VWTVqwcNGsDkybB3L9x6qzx+BGgPxCKJdYNlEV6oBNAaSfwgiXTqVIjS3g9KeaVOHVm/8sor/PPPP14fR5OpKvJsNqhVC6ZMgWPH4JZb5PFUJLE2B6oCa5Geshl+jicT2I3cy80txbG+8UY4fBimTdNEqpQvXnhBZpFJSUlhx44dXh/H42S6ZcsWr0+mVDCLiIDKleHLL+HcOTh9Gm64Qb53DGgFxAH1kQ4/5kwpnNMJoAVQD7mXe8H3T0hsP/wA1aq5JkRXSnknIkJaqHw+jic7L1myhAkTJgDQooXvJ1cqGEVEQIkSUK4czJ0Lyclw/fUQGyvf34skvKbAGqTJNdnHc6YA64DOwCbHYy1bXrhfxYoSm94bVSq4eNRAtH//fgzDoFMnKVGmVFEQGwvz58t2QoI0sf72G+wE2jj2aQZ8DJTL9rw6QDRgIPsa+Rz/DHA/ruEtDRvCkiVQtaoWXFAqVHh1t+Xii80OQ6nQUKYMLF8Op07JjBP//gsHD0onpatz7dsFGAdMBD538/ht2sCPP0oiVUqFDu26oJQXKlSQpApw6BAMGCAJFsAwZNjNYnKOW61bN//OQuXLw4wZUKOGP6NWSvmLJlOlfHTRRbByZc7HNmyQWyEZjq6/Y8fKVGhKqfDkUTKdO3euv+JQKqw0awarV1sdhVIqUDzqEzhz5kwABg70SyxKKaVUSPK4g32PHq6xd0oppVS4MIz8+twXzuNkGhfn9bmUUkqpoDVt2jTsdrtXz/U4mS5YIHVMlVJKqXDw8MOynjNnDgsXLvTqGB4l04YNG5KYCGPGeHUupZRSKuhcey107izbWVlZXh3Do2RarFgxALy8ClZKKaWCUkyMrDMyvJvKwqNk+s8//1CiBDzzjFfnUkoppYKSs3Tn+PHjSU31fBoLr3rzXnaZx+dRSimlgtarr8osTMuXL+fXX3/1+PkeJ1Od8kkppVS4adoUOnaU7TFjxpCc7NlcUDqRk1JKKQV8+KFMb7hy5Uo2btzo0XM1mSqllFLIJOGtWnn3XE2mSimllIO3cwhrMlVKKaV8pMlUKaWU8pEmU6WUUiqXo0ePerS/JlOllFLKoXp1WT/yyCOcOnXK7edpMlVKKaUcpkyB2FjYv38/hw8fdvt5Nk/mb7PZbN5P9qaUUkqFGMMw3Orfq1emSimllI80mSqllFL5sNls893aT5t5lVJKqby528wb5clBe/WCOXO8C0gp5bnc1Vg8+OyrlPJSkyawaRNMnjzZ7edoM69SSimVTefOsn7wwQfdfo4mU6WUUiqbd96B0qXxaJJwTaZKKaVUNhERULGih8/xTyhKKaVU0aHJVCmllPKRJlOllFLKR5pMlVJKKR9pMlVKKaV8pMlUKaWU8pEmU6WUUspHmkyVUkqpXCI8zI6aTJVSSqlcmjb1bH9NpkoppVQ2J0/CkiWePUeTqVJKKZVNxYqwYIFnz9FkqpRSSuXSpg1Urer+/ppMlVJKqTzknk+4IJpMlVJKqVyysuDUKff312SqlFJK5fLAA5Ce7v7+HiXTgwfBMDwNSSmllAotixd7tr9HyXT9ehg/3rMTKKWUUuHO42be1av9EYZSSikVuvSeqVJKKeUjj5Ppli1gt/sjFKWUUio0eZRMY2Nj2bQJxozxVzhKKaWUteLjYd8+z57jUTJ95plnANi717OTKKWUUqFi8GAZZ1q3bl23n+NRMq1Zs6bHQSmllFKh5NAhWX/88cduP0c7ICmllFIOR47Atm2yXdWD4ryaTJVSSimH22+HzExo3LgxtWrVcvt5mkyVUkoph4QEWb/99tuULl3a7edpMlVKKaVyKVu2rEf7azJVSimlfKTJVCmllHLwtiiRJlOllFIK+Ptv+Osv756ryVQppZQCnnhC1tdddx0tWrTw6LmaTJVSSilck4GPHDmS6Ohoj56ryVQppVSRt2wZrFwp2zExMR4/X5OpUkqpIm3ZMrjhBtm+8847adeuncfH0GSqlFKqSPvf/yAlBXr16sUnn3xCRITnqdGrZJqU5M2zlFJKqeA1bNgwrxIpeDGfKcAPP8CSJV6dTymllApKNpvN6+d6lEwHDBjADTfcQFYWzJzp9TmVUkqpsOJRMo2IiKBnz57+ikUppZQKuJMnfT+GdkBSSilVZE2dCr/+KtsVKlTw+jiaTJVSShVZixbJ+v7776dTp05eHyfKpHiUUkEsMxPuvhvmznVv/969YdIkiIz0b1xKBYurr77ap+frlalSYcgw4MknIS5OlmLFpDnr1Cn3ls8+g6goee5jj8nxlFL502SqVJh47z2w2WSJiIA334TkZFkASgI/AycKWeYAJRzHTE6Gd96R4z37rCZVpfKjzbxKhbAzZyArS5pkn3km5/eigC8AZ//7SKC4G8fsBZwCsoD5wGAgA3jlFUhNlfNERED58ia9CKXCgF6ZKhVCDh50LR98IAmtYkVXIv0IsDuWdGAgEOdY3EmkTsUdz+kHpAEzkGQ8fjxUqgQVKsBLL0kcx46Z9eqUCl16ZapUkDh7Fo4edX29c+eF+9SseeFjsY7lZeB+P8RlA/oD3wD3IUk6EXjxRVlsNnjjDem0VKIE1KjhhyCU8oPMTFi/3pxjaTJVyiJJSbBrl2yfOAEPPww7dhT8nItyff0Q8ExeO/pBP8cC8AnwIpAMxBswcqQsUVFy9dq+vexXoULeHwCUCgZjx8LGjRAXF0fz5s19OpYmU6UscPYsdO8Of/yR8/FIoG62r3NfnB70c1zuusexALwJTALigZOZ8Mgjrv1KloR334WuXaFWrYCHqVSBnC1BI0eOpGnTpj4dy+tkunw5pKWBF3OoKlUkpabC779LD9kXX4S1a6XTQhPH9ysDXwIVsz3H+7LbgTPSsRjA80hvYJDEfyoJ7rkHypSBhQvhiiusilKpnFJT4bffZLtq1ao+H8/jDkgdO3akTJkybN4Mr73m8/mVKhJSU+Hmm+UKrXdvSaQXI1ee6x3LL+RMpKHGBozF9XqOAQ8CVYCEBLjuOli92sIAlcrmlVdg61YoW7YsHTp08Pl4HifTxo0b85///AeQpiqlVP7S0+Hrr6FLF1iwQJpxrwW6A0vJ2aQbbiKBD4EDyL1WZ0IdP77we8NK+Zszf40YMYKGDRv6fDyvhsaUKVMGgJ9+0onClcotKwu++gomT5YkOngwrFwJlZArtqXAPMI7kWZXDPgKSahnz8Ljj0PbtrBhg8WBqSIrMdFVWtOZz3zlVTK9+eabqVChAjt2wNtvmxKHUiHPbodp06BHDxgyRGrhrlgB0UBfYAlwucUxWsWZUB8H6iElC7t00YSqrPH229KTvlKlStx0002mHNOrZFqvXj2GDx8OwPTp0nyjVFFlGFLLtmdPGDZMOtpEIWMzbwf+AGZRdBOpUzHgLWATUpVJE6qySkaGrIcPH07duua0EXldAem+++6jUqVK7NoFH39sSixKhZTp02WcWs+ecNddMH++JNEhyFXoDOBzoKWVQQahGOTDRS80oSprZGXJOjY21rRjep1Ma9WqxbBhwwBp3lKqKBk3Dm67DZ5/HubNk842w5AeuV8A7a0NL+jFADPRK1QVeEePygdhs/lUm7dYsWJmxaFUyEhOhueek+1hwGikIPwUwPuphYse5xWqM6Fecw0MHw5btlgcmAprn3wChw5BzZo1z9+uNIMphe5TU804ilLB76OPoFs32Y5FEuhYoKuFMYWy7E2+587J7DedO2tCVf43dOhQKlWqZNrxfEqmUVFSQGniRNi3z5R4lApK//d/0KEDPPigVDGKBKZaHVSYcDb5/g+5v3zsmCZU5T/O+X0jIyNNPa5PyfSJJ56gTp06HDvmnzZopaw2ebKMibzvPik9ZgNGAIuR3rrKHDFI0f4VyFW+JlTlDzt3ygdjgOjoaFOP7VMyLVOmDAMGDADgvfdgzx5TYlIqKHz0kYwVdRajHwEsA94FOloXVliLBWajCVX5x5w5cn++fv36PP7446Ye2+d7ps899xx169blxAlYtMiMkJSy3kcfSZMuwCPAb0gS9b2CpypMLPAjOROqlh9UZjhzRtY33HADcXFxph7b52RasmRJunTpYkYsSgWF7In0feA9oJ2VARVBJZAr1PpIQp01y+KAVMjbvFlaUMG8EoLZmdKbV6lwMWGCK5G+B/zH0miKtligj2P7zTd1HKryzaxZUhv68ssvZ9SoUaYfX5OpUsDnn0O1avDQQ/L1+0jzrrLWS8iwmfh4LeygvLdpE7z1lmz36dPH9M5HoMlUKT7/XMoBHj0qX7+PXpEGi9yVkpo3h/r1tVOS8syPP8pMMY0bN+b555/3yzlMTaZ795p5NKX8a/x4KFEC7rxTitWPAvahiTTYZK+UBDK8oXFjqFdPZv5QqiAbN8pE4CBXpc76CGYzJZnWq1cPkGltVq8244hK+d+WLZCSItuvAS8DF1sZkMpXDPA9sAdXycbdu+HaazWhqoL99JMUamjUqBEvvvii385jSjIdOXIkrVu3Jj0dZs8244hK+ddnn0lBBoAPgKcsjUa5oxhQB5lM4BDSw/rQIU2oKn+bNsGYMbLdr18/06seZWdKMrXZbOeLN7z+Ovz1lxlHVco/Fi2SYgyGIVekD1sdkPJIFFAdmIfMzqMJVeXnp5+kdvxll13m16tSMPGe6ciRI2nRogWZmbB/v1lHVcp8s2ZJIu2KXpGGspLAz2hCVXnbsgVeeEG2Bw0aRESEf/vbmnZ0m81G7dq1Ab1vqoLXhAmuyeyHWhuKMoEmVJWf2bMhPV369LzgzKp+ZGq3pk6dOvH999/zxhvQrx9ccYWZR1fBKisLMjP9c+yICDBr2tyPPnKNI30PuMOcwyqLORNqD2DFIeje3bOEahjyputv0dFgs/n/PAq2b4fRo2V72LBh2ALwg7cZhuHJ/oXu3LJlS9atW8e8efJHrcJPQoIUf//t15Ls2B5NWpqNqCj/NKHY7QY2m51q1bNo2+4sHToYXOxFl9sJE3IWZAiV4S+53wI8+m8tYg4BNRzbmzdDo0Z575eRAevXw2/LY1j7VwnOnrURFeW/jilOmZl2SpY0aN4ymfYdUmnVShKsMt+ff8LVV0OdOnXYvXu3r8nUrSebPuCmcuXKZh9SBZFFi2DSx5Vo3rg+XdteyqP3VCGuRDG/fvJLT8/i333xrFyzh+ee2UqT5kd59LF03O2YF6qJVHmmCjJsZinQp49cneR26BCMHlWaimUv5urWjej/bA0qlIslMtL/9WvsdoPT8Sn8vf4wP87YwoT/7WXMuATq1PH7qYucBQtkXbly5YBclYIfrky7d+/OggULuP9+aVZT4WPRIpjyaU1eGNmXGtVLWxJDenoWr7//C2Uq/cPjTxSeULdskQH+IE27oVYiUK9MPbMHuMSxvX49NGvm+t6hQ/Ds02UZcGMPunSoZ0V4OaxcvY/JX/3E2FdOa0I10c6dUiUL4N1332XEiBG+HtKtbGz6x7Fhw4YB0slj1Sqzj66scuIEfDqxiqWJFCA6OpKnHunG8UONWLiw8P2nTpX1pYReIlWeq4mrUlLnzjlr+Y5/qyR9ul8fFIkUoG2bWtw+sCevvVIaz65pVEGchVhq1aplRiJ1m+nJdMiQIbRs2RKQ8T0qPKxYAVe0aIRfxy0AACAASURBVGhpInWKjo7kxutbsHxZyQL327lTxj0DPBmAuJT1iiGlB3sBp0+7iuMfOwaHD5Wm67XBkUid2l1Vi/TUcvz7r9WRhI8vvpB16dKBfa/yy42CkiXlTc5u98fRlRVWLC/F1a2D542oRZNq7N4Zd36y37xMnCjr2sA9gQhKBQVncfxeSHH8Ll3kDfaqlg0Ccm/UEzabjatbNWLFiuCKK1Tt3QtvvCHbjz76aEDP7dff4Mcfa0INB3Y77NxRjCaNqlgdynnR0ZHUv6R6np1MAMaNk1rRNmBMQCNTwcCZUDshCXXyp3Fc3rCmxVHlrdnlNdiyqeBWFuWejAxZX3TRRdx5550BPbdfkuloxwCfb7/V0oLh4Nw5iC0e7bfhL94qVTKOpKQLHx83Dp57ThLpVOC2QAemgkIM8Ixj+9jRSCJsMVaGk6/SpWJISgyu/61QZBjw7ruyXbx48YCf3y+/weuuu44WLVoAcs8qK8sfZ1GBkpEBxaL9Pw7PUzHRxc5/EnVKTJRECpJIbw14VCqYXAv0Q5pTt26OJCHB4oDyUKxYJBkZWs3BV0eOyDA4gDecbb0B5LePQ+86PiJ89x1s2+avsyjlYhgwdqxsx6GJVEmHpK+AakBGFuzI57aACn3OW4qVK1emb9++AT+/35Jphw4daNCgARCYUl0qOIx761diq40lttrYC77nfHzcW7969Dx3ZWbCm2/K9udeH0WFm2LAZY7t4ycg/nTB++f+WzxwKIG4i8YRW20s73z0h9vn/XLGP+ePs+6fI94Fr9xit8OoUbIdE2NNc75fG+qdL+qZZzShKv8yDBg5UrZjgP6WRqOCTQ2kQpJhwKrVhSfU7GpeVIYuHeoCMO3r9W4/b4pj32aXV6FF02oeRKs8lZAA06bJ9meffWZJDH5Npo0dpWcWLIClS/15JlWUGQY89hi8955chcy0OiAVdGxAI+AipAVj1WqIj3f/+bcPaQ7A1h0nWb32UKH7790fz29/7ANg2KDmngesvFKmTBm6dOliybn9lkzPnDnDsmXLzn/drp2/zqSKupEjpRdf9gH7SuWlBVARSaj797n/vBuvv4wK5WIBmPpV4VenU7/egGFATEwkg/o28S5Y5ba0NFkHqg5vXvyWTMuWLcvSpUupVk2aN3r10qZeZT67HT78ULY/BW60NBoVCi5yrA8dkipJ7oiOjmRw/6YAzPhxMykpGfnua7cbTP/2HwB692hAeUcSVv6RlgZ33SXbpUqVsiwOvzbz1q9f//zV6bJlUp1CKbMYSCJNTZX7pD0Le4JSSP3eioDdgOPH3X/eHY6m3rOJafwwd2u++y397V8OHJIxOLdrE69fpaVB//4wbx6UL1+eOXPmWBaL30cK169f//y902HDuGBcoFLe2rcXfv4ZigOzgfIWx6NCR0UvntO4QWVat6gOwJSvN+S7n7Pj0cU1ytCpvU4H4y/ORPrTT5JIFy9eTLPs0wQFWEDKbjg/Laxa5aror5QvNm6Uq4piwI9AN6sDUiHJ08k47hgsxWiWr9zL3v0X9mCKP5PCnPkymHXYoGZERGgxBn8ZMsSVSJcsWULz5ta2AgQkmdapU+d8W/aJE4E4owpnW7fCvn3SQ/MFNJEqzznvYh46BIcPu/+8ATc3pkRsMQwDpn1z4dXptz9sJjU1k4gIG7cOtO4qKdylpEhBIICZM2daekXqFLCCkHXryjitPn20+L3ygeF686sJtLI0GBWqLgLqI8Oq1q2Dk25+yC9dKoY+vRoCMP3bf7Dbc05E6uzp26ldHWrVLGtixMopJQVuukm2K1euzBVXXGFtQA4BS6bz588HpHlOk6nyRmYmrN8g/0yRgL5VKV/UB0ogCfVsovvPu2OwNCfuP5jA0t9cE5Fu3HKMtY5KR85xqcpcKSnQuzcsXCiJdOnSpeen/LRawJJp1apViYyUYukHDgTqrCpcbNwI8+fDwYOSSK9EOh4p5a2DQLJj25MRFe2urkW9utLdbeo3rjGnUx2dksqXi6V398vyfK7yzZ13wqJFrkTaqFEjq0M6L6Dz/rRs2RKAbnqTS7kpJQXWr5d7pCAdjq5Ee+4q351zrGvWhEqVPHuus6rR7HnbOZOQSkZGFl/P2gjALX0vJyYmysRIFUBSkgyBAZgwYUJQJVIIcDKdO3cuALt2we7dgTyzCkVbtsDixXI1GgFcBVyPJlJlLm+Kydw6sCmRkTZSUzP55vtN/LRgBydPy3WusxlYmScpCXr2hLNnZeLvdkFYUi+gybRChQpcc801AHTqpAlV5W/LFtizR7ZjgTZ4NzZQqfyUR3qEHzsmvXo9Ua1KKbp3qQfA1K/Xn+/Z26JJVZo2rmpuoIoHH4Tly6FatWosXbqUKlWqWB3SBQKaTCMiIpg3bx5t27blwAFNqCpvzkQagSTRLmgiVearBFR3bDtru3ridseY07UbjrBgyS55bEgLc4JT5yUkuJp3X3vtNS699FJrA8pHQJMpSO3E+fPnc9VVV3HggFSwUCo7ZyJtBVS2OhgV1nyZ+bJH10upUikOkHq8xYtHcUufy80JTAGSSK+/Hk6ehNq1a9O1a1erQ8pXwJMpSEL9+OOPAThzxooIVDBJS5N7Ik7ORBp8DTlKuURFRTB0YNPzX9/UowFly2gfc7M4E+mqVZJIly5dSvXq1Qt/okUs63LmrIi0d68Me2iisxSFhdFPdGT0Ex3z/F7KkefyfDwhAdq17ki71h01kSrLFfQ3nNvLo7vy8ujgvVoKVXkl0tq1a1sdVoEsuTIFqFGjxvlJXG+/3aooVDDI3vmjNZpIlSrKQjGRgoXJNDo6mtdffx2Qcl5//21VJMpK8addyfQK9B6pCpwM4KjVQagcQjWRgoXJFKBp06b06dMHgOuug7VrrYxGWSHLUVqyHJ5fkRqGUfhOSuXjH1wVkCpb+CnOMAwZo1PEhXIiBYuTabFixfj666/p06cP8fHQtasm1GAUHQ0Z6VnmH9hwTRjvzR9ienqGT70xVdFRzG6Qlpnzb7hutm0rh+hlZGQRE120PxiGeiIFi5MpSHPv119/TZcuXYiPh2eftToilVtcHKSmpZNuckKNPwNHHe1s3lQyTUg4R3CUuFbBrlRmJgnJrslL0wDnBGqlS0HDhpaEBcCZhDRKlvLDh9UQkJQEo0dDq1ahnUghCJLp/7d35vFNVWkf/yZdaaHsllLassiiIuMgOKCCIugg8qIguIAU2V5ABMQRwW2YARRE5FXkxYVFwGUEoWVkQGSKoIjIUmQQodAKyE4pZSm0dMk988dJmqQtJWmT3Jv0fD+f59Obm5ub5z69Ob97tueAFNRx48YBFZs8rfAuJhPcdHMBu/d6rodJCMhIl9u1cD9F4NXCItKPnKSVxzxSBDKtc3LZfeBI8etjwGWgWjXo0FG2vujF7r1Had3m8vUPDCDy8uCll6BtW3j9ddky4M9CCgYRU4CwMNlgt3EjbNqkry+K0tzd+TI/7Uz32PkuX4YzmXK7ItPcdx4+SaucK7ix2IeiCnM3sHP3QYoszus/Nmyor5AKIdi6cz+dOlWdZl7beqQzZkB6OtSrV4/JkyezadMmvxVSMJCYduvWjcGDB1u34ZFH4McfdXbKy6xfD08+KW3ECMhxY01FX3PXXbDrl/38djjbI+ezWFu1auD+uqS5+YX8c+MO7rl05foHKxRAXaDx+Ut8tfNXp/0mnQf+rP82nZq1zxMXp68fvsImpP/+N9SpU4epU6fy008/8be//Y2EhAS93asUJjdHRHr18UnTNIYNG8bHH38MQFCQXK5t6lTZpu7vpKbClClyOy9P3lCOxMSANxaNDwuDd96RT+GVYds2eHd2DK8814dmTSq+doumwfZtkHVOCqk76z/k5hcy9dM1NN+VxqiCwoAfBFny+qpO/cXzZAEv1a7B/Y/dz83tbiENuPFGaKVTX0HKpnRWrP2aN2ZcrPRv08gUFsKECXD4sJwGeeyYMdcjLQeXihlDiSlIQU1KSmL+/PmsX78egNq14dtv4TaDr2x0/rysYZaVItFikc3XmnMrE72B+4FpwEkv+lajBvzpTzKWH34o/1aEbdtg9qw6NEuIp0Pbm7j1lmiqR4YSGhLk8jlyLsPm7+V2Jyi3qdaiCXILCjmSdYGte9JJ3XOQLqfP8b9VQEhBiamnyQJeq1mdE3HRxLW/me5d42nfLoLwsGCv1lKFgPyCIs5fyGPnz8fZmvor+UWnmTItJ6CFVNPg8cdhxQr7vujoaL799lt/EVLwVzG1UVRUxJo1a5gwYQLp6ek8/zy8/bavvv36FBTAqFFw4IB93y+/yPX2yuM+YDQQBIQC3ZH/qWOAN2YFXUEK9X6HfVFRcl7vokVy213y82Ut+/vvwjmYVo0ruSYKXVwTUhOwf79s0o4A2lzneLOASAQxBUXceSGHu6la65kqMfU8AngemBscRMc/1SCukZm8PO9/b3g4RNXUuO2Pl7m7UxE33wxmw3S0eYfsbKhbV27PmTOH+Ph42rRpQ5MmTfR1zD38W0xtvPXWW7z44otEREBKCnTs6GsPSlNQAP36wVdflX6vCfAG1y7w70WKqC/JBnZa/74EHLHur15dNqMvXSqnv/iCkychNlZup1GxKTFVCSWm3uEN4BVg+HD46CO9vQkcNA3Gj4fNm+Xrn3+Wf2NiYjh50pttb17FJTHVLdG9q9jWrsvNhdWr9RHTPn1kbcrG8eNyNGpt4B2cJ3+3RK6TaCTqAA9Yt7sC3wPjgWOXISlJrhX40EPwySfy6dmbnD8v/8aghFShH9bKEosXw1NPQefOenoTGGgajBwJ8+c77x8y5GnGjn1OH6d8iRDCHfM5Y8eOFcgHcpGQgNi40V2XK2cWC6JBA4p9sFltELtkV4hfWiaIVSBiHK4pJATx2GOI/HzvxDI/H9G2rfyu2wwQA3+wkved3v4EillA3GeN6dSp+LRMCTTTNMT48YjISBnP8PBwsWDBApGamipSU1NFAOBSINyNnM+xWCxi1KhRxYVJRIR3BXXsWES9etJWrkS88IK9IFsLYp/VThugQPCEZYFYDaI+zoV2//6IwkLPxjY7237+owa4dn8wJabes4EoMa2saRpizBj7/RkeHi5SUlJEgOFSMNyNni5omiYOHToknnjiCWET1N27vXNzDBlivzGmTEE0aiS35xrgx+9Ny0Y+LNTBufAeNAhRVOSZ2NrEtJYBrtdfTImpd+wTECZrTD/5BK+UJYFujkIaGhoqlixZIjIzM0UA4lJA3I2grlgsFtG6dWsBiGnTvHODTJ9eugCLAJFhgALAF3YJxDoQUSViMGwY4soVREFBxWO7Z48SU3dNial37P9KxHXWLLxSngSqlRTSNWvWlFVkBwouBcXdKOrO5MmTBSDCwhDr1nnnJhk92vmH9p0Bfvy+tqsgvgERWaLQGTy4YnEtLEQ0bSrPcacBrs9fTImp92y2Q1yHDsXjZUmgWhUTUiFcDIy7kdQdTdOK+1DDwhCff444d87zN8uzz1oH5YBYY4Afvl5mQYqqrdBp3Rpx9Cji+HEZJ1djun+//RzZBrguf7GSYroFRI4B/AoUW+wQ27fekq0vmZny/vbWQDx/NU2TcRk2rEoJqRAuBsjdiBoCR0H11qAkp6cvEGkG+OHradtBhONcsI8ejfjtt+vH8sgRRFyc/EwnA1yLP1lJMQ0HcTdKUD1pjk2+I4cjunSR28s/V4JqM01DTJxoj1MVElIhXAySu1E1DBaLRTz77LMiLCxMeFNQGze29qkY4Eevt60HEQfiBpwL+HHjEPv2XTuOH3wgj2uB7JPV+zr8yUqKqc2eMIBvgWJaidjWrGnf3roVj5Yp/mglhTQmJqYqCakQLgbK3cgaDovFIgYPHiy8JajPPCNvoGAQXxvgh28UWwEivkQh9NlnZcfw9dfl+5MM4Lc/2RGuLaa1QZwv57O5ILaC+NkA1+EP9v414lzVxVTTEC++aC0Dg4PFypUr3SidAwaXguVudA2JtwQ1M1P2EQLiJhCnDPCjN5p9CqKhteCJjUVcvlw6juPHq9q9u7YVRA1KF+4mk0kEBQUJQESD2FCGrQVxl8Nnxlj37zTAdRnZPgDRskS8z57FI2WJP5oS0mJcCpi7ETYsJQXVE30dffrIG6k5SkjLMwuI262Fz8svl47jwIHyvcUG8NUfLI2yhVQOugsTI0eOLPM9V+xDA1yf0e0Fh3gtW0alyxF/NMem3SoupEK4GLSAWbPAbDazYMECQObxfeMNWL5cWlqae+c6e1Z+7tAh+ToaaOBZdwMKM/CedXvWLDh40Pn9rCz5ty4KV8gFrrVOfHx8PO3ataNWrVrlWp06dVi9ejXDhw+nVq1ahFuTLo8AFvrqQvwUWxru1q2hWzddXdGFdetg4EB4800IDg5m2bJl9OnTR2+3jI+rqms1w/PMM8+UehqvWRPx5puIxYsRGRnlX6Jj066jTTXAE7PRbbA1Vg884Dxtpn17a/+TAXw0um0CUY1r1ywrQ3BwcPF5PjLAtRrVulhjtGoV5ZYVgWZr1iASE+33mqqRFuNSAN2NuOHRNE1MnjxZ9OvXT/Tr1080b97cqTCqV09mOZo3T86XdLw8RyENDQ0tVZApQS3fziCzG4HMa2yLqy1ZwzYD+GhkO4bMtmW73zp37iw8Kaa33367iIqKKj7XGQNcsxHNJqYbNuBUPgSyvfmm833WrVs3kZycXKn7LYBwKYjuRt3vyM/PFxMmTBCJiYmiadOmTjdMw4YyLeGsWVJIx42T++Pi4sSpU6fE6tWrRYcOHYpzAgPigAF+7Ea2udY4xcfbByPZJnknGsA/o9tM7PfnqVOnhCfF1IbJZBKAOGGA6zWi3UPVEdPkZMSAAfb7q3v37uLLL7/0yH0WQLgUTHej79dcuXJFvPDCC2LEiBEiLi7OqZCyzSe11UoLCwuFELKmK4S9iWwCcl6a3j94o1oRiD9a4/jqq/K2OXQIERyMMIM4aAAfjWY/Ils9poJYiMy6BYhevXoJb4ip2WwWSkxL234QLzrEOpDFdNUqZxE1mUxi4cKFHrm/AhCXgmoSQrjVxerOwUbmwoULzJgxg6tXr7J8+XJOnTpV/F6LFs1IS0vHZLIvsB4WFkZBQQEAPYC+wGAf++wvbAHuBqpXh8OHoV49GD4cFiyARGCJzv4Zhe3Ax1bLd9hfg2sPQHLz91omQUFBaJrGCaBhpc8WGOwD7gPOWF/37AkrVkBYmI5OeZivv4bNm+HIEfjHP+z7+/Tpw5NPPknfvn11883gmK5/CFVXTB05ffo0c+fOBSAvL4+ePXvSpUsXp2P69OnD3r17SU9PL943BXjNl476EQ8C64CXXpIjqw8fhhYtQCuCNKC5zv7pzU/AA9hFMyoqiujoaKf7qyyUmHqHQcBSID4eRoyAv/zFv4V0+3b4/HP768xMWLYMNM2+79FHH6Vfv348/vjjvnfQv1Bi6g2mT5/OkiVLOHDgAABTgVf1dcmQ/AR0xLl2OmgQLF0KM4CJOvunJxbgVuxTMADat29PSkoKtWrVKlcwlZh6B5uYLlwIQ4aUfYwQ8O67sG+f6+cdMwZuvdUTHpbN8eMwbZqzSOblwZdfQn5+6eMffvhh7rjjDpo1a6ZE1HWUmHqTYcOGsXDhQsKAq3o7Y1BK1k4/+ABGjZLN44t09k1v0oF7gZMO+6Kjo8nMzPSZmP4OxFf6bIGBTUwXL5YPfSURAl55BaZPd++81apBr14QEmLfFxYmz1O/fsV8zcqSv6ncXNl0e/582cd16tSJ3r17F3dX1a9fn/79+zt1XylcQompN8nKyqJ+/fpKTMuhZO10717o0gXuRParVnV+AzYAWcBbwAUXPuMJMQ0ODsZisfAo8AUQXOkz+j/liek778Cnn0JqqnwQGT9+PM2bl99RoWkaixcvZtu2bWW+X68edOoEFdG1zZtlYhkbrVq1YvTo0YSGhhbvCw0N5amnniI4WP13PYBL/yUV6QrSoUMHAMJ19sPIdAC6A+suw+zZsskLZJ+pAppZDWQtvq11u0uXLrRo0YK2bdsyYsQIj3/ve++9x+hnnmElUkQ+xcXSogrx0UeQnAynT8Pu3XJfUFAQX3zxhcsDdZ5++mlWrlyJ5tAGm5uby8yZMzl06BDJyRX3r3HjxkycOJHIyEgeeugh6tSpU/GTKTyCqplWEFtTyQbkKEBF2TjWTo8fh7g4yMmBs0A9nX0zGo2B34GkpCR69+4NUKpJzhM1U4APP/yQkSNHAnIQVHWPnNV/sdVM+/eHX3+F//zH/p7ZbGbixIk88sgj3HHHHZX+rjNnzvDDDz9U6hx33XUXDRqoJKc+QjXzehNbIWeBwElw7CXaAanAhg0waRLs2AGbkdNnFHZuBfYCNWrUICMjgxtuuMFrYgpSJIQQ9AaWASHX+0CAkgyUlXn2+eefp1u3btSvX5927dr52i2FcXBJTJUOVIALF1zp3VLYsD3Lp6ZCy5Zy+4Bu3hgXWz0jJyeH1atXe/37Fi9ejAkpJv29/m3GYy3QEmchDQbGjRvHzp07efvtt3nwwQeVkCpcQvWZusmFCxdo0aIFoJopXeV269/UVLkSB6h+U0c04Fkgxfp6zJgxDB061Ovfm5iYyKVLlxgzZgwrkIkj/HhqpctsAIYDhx32tWzZkgkTJtC0adNSc8wVCldQNVM3sAnpWetQug2oALqCo5i2aiW3Vc3UznjgfYfXvsxEk5iYWNyU3AfnTEyBxg/IqUDdsAvpjTfeyKpVq9i3bx9Dhw5VQqqoMEoL3ODEiRNcvHix+PVNOvriT9yCrPFkZEBMjNynxFRiQU5PAbj//vvZuHGjT5sVo6KiWL58OSaTibXAUz77Zt+xHZmcohNwzLqvcePGpKSkcODAAR5++GHMZlUUKiqHuoPc4JZbbmHHjh3Fr3/X0Rd/IgRoY93OzZVz634DCnT0SS+KrFYADEX2s2QiRzj8/e9/59577yUiIsKnPvXt25cpU6YAsAq5OHmgsB2ZttGWebtRo0Zs376d9PR0unbtqkRU4THUneQmbdq0KS7seqKGN7tKbetfTYPGjWWN7JCO/niCq8AlN+xl5INFCLKm7pgF6v/nzaNjx44+870kAwcOxGw2U0Tg1E53IIX0IhATE0NaWhqHDx+mffv2KpmBwuMoMa0Au3btAmRT5SkC60neF9hG9PrrIKTLyAxOTYGablhZmeimT59OXl4eo0aN8oHn1yYhIYG//vWvAKxBpjv015aDS8A3wP1IIY2NjSUjI4OWLVsqEVV4DSWmFaClTQ2AWOCPwFZkIau4Pv46COkS8B1wM3KO7KnyDy+TMWPGOK2BOGnSJMLDjZFHq3///gQFBVEAtEAuNehPD4rnkdNdmiAzb9mE9ODBgz5vOldUPZSYVhCz2Vzc33IQmW/2TmRmH0X5+GPN9Bjyoele7INY6tevz5kzZ9xalXnOnDm6XcP1aN68OevXryfMuvbYBqAXxhfULOBfyAGBDwHZ1v0JCQlKSBU+Q4lpBbFYLFgsFtLS0qhZsyYAvyDX6VQpHcrHltTHX+J0DOiCvY+3bt26HDt2jMzMTG644QYdPfM89913H1evXmXNmjWYTSY2AHcBeXo7VgZnkQOmbgP+B/vC3nFxcVy8eJEjR44oIVX4DCWmlaRly5ZcuHCBmTNnArJpqROqhloeSUnyb0993ShFLrIZ19FWAvcgRx9HRERw6NAhsrKyaNSokX6O+oAePXrwZH+ZF2k3sv/xG4wxD/Us8CUyTWVv4IR1f2xsLOfOnePo0aNERUXp5p+iaqJ64z2EbZRvbm4ue4HOwBSghvX9TkCkbt4Zh0uXZI7eIOARH37vj8g+z2uRjxwgVPaCWVJI9+7dS5MmTTzum1Fp164dycnJ5ObmsgXZD3kP8ALwZ3yby/c88n9zHngV55HgsbGx7Ny5UyV+V+iKSnTvYfbs2UP79u0pKHAeC9ke+AuyADIja2VV6Unmz8B6YNw4ePdd+XqdF79vG/Yay0ZgrhufjYqKckowHxERwZYtW3QRUm8muneVtWvXMmDAAKec1F2BkcgpPu2RDyvVkTVYTy3ndhl5z+QiH3T2lXg/NjaWH3/8kfh4tcS5wquoVWP0Ys+ePXTv3p2CggKEEGRnZ5c6pity2ScTssb6CIG9puTdyOkkbdvCrl2wAJm0wFP8DPxq3d6LXGxbK3FMZGRkuSNnzWYzS5Ys4cEHH/SgZ5XDCGJqY8WKFYwYMaLM+9lGIlJQo61/3aEQWIGcg1wIvIf8vzpSt25dqlWrxqZNm2jWrFnJUygU3kCJqVH45ptvGDp0KJqmYbFYyMzMLHVMb2QzGkAMckBFoPAdchRseBgUFIJJk4NF6lbyvL8gpyQdB2ZRepBMRERE8eCwHj16sGDBgkp+o+8xkpjaWLp0KZMmTaKoqKg4T3VZDEf2a7qCAD4Hvi/jvQYNGhASEkJSUpJawUWhB0pMjcqCBQt4/fXXAcjLy+PMmTOljkkE2pbYdzv+twZoIXJKya9Ar17w1VeyxrK+kufdiUxYftFhX7Vq1YiOjgbgD3/4A0lJSX6fLs6IYurI7NmzmTx5Mtu3b2fRokWsWLGCS5culVt7vR6xsbGEhIRgMpmYP38+Xbt29aDHCoXbKDH1F6ZNm8Znn30GQHZ2dpk1V5D9rSORNVdHegOtvOlgJZiN7Ctu2hR69IC5cyECOUq2e/kfLZO9wCfAPGSfWnh4OI0bNyY2NpZ169YFXIYbo4spgKZppR5aRo4cyXfffef2uSZNmsSgQYM85ZpC4QmUmPorY8eO5fvvnRu8jhw54rRijSO1gCFIkRoGJHjbQRc5iRT5HOBf/4IHHoDhw2HJEjn4aiGyBu4qe4D7gHPW1w0bNiQ9PT2g5xL6g5gqFAGOEtNAQtM0HnvsMU6cOFG8TwjBJSeEWwAAAgpJREFU7t27yc+3z/6rB/Tn2tMWagETy3nfkwxA9oP16gX//KfcJwS8/DLMmCFfvwlM4Pp3q6OQhoeH07lzZ5KTkwNaSEGJqUJhAJSYVgWys7MZMGAAV69eZcuWLRQWFl73M7cBjuuTtADG4dnRxL8CrYHwcNi3D0rOKpkzB557TorrOGRzsGND4e/ATOQNlw8sA64gU/hlZGRUmUn5SkwVCt1RYlrVOHr0KKNHj75mgZudnc3WrVvLfO9OZCrEknQBfkKOlB2CTEZRFhp2MdSAGcArwODBsGhR2Z9ZtgwSE6GgAB4D3kYmurgKrKZ0usGqJqSgxFShMABKTBWlWb58OUm2fH5ARkYGqampLn02CJlIPLzEvi7AB8iluzYAHyGTje9Fpg7s3fva5/zqK+jbF8qqUIeGhtKzZ09CQkIIDg5m3rx5VUpIQYmpQmEAlJgqXOP9998vU1BTUlL4/fffMZvNREZGkpOTU+55zDgnSjCb5aCj8gbY7tgBjjODgoOD6du3L5GRkbz22mskJBhlOJU+KDFVKHTHK2KqUCh0wGQyrRNCVGQ2kUKh8AFKTBUKhUKhqCT+nR5GoVAoFAoDoMRUoVAoFIpKosRUoVAoFIpKosRUoVAoFIpKosRUoVAoFIpKosRUoVAoFIpKosRUoVAoFIpKosRUoVAoFIpKosRUoVAoFIpK8l/D/Xay54NKGQAAAABJRU5ErkJggg==\n", "text/plain": [ "
    " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.rcParams['figure.figsize'] = [8, 8]\n", "geojsonIO.plotOne(flP.petal); geojsonIO.addOne(flP.spots)\n", "ax = plt.gca()\n", "## moving spines we want\n", "ax.spines['right'].set_position('zero')\n", "ax.spines['bottom'].set_position('zero')\n", "## removing the other spines\n", "ax.spines['left'].set_visible(False)\n", "ax.spines['top'].set_visible(False)\n", "## removing the tick marks\n", "ax.tick_params(bottom=\"off\", right=\"off\")\n", "## get rid of the tick labels\n", "ax.get_xaxis().set_ticks([])\n", "ax.get_yaxis().set_ticks([])\n", "## increase line width\n", "ax.axhline(linewidth=4, color = \"k\")\n", "ax.axvline(linewidth=4, color = \"k\")\n", "props = dict(boxstyle='round', facecolor='wheat', alpha=0.7)\n", "ax.text(0.25, 0.25, \"I\", fontsize=25,\n", " verticalalignment='top', bbox=props)\n", "ax.text(-0.25, 0.25, \"II\", fontsize=25,\n", " verticalalignment='top', bbox=props)\n", "ax.text(-0.25, -0.25, \"III\", fontsize=25,\n", " verticalalignment='top', bbox=props)\n", "ax.text(0.25, -0.25, \"IV\", fontsize=25,\n", " verticalalignment='top', bbox=props)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "***\n", "nuQuadSpots \n", "\n", "Based on centroids of spots, how many spots are in a quadrant. \n", "
    \n", "`\n", "nuQuadIspots = len([ i for i in self.spots if i.centroid.x > 0 and i.centroid.y > 0 ])\n", "`" ] }, { "cell_type": "code", "execution_count": 51, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/plain": [ "2" ] }, "execution_count": 51, "metadata": {}, "output_type": "execute_result" } ], "source": [ "flP.nuQuadISpots" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "***\n", "propSpotsInQuad \n", "\n", "Proportion of total spotted (red) area from petal is found in a quadrant.\n", "\n", "`\n", "propSpotsInQuadI = spottedSurfaceQuadI.area / self.spots.area\n", "`" ] }, { "cell_type": "code", "execution_count": 55, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.2318585808247405" ] }, "execution_count": 55, "metadata": {}, "output_type": "execute_result" } ], "source": [ "flP.propSpotsInQuadI" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "quadCoveredbySpots\n", "\n", "Proportion of quadrant covered by spots (red). \n", "
    \n", "`\n", "quadICoveredbySpots = spottedSurfaceQuadI.area / petalQuadI.area\n", "`" ] }, { "cell_type": "code", "execution_count": 57, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/plain": [ "0.2292068403072505" ] }, "execution_count": 57, "metadata": {}, "output_type": "execute_result" } ], "source": [ "flP.quadICoveredbySpots" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "[back to Traits overview](#TraitOverview)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "

    Notes on updating and maintaining the package

    \n", "\n", "My general system has been to keep the versions under development in the [github repo](https://github.com/danchurch/mimulusSpeckling), and a \"stable\" version at [PyPi](https://pypi.org/project/makeFlowerPolygons-dcthom/).\n", "\n", "The github repo also (mostly) contains the analysis that has been done on these analysis. So it's a bit of a mess, just shoot me emails for questions on what's going on there. I'm betting that anyone tasked with future development of this project will want to fork the repo, then clean it up. Even the \"stable\" version is very raw, but should at least be simpler to understand - it is only the modules.\n", "\n", "So good luck, and send me questions if you have them. " ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.8" } }, "nbformat": 4, "nbformat_minor": 2 }