stimmenfryslan/notebooks/Dialect Regions from image....

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{
"cells": [
{
"cell_type": "code",
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"execution_count": 36,
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"metadata": {},
"outputs": [],
"source": [
"import folium\n",
"\n",
"from collections import Counter\n",
"\n",
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"from math import sqrt, floor\n",
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"import numpy as np\n",
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"from imageio import imread\n",
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"\n",
"%matplotlib notebook\n",
"from matplotlib import pyplot as plt\n",
"\n",
"from skimage.morphology import binary_closing\n",
"from skimage.measure import find_contours, label\n",
"\n",
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"import folium.plugins\n",
"from folium_jsbutton import JsButton"
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]
},
{
"cell_type": "code",
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"execution_count": 18,
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"metadata": {},
"outputs": [],
"source": [
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"im = imread('../data/dialects.png')\n",
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"\n",
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"color_occurence = Counter(map(tuple, im.reshape(-1,3)))\n",
"colors_sorted_by_occurence = [c for c, _ in sorted(\n",
" color_occurence.items(),\n",
" key=lambda x: x[1],\n",
" reverse=True)\n",
"]"
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]
},
{
"cell_type": "code",
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"execution_count": 31,
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"metadata": {},
"outputs": [
{
"data": {
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" function() { $(this).addClass(\"ui-state-hover\");},\n",
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" };\n",
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" var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
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" x0 = Math.floor(x0) + 0.5;\n",
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" x1 = Math.floor(x1) + 0.5;\n",
" y1 = Math.floor(y1) + 0.5;\n",
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" var width = Math.abs(x1 - x0);\n",
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" * Chrome. But how to set the MIME type? It doesn't seem\n",
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"// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
"mpl.findpos = function(e) {\n",
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" }\n",
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" var y = canvas_pos.y * mpl.ratio;\n",
"\n",
" this.send_message(name, {x: x, y: y, button: event.button,\n",
" step: event.step,\n",
" guiEvent: simpleKeys(event)});\n",
"\n",
" /* This prevents the web browser from automatically changing to\n",
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" * to control all of the cursor setting manually through the\n",
" * 'cursor' event from matplotlib */\n",
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" // Handle any extra behaviour associated with a key event\n",
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"\n",
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"\n",
" // Prevent repeat events\n",
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" else\n",
" this._key = event.which;\n",
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"\n",
" var value = '';\n",
" if (event.ctrlKey && event.which != 17)\n",
" value += \"ctrl+\";\n",
" if (event.altKey && event.which != 18)\n",
" value += \"alt+\";\n",
" if (event.shiftKey && event.which != 16)\n",
" value += \"shift+\";\n",
"\n",
" value += 'k';\n",
" value += event.which.toString();\n",
"\n",
" this._key_event_extra(event, name);\n",
"\n",
" this.send_message(name, {key: value,\n",
" guiEvent: simpleKeys(event)});\n",
" return false;\n",
"}\n",
"\n",
"mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
" if (name == 'download') {\n",
" this.handle_save(this, null);\n",
" } else {\n",
" this.send_message(\"toolbar_button\", {name: name});\n",
" }\n",
"};\n",
"\n",
"mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
" this.message.textContent = tooltip;\n",
"};\n",
"mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
"\n",
"mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
"\n",
"mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
" // Create a \"websocket\"-like object which calls the given IPython comm\n",
" // object with the appropriate methods. Currently this is a non binary\n",
" // socket, so there is still some room for performance tuning.\n",
" var ws = {};\n",
"\n",
" ws.close = function() {\n",
" comm.close()\n",
" };\n",
" ws.send = function(m) {\n",
" //console.log('sending', m);\n",
" comm.send(m);\n",
" };\n",
" // Register the callback with on_msg.\n",
" comm.on_msg(function(msg) {\n",
" //console.log('receiving', msg['content']['data'], msg);\n",
" // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
" ws.onmessage(msg['content']['data'])\n",
" });\n",
" return ws;\n",
"}\n",
"\n",
"mpl.mpl_figure_comm = function(comm, msg) {\n",
" // This is the function which gets called when the mpl process\n",
" // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
"\n",
" var id = msg.content.data.id;\n",
" // Get hold of the div created by the display call when the Comm\n",
" // socket was opened in Python.\n",
" var element = $(\"#\" + id);\n",
" var ws_proxy = comm_websocket_adapter(comm)\n",
"\n",
" function ondownload(figure, format) {\n",
" window.open(figure.imageObj.src);\n",
" }\n",
"\n",
" var fig = new mpl.figure(id, ws_proxy,\n",
" ondownload,\n",
" element.get(0));\n",
"\n",
" // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
" // web socket which is closed, not our websocket->open comm proxy.\n",
" ws_proxy.onopen();\n",
"\n",
" fig.parent_element = element.get(0);\n",
" fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
" if (!fig.cell_info) {\n",
" console.error(\"Failed to find cell for figure\", id, fig);\n",
" return;\n",
" }\n",
"\n",
" var output_index = fig.cell_info[2]\n",
" var cell = fig.cell_info[0];\n",
"\n",
"};\n",
"\n",
"mpl.figure.prototype.handle_close = function(fig, msg) {\n",
" var width = fig.canvas.width/mpl.ratio\n",
" fig.root.unbind('remove')\n",
"\n",
" // Update the output cell to use the data from the current canvas.\n",
" fig.push_to_output();\n",
" var dataURL = fig.canvas.toDataURL();\n",
" // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
" // the notebook keyboard shortcuts fail.\n",
" IPython.keyboard_manager.enable()\n",
" $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
" fig.close_ws(fig, msg);\n",
"}\n",
"\n",
"mpl.figure.prototype.close_ws = function(fig, msg){\n",
" fig.send_message('closing', msg);\n",
" // fig.ws.close()\n",
"}\n",
"\n",
"mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
" // Turn the data on the canvas into data in the output cell.\n",
" var width = this.canvas.width/mpl.ratio\n",
" var dataURL = this.canvas.toDataURL();\n",
" this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
"}\n",
"\n",
"mpl.figure.prototype.updated_canvas_event = function() {\n",
" // Tell IPython that the notebook contents must change.\n",
" IPython.notebook.set_dirty(true);\n",
" this.send_message(\"ack\", {});\n",
" var fig = this;\n",
" // Wait a second, then push the new image to the DOM so\n",
" // that it is saved nicely (might be nice to debounce this).\n",
" setTimeout(function () { fig.push_to_output() }, 1000);\n",
"}\n",
"\n",
"mpl.figure.prototype._init_toolbar = function() {\n",
" var fig = this;\n",
"\n",
" var nav_element = $('<div/>')\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) { continue; };\n",
"\n",
" var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></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 = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
" nav_element.append(status_bar);\n",
" this.message = status_bar[0];\n",
"\n",
" // Add the close button to the window.\n",
" var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
" var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
" button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
" button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
" buttongrp.append(button);\n",
" var titlebar = this.root.find($('.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<ncells; i++) {\n",
" var cell = cells[i];\n",
" if (cell.cell_type === 'code'){\n",
" for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
" var data = cell.output_area.outputs[j];\n",
" if (data.data) {\n",
" // IPython >= 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"
],
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"text/plain": [
"<IPython.core.display.Javascript object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
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"<img src=\"data:image/png;base64,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],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
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"output_type": "display_data"
}
],
"source": [
"pallette_width = floor(sqrt(len(colors_sorted_by_occurence)))\n",
"pallette = np.array(colors_sorted_by_occurence[:pallette_width**2]).reshape(pallette_width, pallette_width, 3)\n",
"\n",
"_, (ax0, ax1) = plt.subplots(1, 2)\n",
"ax0.imshow(pallette)\n",
"for x in range(pallette_width):\n",
" for y in range(pallette_width):\n",
" ax0.text(x-0.5, y+0.5, str(x + y * pallette_width))\n",
"ax0.set_xticks([]), ax0.set_yticks([])\n",
"\n",
"\n",
"\n",
"pallette_indices = [3, 4, 7, 8]\n",
"pallette = [colors_sorted_by_occurence[i] for i in pallette_indices]\n",
"pallette = np.array(pallette).reshape(1, -1, 3)\n",
"ax1.imshow(pallette)\n",
"ax1.set_xticks([]), ax1.set_yticks([])\n",
"None"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {},
"outputs": [
{
"ename": "AttributeError",
"evalue": "module 'folium.plugins' has no attribute 'ImageOverlay'",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-37-5561299adb2c>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 10\u001b[0m )\n\u001b[1;32m 11\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 12\u001b[0;31m \u001b[0mfolium\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplugins\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mImageOverlay\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 13\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 14\u001b[0m \u001b[0mm\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mAttributeError\u001b[0m: module 'folium.plugins' has no attribute 'ImageOverlay'"
]
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}
],
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"source": [
"bounds = [\n",
" [52.832432288794514, 5.354483127593994],\n",
" [53.41434089638827, 6.330699920654297]\n",
"]\n",
"\n",
"m = folium.Map(\n",
" location=[(bounds[0][0] + bounds[1][0]) / 2, (bounds[0][1] + bounds[1][1]) / 2],\n",
" tiles='stamentoner',\n",
" zoom_start=9\n",
")\n",
"\n",
"folium.raster_layers.ImageOverlay()\n",
"\n",
"m"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"print(geojson)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
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"source": [
"plt.rcParams['figure.figsize'] = (9.5, 3)\n",
"ax0, ax1, ax2 = plt.subplots(1,3)[1]\n",
"ax0.imshow(im)\n",
"ax1.imshow(composed_49.astype(int))\n",
"ax2.imshow(composed_4.astype(int))\n",
"ax0.set_xticks([]); ax0.set_yticks([])\n",
"ax1.set_xticks([]); ax1.set_yticks([])\n",
"ax2.set_xticks([]); ax2.set_yticks([])\n",
"plt.tight_layout()\n",
"\n",
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"\n",
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"\n",
"stavoren_to_east_pixels = [295, 717]\n",
"north_to_south_pixels = [99, 525]"
]
},
{
"cell_type": "code",
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"execution_count": null,
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"metadata": {
"scrolled": false
},
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"outputs": [],
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"source": [
"axes = plt.subplots(2,2)[1].ravel()\n",
"contours = []\n",
"for axis, c in zip(axes, relevant_colors):\n",
" bi = (im[:-100] == c[None,None]).min(axis=2)\n",
" bi = binary_closing(bi, np.ones((5,5)))\n",
" \n",
" labels = label(bi, background=False)\n",
" \n",
" contours.append(find_contours(bi, 0.5))\n",
"\n",
" axis.imshow(bi)\n",
" for n, contour in enumerate(contours[-1][:1]):\n",
" axis.plot(contour[:, 1], contour[:, 0], linewidth=2)\n",
" axis.set_xticks([]); axis.set_yticks([])\n",
"plt.tight_layout()"
]
},
{
"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
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"outputs": [
{
"ename": "NameError",
"evalue": "name 'stavoren_to_east_coords' is not defined",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-3-7f649ab43c0e>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0ma0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mb0\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mstavoren_to_east_coords\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0mc0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0md0\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mstavoren_to_east_pixels\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0mscale_x\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mlambda\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mc0\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0md0\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mc0\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mb0\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0ma0\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0ma0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mNameError\u001b[0m: name 'stavoren_to_east_coords' is not defined"
]
}
],
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"source": [
"a0, b0 = stavoren_to_east_coords\n",
"c0, d0 = stavoren_to_east_pixels\n",
"\n",
"scale_x = lambda x: (x - c0) / (d0 - c0) * (b0 - a0) + a0\n",
"\n",
"a1, b1 = north_to_south_coords\n",
"c1, d1 = north_to_south_pixels\n",
"\n",
"scale_y = lambda x: (x - c1) / (d1 - c1) * (b1 - a1) + a1\n",
"\n",
"contours_scaled = [\n",
" list(zip(scale_x(c[0][:, 1]), scale_y(c[0][:, 0])))\n",
" for c in contours\n",
"]"
]
},
{
"cell_type": "code",
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"execution_count": 4,
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"metadata": {
"scrolled": true
},
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"outputs": [
{
"ename": "NameError",
"evalue": "name 'contours_scaled' is not defined",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-4-fc568b2f2fd5>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 10\u001b[0m }\n\u001b[1;32m 11\u001b[0m }\n\u001b[0;32m---> 12\u001b[0;31m \u001b[0;32mfor\u001b[0m \u001b[0mcontour\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdialect\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcontours_scaled\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mregions\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 13\u001b[0m ]\n\u001b[1;32m 14\u001b[0m })\n",
"\u001b[0;31mNameError\u001b[0m: name 'contours_scaled' is not defined"
]
}
],
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"source": [
"geojson = json.dumps({\n",
" \"type\": \"FeatureCollection\",\n",
" \"features\": [\n",
" {\n",
" \"type\": \"Feature\",\n",
" \"properties\": {'dialect': dialect},\n",
" \"geometry\": {\n",
" \"type\": \"Polygon\",\n",
" \"coordinates\": [list(map(list, contour))]\n",
" }\n",
" }\n",
" for contour, dialect in zip(contours_scaled, regions)\n",
" ]\n",
"})\n",
"\n",
"with open('dialect_regions.geojson', 'w') as f:\n",
" f.write(geojson)"
]
},
{
"cell_type": "code",
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"execution_count": 5,
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"metadata": {},
"outputs": [
{
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"ename": "NameError",
"evalue": "name 'north_to_south_coords' is not defined",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-5-732d7d519e9d>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m m = folium.Map(\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mlocation\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0msum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnorth_to_south_coords\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstavoren_to_east_coords\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3\u001b[0m \u001b[0mtiles\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'Mapbox Bright'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0mzoom_start\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m9\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m )\n",
"\u001b[0;31mNameError\u001b[0m: name 'north_to_south_coords' is not defined"
]
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}
],
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"source": []
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},
{
"cell_type": "code",
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"execution_count": 6,
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"metadata": {},
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"outputs": [
{
"ename": "NameError",
"evalue": "name 'contours_scaled' is not defined",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-6-1008e368979e>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m shapes = {\n\u001b[1;32m 2\u001b[0m \u001b[0mdialect\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mshape\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m{\u001b[0m\u001b[0;34m\"type\"\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m\"Polygon\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"coordinates\"\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcontour\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0;32mfor\u001b[0m \u001b[0mcontour\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdialect\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcontours_scaled\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mregions\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 4\u001b[0m }\n\u001b[1;32m 5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mNameError\u001b[0m: name 'contours_scaled' is not defined"
]
}
],
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"source": [
"shapes = {\n",
" dialect: shape({\"type\": \"Polygon\", \"coordinates\": [list(map(list, contour))]})\n",
" for contour, dialect in zip(contours_scaled, regions)\n",
"}\n",
"\n",
"def regions_for(coordinate):\n",
" regions = {\n",
" region_name\n",
" for region_name, shape in shapes.items()\n",
" if shape.contains(Point(*coordinate))\n",
" }\n",
" return regions\n",
"\n",
"def distance(shape, longitude, latitude):\n",
" ext = shape.exterior\n",
" p = ext.interpolate(ext.project(Point(longitude, latitude)))\n",
" return vincenty((latitude, longitude), (p.y, p.x))"
]
},
{
"cell_type": "code",
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"execution_count": 7,
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"metadata": {},
"outputs": [],
"source": [
"# SELECT user_lat, user_lng, question_text, answer_text\n",
"picture_games = pandas.read_sql('''\n",
"SELECT language.name as language, item.name as picture,\n",
" survey.user_lat as latitude, survey.user_lng as longitude,\n",
" survey.area_name as area, survey.country_name as country,\n",
" result.recording as filename,\n",
" result.submitted_at as date\n",
"FROM core_surveyresult as survey\n",
"INNER JOIN core_picturegameresult as result ON survey.id = result.survey_result_id\n",
"INNER JOIN core_language as language ON language.id = result.language_id\n",
"INNER JOIN core_picturegameitem as item\n",
" ON result.picture_game_item_id = item.id\n",
"''', db)\n",
"# picture_games['filename'] = [filename.split('/')[-1] for filename in picture_games['filename']]\n",
"picture_games.set_index('filename', inplace=True)"
]
},
{
"cell_type": "code",
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"execution_count": 8,
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"metadata": {},
"outputs": [
{
"data": {
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"application/vnd.jupyter.widget-view+json": {
"model_id": "c9ac8f69c77e461fa654e81dba282ca1",
"version_major": 2,
"version_minor": 0
},
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"text/plain": [
"VBox(children=(HBox(children=(FloatProgress(value=0.0, max=1.0), HTML(value='<b>0</b>s passed', placeholder='0…"
]
},
"metadata": {},
"output_type": "display_data"
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},
{
"ename": "NameError",
"evalue": "name 'regions_for' is not defined",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-8-aabb5cdda548>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 12\u001b[0m for filename, (latitude, longitude) in ProgressBar(\n\u001b[1;32m 13\u001b[0m \u001b[0mpicture_games\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'latitude'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'longitude'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0miterrows\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 14\u001b[0;31m \u001b[0msize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpicture_games\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 15\u001b[0m )\n\u001b[1;32m 16\u001b[0m ]\n",
"\u001b[0;32m<ipython-input-8-aabb5cdda548>\u001b[0m in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m 10\u001b[0m \u001b[0;34m'filename'\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mfilename\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 11\u001b[0m }\n\u001b[0;32m---> 12\u001b[0;31m for filename, (latitude, longitude) in ProgressBar(\n\u001b[0m\u001b[1;32m 13\u001b[0m \u001b[0mpicture_games\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'latitude'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'longitude'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0miterrows\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 14\u001b[0m \u001b[0msize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpicture_games\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mNameError\u001b[0m: name 'regions_for' is not defined"
]
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}
],
"source": [
"region_per_picture_game = [\n",
" {\n",
" 'dialects': [\n",
" {\n",
" 'dialect': dialect,\n",
" 'boundary_distance': distance(shapes[dialect], longitude, latitude),\n",
" }\n",
" for dialect in regions_for((longitude, latitude))\n",
" ],\n",
" 'filename': filename,\n",
" }\n",
" for filename, (latitude, longitude) in ProgressBar(\n",
" picture_games[['latitude', 'longitude']].iterrows(),\n",
" size=len(picture_games)\n",
" )\n",
"]"
]
},
{
"cell_type": "code",
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"execution_count": null,
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"metadata": {
"scrolled": false
},
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"outputs": [],
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"source": [
"Counter(len(x['dialects']) for x in region_per_picture_game)"
]
},
{
"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
"df = pandas.DataFrame([\n",
" [r['filename'], r['dialects'][0]['dialect'], r['dialects'][0]['boundary_distance']]\n",
" for r in region_per_picture_game\n",
" if len(r['dialects']) == 1\n",
"], columns = ['filename', 'dialect', 'boundary_distance'])\n",
"\n",
"df.to_excel('picture_game_recordings_by_dialect.xlsx')\n",
"df.to_csv('picture_game_recordings_by_dialect.csv')\n",
"df"
]
},
{
"cell_type": "code",
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"execution_count": 9,
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"metadata": {},
"outputs": [],
"source": [
"# SELECT user_lat, user_lng, question_text, answer_text\n",
"free_speech_games = pandas.read_sql('''\n",
"SELECT language.name as language,\n",
" survey.user_lat as latitude, survey.user_lng as longitude,\n",
" survey.area_name as area, survey.country_name as country,\n",
" result.recording as filename,\n",
" result.submitted_at as date\n",
"FROM core_surveyresult as survey\n",
"INNER JOIN core_freespeechresult as result ON survey.id = result.survey_result_id\n",
"INNER JOIN core_language as language ON language.id = result.language_id\n",
"''', db)\n",
"# free_speech_games['filename'] = [filename.split('/')[-1] for filename in games['filename']]\n",
"free_speech_games.set_index('filename', inplace=True)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
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"version": 3
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},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.5"
}
},
"nbformat": 4,
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"nbformat_minor": 1
}