LtL-settleTime-density-plots.ipynb

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   "source": [
    "%matplotlib notebook\n",
    "#%matplotlib inline\n",
    "\n",
    "#https://stackoverflow.com/questions/20961287/what-is-pylab\n",
    "##from matplotlib.pylab import *\n",
    "from pylab import *\n",
    "import numpy as np\n",
    "\n",
    "\n",
    "#parameters are described at  https://github.com/martin12333/PyMartinCA/blob/master/PARAMETERS.txt\n",
    "\n",
    "r=1\n",
    "am51printthres=      0.175\n",
    "settleTimeThres=200 #100: #150:#200:#50:#500:#1000:#400:#600: #1200:\n",
    "\n",
    "diam = 2*r + 1\n",
    "\n",
    "\n",
    "\n",
    "bmin0=2; bmax0=2; smin0=2; smax0=2\n",
    "bmin0=3; bmax0=3; smin0=3; smax0=4\n",
    "\n",
    "\n",
    "ndbmin=3; ndbmax=3; ndsmin=3; ndsmax=3\n",
    "ndbmin=3; ndbmax=7; ndsmin=3; ndsmax=1\n",
    "ndbmin=3; ndbmax=7; ndsmin=3; ndsmax=7\n",
    "#ndbmin=3; ndbmax=1; ndsmin=3; ndsmax=1\n",
    "ndbmin=4; ndbmax=4; ndsmin=4; ndsmax=4\n",
    "ndbmin=3; ndbmax=4; ndsmin=3; ndsmax=4\n",
    "ndbmin=1; ndbmax=1; ndsmin=1; ndsmax=1\n",
    "pskip=0\n",
    "\n",
    "\n",
    "\n",
    "#=============================\n",
    "\n",
    "w=128#80#64#40#32#40#64#80#128#160#256##320#240#320 ##64*diam #256 #1850 #2000 #2250 #1800\n",
    "h=w #256 #1300 #1400 #950\n",
    "\n",
    "w0=10#5#10#15#20#30#40#64#128\n",
    "h0=w0#64#128\n",
    "\n",
    "w2=w-2*w0#240#128#16#25#16\n",
    "h2=w2##240#128#16#25#16\n",
    "\n",
    "#\n",
    "\n",
    "np.random.seed()      \n",
    "\n",
    "\n",
    "#=============================\n",
    "\n",
    "###x11 = floor(linspace(xlo, xhi + 0.999, w))\n",
    "###y11 = floor(linspace(yhi + 0.999 , ylo, h))\n",
    "#x11 = (linspace(xlo, xhi , w))\n",
    "#y11 = (linspace(yhi  , ylo, h))\n",
    "#xx, yy = meshgrid(x11, y11)\n",
    " \n",
    "#\n",
    "\n",
    "ns=9#15#30#1 #7#10 #3\n",
    "\n",
    "\n",
    "\n",
    "niter =2000 #6000#2400 #300#150# 600 #300 #400 #200 #1000 #3000   \n",
    "#niter0=niter-150\n",
    "#sleep1=    0 #0.3      #0.1 #0 #0.01\n",
    "step1=12 #4 #20 #8 #15 #5 #1 #37 #2\n",
    "step2=60 #120 #1\n",
    "\n",
    "bfade = False#True  # False\n",
    "q = 0.1#0.9 #0.8#0.7 #0.3 #0.1 \n",
    "###qqq=0.2 ###0 #0.5 #1#2 #4 #8 #1.8\n"
   ]
  },
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       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "              'Firefox 4 and 5 are also supported but you ' +\n",
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       "\n",
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       "\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 = $('<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 = $('<canvas/>');\n",
       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
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       "            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",
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       "\n",
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       "            return fig.mouse_event(event, event['data']);\n",
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       "    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",
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       "\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",
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       "        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 = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option)\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'] / mpl.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
       "    var x1 = msg['x1'] / mpl.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * mpl.ratio;\n",
       "    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",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\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 overriden (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"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
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     "output_type": "display_data"
    },
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\" width=\"640\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "#format pmca 330\n",
      "#comment program 313 1\n",
      "#comment 1\n",
      "#comment 9\n",
      "event1 time_s time11 period2 pop_s population strdensity sdam51 space_s w2 h2 w h rule_s rulespace nstates r bmin bmax smin smax\n",
      "settle time 1416 12 pop 544 0.033 0.001 space 108 108 128 128 rule gltl 2 1 3 3 3 4\n",
      "settle time 1999 0 pop 538 0.033 0.007 space 108 108 128 128 rule gltl 2 1 3 3 3 4\n",
      "settle time 1999 0 pop 678 0.041 0.005 space 108 108 128 128 rule gltl 2 1 3 3 3 4\n",
      "settle time 1999 0 pop 635 0.039 0.027 space 108 108 128 128 rule gltl 2 1 3 3 3 4\n",
      "settle time 1999 0 pop 465 0.028 0.004 space 108 108 128 128 rule gltl 2 1 3 3 3 4\n",
      "settle time 1068 12 pop 503 0.031 0.002 space 108 108 128 128 rule gltl 2 1 3 3 3 4\n",
      "settle time 984 12 pop 434 0.026 0.002 space 108 108 128 128 rule gltl 2 1 3 3 3 4\n",
      "settle time 1999 0 pop 664 0.041 0.038 space 108 108 128 128 rule gltl 2 1 3 3 3 4\n",
      "settle time 1836 12 pop 535 0.033 0.000 space 108 108 128 128 rule gltl 2 1 3 3 3 4\n"
     ]
    }
   ],
   "source": [
    "from __future__ import print_function\n",
    "\n",
    "from scipy.ndimage import filters\n",
    "import time\n",
    "\n",
    "from pylab import *\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "\n",
    "#plt.figure(figsize = (11,5.5))\n",
    "#plt.figure(figsize = (10,5))\n",
    "plt.figure()\n",
    "#plt.figure(figsize = (8,4))\n",
    "\n",
    "#print('#pmco27-gltl')\n",
    "#print('#pmco30')\n",
    "#print('#pmco31')\n",
    "#print('#pmco32') #removed ';'\n",
    "print('#format pmca 330')\n",
    "print('#comment program 313 1')\n",
    "\n",
    "print('#comment',ndbmin* ndbmax* ndsmin* ndsmax)\n",
    "\n",
    "print('#comment',ns*(1-pskip)*ndbmin* ndbmax* ndsmin* ndsmax)\n",
    "\n",
    "###print('settleTime, period2,    strdensity,  population   ,sdam51,      w,w2,id1,  aux1, aux2,     nstates, r,bmin,bmax,smin,smax')\n",
    "###print('settleTime period2 strdensity population sdam51 w w2 id1 aux1 aux2 nstates r bmin bmax smin smax')\n",
    "schema1='event1,time_s,time11,period2,pop_s,population,strdensity,sdam51,space_s,w2,h2,w,h,rule_s,rulespace,nstates,r,bmin,bmax,smin,smax'\n",
    "print(schema1.replace(',',' '))\n",
    "\n",
    "\n",
    "#matplotlib.rc(\"image\",cmap=\"gray\")\n",
    "#matplotlib.rc(\"image\",cmap=\"hot\")\n",
    "##matplotlib.rc(\"image\",cmap=\"RdYlGn\")\n",
    "##matplotlib.rc(\"image\",cmap=\"BrBG\")\n",
    "#matplotlib.rc(\"image\",cmap=\"copper\")\n",
    "#matplotlib.rc(\"image\",cmap=\"afmhot\")\n",
    "#matplotlib.rc(\"image\",cmap=\"autumn\")\n",
    "#matplotlib.rc(\"image\",cmap=\"summer\")\n",
    "\n",
    "#matplotlib.rc(\"image\",interpolation=\"nearest\")\n",
    "\n",
    "\n",
    "plt.ion()\n",
    "#plt.ioff()\n",
    "\n",
    "\n",
    "#img_plot = plt.imshow(a)\n",
    "#plt.show(block=False)\n",
    "\n",
    "\n",
    "####plt.matshow())\n",
    "##plt.figure()\n",
    "#### wrong: extent=(xlo, xhi, ylo, yhi) ) \n",
    "##, interpolation=\"nearest\")\n",
    "##, cmap = plt.cm.gray)\n",
    "\n",
    "time1 = time.clock()\n",
    "\n",
    "\n",
    "OR = logical_or\n",
    "AND = logical_and\n",
    "\n",
    "id1=-1\n",
    "\n",
    "plotysum=zeros( (niter,1) )\n",
    "\n",
    "for j in range(ns):\n",
    "\n",
    "\n",
    "    for dbmax in range(ndbmax):\n",
    "\n",
    "        bmax=bmax0+dbmax\n",
    "\n",
    "        for dsmax in range(ndsmax):\n",
    "\n",
    "            smax=smax0+dsmax\n",
    "\n",
    "\n",
    "            for dbmin in range(ndbmin):\n",
    "\n",
    "                bmin=bmin0+dbmin\n",
    "\n",
    "                for dsmin in range(ndsmin):\n",
    "\n",
    "                    smin=smin0+dsmin\n",
    "\n",
    "\n",
    "                    if   rand()<pskip  or  bmax<bmin or (smax+1) < smin: continue #####not break\n",
    "                    \n",
    "                    \n",
    "                    \n",
    "                    id1+=1\n",
    "                    \n",
    "                    \n",
    "                    \n",
    "                    \n",
    "\n",
    "                    p=0.5#0.3#0.5 #0.25 #0.16\n",
    "                    \n",
    "                    a = zeros( ( h, w ), dtype=bool) \n",
    "                    a[ h0 : h0+h2 , w0 : w0+w2 ] = (rand( h2, w2 )<p) \n",
    "                    \n",
    "                    #a=(rand( h, w )<p) \n",
    "\n",
    "\n",
    "                    \n",
    "                    afade = 0.1 * a\n",
    "                    ##averyold = a ###\n",
    "                    averyold = 0 \n",
    "                    aold360=0\n",
    "                    aold420=0\n",
    "                    aold840=0\n",
    "                    \n",
    "                    \n",
    "                    \n",
    "                    plotx=zeros( (niter,1) )\n",
    "                    ploty=zeros( (niter,1) )\n",
    "                    plotz=zeros( (niter,1) )\n",
    "                    \n",
    "                    dam51=0\n",
    "                    \n",
    "                    \n",
    "                    \n",
    "                    \n",
    "                    iper=0\n",
    "                    \n",
    "                    ahistory=[0]*int(3+niter/12)\n",
    "                    assert step1==12\n",
    "                    \n",
    "                    \n",
    "                    settleTime=2e9\n",
    "                    period2=0\n",
    "\n",
    "\n",
    "                    for i in range(1,niter):\n",
    "\n",
    "                        n41 =   diam*diam*filters.uniform_filter(0.0+a,diam )   ##,mode='wrap')  )  \n",
    "                        n=n41.round()   \n",
    "                        birth = AND(  n>=bmin,  AND( n<=bmax, logical_not(a) )  )\n",
    "                        survi = AND(  n>=smin,  AND( n<=smax, a )  )\n",
    "                        a =  OR( birth , survi )  \n",
    "\n",
    "                        #population=(0.0+a).sum()\n",
    "                        density=(0.0+a).mean()\n",
    "\n",
    "                        ######settleTime=i\n",
    "\n",
    "                        plotx[i]=i\n",
    "                        ploty[i]=density #population\n",
    "\n",
    "\n",
    "\n",
    "                        if (i % 12)==0:\n",
    "                            ahistory[i//12] =a\n",
    "                            #ahistory[i/12] =a\n",
    "\n",
    "                        \n",
    "                        \n",
    "                        if (i % 24)==0:\n",
    "                            aslower=ahistory[i//24]\n",
    "                            #aslower=ahistory[i/24]\n",
    "                            if (a==aslower).all() :\n",
    "                                #print(' ',i,id1)\n",
    "                                if iper==0:\n",
    "                                    settleTime=i//2 ##/2\n",
    "                                    iper=i\n",
    "                                else:\n",
    "                                    #print(' i-iper',i-iper,id1)\n",
    "                                    period2=i-iper\n",
    "                                    break\n",
    "                            \n",
    "\n",
    "\n",
    "\n",
    "\n",
    "                        if (i % step1)==0:\n",
    "\n",
    "                            if bfade:\n",
    "                                afade = q*a + (1-q)*afade\n",
    "                                asho=afade\n",
    "                            else:\n",
    "                                asho=0+a\n",
    "                                \n",
    "                            ada51= logical_xor(a, averyold)  \n",
    "                            averyold =a \n",
    "\n",
    "\n",
    "                            olddam51=dam51\n",
    "                            dam51=ada51.mean()\n",
    "                            \n",
    "                            plotz[i]=dam51\n",
    "\n",
    "                            if dam51 == 0 :\n",
    "                                settleTime=i\n",
    "                                period2=step1\n",
    "                                #print('dam51==0',id1)\n",
    "                                break\n",
    "                            \n",
    "                            \n",
    "                            \n",
    "                            \n",
    "                            \n",
    "                        if (i % 360)==0:\n",
    "                            if (a==aold360).all():\n",
    "                                #print(' p360:',i,iper,id1)\n",
    "                                #print(i,density,dam51,bmin,bmax,smin,smax)\n",
    "                                settleTime=i-360\n",
    "                                period2=360\n",
    "                                break\n",
    "                            aold360=a\n",
    "\n",
    "                        if (i % 840)==0:\n",
    "                            if (a==aold840).all():\n",
    "                                #print(' p840:',i,iper,id1)\n",
    "                                #print(i,density,dam51,bmin,bmax,smin,smax)\n",
    "                                settleTime=i-840\n",
    "                                period2=840\n",
    "                                break\n",
    "                            aold840=a\n",
    "\n",
    "\n",
    "                        #250#175: #0.200 \n",
    "                        if i ==100 and density >0.320:\n",
    "                            break\n",
    "\n",
    "                        if i ==200 and density >0.300:\n",
    "                            break\n",
    "\n",
    "\n",
    "\n",
    "                        if i ==300 and density >0.290:\n",
    "                            break\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "                        if i ==500 and density >0.270:\n",
    "                            break\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "                        if i ==700 and density >0.240:\n",
    "                            break\n",
    "\n",
    "                        if i ==1200 and density >0.200:\n",
    "                            break\n",
    "\n",
    "                        if i ==2000 and density >0.175:\n",
    "                            break\n",
    "                            \n",
    "\n",
    "                        if (i % step2)==0:\n",
    "                            strdensity='{:.3f}'.format(density)\n",
    "                            sdam51='{:.3f}'.format(dam51)\n",
    "                            #print( i, strdensity, sdam51)\n",
    "                            \n",
    "                            #if density >0.250:#175: #0.200 :\n",
    "                                #break #################250\n",
    "\n",
    "\n",
    "                    dam51=olddam51\n",
    "                    \n",
    "                    population=(0+a).sum()\n",
    "                    \n",
    "                    if i<settleTime:\n",
    "                        settleTime=i\n",
    "                        \n",
    "                    strdensity='{:.3f}'.format(density)\n",
    "                    sdam51='{:.3f}'.format(dam51)\n",
    "                    \n",
    "                    nstates=2\n",
    "                    aux1=0\n",
    "                    aux2=0\n",
    "                    \n",
    "                    event1='settle'\n",
    "                    time_s='time'\n",
    "                    time11=settleTime\n",
    "                    pop_s='pop'\n",
    "                    space_s='space'\n",
    "                    rule_s='rule'\n",
    "                    rulespace='gltl'\n",
    "                    \n",
    "                    #if dam51>0.005 and dam51<0.150 and density>0.005 and density<0.150:\n",
    "                    #if dam51>0.001 and dam51<0.150 and density>0.001 and density<0.150:\n",
    "                    #if dam51>0.000 and dam51<0.150 and density>0.000 and density<0.150 and settleTime>70:\n",
    "                    #if dam51>0.000 and dam51<0.150 and density>0.000 and density<0.150 and settleTime>150:\n",
    "                    #if dam51>0.001 and dam51<0.150 and density>0.000 and density<0.150 and settleTime>450:\n",
    "                    #if dam51>0.000 and dam51<0.150 and density>0.000 and density<0.150 and settleTime>450:\n",
    "                    #if  density>0.000 and density<0.175 and settleTime>400:\n",
    "                    #if  dam51<0.150 and density>0.000 and density<0.175 and settleTime>200:#50:#500:#1000:#400:#600: #1200:\n",
    "                    #if  dam51<0.150  and density<am51printthres        and settleTime>settleTimeThres: #100: #150:#200:#50:#500:#1000:#400:#600: #1200:\n",
    "                    #if   density>0.000  and density<am51printthres        and settleTime>settleTimeThres: #100: #150:#200:#50:#500:#1000:#400:#600: #1200:\n",
    "                    if   density<am51printthres        and settleTime>settleTimeThres  and density>0.002:#000 : #100: #150:#200:#50:#500:#1000:#400:#600: #1200:\n",
    "                        #print('pmc11',settleTime,strdensity,sdam51,'ltl',r,bmin,bmax,smin,smax,w,w2,id1)\n",
    "                        #print('pmc12-ltl',settleTime, period2, strdensity,sdam51,r,bmin,bmax,smin,smax,w,w2,id1)\n",
    "                        #print('pmc13-ltl',settleTime, period2, strdensity,  population   ,sdam51,r,bmin,bmax,smin,smax,w,w2,id1)\n",
    "                        #print(settleTime, period2,    strdensity,  population   ,sdam51,      w,w2,id1,  aux1, aux2,     nstates, r,bmin,bmax,smin,smax)\n",
    "                        #print('settleTime',settleTime, period2, ';pop', population,   strdensity, sdam51, ';wh', w,h, ';rule gltl',  nstates, r,bmin,bmax,smin,smax)\n",
    "                        #print('settleTime',settleTime, period2, ';pop', population,   strdensity, sdam51, ';w2h2', w2,h2,w,h, ';rule gltl',  nstates, r,bmin,bmax,smin,smax)\n",
    "                        #print('settleTime',settleTime, period2, 'pop', population,   strdensity, sdam51, 'w2h2', w2,h2,w,h, 'rule gltl',  nstates, r,bmin,bmax,smin,smax)\n",
    "                        print(event1,time_s,time11,period2,pop_s,population,strdensity,sdam51,space_s,w2,h2,w,h,rule_s,rulespace,nstates,r,bmin,bmax,smin,smax)\n",
    "                        \n",
    "                        #plot(ploty[:settleTime],'x')\n",
    "                        #plot(ploty,'x-')\n",
    "                       \n",
    "                    \n",
    "                    \n",
    "                    #if density<#0.200: #999:#500:#100: #500: #0.200 :\n",
    "                    if density<am51printthres        and settleTime>settleTimeThres: #0.200: #999:#500:#100: #500: #0.200 :\n",
    "                        plot(ploty[:settleTime],'x')\n",
    "                        #plot(ploty,'x-')\n",
    "                        ##plot(plotz,'x')\n",
    "                        ##plot( ploty[niter0:], plotz[niter0:],'x')\n",
    "                    \n",
    "                    \n",
    "                    \n",
    "                    \n",
    "                    ##plotysum += ploty\n",
    "                    \n",
    "time2 = time.clock()\n",
    "#print( round(time2-time1,0) )\n",
    "                    \n",
    "                    \n",
    "                    \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pmcostr='''\n",
    "settleTime 912 12 pop 482 0.029 0.001 w2h2 108 108 128 128 rule gltl 2 1 3 3 3 4\n",
    "\n",
    "settleTime 1284 12 ;pop 531 0.032 0.002 ;wh 128 128 ;rule gltl 2 1 3 3 3 4\n",
    "\n",
    "1008 12 0.071 456 0.010 80 60 240 0 0 2 3 16 20 11 24\n",
    "\n",
    "204 12 0.004 1 0.051 16 16 588 0 0 2 1 3 5 4 4\n",
    "\n",
    "564 12 0.000 0 0.008 80 60 1 0 0 2 3 11 13 12 18\n",
    "\n",
    "1860 12 0.032 528 0.002 128 108 5 0 0 2 1 3 3 3 4\n",
    "\n",
    "pmc11 1404 0.000 0.003 ltl 3 12 16 13 19 128 108 68\n",
    "\n",
    "pmc13-ltl 1999 0 0.045 742 0.023 1 3 3 3 4 128 108 4\n",
    "\n",
    "pmc11 7999 0.010 0.013 ltl 3 12 15 12 19 128 108 12\n",
    "pmc11 5940 0.002 0.002 ltl 3 12 16 12 19 128 108 39\n",
    "\n",
    "pmc11 5556 0.001 0.002 ltl 3 12 15 12 19 128 108 13\n",
    "pmc11 5904 0.000 0.001 ltl 3 12 16 12 19 128 108 40\n",
    "\n",
    "pmc11 3999 0.019 0.071 ltl 3 12 16 12 19 128 108 40\n",
    "pmc11 3999 0.036 0.074 ltl 3 12 15 12 19 128 108 13\n",
    "\n",
    "pmc11 1999 0.030 0.061 ltl 3 12 16 12 19 128 108 39\n",
    "pmc11 1999 0.073 0.076 ltl 3 12 17 12 19 128 108 66\n",
    "\n",
    "pmc11 1080 0.004 0.005 ltl 3 12 16 13 19 128 108 68\n",
    "pmc11 1999 0.076 0.124 ltl 3 12 15 12 19 128 108 40\n",
    "pmc11 1320 0.004 0.005 ltl 3 12 15 12 19 128 108 67\n",
    "pmc11 252 0.001 0.001 ltl 3 12 14 12 19 128 108 40\n",
    "'''\n",
    "sp0=pmcostr.split('\\n')\n",
    "###sp1=sp0[1].split(' ')\n",
    "#sp03=sp0[1].split(';')\n",
    "#sp1=sp03[3].split(' ')\n",
    "sp5=sp0[1].split()\n",
    "#print(sp5)\n",
    "\n",
    "i5=sp5.index('gltl')\n",
    "#print(i5)\n",
    "print(sp5[i5+2])\n",
    "\n",
    "bmin0=int(sp5[i5+3])\n",
    "bmax0=int(sp5[i5+4])\n",
    "smin0=int(sp5[i5+5])\n",
    "smax0=int(sp5[i5+6])\n",
    "\n",
    "#bmin0=int(sp1[4]) #12])#   6])\n",
    "#bmax0=int(sp1[5]) #13])#   7])\n",
    "#smin0=int(sp1[6]) #14])#   8])\n",
    "#smax0=int(sp1[7]) #15])#   9])\n",
    "\n",
    "\n",
    "bmin0 -= 1; bmax0 -= 1; smin0 -= 1; smax0 -= 1\n",
    "#sp1#[0]\n",
    "#sp0\n",
    "#smin0\n",
    "smax0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "r=3\n",
    "am51printthres=     0.100# 0.175\n",
    "settleTimeThres=1000#200#50:#500:#1000:#400:#600: #1200:\n",
    "\n",
    "diam = 2*r + 1\n",
    "\n",
    "\n",
    "##bmin0=7; bmax0=11; smin0=7; smax0=10\n",
    "##bmin0=7; bmax0=8; smin0=6; smax0=10\n",
    "bmin0=14; bmax0=18; smin0=12; smax0=20\n",
    "bmin0=12; bmax0=14; smin0=12; smax0=18\n",
    "bmin0=12; bmax0=14; smin0=12; smax0=19\n",
    "bmin0=14; bmax0=20; smin0=13; smax0=22\n",
    "bmin0=14; bmax0=20; smin0=12; smax0=21\n",
    "bmin0=15; bmax0=21; smin0=11; smax0=23\n",
    "bmin0=14; bmax0=18; smin0=14; smax0=23\n",
    "\n",
    "#r3b12t14s12t19\t\t1\n",
    "#r3b12t16s12t18\t\t1\n",
    "bmin0 -= 1; bmax0 -= 1; smin0 -= 1; smax0 -= 1\n",
    "#bmin0 -= 1; bmax0 -= 1; smin0 -= 1; smax0 -= 1\n",
    "#bmin0 -= 1; bmax0 -= 1; smin0 -= 1; smax0 -= 1\n",
    "\n",
    "#bmin0=12; bmax0=12; smin0=12; smax0=12\n",
    "#bmin0=12; bmax0=15; smin0=12; smax0=18\n",
    "#bmin0 -= 1; bmax0 -= 1; smin0 -= 1; smax0 -= 1\n",
    "#bmin0 -= 1; bmax0 -= 1; smin0 -= 1; smax0 -= 1\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "pskip=0;ndbmin=1; ndbmax=1; ndsmin=1; ndsmax=1\n",
    "pskip=0;ndbmin=3; ndbmax=3; ndsmin=3; ndsmax=3\n",
    "##ndbmin=3; ndbmax=1; ndsmin=3; ndsmax=1\n",
    "#pskip=0;ndbmin=1; ndbmax=3; ndsmin=1; ndsmax=2\n",
    "##pskip=0.5; ndbmin=4; ndbmax=12; ndsmin=4; ndsmax=10\n",
    "##pskip=0.95; ndbmin=6; ndbmax=12; ndsmin=6; ndsmax=10\n",
    "##pskip=0.9; ndbmin=6; ndbmax=12; ndsmin=6; ndsmax=10\n",
    "#pskip=0.7; ndbmin=6; ndbmax=12; ndsmin=6; ndsmax=10\n",
    "#pskip=0.3; ndbmin=6; ndbmax=12; ndsmin=6; ndsmax=10\n",
    "#pskip=0;ndbmin=4; ndbmax=11; ndsmin=5; ndsmax=7\n",
    "ndbmin += 2; ndbmax += 2; ndsmin  += 2 ; ndsmax += 2\n",
    "#ndbmin += 2; ndbmax += 2; ndsmin  += 2 ; ndsmax += 2\n",
    "#ndbmin += 2; ndbmax += 2; ndsmin  += 2 ; ndsmax += 2\n",
    "\n",
    "#=============================\n",
    "\n",
    "w=180#128#80#64#40#32#40#64#80#128#160#256##320#240#320 ##64*diam #256 #1850 #2000 #2250 #1800\n",
    "h=w #256 #1300 #1400 #950\n",
    "\n",
    "w0=16#10#20#30#40#64#128\n",
    "h0=w0#64#128\n",
    "\n",
    "w2=w-2*w0#240#128#16#25#16\n",
    "h2=w2##240#128#16#25#16\n",
    "#=============================\n",
    "\n",
    "\n",
    "ns=1#9#15#30#1 #7#10 #3\n",
    "\n",
    "niter =2500#1500 #300#150# 600 #300 #400 #200 #1000 #3000   \n",
    "#niter0=niter-150\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "r=3\n",
    "am51printthres=     0.100# 0.175\n",
    "settleTimeThres=200#50:#500:#1000:#400:#600: #1200:\n",
    "\n",
    "diam = 2*r + 1\n",
    "\n",
    "\n",
    "##bmin0=7; bmax0=11; smin0=7; smax0=10\n",
    "##bmin0=7; bmax0=8; smin0=6; smax0=10\n",
    "bmin0=14; bmax0=18; smin0=12; smax0=20\n",
    "bmin0=12; bmax0=14; smin0=12; smax0=18\n",
    "bmin0=12; bmax0=14; smin0=12; smax0=19\n",
    "#r3b12t14s12t19\t\t1\n",
    "#r3b12t16s12t18\t\t1\n",
    "bmin0 -= 1; bmax0 -= 1; smin0 -= 1; smax0 -= 1\n",
    "\n",
    "#bmin0=12; bmax0=12; smin0=12; smax0=12\n",
    "bmin0=12; bmax0=15; smin0=12; smax0=18\n",
    "bmin0 -= 1; bmax0 -= 1; smin0 -= 1; smax0 -= 1\n",
    "bmin0 -= 1; bmax0 -= 1; smin0 -= 1; smax0 -= 1\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "pskip=0;ndbmin=1; ndbmax=1; ndsmin=1; ndsmax=1\n",
    "pskip=0;ndbmin=3; ndbmax=3; ndsmin=3; ndsmax=3\n",
    "##ndbmin=3; ndbmax=1; ndsmin=3; ndsmax=1\n",
    "#pskip=0;ndbmin=1; ndbmax=3; ndsmin=1; ndsmax=2\n",
    "##pskip=0.5; ndbmin=4; ndbmax=12; ndsmin=4; ndsmax=10\n",
    "##pskip=0.95; ndbmin=6; ndbmax=12; ndsmin=6; ndsmax=10\n",
    "##pskip=0.9; ndbmin=6; ndbmax=12; ndsmin=6; ndsmax=10\n",
    "#pskip=0.7; ndbmin=6; ndbmax=12; ndsmin=6; ndsmax=10\n",
    "#pskip=0.3; ndbmin=6; ndbmax=12; ndsmin=6; ndsmax=10\n",
    "pskip=0;ndbmin=4; ndbmax=11; ndsmin=5; ndsmax=7\n",
    "ndbmin += 2; ndbmax += 2; ndsmin  += 2 ; ndsmax += 2\n",
    "ndbmin += 2; ndbmax += 2; ndsmin  += 2 ; ndsmax += 2\n",
    "\n",
    "#=============================\n",
    "\n",
    "w=64#80#64#40#32#40#64#80#128#160#256##320#240#320 ##64*diam #256 #1850 #2000 #2250 #1800\n",
    "h=w #256 #1300 #1400 #950\n",
    "\n",
    "w0=10#5#10#15#20#30#40#64#128\n",
    "h0=w0#64#128\n",
    "\n",
    "w2=w-2*w0#240#128#16#25#16\n",
    "h2=w2##240#128#16#25#16\n",
    "#=============================\n",
    "\n",
    "\n",
    "ns=1#9#15#30#1 #7#10 #3\n",
    "\n",
    "niter =900 #300#150# 600 #300 #400 #200 #1000 #3000   \n",
    "#niter0=niter-150\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "w=128#80#64#40#32#40#64#80#128#160#256##320#240#320 ##64*diam #256 #1850 #2000 #2250 #1800\n",
    "h=w #256 #1300 #1400 #950\n",
    "\n",
    "w0=10#5#10#15#20#30#40#64#128\n",
    "h0=w0#64#128\n",
    "\n",
    "w2=w-2*w0#240#128#16#25#16\n",
    "h2=w2##240#128#16#25#16\n",
    "\n",
    "\n",
    "ns=1#9#15#30#1 #7#10 #3\n",
    "\n",
    "niter =8000 #4000#2000#1000 #300#150# 600 #300 #400 #200 #1000 #3000   \n",
    "#niter0=niter-150\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "r=1\n",
    "am51printthres=      0.050#100\n",
    "settleTimeThres=100 #150:#200:#50:#500:#1000:#400:#600: #1200:\n",
    "\n",
    "diam = 2*r + 1\n",
    "\n",
    "bmin0=2; bmax0=2; smin0=2; smax0=2\n",
    "bmin0=3; bmax0=3; smin0=3; smax0=4\n",
    "bmin0=2; bmax0=2; smin0=2; smax0=3\n",
    "bmin0=1; bmax0=1; smin0=1; smax0=1\n",
    "\n",
    "ndbmin=3; ndbmax=7; ndsmin=3; ndsmax=1\n",
    "ndbmin=3; ndbmax=7; ndsmin=3; ndsmax=7\n",
    "#ndbmin=3; ndbmax=1; ndsmin=3; ndsmax=1\n",
    "ndbmin=4; ndbmax=4; ndsmin=4; ndsmax=4\n",
    "ndbmin=3; ndbmax=4; ndsmin=3; ndsmax=4\n",
    "ndbmin=1; ndbmax=1; ndsmin=1; ndsmax=1\n",
    "ndbmin=3; ndbmax=3; ndsmin=3; ndsmax=3\n",
    "ndbmin=8; ndbmax=8; ndsmin=9; ndsmax=9\n",
    "pskip=0#0.5 #0\n",
    "\n",
    "\n",
    "#=============================\n",
    "\n",
    "\n",
    "w=32#16#32#40#64#80#128#160#256##320#240#320 ##64*diam #256 #1850 #2000 #2250 #1800\n",
    "h=w #256 #1300 #1400 #950\n",
    "\n",
    "w0=4#0#5#10#15#20#30#40#64#128\n",
    "h0=w0#64#128\n",
    "\n",
    "w2=w-2*w0#240#128#16#25#16\n",
    "h2=w2##240#128#16#25#16\n",
    "#=============================\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "ns=1#9#15#30#1 #7#10 #3\n",
    "\n",
    "niter =700 #600#1100 #300#150# 600 #300 #400 #200 #1000 #3000   \n",
    "#niter0=niter-150\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "r=2\n",
    "am51printthres=  0.150 #    0.050\n",
    "settleTimeThres=200#50:#500:#1000:#400:#600: #1200:\n",
    "\n",
    "diam = 2*r + 1\n",
    "\n",
    "bmin0=7; bmax0=11; smin0=7; smax0=10\n",
    "bmin0=7; bmax0=8; smin0=6; smax0=10\n",
    "bmin0=7; bmax0=9; smin0=7; smax0=10\n",
    "#bmin0=6; bmax0=6; smin0=6; smax0=6\n",
    "#bmin0=5; bmax0=6; smin0=5; smax0=6\n",
    "#bmin0=4; bmax0=5; smin0=4; smax0=5\n",
    "\n",
    "ndbmin=1; ndbmax=1; ndsmin=1; ndsmax=1\n",
    "#ndbmin=3; ndbmax=1; ndsmin=3; ndsmax=1\n",
    "#ndbmin=2; ndbmax=3; ndsmin=2; ndsmax=3\n",
    "ndbmin=3; ndbmax=3; ndsmin=3; ndsmax=3\n",
    "#ndbmin=3; ndbmax=4; ndsmin=3; ndsmax=4\n",
    "#ndbmin=2; ndbmax=6; ndsmin=2; ndsmax=5\n",
    "#ndbmin=3; ndbmax=6; ndsmin=3; ndsmax=5\n",
    "#ndbmin=5; ndbmax=8; ndsmin=5; ndsmax=8\n",
    "pskip=0\n",
    "\n",
    "\n",
    "#=============================\n",
    "\n",
    "w=160#128#80#64#40#32#40#64#80#128#160#256##320#240#320 ##64*diam #256 #1850 #2000 #2250 #1800\n",
    "h=w #256 #1300 #1400 #950\n",
    "\n",
    "w0=10#5#10#15#20#30#40#64#128\n",
    "h0=w0#64#128\n",
    "\n",
    "w2=w-2*w0#240#128#16#25#16\n",
    "h2=w2##240#128#16#25#16\n",
    "#=============================\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "ns=1#9#15#30#1 #7#10 #3\n",
    "\n",
    "niter =600 #300#150# 600 #300 #400 #200 #1000 #3000   \n",
    "#niter0=niter-150\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "r=1\n",
    "am51printthres=      0.175\n",
    "settleTimeThres=200#50:#500:#1000:#400:#600: #1200:\n",
    "\n",
    "diam = 2*r + 1\n",
    "\n",
    "bmin0=2; bmax0=2; smin0=2; smax0=2\n",
    "bmin0=3; bmax0=3; smin0=3; smax0=4\n",
    "bmin0=2; bmax0=2; smin0=2; smax0=3\n",
    "\n",
    "ndbmin=3; ndbmax=7; ndsmin=3; ndsmax=1\n",
    "ndbmin=3; ndbmax=7; ndsmin=3; ndsmax=7\n",
    "#ndbmin=3; ndbmax=1; ndsmin=3; ndsmax=1\n",
    "ndbmin=4; ndbmax=4; ndsmin=4; ndsmax=4\n",
    "ndbmin=3; ndbmax=4; ndsmin=3; ndsmax=4\n",
    "ndbmin=1; ndbmax=1; ndsmin=1; ndsmax=1\n",
    "ndbmin=3; ndbmax=3; ndsmin=3; ndsmax=3\n",
    "pskip=0\n",
    "\n",
    "\n",
    "#=============================\n",
    "\n",
    "\n",
    "w=40#32#40#64#80#128#160#256##320#240#320 ##64*diam #256 #1850 #2000 #2250 #1800\n",
    "h=w #256 #1300 #1400 #950\n",
    "\n",
    "w0=5#10#15#20#30#40#64#128\n",
    "h0=w0#64#128\n",
    "\n",
    "w2=w-2*w0#240#128#16#25#16\n",
    "h2=w2##240#128#16#25#16\n",
    "#=============================\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "ns=1#9#15#30#1 #7#10 #3\n",
    "\n",
    "niter =400 #600#1100 #300#150# 600 #300 #400 #200 #1000 #3000   \n",
    "#niter0=niter-150\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "10//12\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "12//12"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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