diff --git a/.gitignore b/.gitignore index 0f895e7..139225e 100644 --- a/.gitignore +++ b/.gitignore @@ -95,4 +95,8 @@ scratch.ipynb stimuli.mat # svg images -*.svg \ No newline at end of file +*.svg + +# the directories containing outputs +Kay2013_comparison/ +Metamer_images/ \ No newline at end of file diff --git a/Compare_with_Kay2013.ipynb b/Compare_with_Kay2013.ipynb index 757cb5f..d9a436e 100644 --- a/Compare_with_Kay2013.ipynb +++ b/Compare_with_Kay2013.ipynb @@ -2,18 +2,24 @@ "cells": [ { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "ExecuteTime": { + "end_time": "2017-02-03T12:20:16.434797", + "start_time": "2017-02-03T12:20:16.431011" + }, + "collapsed": true + }, "source": [ "This first block handles all the most important imports and needs to be run every time." ] }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 2, "metadata": { "ExecuteTime": { - "end_time": "2016-12-06T17:09:55.583435", - "start_time": "2016-12-06T17:09:50.236165" + "end_time": "2017-02-13T13:31:48.490187", + "start_time": "2017-02-13T13:31:48.452958" }, "autoscroll": "json-false", "collapsed": false, @@ -78,6 +84,9 @@ " reload(sys.modules['sco.model_comparison'])\n", " reload(sys.modules['sco.model_comparison.core'])\n", " reload(sys.modules['sco.model_comparison.compare_with_Kay2013'])\n", + " reload(sys.modules['sco.util'])\n", + " reload(sys.modules['sco.util.metamers'])\n", + " reload(sys.modules['sco.plot'])\n", " import sco" ] }, @@ -114,61 +123,18 @@ } }, "source": [ - "Now, use the Makefile to create the full dataframe. This works but I feel like it takes longer then it should and it doesn't print things along the way, which is annoying. Can also call this from the command line, in which case each of the make steps will announce themselves (and so is a little easier to see progress).\n", + "To create the data required to do this, use the `Makefile` (see README for details)\n", "\n", - "If you do that, skip the following and go right to the next code block" - ] - }, - { - "cell_type": "code", - "execution_count": 38, - "metadata": { - "autoscroll": "json-false", - "collapsed": false, - "ein.tags": [ - "worksheet-0" - ], - "slideshow": { - "slide_type": "-" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Process is terminated.\n" - ] - } - ], - "source": [ - "%%bash\n", - "export PATH=$PATH:/Applications/MATLAB_R2014a8.3.app/bin/\n", - "make fullclean\n", - "make MATLAB_soc_model_params_full.csv" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "ein.tags": [ - "worksheet-0" - ], - "slideshow": { - "slide_type": "-" - } - }, - "source": [ "Load in the stimuli, stimuli info, and model dataframe so we don't need to do this in the call to visualize_model_comparison" ] }, { "cell_type": "code", - "execution_count": 89, + "execution_count": 2, "metadata": { "ExecuteTime": { - "end_time": "2016-11-29T17:47:12.027547", - "start_time": "2016-11-29T17:46:57.782311" + "end_time": "2017-02-04T14:33:29.341385", + "start_time": "2017-02-04T14:33:22.786340" }, "autoscroll": "json-false", "collapsed": false, @@ -182,12 +148,12 @@ "outputs": [], "source": [ "stimuli_idx = np.asarray(range(156))+69\n", - "model_df = pd.read_csv(\"MATLAB_soc_model_params_full.csv\")\n", - "stimulus_model_names = sco.model_comparison.core._load_pkl_or_mat(\"soc_model_params_full_image_names.mat\", 'image_names')\n", + "model_df = pd.read_csv(\"/home/billbrod/Documents/SCO-test-data/Compare_with_Kay2013/MATLAB_soc_model_params_full.csv\")\n", + "stimulus_model_names = sco.model_comparison.core._load_pkl_or_mat(\"/home/billbrod/Documents/SCO-test-data/Compare_with_Kay2013//soc_model_params_full_image_names.mat\", 'image_names')\n", "stimuli_descriptions = sco.model_comparison.core._load_pkl_or_mat(\"sco/model_comparison/stimuliNames.mat\", 'stimuliNames')\n", "stimuli_descriptions = stimuli_descriptions[0, stimuli_idx]\n", "stimuli_descriptions = np.asarray([i[0] for i in stimuli_descriptions])\n", - "stimuli = sco.model_comparison.core._load_pkl_or_mat(\"full_stimuli.mat\", 'images')\n", + "stimuli = sco.model_comparison.core._load_pkl_or_mat(\"/home/billbrod/Documents/SCO-test-data/Compare_with_Kay2013/full_stimuli.mat\", 'images')\n", "stimuli = stimuli[0, :]" ] }, @@ -207,11 +173,11 @@ }, { "cell_type": "code", - "execution_count": 90, + "execution_count": 259, "metadata": { "ExecuteTime": { - "end_time": "2016-11-29T17:47:44.805805", - "start_time": "2016-11-29T17:47:44.783626" + "end_time": "2017-02-04T17:03:31.158991", + "start_time": "2017-02-04T17:03:31.150196" }, "autoscroll": "json-false", "collapsed": false, @@ -235,17 +201,17 @@ }, { "cell_type": "code", - "execution_count": 91, + "execution_count": 260, "metadata": { "ExecuteTime": { - "end_time": "2016-11-29T17:47:44.944561", - "start_time": "2016-11-29T17:47:44.940634" + "end_time": "2017-02-04T17:03:31.765838", + "start_time": "2017-02-04T17:03:31.759793" }, "collapsed": false }, "outputs": [], "source": [ - "plot_idx = 0\n", + "plot_idx = 4\n", "conditions = [all_conditions[plot_idx]]\n", "titles = [all_condition_titles[plot_idx]]\n", "plot_kwargs = [all_plot_kwargs[plot_idx]]\n", @@ -254,11 +220,11 @@ }, { "cell_type": "code", - "execution_count": 92, + "execution_count": 261, "metadata": { "ExecuteTime": { - "end_time": "2016-11-29T17:47:55.460435", - "start_time": "2016-11-29T17:47:45.400186" + "end_time": "2017-02-04T17:03:36.956428", + "start_time": "2017-02-04T17:03:32.378231" }, "autoscroll": "json-false", "collapsed": false, @@ -1034,7 +1000,7 @@ { "data": { "text/html": [ - "" + "" ], "text/plain": [ "" @@ -1807,7 +1773,7 @@ { "data": { "text/html": [ - "" + "" ], "text/plain": [ "" @@ -1825,105 +1791,46 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Spatial frequency preference\n", - "\n", - "In the matlab version of this model (as described in Kay et al, 2013), we only dealt with one spatial frequency (3 cycles per degree). In this version, we want to be able to handle multiple spatial frequencies. In order to check that we're doing what we think we are, we use `freq_pref_check.py`. \n", - "\n", - "There are three things we want to check, and this script enables us to look at all three:\n", - "\n", - "1. For a given preferred spatial frequency (in cycles per degree) and stimulus pixels per degree, generate model predictions on a collection of images with different spatial frequencies (cycles per image), then see which produces the greatest predicted response. In this case, we set our parameters so that the cycles per image in the stimulus end up being the cyles per image in the normalized image that the model operates on.\n", - "\n", - "2. For a given preferred spatial frequency (in cycles per degree) and image (with a given cycles per image), vary the stimulus pixels per degree so that the effective cycles per degree in the normalized image varies, and see which produces the greatest predicted response.\n", - "\n", - "3. For a given image and stimulus pixels per degree, vary the preferred spatial frequency and see which produces the greatest predicted response.\n", - "\n", - "Note that number 3 takes much longer than the others. And it appears that the lower the preferred spatial frequency, the longer it takes.\n", - "\n", - "Exploring the full space takes a fair amount of time, looking at a variety of images, stimulus pixels per degree, and preferred spatial frequencies. Calling the script from the command line (which calls the function `check_full_space`) will do this, but it takes some time and it's recommended to submit as an array job to the cluster (and has been built to facilitate this).\n", - "\n", - "For speed, in this notebook, we instead do what the points above suggest and hold two of the components constant while varying the third. We can do this all in one call (to `main`) or in separate calls (to `check_pref_across_frequencies`, `check_pref_across_pixels_per_degree`, and `check_response_across_prefs`, respectively). We provide cells that do it both ways, but for speed it's recommended to do it in separate calls." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Separate calls\n", + "### Plotting directly from data\n", "\n", - "Run the following two cells, then pick which one you want to see" + "This shows the same info as the above section, but here I'm playing more directly with the data to get better control of how they're visualized" ] }, { "cell_type": "code", - "execution_count": 83, + "execution_count": 218, "metadata": { "ExecuteTime": { - "end_time": "2016-11-29T17:04:56.748389", - "start_time": "2016-11-29T17:04:56.716222" + "end_time": "2017-02-04T16:47:22.508338", + "start_time": "2017-02-04T16:47:22.309234" }, "collapsed": true }, "outputs": [], "source": [ - "# These are the parameters\n", - "\n", - "#NOTHING ELSE SHOULD BE IN THIS FOLDER\n", - "img_folder = os.path.expanduser(\"~/Desktop/freq_pref_imgs\")\n", - "model_df_path = \"./sco_freq_prefs.csv\"\n", - "subject = \"test-sub\"\n", - "subject_dir = None\n", - "output_img_path = \"./sco_freq_prefs.svg\"\n", - "normalized_pixels_per_degree = 12\n", - "img_res = 1000\n", - "max_eccentricity = 7.5\n", - "#this needs to be an array\n", - "freqs = np.asarray(range(30))" - ] - }, - { - "cell_type": "code", - "execution_count": 84, - "metadata": { - "ExecuteTime": { - "end_time": "2016-11-29T17:05:05.851406", - "start_time": "2016-11-29T17:04:57.308414" - }, - "collapsed": false - }, - "outputs": [], - "source": [ - "# we only want those we just created\n", - "if os.path.isdir(img_folder):\n", - " shutil.rmtree(img_folder)\n", - "\n", - "# 2*max_eccentricity is the size of the image in degrees\n", - "stimulus_pixels_per_degree = float(img_res) / (2*max_eccentricity)\n", - " \n", - "freq_pref_check.generate_stimuli(img_folder, freqs+1, img_res, stimulus_pixels_per_degree)\n", + "stimuli_short = stimuli[69:225]\n", + "stimuli_short = np.array([i[:,:,0] for i in stimuli_short])\n", + "stimulus_model_names_short = np.array([i for i in stimulus_model_names if 'sub00' in i])\n", + "stimulus_model_names_short = [i+'_name_'+j for i,j in zip(stimulus_model_names_short, stimuli_descriptions)]\n", + "img_dict = {'stimulus_images':stimuli_short, 'stimulus_image_filenames': stimulus_model_names_short,}\n", "\n", - "model_kwargs = dict(subject=subject, subject_dir=subject_dir,\n", - " stimulus_pixels_per_degree=stimulus_pixels_per_degree,\n", - " normalized_pixels_per_degree=normalized_pixels_per_degree,\n", - " # in order to avoid having edge effects be very large, we set\n", - " # stimulus_aperture_edge_width to 20 to get some smoothness between the image and\n", - " # the surrounding area.\n", - " stimulus_aperture_edge_width=20, max_eccentricity=max_eccentricity)" + "image_struct = sco.util.metamers.create_image_struct(img_dict, {'image_type': (r'name_([A-Za-z_]+)', None), 'image_seed': (r'_sub([0-9]+)' ,'1'), 'image_name': (r'([0-9]+)', None)})" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "The following shows an arbitrary image generated by our code (which will serve as one of the stimuli for the model). The image is a 2d horizontal sinusoidal grating at a given frequency, with a second sine wave at a much lower frequency crossfaded in. Because our model has the second order contrast component, if our input image is a simple sinusoidal graitng, we have no variation in contrast and thus our response is driven by edge effects. With the crossfade, more of the image contributes and the signal at those points has the proper frequency." + "The following plotting is a nice way to visualize stimuli and predictions using seaborn's FacetGrid (see seaborn's website for what that means), but isn't quite optimized for the layout of the data for this analysis. See Metamers notebook for more examples of what this would look like." ] }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 256, "metadata": { "ExecuteTime": { - "end_time": "2016-11-17T15:27:53.873776", - "start_time": "2016-11-17T15:27:53.756385" + "end_time": "2017-02-04T17:02:41.458032", + "start_time": "2017-02-04T17:02:40.501163" }, "collapsed": false }, @@ -2692,7 +2599,7 @@ { "data": { "text/html": [ - "" + "" ], "text/plain": [ "" @@ -2704,52 +2611,39 @@ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 29, + "execution_count": 256, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "img=plt.imread(glob.glob(img_folder+\"/*.png\")[4])\n", - "plt.figure()\n", - "plt.imshow(img)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "For given preferred frequency and pixels per degree, check across frequencies. The preferred frequency is one cycle per degree, so that's where we should see the peak response.\n", + "plot_df = sco.model_comparison._create_plot_df(model_df, stimuli_idx, stimuli_descriptions)\n", + "plot_df = plot_df.rename(columns={'image_name': 'image_type'})\n", + "restr = {'language': 'python', 'image_type': ['pattern_central', 'grating_ori', 'grating_contrast', 'plaid_contrast']}\n", + "tmp_df = plot_df.copy()\n", + "for k, v in restr.iteritems():\n", + " if isinstance(v, basestring):\n", + " v = [v]\n", + " tmp_df = tmp_df[tmp_df[k].isin(v)]\n", "\n", - "For some reason, it's shifted slightly to the right. Not sure why" + "g = sns.factorplot(data=tmp_df, y='predicted_responses', x='image', hue='image_type',legend_out=False, size=10)\n", + "g.set_xticklabels(rotation=30)" ] }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 257, "metadata": { "ExecuteTime": { - "end_time": "2016-11-17T15:30:34.933846", - "start_time": "2016-11-17T15:29:48.288491" + "end_time": "2017-02-04T17:02:55.376956", + "start_time": "2017-02-04T17:02:46.738125" }, "collapsed": false }, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "sco/model_comparison/core.py:64: UserWarning: Results key pRF_views is not in any of our default key sets!\n", - " warnings.warn(\"Results key %s is not in any of our default key sets!\" % k)\n", - "sco/model_comparison/core.py:64: UserWarning: Results key effective_pRF_sizes is not in any of our default key sets!\n", - " warnings.warn(\"Results key %s is not in any of our default key sets!\" % k)\n", - "sco/model_comparison/core.py:64: UserWarning: Results key stimulus_gamma is not in any of our default key sets!\n", - " warnings.warn(\"Results key %s is not in any of our default key sets!\" % k)\n" - ] - }, { "data": { "application/javascript": [ @@ -3514,7 +3408,7 @@ { "data": { "text/html": [ - "" + "" ], "text/plain": [ "" @@ -3525,28 +3419,56 @@ } ], "source": [ - "model_df, results, plot_df, g = freq_pref_check.check_pref_across_frequencies(\n", - " img_folder, os.path.splitext(output_img_path)[0]+\"_pref_across_freqs.svg\",\n", - " os.path.splitext(model_df_path)[0]+\"_pref_across_freqs.csv\", np.array(range(30)),\n", - " img_size=img_res, **model_kwargs)" + "# this is a super wide image because none of the different image types have overlapping names, so you may want to \n", + "# restrict restr and show only one image_type at a time\n", + "# restr = {'language': 'python', 'image_type': ['plaid_contrast']}\n", + "fig = sco.plot.plot_images(image_struct, 'stimulus_images', restr, facet_col='image_name', facet_row='image_type')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "For a given preferred frequency and cycles per image, check across stimulus pixels per degre (and thus effective cycles per degree). The preferred frequency is one cycle per degree, so that's where we should see the peak response." + "## pRF check\n", + "\n" ] }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 3, "metadata": { "ExecuteTime": { - "end_time": "2016-11-16T15:42:18.714741", - "start_time": "2016-11-16T15:41:29.038135" + "end_time": "2017-02-02T14:58:28.097850", + "start_time": "2017-02-02T14:56:50.885419" }, - "collapsed": false + "autoscroll": "json-false", + "collapsed": false, + "ein.tags": [ + "worksheet-0" + ], + "slideshow": { + "slide_type": "-" + } + }, + "outputs": [], + "source": [ + "stimuli_idx = np.asarray(range(34))\n", + "model_df = pd.read_csv(\"Kay2013_comparison/MATLAB_soc_model_params_sweep.csv\")\n", + "stimulus_model_names = sco.model_comparison.core._load_pkl_or_mat(\"Kay2013_comparison/soc_model_params_sweep_image_names.mat\", 'image_names')\n", + "stimuli = sco.model_comparison.core._load_pkl_or_mat(\"Kay2013_comparison/sweep_stimuli.mat\", 'images')\n", + "with open(\"Kay2013_comparison/soc_model_params_sweep_results_dict.pkl\") as f:\n", + " results = pickle.load(f)" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": { + "ExecuteTime": { + "end_time": "2017-02-02T15:11:19.458871", + "start_time": "2017-02-02T15:11:17.394712" + }, + "collapsed": true }, "outputs": [ { @@ -4262,9 +4184,12 @@ " // Check for shift+enter\n", " if (event.shiftKey && event.which == 13) {\n", " this.canvas_div.blur();\n", - " // select the cell after this one\n", - " var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n", - " IPython.notebook.select(index + 1);\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", @@ -4313,7 +4238,7 @@ { "data": { "text/html": [ - "" + "" ], "text/plain": [ "" @@ -4321,43 +4246,62 @@ }, "metadata": {}, "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" } ], "source": [ - "model_df, results, plot_df, g = freq_pref_check.check_pref_across_pixels_per_degree(\n", - " img_folder, os.path.splitext(output_img_path)[0]+\"_pref_across_d2p.svg\",\n", - " os.path.splitext(model_df_path)[0]+\"_pref_across_d2p.csv\", np.array([29]), img_size=img_res,\n", - " **model_kwargs)" + "stim_indices = stimuli_idx\n", + "#stim_indices = range(18)\n", + "vox_indices = range(model_df.shape[0])\n", + "vox_indices=[1, 2, 3]\n", + "fig = plt.figure(figsize=[10,10])\n", + "norm_stim = results['normalized_stimulus_images']\n", + "vox_colors = sns.palettes.color_palette('Set1', len(vox_indices))\n", + "for i, idx in enumerate(stim_indices):\n", + " ax = plt.subplot(np.ceil(np.sqrt(len(stim_indices))),np.ceil(np.sqrt(len(stim_indices))),i+1)\n", + " stim_idx = np.where(stimulus_model_names=='%04d_sub00'%idx)[0][0]\n", + " plt.imshow(norm_stim[stim_idx,:,:], vmin=0, vmax=1, cmap='gray')\n", + " ax.set_title(stimulus_model_names[stim_idx])\n", + " ax.yaxis.set_visible(False)\n", + " ax.xaxis.set_visible(False)\n", + " circles = []\n", + " for j, vox_idx in enumerate(vox_indices):\n", + " circles.append(plt.Circle((int(model_df['pRF_pixel_centers_col_image_%s'%stimulus_model_names[stim_idx]][vox_idx]), \n", + " int(model_df['pRF_pixel_centers_row_image_%s'%stimulus_model_names[stim_idx]][vox_idx])),\n", + " int(results['effective_pRF_sizes'][vox_idx]*results['normalized_pixels_per_degree'][stim_idx]),\n", + "# 2*int(model_df['pRF_pixel_sizes_image_%s'%stimulus_model_names[stim_idx]][vox_idx]),\n", + " color=vox_colors[j], fill=True))\n", + " ax.add_artist(circles[-1])\n", + "fig.legend(circles, ['vox %d' % i for i in vox_indices])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "For a given cycles per degree (so holding both cycles per image and stimulus pixels per degree constant), check across preferred frequencies.\n", - "\n", - "Why is this not symmetric?" + "MATLAB pRF looks larger and thsi is probably due to the fact that we cut off the python pRFs after 3 sigmas, whereas there's no such limitation on the MATLAB" ] }, { "cell_type": "code", - "execution_count": 62, + "execution_count": 29, "metadata": { "ExecuteTime": { - "end_time": "2016-11-28T13:39:58.797298", - "start_time": "2016-11-28T13:35:00.567008" + "end_time": "2017-02-02T15:11:25.967713", + "start_time": "2017-02-02T15:11:23.933426" }, - "collapsed": false + "collapsed": true }, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "sco/model_comparison/core.py:73: UserWarning: Results key stimulus_gamma is not in any of our default key sets!\n", - " warnings.warn(\"Results key %s is not in any of our default key sets!\" % k)\n" - ] - }, { "data": { "application/javascript": [ @@ -5071,9 +5015,12 @@ " // Check for shift+enter\n", " if (event.shiftKey && event.which == 13) {\n", " this.canvas_div.blur();\n", - " // select the cell after this one\n", - " var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n", - " IPython.notebook.select(index + 1);\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", @@ -5122,7 +5069,7 @@ { "data": { "text/html": [ - "" + "" ], "text/plain": [ "" @@ -5130,86 +5077,139 @@ }, "metadata": {}, "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" } ], "source": [ - "model_df, results, plot_df, g = freq_pref_check.check_response_across_prefs(\n", - " img_folder, os.path.splitext(output_img_path)[0]+\"_resp_across_prefs.svg\",\n", - " os.path.splitext(model_df_path)[0]+\"_resp_across_prefs.csv\", np.array([14]), img_size=img_res,\n", - " **model_kwargs)" + "plot_df = sco.model_comparison._create_plot_df(model_df, stimuli_idx)\n", + "plot_df = plot_df[plot_df.language=='python']\n", + "facet_col = 'language'\n", + "facet_hue = 'voxel'\n", + "plot_df = plot_df[plot_df.voxel.isin(vox_indices)]\n", + "plot_df = plot_df[plot_df.image.isin(['%04d'% i for i in stim_indices])]\n", + "if plot_df[facet_col].nunique() < 3:\n", + " kwargs = {'size': 12./plot_df[facet_col].nunique(), 'col_wrap': plot_df[facet_col].nunique()}\n", + "else:\n", + " kwargs = {'size': 4, 'col_wrap': 3}\n", + "g = sns.factorplot(data=plot_df, y='predicted_responses', x='image', hue=facet_hue, col=facet_col,\n", + " palette = 'Set1', **kwargs)\n", + "g.fig.suptitle('sweep')\n", + "g.fig.subplots_adjust(right=.9, top=.9)\n", + "g.set_xticklabels(rotation=45)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "### All in one call" + "## Spatial frequency preference\n", + "\n", + "In the matlab version of this model (as described in Kay et al, 2013), we only dealt with one spatial frequency (3 cycles per degree). In this version, we want to be able to handle multiple spatial frequencies. In order to check that we're doing what we think we are, we use `freq_pref_check.py`. \n", + "\n", + "There are three things we want to check, and this script enables us to look at all three:\n", + "\n", + "1. For a given preferred spatial frequency (in cycles per degree) and stimulus pixels per degree, generate model predictions on a collection of images with different spatial frequencies (cycles per image), then see which produces the greatest predicted response. In this case, we set our parameters so that the cycles per image in the stimulus end up being the cyles per image in the normalized image that the model operates on.\n", + "\n", + "2. For a given preferred spatial frequency (in cycles per degree) and image (with a given cycles per image), vary the stimulus pixels per degree so that the effective cycles per degree in the normalized image varies, and see which produces the greatest predicted response.\n", + "\n", + "3. For a given image and stimulus pixels per degree, vary the preferred spatial frequency and see which produces the greatest predicted response.\n", + "\n", + "Note that number 3 takes much longer than the others. And it appears that the lower the preferred spatial frequency, the longer it takes.\n", + "\n", + "Exploring the full space takes a fair amount of time, looking at a variety of images, stimulus pixels per degree, and preferred spatial frequencies. Calling the script from the command line (which calls the function `check_full_space`) will do this, but it takes some time and it's recommended to submit as an array job to the cluster (and has been built to facilitate this).\n", + "\n", + "For speed, in this notebook, we instead do what the points above suggest and hold two of the components constant while varying the third. We can do this all in one call (to `main`) or in separate calls (to `check_pref_across_frequencies`, `check_pref_across_pixels_per_degree`, and `check_response_across_prefs`, respectively). We provide cells that do it both ways, but for speed it's recommended to do it in separate calls." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Separate calls\n", + "\n", + "Run the following two cells, then pick which one you want to see" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 83, "metadata": { + "ExecuteTime": { + "end_time": "2016-11-29T17:04:56.748389", + "start_time": "2016-11-29T17:04:56.716222" + }, "collapsed": true }, "outputs": [], "source": [ - "freq_pref_check.main()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Full space\n", + "# These are the parameters\n", "\n", - "The following cells are for after you've run `check_full_space` and then want to visualize the results." + "#NOTHING ELSE SHOULD BE IN THIS FOLDER\n", + "img_folder = os.path.expanduser(\"~/Desktop/freq_pref_imgs\")\n", + "model_df_path = \"./sco_freq_prefs.csv\"\n", + "subject = \"test-sub\"\n", + "subject_dir = None\n", + "output_img_path = \"./sco_freq_prefs.svg\"\n", + "normalized_pixels_per_degree = 12\n", + "img_res = 1000\n", + "max_eccentricity = 7.5\n", + "#this needs to be an array\n", + "freqs = np.asarray(range(30))" ] }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 84, "metadata": { "ExecuteTime": { - "end_time": "2016-11-16T15:00:33.822264", - "start_time": "2016-11-16T15:00:32.873069" + "end_time": "2016-11-29T17:05:05.851406", + "start_time": "2016-11-29T17:04:57.308414" }, "collapsed": false }, "outputs": [], "source": [ - "template_string = \"/Users/winawerlab/sco-data/sco_freq_prefs_freq*.csv\"\n", - "img_size_in_pixels = 1000\n", - "img_size_in_degrees = 15\n", - "df = freq_pref_check.combine_dfs(template_string, \"./sco_full_space_freq_prefs.csv\")\n", - "plot_df = sco.model_comparison.core._create_plot_df(df)\n", - "plot_df = plot_df[plot_df.language=='python']\n", - "# in the naive case, we assume none of the image was cut out of the aperture, and so the number\n", - "# of cycles per image is given by the frequency of the generating sine wave.\n", - "plot_df['cycles_per_full_image'] = plot_df['image'].apply(lambda x: int(re.search(r'freq_([0-9]+)', x).groups()[0]))\n", - "# we try to grab the pixels per degree, if it's there.\n", - "plot_df['pix_per_deg'] = plot_df['image'].apply(lambda x: float(re.search(r'pix_per_deg_([\\.0-9]+)', x).groups()[0]))\n", - "plot_df['cycles_per_norm_image'] = plot_df.apply(lambda x: x['cycles_per_full_image']*(x['pix_per_deg']/(img_size_in_pixels/img_size_in_degrees)), axis=1)\n", - "plot_df['cycles_per_degree'] = plot_df['cycles_per_norm_image'].astype(float)/img_size_in_degrees\n", - "# want to make this look good\n", - "plot_df['cycles_per_degree'] = plot_df['cycles_per_degree'].map(lambda x: \"{:.02f}\".format(x)).astype(float)\n", - "plot_df['preferred_frequency'] = plot_df['image'].apply(lambda x: float(re.search(r'freq_pref_([\\.0-9]+)', x).groups()[0]))" + "# we only want those we just created\n", + "if os.path.isdir(img_folder):\n", + " shutil.rmtree(img_folder)\n", + "\n", + "# 2*max_eccentricity is the size of the image in degrees\n", + "stimulus_pixels_per_degree = float(img_res) / (2*max_eccentricity)\n", + " \n", + "freq_pref_check.generate_stimuli(img_folder, freqs+1, img_res, stimulus_pixels_per_degree)\n", + "\n", + "model_kwargs = dict(subject=subject, subject_dir=subject_dir,\n", + " stimulus_pixels_per_degree=stimulus_pixels_per_degree,\n", + " normalized_pixels_per_degree=normalized_pixels_per_degree,\n", + " # in order to avoid having edge effects be very large, we set\n", + " # stimulus_aperture_edge_width to 20 to get some smoothness between the image and\n", + " # the surrounding area.\n", + " stimulus_aperture_edge_width=20, max_eccentricity=max_eccentricity)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "From the plot below, which shows the differetn preferred frequencies in different axes, with the cycles per degree on the x axis, predicted response on the y axis, and visual area shown via the color, we see that the peak occurs where it should." + "The following shows an arbitrary image generated by our code (which will serve as one of the stimuli for the model). The image is a 2d horizontal sinusoidal grating at a given frequency, with a second sine wave at a much lower frequency crossfaded in. Because our model has the second order contrast component, if our input image is a simple sinusoidal graitng, we have no variation in contrast and thus our response is driven by edge effects. With the crossfade, more of the image contributes and the signal at those points has the proper frequency." ] }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 29, "metadata": { "ExecuteTime": { - "end_time": "2016-11-16T15:00:54.984430", - "start_time": "2016-11-16T15:00:53.650728" + "end_time": "2016-11-17T15:27:53.873776", + "start_time": "2016-11-17T15:27:53.756385" }, "collapsed": false }, @@ -5978,7 +5978,7 @@ { "data": { "text/html": [ - "" + "" ], "text/plain": [ "" @@ -5986,73 +5986,56 @@ }, "metadata": {}, "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" } ], "source": [ - "g= sns.lmplot(data=plot_df, x='cycles_per_degree', y='predicted_responses', col='preferred_frequency', hue='v123_label', size=4, col_wrap=3, fit_reg=False,\n", - " legend_out=True)\n", - "ylim = None\n", - "xticklabels = None\n", - "xlim = [plot_df.cycles_per_degree.min(), plot_df.cycles_per_degree.max()]\n", - "for (i,j,k), data in g.facet_data():\n", - " ax = g.facet_axis(i,j)\n", - " if ylim is None:\n", - " ylim = list(ax.get_ylim())\n", - " if xticklabels is None:\n", - " xticklabels = ax.get_xticklabels()\n", - " if not data.empty:\n", - " pref_freq = data.preferred_frequency.unique()[0] #there will only be one\n", - " ax.plot([pref_freq, pref_freq], ylim, 'k--')\n", - "g.set(ylim=ylim, xlim=xlim, xticks=[i/5. for i in range(10)])\n", - "g.fig.subplots_adjust(right=.9)" + "img=plt.imread(glob.glob(img_folder+\"/*.png\")[4])\n", + "plt.figure()\n", + "plt.imshow(img)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "## pRF check\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "ExecuteTime": { - "end_time": "2016-12-06T17:11:15.129164", - "start_time": "2016-12-06T17:10:03.503004" - }, - "autoscroll": "json-false", - "collapsed": false, - "ein.tags": [ - "worksheet-0" - ], - "slideshow": { - "slide_type": "-" - } - }, - "outputs": [], - "source": [ - "stimuli_idx = np.asarray(range(34))\n", - "model_df = pd.read_csv(\"MATLAB_soc_model_params_sweep.csv\")\n", - "stimulus_model_names = sco.model_comparison.core._load_pkl_or_mat(\"soc_model_params_sweep_image_names.mat\", 'image_names')\n", - "stimuli = sco.model_comparison.core._load_pkl_or_mat(\"sweep_stimuli.mat\", 'images')\n", - "with open(\"soc_model_params_sweep_results_dict.pkl\") as f:\n", - " results_prf = pickle.load(f)" + "For given preferred frequency and pixels per degree, check across frequencies. The preferred frequency is one cycle per degree, so that's where we should see the peak response.\n", + "\n", + "For some reason, it's shifted slightly to the right. Not sure why" ] }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 31, "metadata": { "ExecuteTime": { - "end_time": "2016-12-06T17:28:09.534374", - "start_time": "2016-12-06T17:28:08.767940" + "end_time": "2016-11-17T15:30:34.933846", + "start_time": "2016-11-17T15:29:48.288491" }, "collapsed": false }, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "sco/model_comparison/core.py:64: UserWarning: Results key pRF_views is not in any of our default key sets!\n", + " warnings.warn(\"Results key %s is not in any of our default key sets!\" % k)\n", + "sco/model_comparison/core.py:64: UserWarning: Results key effective_pRF_sizes is not in any of our default key sets!\n", + " warnings.warn(\"Results key %s is not in any of our default key sets!\" % k)\n", + "sco/model_comparison/core.py:64: UserWarning: Results key stimulus_gamma is not in any of our default key sets!\n", + " warnings.warn(\"Results key %s is not in any of our default key sets!\" % k)\n" + ] + }, { "data": { "application/javascript": [ @@ -6817,7 +6800,7 @@ { "data": { "text/html": [ - "" + "" ], "text/plain": [ "" @@ -6825,51 +6808,29 @@ }, "metadata": {}, "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" } ], "source": [ - "stim_indices = range(18)\n", - "#stim_indices = range(16)\n", - "vox_indices = range(model_df.shape[0])\n", - "vox_indices=[0]\n", - "fig = plt.figure(figsize=[10,10])\n", - "norm_stim = results_prf['normalized_stimulus_images']\n", - "vox_colors = sns.palettes.color_palette('Set1', len(vox_indices))\n", - "for i, idx in enumerate(stim_indices):\n", - " ax = plt.subplot(np.ceil(np.sqrt(len(stim_indices))),np.ceil(np.sqrt(len(stim_indices))),i+1)\n", - " stim_idx = np.where(stimulus_model_names=='%04d_sub00'%idx)[0][0]\n", - " plt.imshow(norm_stim[stim_idx,:,:], vmin=0, vmax=1, cmap='gray')\n", - " ax.set_title(stimulus_model_names[stim_idx])\n", - " ax.yaxis.set_visible(False)\n", - " ax.xaxis.set_visible(False)\n", - " circles = []\n", - " for j, vox_idx in enumerate(vox_indices):\n", - " circles.append(plt.Circle((int(model_df['pRF_pixel_centers_col_image_%s'%stimulus_model_names[stim_idx]][vox_idx]), \n", - " int(model_df['pRF_pixel_centers_row_image_%s'%stimulus_model_names[stim_idx]][vox_idx])),\n", - " int(results_prf['effective_pRF_sizes'][vox_idx]*results_prf['normalized_pixels_per_degree'][stim_idx]),\n", - "# 2*int(model_df['pRF_pixel_sizes_image_%s'%stimulus_model_names[stim_idx]][vox_idx]),\n", - " color=vox_colors[j], fill=True))\n", - " ax.add_artist(circles[-1])\n", - "fig.legend(circles, ['vox %d' % i for i in vox_indices])" + "model_df, results, plot_df, g = freq_pref_check.check_pref_across_frequencies(\n", + " img_folder, os.path.splitext(output_img_path)[0]+\"_pref_across_freqs.svg\",\n", + " os.path.splitext(model_df_path)[0]+\"_pref_across_freqs.csv\", np.array(range(30)),\n", + " img_size=img_res, **model_kwargs)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For a given preferred frequency and cycles per image, check across stimulus pixels per degre (and thus effective cycles per degree). The preferred frequency is one cycle per degree, so that's where we should see the peak response." ] }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 12, "metadata": { "ExecuteTime": { - "end_time": "2016-12-06T17:27:11.213380", - "start_time": "2016-12-06T17:27:10.415123" + "end_time": "2016-11-16T15:42:18.714741", + "start_time": "2016-11-16T15:41:29.038135" }, "collapsed": false }, @@ -7638,7 +7599,7 @@ { "data": { "text/html": [ - "" + "" ], "text/plain": [ "" @@ -7646,32 +7607,1693 @@ }, "metadata": {}, "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" } ], "source": [ - "plot_df = sco.model_comparison._create_plot_df(model_df, stimuli_idx)\n", - "#plot_df = plot_df[plot_df.language=='python']\n", - "plot_df = plot_df[plot_df.voxel.isin(vox_indices)]\n", - "plot_df = plot_df[plot_df.image.isin(['%04d'% i for i in stim_indices])]\n", - "if plot_df.voxel.nunique() < 3:\n", - " kwargs = {'size': 12./plot_df.voxel.nunique(), 'col_wrap': plot_df.voxel.nunique()}\n", - "else:\n", - " kwargs = {'size': 4, 'col_wrap': 3}\n", - "g = sns.factorplot(data=plot_df, y='predicted_responses', x='image', hue='language', col='voxel',\n", - " **kwargs)\n", - "g.fig.suptitle('sweep')\n", - "g.fig.subplots_adjust(right=.9, top=.9)\n", - "g.set_xticklabels(rotation=45)" + "model_df, results, plot_df, g = freq_pref_check.check_pref_across_pixels_per_degree(\n", + " img_folder, os.path.splitext(output_img_path)[0]+\"_pref_across_d2p.svg\",\n", + " os.path.splitext(model_df_path)[0]+\"_pref_across_d2p.csv\", np.array([29]), img_size=img_res,\n", + " **model_kwargs)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For a given cycles per degree (so holding both cycles per image and stimulus pixels per degree constant), check across preferred frequencies.\n", + "\n", + "Why is this not symmetric?" + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "metadata": { + "ExecuteTime": { + "end_time": "2016-11-28T13:39:58.797298", + "start_time": "2016-11-28T13:35:00.567008" + }, + "collapsed": false + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "sco/model_comparison/core.py:73: UserWarning: Results key stimulus_gamma is not in any of our default key sets!\n", + " warnings.warn(\"Results key %s is not in any of our default key sets!\" % k)\n" + ] + }, + { + "data": { + "application/javascript": [ + "/* Put everything inside the global mpl namespace */\n", + "window.mpl = {};\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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');\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 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);\n", + " canvas.attr('height', height);\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": [ + "g= sns.lmplot(data=plot_df, x='cycles_per_degree', y='predicted_responses', col='preferred_frequency', size=4, col_wrap=3, fit_reg=False,\n", + " legend_out=True, hue='v123_label',)\n", + "ylim = None\n", + "xticklabels = None\n", + "xlim = [plot_df.cycles_per_degree.min(), plot_df.cycles_per_degree.max()]\n", + "for (i,j,k), data in g.facet_data():\n", + " ax = g.facet_axis(i,j)\n", + " if ylim is None:\n", + " ylim = list(ax.get_ylim())\n", + " if xticklabels is None:\n", + " xticklabels = ax.get_xticklabels()\n", + " if not data.empty:\n", + " pref_freq = data.preferred_frequency.unique()[0] #there will only be one\n", + " ax.plot([pref_freq, pref_freq], ylim, 'k--')\n", + "g.set(ylim=ylim, xlim=xlim, xticks=[i/5. for i in range(10)])\n", + "g.fig.subplots_adjust(right=.9)" ] } ], diff --git a/Makefile b/Makefile index a4fd6d8..b226102 100644 --- a/Makefile +++ b/Makefile @@ -3,68 +3,108 @@ SHELL := /bin/bash # for all full stimuli, use the following: STIMULI_IDX_full = $(shell seq 69 224) +# for testing: +# STIMULI_IDX_full := $(shell seq 69 73) + # for all sweep stimuli, use the following (and honestly, this is # small enough that you don't need to use something else for testing) STIMULI_IDX_sweep = $(shell seq 0 33) -# for testing: -# STIMULI_IDX_full := $(shell seq 69 73) +VOXEL_IDX = $(shell seq 0 3) -VOXEL_IDX = $(shell seq 0 9) +# these defaults are probably fine +BRODATZ_METAMER_IDX = $(shell seq 1 15) +FREEMAN2013_METAMER_IDX = $(shell seq 1 15) +IMAGENET_METAMER_IDX = $(shell seq 1 20) +NUM_SCALES=4 +NUM_ORIENTATIONS=4 +SIZE_NEIGHBORHOOD=7 +METAMER_SEEDS = $(shell seq 1 5) -# KNK_PATH=/home/billbrod/Documents/Kendrick-socmodel/code/ -KNK_PATH=/Users/winawerlab/matlab/git/knkutils/ +# these you probably need to change +KNK_PATH=~/matlab/git/knkutils/ SUBJ=test-sub SUBJ_DIR=/Volumes/server/Freesurfer_subjects -# SUBJ_DIR=/home/billbrod/Documents/SCO-test-data/Freesurfer_subjects +TEXTURE_SYNTH_PATH=~/matlab/git/textureSynth/ +PYR_TOOLS_PATH=~/matlab/git/matlabPyrTools-master + # make sure matlab is in your path, which it may not be by default if you're on Mac. # for our stimuli, we use the pictures from Kay2013, which Kendrick # provides on his website. -full_stimuli.mat : - wget -q http://kendrickkay.net/socmodel/stimuli.mat -O ./full_stimuli.mat +Kay2013_comparison : + mkdir $@ + +Kay2013_comparison/full_stimuli.mat : Kay2013_comparison + wget -q http://kendrickkay.net/socmodel/stimuli.mat -O ./Kay2013_comparison/full_stimuli.mat # we need to do this to get the stimuli.mat into the format we want matlab -nodesktop -nodisplay -r "load('$@','images'); save('$@','images'); quit" -sweep_stimuli.mat : full_stimuli.mat - python2.7 pRF_check.py $< {}_stimuli.mat +Kay2013_comparison/sweep_stimuli.mat : Kay2013_comparison/full_stimuli.mat + python2.7 pRF_check.py $< Kay2013_comparison/{}_stimuli.mat + +Metamer_images : + mkdir $@ + +# primarily using Freeman 2013 stimuli now, so this might not be relevant. +Metamer_images/Original_Brodatz_seed_images : Metamer_images + wget -q http://multibandtexture.recherche.usherbrooke.ca/images/Original_Brodatz.zip -O ./Original_Brodatz.zip + unzip ./Original_Brodatz.zip -d Metamer_images + mv Metamer_images/Original\ Brodatz Metamer_images/Original_Brodatz_seed_images + rm Original_Brodatz.zip + +Metamer_images/Brodatz_metamers : Metamer_images/Original_Brodatz_seed_images/ + mkdir $@ + matlab -nodesktop -nodisplay -r "createMetamers('$(TEXTURE_SYNTH_PATH)', '$(PYR_TOOLS_PATH)', '$(KNK_PATH)','$ $@ - -%_stim_idx.txt : - echo $(STIMULI_IDX_$*) > $@ - # for stimuli indices, we need to increment them by one to - # turn them from python into matlab indices. - python2.7 sco/model_comparison/py_to_matlab.py -p2m $@ - -MATLAB_soc_model_params_%.csv : soc_model_params_%.csv voxel_idx.txt %_stim_idx.txt - matlab -nodesktop -nodisplay -r "cd $(shell pwd)/sco/model_comparison; compareWithKay2013('$(KNK_PATH)', '$(shell pwd)/$*_stimuli.mat', '$(shell pwd)/$*_stim_idx.txt', '$(shell pwd)/voxel_idx.txt', '$(shell pwd)/$<', '$(shell pwd)/soc_model_params_$*_image_names.mat', '$(shell pwd)/$@'); quit;" +Kay2013_comparison/MATLAB_soc_model_params_%.csv : Kay2013_comparison/soc_model_params_%.csv + # we increment the stimuli index and not the voxel index, + # because the voxel indices refer to a column in the + # dataframe/table, while the stimuli indices will actually be + # used to grab something from an array in matlab + matlab -nodesktop -nodisplay -r "cd $(shell pwd)/sco/model_comparison; compareWithKay2013('$(KNK_PATH)', '$(shell pwd)/Kay2013_comparison/$*_stimuli.mat', [$(STIMULI_IDX_$*)]+1, [$(VOXEL_IDX)], '$(shell pwd)/$<', '$(shell pwd)/Kay2013_comparison/soc_model_params_$*_image_names.mat', '$(shell pwd)/$@'); quit;" .PHONY : %_images # this will create several images, with names based on the default options in sco/model_comparison/core.py -%_images : MATLAB_soc_model_params_%.csv %_stimuli.mat soc_model_params_%.csv - python2.7 sco/model_comparison/core.py $* $< soc_model_params_$*_image_names.mat sco/model_comparison/stimuliNames.mat $*_stimuli.mat $(STIMULI_IDX_$*) - -.PHONY : cleantmps -cleantmps : - -rm voxel_idx.txt - -rm stim_idx.txt +%_images : Kay2013_comparison/MATLAB_soc_model_params_%.csv Kay2013_comparison/%_stimuli.mat Kay2013_comparison/soc_model_params_%.csv + python2.7 sco/model_comparison/core.py $* $< Kay2013_comparison/soc_model_params_$*_image_names.mat sco/model_comparison/stimuliNames.mat Kay2013_comparison/$*_stimuli.mat $(STIMULI_IDX_$*) + mv SCO_model_comparison*.svg Kay2013_comparison/ .PHONY : %clean -%clean : cleantmps - -rm soc_model_params_$*.csv - -rm soc_model_params_$*_image_names.mat - -rm MATLAB_soc_model_params_$*.csv - -rm soc_model_params_$*_results_dict.pkl +%clean : + -rm Kay2013_comparison/soc_model_params_$*.csv + -rm Kay2013_comparison/soc_model_params_$*_image_names.mat + -rm Kay2013_comparison/MATLAB_soc_model_params_$*.csv + -rm Kay2013_comparison/soc_model_params_$*_results_dict.pkl # this way these won't be deleted as unnecessary intermediates. These # take a while to make, so we don't want to do that. -.PRECIOUS : soc_model_params_%.csv MATLAB_soc_model_params_%.csv +.PRECIOUS : Kay2013_comparison/soc_model_params_%.csv Kay2013_comparison/MATLAB_soc_model_params_%.csv Kay2013_comparison/full_stimuli.mat diff --git a/Metamers.ipynb b/Metamers.ipynb new file mode 100644 index 0000000..c180091 --- /dev/null +++ b/Metamers.ipynb @@ -0,0 +1,19790 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This notebook is to see how the model handles V1 and V2 metamers, as used in [\"A functional and perceptual signature of the second visual area in primates\"](http://www.nature.com/neuro/journal/v16/n7/full/nn.3402.html), by Jeremy Freeman, Corey M Ziemba,\tDavid J Heeger,\tEero P Simoncell & J Anthony Movshon.\n", + "\n", + "# Creating stimuli\n", + "\n", + "We use the stimuli from that paper (uploaded to our OSF project, can be downloaded with the `Makefile`), though they no longer have the original images used to create them. Once you have the stimuli, you can use `createMetamers.m`, in this directory, to create the images for this model. To do so using the default options, make sure MATLAB is in your path and type \n", + "\n", + " make Metamer_images/Freeman2013_metamers\n", + "\n", + "on the command line in order to have the `Makefile` take care of this for you. This will take the first sample of each of the 15 image classes from `Metamer_images/Freeman2013_stimuli` and create 5 V1 metamers and 5 V2 metamers.\n", + "\n", + "The V1 metamers are phase-randomized, spectrally matched noise (using Kendrick Kay's [knkutils](https://github.com/kendrickkay/knkutils)), while the V2 metamers are based on the texture synthesis model from \"A Parametric Texture Model Based on Joint Statistics of Complex Wavelet Coefficients\" by Portilla and Simoncelli, 2000, and so rely on matching a variety of correlations and higher order properties of the output of convolving steerable pyramid filters with the image. This is done using the Simoncelli lab's [textureSynth](https://github.com/LabForComputationalVision/textureSynth) and [matlabPyrTools](https://github.com/LabForComputationalVision/matlabPyrTools).\n", + "\n", + "The `Makefile` includes recipes to create two other classes of metamers, one from [Brodatz texture images](http://multibandtexture.recherche.usherbrooke.ca/index.html) and one from a sampling of cliff and mosaic images from [ImageNet](http://image-net.org/explore) (combined with a small number of simple stimuli used in the `Compare_with_Kay2013.ipynb`). In order to create these, type\n", + "\n", + " make Metamer_images/Brodatz_metamers\n", + "\n", + "or\n", + "\n", + " make Metamer_images/ImageNet_metamers\n", + "\n", + "respectively.\n", + "\n", + "To create your own metamer images, see the documentation in the `createMetamers.m` script and look at the recipes above in the `Makefile`. The model should run without any problem on these images, but you may need to change the regex keyword arguments to `create_met_df` and `create_image_struct` in order to correctly define image type, image seed, and image name.\n", + "\n", + "# Generating model predictions\n", + "\n", + "This notebook contains the code to run the model and shape the predictions for visualization, but when running 100+ images and bootstrapping 100 to 1000 times to generate the SNR values, one model can take >3 hours to run. If you're running this many images for several models, this can be infeasible for completion in a notebook. In that case, it's recommended you submit the model to a cluster to speed things up. `cluster_submit.py` is provided to help you with this; see that script and the README for more details.\n", + "\n", + "Finally, the results of running several versions of the model have been uploaded to Google Drive (linked to on the OSF project page). There is no guarantee they will always be there, be updated, or be maintained in any way, but for now you can download them and then run the loading block to examine the results.\n", + "\n", + "# Initialization\n", + "\n", + "These first four blocks are all initialization blocks, importing relevant packages and defining some functions that we'll use in our initial calls to run the model and save the results. They should thus be run everytime" + ] + }, + { + "cell_type": "code", + "execution_count": 171, + "metadata": { + "ExecuteTime": { + "end_time": "2017-02-16T14:05:23.535762", + "start_time": "2017-02-16T14:05:21.843834" + }, + "collapsed": false, + "init_cell": true + }, + "outputs": [], + "source": [ + "import numpy as np\n", + "import numpy.linalg as npl \n", + "import scipy as sp\n", + "from scipy import ndimage as ndi\n", + "import pandas as pd\n", + "import seaborn as sns\n", + "import re\n", + "import shutil\n", + "import glob\n", + "import pickle\n", + "\n", + "from skimage import data\n", + "from skimage.util import img_as_float\n", + "from skimage.filters import gabor_kernel\n", + "from sklearn.preprocessing import scale\n", + "\n", + "from numbers import Number \n", + "\n", + "import os, sys, math, itertools, collections\n", + "\n", + "%matplotlib notebook\n", + "#%gui qt\n", + "import matplotlib\n", + "import matplotlib.pyplot as plt\n", + "\n", + "import neuropythy as neuro\n", + "import neuropythy.freesurfer as nfs\n", + "\n", + "for p in ['/home/billbrod/Documents/SCO-test-data/Freesurfer_subjects', '/Volumes/server/Freesurfer_subjects/']:\n", + " if p not in nfs.subject_paths():\n", + " nfs.add_subject_path(p)\n", + "\n", + "import sco\n", + "import freq_pref_check\n", + "import model_comparison_script\n", + "import pRF_check\n", + "\n", + "def reload_sco():\n", + " reload(sys.modules['sco.core'])\n", + " reload(sys.modules['sco.anatomy.core'])\n", + " reload(sys.modules['sco.stimulus.core'])\n", + " reload(sys.modules['sco.pRF.core'])\n", + " reload(sys.modules['sco.normalization.core'])\n", + " reload(sys.modules['sco.anatomy'])\n", + " reload(sys.modules['sco.stimulus'])\n", + " reload(sys.modules['sco.pRF'])\n", + " reload(sys.modules['sco.normalization'])\n", + " reload(sys.modules['sco'])\n", + " reload(sys.modules['sco.model_comparison'])\n", + " reload(sys.modules['sco.model_comparison.core'])\n", + " reload(sys.modules['sco.model_comparison.compare_with_Kay2013'])\n", + " reload(sys.modules['sco.plot'])\n", + " reload(sys.modules['sco.util.core'])\n", + " reload(sys.modules['sco.util'])\n", + " import sco" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Run / load the model\n", + "\n", + "Using the images found in `Freeman2013_metamers`, we don't have any true original images. The seed images we used for metamer creation are still examples of the texture metamers (from that paper), since they no longer have the original images, so we should probably ignore those. I keep the code here because we might want it for future analyses.\n", + "\n", + "This block take a long time to run (around 2 or 3 hours) and saves all the relevant outputs. You can also generate the relevant data using the `sco.util.metamers.main` function, which can be run on the cluster using `cluster_submit.py`. If you do that, use the second code block to load in your results.\n", + "\n", + "This block runs the model on these V1 and V2 metamers with the defualt parameters and then runs a \"dumb V1\" version of the model, with the `SOC_constant=0` and `output_nonlinearity=1`, which effectively removes the second order contrast and output nonlinearity steps of the model.\n", + "\n", + "We hypothesize that the dumb V1 version of the model will not distinguish between the V1 and V2 metamers, while the full model will." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "ExecuteTime": { + "end_time": "2017-01-30T15:43:16.234144", + "start_time": "2017-01-30T15:38:22.700308" + }, + "collapsed": false + }, + "outputs": [], + "source": [ + "SCO_kwargs = {'full_test': {}, \n", + " 'dumb_V1_test' : {'Kay2013_SOC_constant': 0, 'Kay2013_output_nonlinearity': 1}}\n", + "results, model_df, plot_df, SNR_df = {}, {}, {}, {}\n", + "for model_name, kwargs in SCO_kwargs.iteritems():\n", + " images = glob.glob(os.path.expanduser('~/sco/Metamer_images/Freeman2013_metamers/*png'))\n", + " results[model_name] = sco.calc_surface_sco(subject='test-sub', stimulus_image_filenames=images, max_eccentricity=7.5, **kwargs)\n", + " sco.util.save_results_dict(results[model_name], './Metamer_images/results_%s.pkl' % model_name)\n", + "\n", + " images = [os.path.split(i)[1] for i in images]\n", + " model_df[model_name] = sco.model_comparison.create_model_dataframe(results[model_name], images, './Metamer_images/model_df_%s.csv' % model_name)\n", + " # there are a couple other columns we want in plot_df and SNR_df\n", + " plot_df[model_name] = sco.model_comparison.core._create_plot_df(model_df[model_name], extra_cols=['Kay2013_output_nonlinearity', 'Kay2013_SOC_constant'])\n", + " plot_df[model_name] = sco.util.metamers.create_met_df(plot_df[model_name])\n", + " SNR_df[model_name] = sco.util.create_SNR_df(plot_df[model_name], file_name='./Metamer_images/SNR_df_%s.csv' % model_name,\n", + " extra_cols=['Kay2013_output_nonlinearity', 'Kay2013_SOC_constant'])\n", + " plot_df[model_name]['model'] = model_name\n", + " SNR_df[model_name]['model'] = model_name\n", + " model_df[model_name]['model'] = model_name\n", + "SNR_df = pd.concat(SNR_df.values()).reset_index(drop=True)\n", + "plot_df = pd.concat(plot_df.values()).reset_index(drop=True)\n", + "model_df = pd.concat(model_df.values()).reset_index(drop=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If you've run and saved your results beforehand, load in the results dictionary, and the model and SNR dataframes. The `results` dict won't be complete because we cannot pickle functions or other callable python objects, but among them they will contain all information used in this notebook.\n", + "\n", + "Make sure the list in the for loop includes all the models you want to load in. We will not hold onto the `results` dictionary or the `model_df` DataFrame, since these are large and unnecessary for this notebook" + ] + }, + { + "cell_type": "code", + "execution_count": 169, + "metadata": { + "ExecuteTime": { + "end_time": "2017-02-16T14:03:38.486229", + "start_time": "2017-02-16T13:56:40.738923" + }, + "collapsed": false + }, + "outputs": [], + "source": [ + "# for the rest of this notebook, model_df and results are only used in such a way that it doesn't matter which model\n", + "# they came from, so we only hold onto one value for each.\n", + "\n", + "plot_df, SNR_df = {}, {}\n", + "for name in ['full', 'dumb_V1', 'intermediate_1', 'intermediate_2', 'intermediate_3']:\n", + " model_df = pd.read_csv('Metamer_images/Freeman2013_metamers_results/model_df_%s.csv' % name)\n", + " # there are a couple other columns we want in plot_df and SNR_df\n", + " plot_df[name] = sco.model_comparison.core._create_plot_df(model_df, extra_cols=['Kay2013_output_nonlinearity', 'Kay2013_SOC_constant'])\n", + " plot_df[name] = sco.util.metamers.create_met_df(plot_df[name])\n", + " plot_df[name]['model'] = name\n", + " SNR_df[name] = pd.read_csv('Metamer_images/Freeman2013_metamers_results/SNR_df_%s.csv' % name)\n", + " SNR_df[name]['model'] = name\n", + "SNR_df = pd.concat(SNR_df.values())\n", + "plot_df = pd.concat(plot_df.values())\n", + "with open('Metamer_images/Freeman2013_metamers_results/results_full.pkl') as f:\n", + " results = pickle.load(f)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To ease visualization, we make a new column that contains the relevant parameters" + ] + }, + { + "cell_type": "code", + "execution_count": 160, + "metadata": { + "ExecuteTime": { + "end_time": "2017-02-14T16:50:11.772219", + "start_time": "2017-02-14T16:43:24.116030" + }, + "collapsed": false + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/winawerlab/anaconda2/lib/python2.7/site-packages/ipykernel/__main__.py:10: UserWarning: SNR_df does not contain the appropriate columns, will have to rely on column 'model' instead\n" + ] + } + ], + "source": [ + "def make_param_summary(df):\n", + " tmp_df = df.groupby('model')[['Kay2013_output_nonlinearity']].transform(np.mean)\n", + " tmp_df = pd.concat([tmp_df, df.groupby('model')[['Kay2013_SOC_constant']].transform(np.mean)], axis=1)\n", + " df['(n, c)'] = tmp_df.apply(lambda x: '%.02f, %.02f' % (x['Kay2013_output_nonlinearity'], x['Kay2013_SOC_constant']), axis=1)\n", + " return df\n", + "try:\n", + " SNR_df = make_param_summary(SNR_df)\n", + "except KeyError:\n", + " import warnings\n", + " warnings.warn(\"SNR_df does not contain the appropriate columns, will have to rely on column 'model' instead\")\n", + "plot_df = make_param_summary(plot_df)\n", + "\n", + "# we use these to get a better order for plotting\n", + "param_order = sorted(plot_df['(n, c)'].unique())\n", + "model_order = ['full', 'intermediate_3', 'intermediate_2', 'intermediate_1', 'dumb_V1']\n", + "img_type_order = ['original', 'V1-metamer', 'V2-metamer', 'V2Scl-metamer']" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "`image_struct` contains the stimuli images, normalized images, and a label DataFrame that makes it easy (with `sco.plot.plot_images`) to visualize the stimuli or normalized images, especially for comparing them visually to the [\"cortical images\"](#Cortical-images). It allows the user to easily arrange images of different names (based on the seed image), type (original, V1 metamer, etc), or noise seed along different columns or rows. See [Stimuli examples](#Stimuli-examples) for some examples" + ] + }, + { + "cell_type": "code", + "execution_count": 161, + "metadata": { + "ExecuteTime": { + "end_time": "2017-02-14T16:50:19.329397", + "start_time": "2017-02-14T16:50:19.123801" + }, + "collapsed": false + }, + "outputs": [], + "source": [ + "# all results dicts should be the same for stimulus_image_filenames and stimulus_images, so it doesn't \n", + "# matter which we use\n", + "image_struct = sco.util.metamers.create_image_struct(results)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Stimuli examples\n", + "\n", + "* `original`: the seed image that the metamers are created from (though note with the Freeman 2013 stimuli, there are no true original images, these are just specific V2 metamers)\n", + "\n", + "* `V1_metamer`: phase-scrambled, spectrally matched noise. These have been clipped after phase-scrambling so the pixel values lie within [0, 255]. \n", + "\n", + "* `V2_metamer`: synthesized images adjusted so as to match a variety of correlations and higher-order statistical properties of the steerable pyramid outputs of the seed image, decsrbited in Portilla, 2000.\n", + "\n", + "* `V2Scl_metamer`: the same synthesized images as `V2_metamer`, except we have scaled them by the ratio of variances between the corresponding V1 and V2 metamers so that it has the same energy as the V1 metamer (though its range is reduced)\n", + "\n", + "In Freeman, 2013, the authors show that V1 neurons are unable to distinguish between V1 and V2 metamers, while V2 and V3 neurons are. They do not use what we're calling `V2Scl_metamers`, which we're including as a check." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "ExecuteTime": { + "end_time": "2017-02-13T16:00:02.630533", + "start_time": "2017-02-13T16:00:02.625474" + }, + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['im01', 'im02', 'im03', 'im04', 'im05', 'im06', 'im07', 'im08', 'im09', 'im10', 'im11', 'im12', 'im13', 'im70', 'im71', 'im74', 'im79', 'im83', 'im84', 'im85']\n", + "['V2Scl-metamer' 'V1-metamer' 'original' 'V2-metamer']\n", + "['2' '3' '1' '5' '4']\n" + ] + } + ], + "source": [ + "print sorted(image_struct['labels'].image_name.unique())\n", + "print image_struct['labels'].image_type.unique()\n", + "print image_struct['labels'].image_seed.unique()" + ] + }, + { + "cell_type": "code", + "execution_count": 80, + "metadata": { + "ExecuteTime": { + "end_time": "2017-02-14T14:37:11.975630", + "start_time": "2017-02-14T14:37:10.430942" + }, + "collapsed": false, + "scrolled": false + }, + "outputs": [ + { + "data": { + "application/javascript": [ + "/* Put everything inside the global mpl namespace */\n", + "window.mpl = {};\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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');\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": 164, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "g = sns.factorplot(data=plot_df, y='predicted_responses', row='v123_label', x='image_type', \n", + " kind='violin', size=3, col='(n, c)', col_order=param_order)\n", + "g.set_xticklabels(rotation=30)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "It is also useful to examine this data as a point plot, with the image name (seed image used to create the metamers) along the x-axis and image type as hue. This allows us to see if there are differences between the different seed images and find which has the smallest and largest differences among image types. This takes longer to run than the previous code block" + ] + }, + { + "cell_type": "code", + "execution_count": 166, + "metadata": { + "ExecuteTime": { + "end_time": "2017-02-14T17:01:27.379397", + "start_time": "2017-02-14T16:57:45.447642" + }, + "collapsed": false + }, + "outputs": [ + { + "data": { + "application/javascript": [ + "/* Put everything inside the global mpl namespace */\n", + "window.mpl = {};\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 = $('
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')\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": 170, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "g = sns.factorplot(data=SNR_df[(SNR_df.variable.isin(['V2 - V1', 'V2 - original', 'V1 - original']))], col='v123_label', hue='variable', kind='point', y='value', x='model', legend_out=True, size=7, x_order=model_order)\n", + "g.fig.subplots_adjust(right=.9)\n", + "g.set_ylabels('SNR')" + ] + }, + { + "cell_type": "code", + "execution_count": 64, + "metadata": { + "ExecuteTime": { + "end_time": "2017-02-04T19:19:40.266508", + "start_time": "2017-02-04T19:19:29.248060" + }, + "collapsed": false + }, + "outputs": [ + { + "data": { + "application/javascript": [ + "/* Put everything inside the global mpl namespace */\n", + "window.mpl = {};\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", + " 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", + " this.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 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);\n", + " canvas.attr('height', height);\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": 168, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "g = sns.factorplot(data=SNR_df, kind='violin', x='variable', y='value', col='model', size=4, \n", + " row='v123_label', col_order=model_order,)\n", + "g.set_xticklabels(rotation=30)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We noticed that a lot of the response is around the edge of the image, especially to the V1 metamers. In order to visualize how the SNR would look if we ignored those voxels, we only look at those with eccentricity less than 6 degrees" + ] + }, + { + "cell_type": "code", + "execution_count": 281, + "metadata": { + "ExecuteTime": { + "end_time": "2017-02-01T15:45:51.175223", + "start_time": "2017-02-01T15:45:47.058937" + }, + "collapsed": false + }, + "outputs": [ + { + "data": { + "application/javascript": [ + "/* Put everything inside the global mpl namespace */\n", + "window.mpl = {};\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", + " 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", + " this.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 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);\n", + " canvas.attr('height', height);\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": [ + "restr = {'image_name': ['im02'], 'image_seed': ['1', None], 'model': ['full'], }\n", + "facet_col='image_type'\n", + "facet_row='v123_label'\n", + "g = sco.plot.plot_images(image_struct, 'normalized_stimulus_images', restr, facet_col=facet_col, facet_row=facet_row, size=3, col_order=img_type_order, )" + ] + }, + { + "cell_type": "code", + "execution_count": 59, + "metadata": { + "ExecuteTime": { + "end_time": "2017-02-14T14:24:13.549140", + "start_time": "2017-02-14T14:24:04.787639" + }, + "collapsed": false + }, + "outputs": [ + { + "data": { + "application/javascript": [ + "/* Put everything inside the global mpl namespace */\n", + "window.mpl = {};\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", + " 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", + " this.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 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);\n", + " canvas.attr('height', height);\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": [ + "restr = {'image_name': ['im02'], 'image_type': 'V2-metamer', 'model': ['full'],}\n", + "facet_col='image_seed'\n", + "facet_row='v123_label'\n", + "g = sco.plot.plot_images(image_struct, 'normalized_stimulus_images', restr, facet_col=facet_col, facet_row=facet_row, size=2.5, row_order=['im01', 'im02', 'im03', 'im04'], )" + ] + }, + { + "cell_type": "code", + "execution_count": 64, + "metadata": { + "ExecuteTime": { + "end_time": "2017-02-14T14:31:16.789304", + "start_time": "2017-02-14T14:31:04.097199" + }, + "collapsed": false + }, + "outputs": [ + { + "data": { + "application/javascript": [ + "/* Put everything inside the global mpl namespace */\n", + "window.mpl = {};\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", + " 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", + " this.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 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);\n", + " canvas.attr('height', height);\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": [ + "restr = {'image_name': ['im02', 'im03', 'im12', 'im07', 'im71'], 'image_type':'V2-metamer','image_seed': ['1', None], 'v123_label': 1}\n", + "facet_col='image_name'\n", + "facet_row='(n, c)'\n", + "g = sco.plot.plot_images(image_struct, 'normalized_stimulus_images', restr, facet_col=facet_col, facet_row=facet_row, size=3, )" + ] + }, + { + "cell_type": "code", + "execution_count": 76, + "metadata": { + "ExecuteTime": { + "end_time": "2017-02-14T14:31:16.789304", + "start_time": "2017-02-14T14:31:04.097199" + }, + "collapsed": false + }, + "outputs": [ + { + "data": { + "application/javascript": [ + "/* Put everything inside the global mpl namespace */\n", + "window.mpl = {};\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", + " 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", + " this.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 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);\n", + " canvas.attr('height', height);\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": [ + "g = sco.plot.plot_cortical_images(plot_df, restr, facet_col=facet_col, facet_row=facet_row, row_order=param_order, cmap='Blues', set_minmax=False, size=2)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Finally, since we're comparing models, it is useful to not only plot the predictions for each model separately but to also plot the differences in predictions of the models as a cortical image. To demonstrate, we show the differences between model `full` and model `dumb_V1` across all three visual areas for one image, one seed.\n", + "\n", + "We cannot use `SNR_df` for this, since in the bootstrapping process we throw away image information." + ] + }, + { + "cell_type": "code", + "execution_count": 84, + "metadata": { + "ExecuteTime": { + "end_time": "2017-02-14T14:50:42.826354", + "start_time": "2017-02-14T14:50:41.387766" + }, + "collapsed": false + }, + "outputs": [ + { + "data": { + "application/javascript": [ + "/* Put everything inside the global mpl namespace */\n", + "window.mpl = {};\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", + " 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", + " this.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 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);\n", + " canvas.attr('height', height);\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": [ + "g = sco.plot.plot_cortical_images_diff(plot_df, {'model': ['full', 'dumb_V1']}, restr, facet_col=facet_col, facet_row=facet_row, )" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Finally, we examine this difference in predictions for all image types, in V1, and several different images" + ] + }, + { + "cell_type": "code", + "execution_count": 87, + "metadata": { + "ExecuteTime": { + "end_time": "2017-02-14T14:52:58.389307", + "start_time": "2017-02-14T14:52:55.864516" + }, + "collapsed": false + }, + "outputs": [ + { + "data": { + "application/javascript": [ + "/* Put everything inside the global mpl namespace */\n", + "window.mpl = {};\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", + " 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", + " this.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 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);\n", + " canvas.attr('height', height);\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": [ + "g = sco.plot.plot_cortical_images_diff(plot_df, {'model': ['full', 'dumb_V1']}, restr, facet_col=facet_col, facet_row=facet_row, col_order=img_type_order, row_order=['im01', 'im04', 'im13', 'im74'],)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Predictions on the cortex\n", + "\n", + "These blocks average the predicted responses across all images, separating them out by model and image type, then plotting these averages on the cortex in order to visualize how they're distributed. The first section plots them on an inflated brain using pysurfer, while the second projects the cortex onto a flattened surface using neuropythy. I think the second is more useful, since we're only looking at V1, V2, and V3, all fo which lie right around the occipital pole. Because we're not relying on pysurfer and its interface with freesurfer, there's also more flexibility in the flat cortex plotting, allowing us to facet the plots and control more aspects of the aesthetics." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Plot using pysurfer\n", + "\n", + "The following small code block will save the predicted results for the `full` and `dumb_V1` models on the inflated brain using freesurfer (separately for original, V1 metamers, and V2 metamers). Each hemisphere must be done separately. If you want to add more models or image types, expand the lists in the for loop.\n", + "\n", + "NOTE: There is a chance that, for some reason, the plots will not save correctly. This is a known bug, but I'm not sure how to fix it. If that happens, uncomment the brain.save_image line in the for loop below." + ] + }, + { + "cell_type": "code", + "execution_count": 132, + "metadata": { + "ExecuteTime": { + "end_time": "2017-02-14T15:31:44.449752", + "start_time": "2017-02-14T15:29:29.560422" + }, + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "setting roll\n", + "setting roll\n", + "setting roll\n", + "setting roll\n", + "setting roll\n", + "setting roll\n", + "setting roll\n", + "setting roll\n", + "setting roll\n", + "setting roll\n", + "setting roll\n", + "setting roll\n", + "setting roll\n", + "setting roll\n", + "setting roll\n", + "setting roll\n" + ] + } + ], + "source": [ + "subjects_dir = '/Volumes/server/Freesurfer_subjects/'\n", + "for hemi, model, image_type in itertools.product(['lh', 'rh'], ['full', 'dumb_V1'], ['original', 'V1-metamer', 'V2-metamer',]):\n", + " brain = sco.plot.pysurfer_plot_predictions('test-sub', hemi, model, image_type, model_df, plot_df, subjects_dir=subjects_dir, save_path='Metamer_images/ImageNet_metamers_results/SCO_%s_%s_%s.png' % (hemi, model, image_type), mode='data')\n", + "# brain.save_image('Metamer_images/ImageNet_metamers_results/SCO_%s_%s_%s.png' % (hemi, model, image_type))\n", + " brain.close()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Predictions on the \"flat cortex\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "More useful, I think, is to look at the predicted results or SNR values, averaged within model and image type, plotted on the flat cortex. Note that these plots can take a while to create, because a lot of data is being plotted on each one. Similar to [Plot using pysurfer](#Plot-using-pysurfer), this averages across all images within each model and image type to create a summary of each voxels' predictions.\n", + "\n", + "When creating these plots, the first two arguments, `model_df` and `meshes` will always be the same; they provide the locational information for these plots (allowing us to map between the voxels as the model sees them and the vertices on the flat map). \n", + "\n", + "`data_df` is the DataFrame that contains the information you want to plot on the cortical surface and is thus the most important of the arguments. In this notebook, it's either `SNR_df`, `plot_df`, or `None`. If `None`, then you're relying on information contained within the neuropythy meshes (for example, `'benson14_visual_area'`) and NOT those that come from running the model. The next argument, `plot_val` is the name of the column within `data_df` that you wish to plot on the cortical surface. `facet_col` and `facet_row` (`'image_type'` and `'model'` by default) allow you to specify how you wish to facet the data. Since this can be an overwhelming amount of data to examine at once, `facet_col_vals` and `facet_row_vals` allow you to limit which are shown.\n", + "\n", + "Note that in most of these plots, I am not plotting the values for `V2Scl-metamer`. These can be easily included by removing or modifying the appropriate `facet_col_vals` / `facet_row_vals` argument.\n", + "\n", + "See the documentation for `sco.plot.plot_flat_cortex` for more details on the arguments being used here.\n", + "\n", + "This first block creates the projection mesh of the cortex and so needs to be run everytime. Both the method and the radius can be changed (method can take the following values: `'orthographic', 'equirectangular', 'mercator', sinusoidal'`)]" + ] + }, + { + "cell_type": "code", + "execution_count": 172, + "metadata": { + "ExecuteTime": { + "end_time": "2017-02-16T14:06:25.510153", + "start_time": "2017-02-16T14:05:23.537267" + }, + "collapsed": false, + "init_cell": true, + "scrolled": false + }, + "outputs": [], + "source": [ + "subject = neuro.freesurfer_subject('test-sub')\n", + "meshes, hemis = {}, {}\n", + "\n", + "# you can change the radius here (how far out on the cortex you want to go), but pi/4 is sufficient\n", + "# for eccentricity < 7.5\n", + "\n", + "hemis['L'] = subject.LH\n", + "meshes['L'] = hemis['L'].projection(method='equirectangular',radius=np.pi/4)\n", + "\n", + "hemis['R'] = subject.RH\n", + "meshes['R'] = hemis['R'].projection(method='equirectangular',radius=np.pi/4)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Note that these can take a while (about a minute) to plot, depending on how many of the possible subplots you want to see.\n", + "\n", + "These first two plots simply show the labels for V1, V2, and V3, to help you orient yourself on the flat cortex. The first one shows labels for all 90 degrees of eccentricity, while the second plot will only show the eccentricities included in the model (by default, 7.5) and therefore not cover as much of the cortical surface. Note that the rest of these plots will cover this smaller amount of cortical surface." + ] + }, + { + "cell_type": "code", + "execution_count": 138, + "metadata": { + "ExecuteTime": { + "end_time": "2017-02-14T16:09:32.067404", + "start_time": "2017-02-14T16:09:16.308550" + }, + "collapsed": false + }, + "outputs": [ + { + "data": { + "application/javascript": [ + "/* Put everything inside the global mpl namespace */\n", + "window.mpl = {};\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 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);\n", + " canvas.attr('height', height);\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": [ + "# Note the use of facet_row_vals and facet_col_vals here. Since v123_label does not depend on either\n", + "# the stimulus image being fit or the parameters we varied in differentiating the models, all of them\n", + "# will be the same. Thus, we only want one subplot \n", + "g, df = flat_cort_df = sco.plot.plot_flat_cortex(model_df, meshes, plot_df, plot_val='v123_label', facet_row_vals=['full'], facet_col_vals=['original'])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We now visualize the predicted responses across the cortex. To get a sense for what this looks like, we plot the average predicted responses for the full model to original images, V1 metamers, and V2 metamers. The main thing we notice is the steadily increasing response in V1, V2, and V3 to V1 and V2 metamers. The predicted responses to original images appears to be a little more varied." + ] + }, + { + "cell_type": "code", + "execution_count": 143, + "metadata": { + "ExecuteTime": { + "end_time": "2017-02-14T16:17:36.057415", + "start_time": "2017-02-14T16:16:56.830977" + }, + "collapsed": false + }, + "outputs": [ + { + "data": { + "application/javascript": [ + "/* Put everything inside the global mpl namespace */\n", + "window.mpl = {};\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", + " 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", + " this.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 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);\n", + " canvas.attr('height', height);\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": [ + "g, _ = sco.plot.plot_flat_cortex(model_df, meshes, plot_df, facet_row='(n, c)', row_order=param_order, \n", + " facet_col_vals=['V2-metamer', 'original', 'V1-metamer'], col_order=['original', 'V1-metamer', 'V2-metamer'],\n", + " v123_cmaps='viridis')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Similar to with cortical images, it can be difficult to tell what exactly each model is doing because the overall magnitude of predictions is so much higher in the full model. In order to get a better sense of what the patterns look like across the cortex for each model, we allow each plot to have its own min and max values (therefore, no colorbar is created)." + ] + }, + { + "cell_type": "code", + "execution_count": 142, + "metadata": { + "ExecuteTime": { + "end_time": "2017-02-14T16:15:31.767707", + "start_time": "2017-02-14T16:13:41.809987" + }, + "collapsed": false + }, + "outputs": [ + { + "data": { + "application/javascript": [ + "/* Put everything inside the global mpl namespace */\n", + "window.mpl = {};\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 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": [ + "g, _ = sco.plot.plot_flat_cortex(model_df, meshes, SNR_df, facet_row='(n, c)', row_order=param_order, \n", + " plot_val='value', facet_col='variable', \n", + " facet_col_vals=['V2 - V1', 'V2 - original', 'V1 - original'], \n", + " v123_cmaps='RdBu')" + ] + } + ], + "metadata": { + "anaconda-cloud": {}, + "kernelspec": { + "display_name": "Python [conda root]", + "language": "python", + "name": "conda-root-py" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.12" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/README.md b/README.md index d77ad2a..1fa0ea2 100644 --- a/README.md +++ b/README.md @@ -16,6 +16,22 @@ available and listed in the requirements file. In order to install, type `pip install -r requirements.txt`. If you wish to use the included Jupyter notebooks, make sure to also install `jupyter`. +One included function, `sco.plot.pysurfer_plot_predictions`, requires +pysurfer to run. Since this library can be difficult to get installed +(it requires mayavi), it is not listed as an official requirement. + +If you wish to compare the output of this model to the output of +Kendrick Kay's MATLAB implementation, you will need +his [knkutils](https://github.com/kendrickkay/knkutils). + +If you wish to see how this model handles Eero Simoncelli's texture +metamers, you will need +his [textureSynth](https://github.com/billbrod/textureSynth) (note +that this is actually billbrod's fork of the textureSynth code, +because a small change was needed) +and +[matlabPyrTools](https://github.com/LabForComputationalVision/matlabPyrTools). + ## License ######################################################################################### This README file is part of the SCO Python library. @@ -87,7 +103,10 @@ values. The key you will most likely be interested in is `predicted_responses`, which contains the predicted response of each voxel to each image. -In order to get an mgh for viewing the predictions on a brain, call +## Visualizing model predictions + +In order to get an mgh for viewing the predictions of the model to a +single image on a brain, call `results=sco.export_predicted_responses(results, export_path='...')`, after filling out `results` above and specifying where you want to export the image (this step can be added to the calc chain if you @@ -96,7 +115,20 @@ will take the `predicted_responses` key from `results` and create a corresponding mgh image, which you can view using `freesurfer` or `nibabel`. -## Makefile +The module `sco.plot` also contains several functions to help +visualize model predictions and examples can also be found in +`sco.model_comparison.visualize_model_comparison` or in the +`Metamers.ipynb` and `Compare_with_Kay2013.ipynb` notebooks. + +## Compare with Kay2013 + +The SCO model is an extension of the Second-Order Contrast model +proposed in Kay2013a. We therefore wanted to compare the model's +performance on some simple, systematic stimuli to that of the +Second-Order Contrast model, implemented in MATLAB as part +of [knkutils](https://github.com/kendrickkay/knkutils) +(see [Kendrick's website](http://kendrickkay.net/socmodel/) for more +details). This repository contains a Makefile, which is used to quickly create the images comparing the python and matlab implementations of the @@ -161,6 +193,77 @@ preference" in the jupyter notebook. It will load in all of the dataframes and combine them in a way that allows you to visualize the model behavior. +If you want to delete the results you have generated (so you can +re-run the model), type `make fullclean` or `make sweepclean`. + +## Texture Metamers + +The notebook `Metamers.ipynb` analyzes how the SCO model handles V1 +and V2 metamers, as used in Freeman2013. + +To generate the stimuli for that notebook, the script +`createMetamers.m` is provided. You can use to create your own V1 and +V2 metamers for a given batch of images or you can use the recipes +found in the `Makefile` to create metamers from three defined +examples: the stimuli from Freeman2013, +some +[Brodatz texture images](http://multibandtexture.recherche.usherbrooke.ca/index.html), +or some images from [ImageNet](http://image-net.org/explore) (plus +some simple stimuli from Kay2013a). To do so, type `make +Metamer_images/{img}_metamers`, where `{img}` is either: +`Freeman2013`, `Brodatz`, or `ImageNet` from the command line in the +main sco directory (note that this requires `python2.7`, `make`, and +`matlab` are in your path and you should check to make sure that paths +`KNK_PATH`, `SUBJ_DIR`, `TEXTURE_SYNTH_PATH`, and `PYR_TOOLS_PATH` are +correct, and that `SUBJ` refers to a valid freesurfer subject. The +seed images will then be downloaded from the internet and the metamers +created. + +After the stimuli are created, you can either start up the +`Metamers.ipynb` notebook and run the model from there or submit the +model to the cluster. If you are running 100+ images, calculating the +SNR values (bootstrapping 100 to 1000 times), and running multiple +versions of the model, this can take a very long time (3+ hours for +each model), so it's recommended you run the model on a +cluster. See +[Running model on the cluster](#running-model-on-the-cluster) for +details. Regardless of whether you run the model in the notebook or +using `cluster_submit.py`, the predicted responses will be saved to a +.csv. + +All the visualization of model results is handled by the notebook, so +look there for more details. + +## Running model on the cluster + +`cluster_submit.py` is provided in order to help run the model on a +computing cluster. Note that because not all of the items in the data +pool are pickle-able (some are functions and thus can't be saved), +it's recommended to do this when you want to view only the outputs of +the model (the predicted responses), not the intermediate stages. + +`cluster_submit.py` should work as is if you are submitting the script +to NYU's mercer (PBS) cluster. The `TEMPLATE_SUBMISSION_SCRIPT` +constant at the top of the script shows the shell script that will be +created and submitted to run the job. By default, the shell script +will simply call `cluster_submit.py`, causing the bottom part of the +script (after `if __name__ == '__main__'`) to be called, which will +run `metamers.main`. If you want to use another script, specify it +when calling `cluster_submit.main` as the `script_path` argument. The +`submission_command` argument can be modified if you wish to specify +submission options (these can also be added to the top of the +`TEMPLATE_SUBMISSION_SCRIPT`). + +As is, `cluster_submit.py` will run the model on a specified directory +of images. You can run multiple versions of the model by adding +entries to the `SCO_KWARGS` dictionary in the script; currently, +`'full'`, `'dumb_V1`, `'intermediate_1'`, `'intermediate_2'`, and +`'intermediate_3'` are supported. The `model_names` argument is used +to specify which model(s) should be run. For example, to run the +`'full'` and `'dumb_V1'` versions of the model in the directory found +at `~/imgs`, you would run `cluster_submit.main(['full', 'dumb_V1'], +'~/imgs')` from within a python interpeter (e.g., Ipython). + # Data pool The model has a large data pool (found in the `results` dictionary), @@ -168,7 +271,7 @@ which I describe below. The data pool contains the inputs and outputs for every step of the model. For these descriptions, we assume your subject -has n voxels and you're predicting the responses for m images. +has `n` voxels and you're predicting the responses for `m` images. Any parameter that is specified as an array can also be specified as a list. However, the model will turn it into an array for ease of @@ -455,6 +558,11 @@ areas V1, V2, and V3). # References +- Freeman, J., Ziemba, C. M., Heeger, D. J., Simoncelli, E. P., & + Movshon, J. A. (2013). A functional and perceptual signature of the + second visual area in primates. Nature Neuroscience, 16(7), + 974–981. http://dx.doi.org/10.1038/nn.3402 + - Kay2013a: Kay, K. N., Winawer, J., Rokem, A., Mezer, A., & Wandell, B. A. (2013). A two-stage cascade model of BOLD responses in human visual cortex. {PLoS} Comput Biol, diff --git a/cluster_submit.py b/cluster_submit.py new file mode 100644 index 0000000..319b1ab --- /dev/null +++ b/cluster_submit.py @@ -0,0 +1,119 @@ +# cluster_submit.py +# +# by William F. Broderick +# +# This little script will help you submit the SCO model to the cluster, saving the outputs for +# later inspection (since they can take a while to run). Note that this was created for use with +# the NYU mercer HPC cluster and may need to be modified for use with others. +# +# Most important is the TEMPLATE_SUBMISSION_SCRIPT global variable at the top of the file. This bit +# of text should be modified to create the basic style of submission script that you need, as it +# will be filled in, turned into a .sh file, and submitted. + +TEMPLATE_SUBMISSION_SCRIPT = """ +#!/bin/bash +#PBS -l nodes=1:ppn=1 +#PBS -l walltime=12:00:00 +#PBS -l mem=23GB +#PBS -N {job_name} +#PBS -M {username} +#PBS -j oe +#PBS -m bae +#PBS -o {output_dir}/scripts + +module purge +module load anaconda/2.3.0 + +cd $HOME/sco +python2.7 cluster_submit.py {model_name} {image_dir} {output_dir} + +# leave a blank line at the end +""" + +import glob +import os +import subprocess +from sco.util import metamers + +SCO_KWARGS = {'full': {}, + 'dumb_V1': {'Kay2013_SOC_constant': 0, 'Kay2013_output_nonlinearity': 1}, + 'intermediate_1': {'Kay2013_SOC_constant': .25, 'Kay2013_output_nonlinearity': .8,}, + 'intermediate_2': {'Kay2013_SOC_constant': .5, 'Kay2013_output_nonlinearity': .6}, + 'intermediate_3': {'Kay2013_SOC_constant': .75, 'Kay2013_output_nonlinearity': .4}} + +def main(model_names, image_dir, output_dir='~/SCO_metamer_data', job_name="SCO", username="", + submission_command="qsub {script}", script_path=None): + """Run the given variants of the models on the specified `image_dir` + + Because I can't come up with a good way to pass a dictionary around on the command line, the + user is limited to the supported variants of the model (given in SCO_KWARGS), though this can + always be modified to add more. I think json could actually be used to accomplish this + (http://stackoverflow.com/questions/18006161/how-to-pass-dictionary-as-command-line-argument-to-python-script), + but I'm not sure it's worth the work. + + Note that if you're using this for metamer (main use right now), make sure that type_regex, + seed_regex, and name_regex are all set correctly (see metamers.main for examples) and include + these either in the metamers.main call in the if-main block or in the SCO_KWARGS dictionary + above. + + The model steps argument of SCO_KWARGS allows you to specify which outputs to create: results, + model_df, SNR_df + + Arguments + ============== + + model_names: string or list. The model(s) to run. See the keys of SCO_KWARGS in this script for + supported values and what they mean. + + image_dir: string. The path to the directory containing the images to run the model on. + + output_dir: string, optional. Path to the directory to place outputs in + + job_name: string, optional. Name on the cluster of this/these jobs + + username: string, optional. If you want email updates on your job, enter your + 'username@domain.something' here + + submission_command: string, optional. What command should be used to submit the script that + this function will create. Must contain {script}. + + script_path: string, optional. If you do not want to use the TEMPLATE_SUBMISSION_SCRIPT, set + script_path to the path to an example script. If script_path is None (default), + will use the TEMPLATE_SUBMISSION_SCRIPT. If you have multiple scripts for different + model_names, include {model_name} in your `script_path` + """ + if isinstance(model_names, basestring): + model_names = [model_names] + output_dir = _check_path(output_dir) + if not os.path.isdir(output_dir): + os.makedirs(output_dir) + if not os.path.isdir(output_dir+'/scripts'): + os.makedirs(output_dir+'/scripts') + for name in model_names: + assert name in SCO_KWARGS.keys(), "Don't know how to run model %s!" % name + if script_path is None: + script_file = "%s/scripts/SCO_%s.sh" % (output_dir, name) + with open(script_file, 'w') as f: + f.write(TEMPLATE_SUBMISSION_SCRIPT.format(job_name=job_name, username=username, + model_name=name, image_dir=image_dir, + output_dir=output_dir)) + else: + script_file = script_path.format(model_name=model_name) + print("Submitting model %s" % name) + subprocess.call(submission_command.format(script=script_file).split(' ')) + +def _check_path(path_name): + path_name = os.path.expanduser(path_name) + if path_name[-1] == '/': + path_name = path_name[:-1] + return path_name + +if __name__ == '__main__': + import sys + model_name = sys.argv[1] + img_dir = _check_path(sys.argv[2]) + output_dir = _check_path(sys.argv[3]) + images = glob.glob(os.path.expanduser('%s/*png' % img_dir)) + assert model_name in SCO_KWARGS.keys(), "Don't know how to run model %s!" % model_name + metamers.main(images, output_dir, model_name, subject_path='~/Freesurfer_subjects', + **SCO_KWARGS[model_name]) diff --git a/createMetamers.m b/createMetamers.m new file mode 100644 index 0000000..d89381d --- /dev/null +++ b/createMetamers.m @@ -0,0 +1,129 @@ +% createMetamers.m +% +% by William F. Broderick +% +% creates the V1 and V2 metamers used to test the sco model. Note +% that these are spectrally matched noise (V1) and the outputs of +% Eero's texture model (V2). +% +% based off of example1.m in textureSynth +% +% this requires both Eero's textureSynth and matlabPyrTools + +function createMetamers(textureSynthPath, pyrToolsPath, knkpath, imgDir, outputDir, imgIdx, numScales, numOrientations, sizeNeighborhood, Niter, seeds) +% arguments: +% +% textureSynthPath: string, path to your textureSynth folder, which +% we'll add to the matlab path +% +% pyrToolsPath: string, path to your matlabPyrTools folder, which +% we'll add to the matlab path +% +% imgDir: template string to the images you want are contained. Should +% be something like the following: /path/to/imgs/*.jpg, so that we +% only find the images of the right type (we will call dir(imgDir)). +% +% outputDir: string, directory to save these metamers (and the +% original image used) in. +% +% imgIdx: vector, specifying which images from imgDir to use. +% +% numScales: integer, the number of scales to use in filters for +% generating metamers. recommended value 4. +% +% numOrientations: integer, the number of orientations in the +% filters for generating metamers. recommended value 4 +% +% sizeNeighborhood: integer, spatial neighborhood for generating metamers +% (must be an odd number!). recommended value 7 +% +% for details on these three, see the textureSynth library and +% the paper: "A Parametric Texture Model based on Joint Statistics of +% Complex Wavelet Coefficients". J Portilla and E P Simoncelli. Int'l +% Journal of Computer Vision, vol.40(1), pp. 49-71, Dec 2000. +% +% Niter: integer, how many iterations the textureSynthesis +% algorithm should go through. recommended 10 to 50. +% +% seeds: vector of integers, random seeds to use for generating the +% initial random images. The number of metamers we make will be +% equal to the length of this vector. + + addpath(genpath(textureSynthPath)); + addpath(genpath(pyrToolsPath)); + addpath(genpath(knkpath)); + + if isempty(find(imgDir=='*')) + error('imgDir must contain a wildcard in order for this to work! see docstring for example') + end + imgPaths = dir(imgDir); + lastSlash = find(imgDir=='/'); + lastSlash = lastSlash(end); + imgDir = imgDir(1:lastSlash); + + if ~strcmp(outputDir(end), '/') + outputDir = [outputDir '/']; + end + + for ii=imgIdx + % our input image must be a double + imgName = imgPaths(ii).name; + baseImage = double(imread([imgDir, imgName])); + % need to make sure image is grayscale + baseImage = mean(baseImage, 3); + % need to make sure image is square and can be divided by 2 five times + % evenly + cutSize = floor(min(size(baseImage))/2^6) * 2^6; + baseImage = baseImage(1:cutSize, 1:cutSize); + + lastPeriod = find(imgName=='.'); + imgName = imgName(1:lastPeriod-1); + + imwrite(baseImage/255, [outputDir imgName '.png']); + + params = textureAnalysis(baseImage, numScales, numOrientations, sizeNeighborhood); + + % metamer size must be some multiple of + % 2^(numScales+2). for now, we try to make the size of the + % metamer image about the same as the size of the input image + metamerSize = 2^(numScales + 2); + metamerSize = ceil(size(baseImage, 1) / metamerSize) * metamerSize; + + for jj=1:length(seeds) + % Based on the methods of Freeman, 2013 + % (http://www.nature.com/neuro/journal/v16/n7/full/nn.3402.html#methods), + % the V1 metamer should be spectrally matched noise, + % generated by "randomizing the phase but matching the + % complete two-dimensional power spectra", which is what + % this does. We only want the real component, the + % imaginary ends up being some constant. + V1Metamer = real(ifft2(fft2(baseImage) .* generaterandomphase(size(baseImage, 1)))); + % The V1Metamer can have some weird values. This makes + % sure it lies between 0 and 1. Unfortunately, I can't + % figure out what the theoretical min and max values + % would be. + % [mn, mx] = range2(V1Metamer); + % V1Metamer = (V1Metamer + abs(mn)) / (abs(mn) + abs(mx)); + % center the mean and then clip + V1Metamer = V1Metamer - mean(V1Metamer(:)) + 127.5; + V1Metamer(V1Metamer>255) = 255; + V1Metamer(V1Metamer<0) = 0; + + % with these inputs, textureSynthesis uses white noise + % as the seed image + V2Metamer = textureSynthesis(params, [metamerSize metamerSize seeds(jj)], Niter); + + V2MetamerScaled = V2Metamer * (var(V1Metamer(:)) / var(V2Metamer(:))); + + % imwrite requires them to lie between 0 and 1, so we scale + % them down + imwrite(V1Metamer/255, [outputDir 'V1Met-' imgName '-' num2str(seeds(jj)) '.png']); + imwrite(V2Metamer/255, [outputDir 'V2Met-' imgName '-' num2str(seeds(jj)) '.png']); + imwrite(V2MetamerScaled/255, [outputDir 'V2MetScaled-' imgName '-' num2str(seeds(jj)) '.png']); + end + end + + close all; + + +end diff --git a/model_comparison_script.py b/model_comparison_script.py index 905ad26..050be0b 100644 --- a/model_comparison_script.py +++ b/model_comparison_script.py @@ -16,7 +16,7 @@ import neuropythy import numpy as np from sco.model_comparison import (create_model_dataframe, compare_with_Kay2013) - +from sco.util import save_results_dict def main(image_base_path, stimuli_idx, voxel_idx, subject, model_df_path="./soc_model_params.csv", subject_dir=None): @@ -31,41 +31,11 @@ def main(image_base_path, stimuli_idx, voxel_idx, subject, model_df_path="./soc_ results, stimulus_model_names = compare_with_Kay2013(image_base_path, np.asarray(stimuli_idx), voxel_idx, subject, subject_dir) - # we want to save the results dictionary but we can't pickle functions. We need to do this - # because our results dict contains both functions and arrays of functions. - save_results = dict() - for k, v in results.iteritems(): - # we can't pickle the free surfer subject - if isinstance(v, neuropythy.freesurfer.subject.Subject): - continue - # we can't pickle functions or lambdas - if _check_uncallable(v): - save_results[k] = v - with open(os.path.splitext(model_df_path)[0] + "_results_dict.pkl", 'w') as f: - pickle.dump(save_results, f) + save_results_dict(results, os.path.splitext(model_df_path)[0]) model_df = create_model_dataframe(results, stimulus_model_names, model_df_path) return model_df, results -def _check_uncallable(x): - # if this is an iterable, we want to call this again (we don't know the structure of this - # iterable, so x[0] might be another iterable). - if hasattr(x, '__iter__'): - # we can have an array that just contains None, in which case v[0] will throw an - # exception - try: - return _check_uncallable(x[0]) - except IndexError: - return False - # if this is a dictionary, it may return a keyerror, in which case we want to check the - # first value. - except KeyError: - return _check_uncallable(x.values()[0]) - else: - if not hasattr(x, '__call__'): - return True - else: - return False if __name__ == '__main__': parser = argparse.ArgumentParser(description=("Create the dataframe with predicted responses for" diff --git a/sco/__init__.py b/sco/__init__.py index f5bca82..41cdffc 100644 --- a/sco/__init__.py +++ b/sco/__init__.py @@ -18,7 +18,7 @@ export_predicted_response_surface) from sco.model_comparison import create_model_dataframe -from sco.util import (cortical_image) +from sco.plot import (cortical_image) # Version information... _version_major = 0 diff --git a/sco/model_comparison/core.py b/sco/model_comparison/core.py index 29e6ffc..6df76aa 100644 --- a/sco/model_comparison/core.py +++ b/sco/model_comparison/core.py @@ -41,13 +41,13 @@ def _get_pRF_pixel_size(pRF_views, normalized_stimulus_images, normalized_pixels # Keys from the results dict that correspond to model parameters and so that we want to save in the # output dataframe -MODEL_DF_KEYS = ['pRF_centers', 'pRF_hemispheres', 'pRF_voxel_indices', 'SOC_responses', - 'pRF_eccentricity', 'pRF_v123_labels', 'predicted_responses', 'pRF_sizes', - 'pRF_polar_angle', 'Kay2013_output_nonlinearity', 'Kay2013_pRF_sigma_slope', - 'Kay2013_SOC_constant', 'Kay2013_normalization_r', 'Kay2013_normalization_s', - 'Kay2013_response_gain', 'pRF_frequency_preferences', 'voxel_idx', - {'pRF_pixel_centers_row': _get_pRF_pixel_centers_row}, 'effective_pRF_sizes', - {'pRF_pixel_sizes': _get_pRF_pixel_size}, +MODEL_DF_KEYS = ['pRF_centers', 'pRF_hemispheres', 'pRF_voxel_indices', 'pRF_vertex_indices', + 'SOC_responses', 'pRF_eccentricity', 'pRF_v123_labels', 'predicted_responses', + 'pRF_sizes', 'pRF_polar_angle', 'Kay2013_output_nonlinearity', + 'Kay2013_pRF_sigma_slope', 'Kay2013_SOC_constant', 'Kay2013_normalization_r', + 'Kay2013_normalization_s', 'Kay2013_response_gain', 'pRF_frequency_preferences', + 'voxel_idx', {'pRF_pixel_centers_row': _get_pRF_pixel_centers_row}, + 'effective_pRF_sizes', {'pRF_pixel_sizes': _get_pRF_pixel_size}, {'pRF_pixel_centers_col': _get_pRF_pixel_centers_col}, 'pRF_blob_stds'] # Keys from the results dict that correspond to model setup and so we want @@ -189,13 +189,16 @@ def create_model_dataframe(results, image_names, model_df_path="./soc_model_para k, tmp_func = k.items()[0] tmp_args = inspect.getargspec(tmp_func).args value = tmp_func(*[results.get(i) for i in tmp_args]) - else: - value = results[k] - try: - # they should all be arrays, but just in case. + # they should all be arrays, but just in case. model_df_dict[k] = np.asarray(value) - except KeyError: - warnings.warn("Results dict does not contain key %s, skipping" % k) + else: + try: + value = results[k] + # they should all be arrays, but just in case. + model_df_dict[k] = np.asarray(value) + except KeyError: + warnings.warn("Results dict does not contain key %s, skipping" % k) + # We assume that we have four types of variables here, as defined by their shape: # # 1. One-dimensional. For these, we simply want their values to be one of the columns of our @@ -290,7 +293,9 @@ def create_model_dataframe(results, image_names, model_df_path="./soc_model_para # infer voxel label from the indices directly. if 'voxel_idx' in model_df.columns: model_df = model_df.rename(columns={'voxel_idx': 'voxel'}) - model_df = model_df.set_index('voxel') + else: + model_df['voxel'] = range(model_df.shape[0]) + model_df = model_df.set_index('voxel') # Finally, we save model_df as a csv for easy importing / exporting model_df.to_csv(model_df_path, index_label='voxel') sio.savemat(os.path.splitext(model_df_path)[0] + "_image_names.mat", @@ -310,12 +315,15 @@ def _load_pkl_or_mat(path, mat_field): return path -def _create_plot_df(model_df, stimuli_idx=None, stimuli_descriptions=None): +def _create_plot_df(model_df, stimuli_idx=None, stimuli_descriptions=None, extra_cols=[]): """create dataframe stimuli associated with condition to plot from condition can be either a boolean array, in which case we grab the values from stimuli_idx corresponding to the Trues, or an array of integers, in which case we assume it's just the index numbers themselves. + + extra_cols is a list of strings corresponding to columns in model_df that you would also like + to add to the plot_df """ # this grabs the columns that contain the predicted responses from both the matlab and the # python code. @@ -344,7 +352,10 @@ def _create_plot_df(model_df, stimuli_idx=None, stimuli_descriptions=None): plot_df['image_name'] = plot_df.image.map(mapping) plot_df = plot_df.set_index('voxel') - plot_df['v123_label'] = model_df['pRF_v123_labels'] + for col in extra_cols+['pRF_v123_labels', 'pRF_centers_dim0', 'pRF_centers_dim1', 'pRF_hemispheres']: + # we want to rename some of these columns + plot_col = {'pRF_v123_labels': 'v123_label'}.get(col, col) + plot_df[plot_col] = model_df[col] plot_df = plot_df.reset_index() return plot_df diff --git a/sco/plot/__init__.py b/sco/plot/__init__.py new file mode 100644 index 0000000..d2b7ddc --- /dev/null +++ b/sco/plot/__init__.py @@ -0,0 +1,736 @@ +# plot/__init__.py +# +# various functions to help plot SCO model performance. +# +# by William F Broderick and Noah Benson + +import matplotlib +import itertools +import matplotlib.pyplot as plt +import matplotlib.tri as tri +import numpy as np +import seaborn as sns +import pandas as pd +import neuropythy + +# pysurfer is optional +try: + from surfer import Brain +except ImportError, e: + if e.message != 'No module named surfer': + raise + +def cortical_image(datapool, visual_area=1, image_number=None, image_size=200, n_stds=1, + size_gain=1, method='triangulation', subplot=None): + ''' + cortical_image(datapool) yields an array of figures that reconstruct the cortical images for the + given sco results datapool. The image is always sized such that the width and height span + 2 * max_eccentricity degrees (where max_eccentricity is stored in the datapool). The following + options may be given: + * visual_area (default: 1) must be 1, 2, or 3 and specifies whether to construct the cortical + image according to V1, V2, or V3. + * image_number (default: None) specifies which image to construct a cortical image of. If this + is given as None, then a list of all images in the datapool is returned; otherwise only the + figure for the image number specified is returned. + * image_size (default: 200) specifies the width and height of the image in pixels (only for + method equal to 'pRF_projection'. + * n_stds (default: 1) specifies how many standard deviations should be used with each pRF when + projecting the values from the cortical surface back into the image. + * size_gain (default: 1) specifies a number to multiply the pRF size by when projecting into + the image. + * method (default: 'triangulation') should be either 'triangulation' or 'pRF_projection'. The + former makes the plot by constructing the triangulation of the pRF centers and filling the + triangles in while the latter creates an image matrix and projects the pRF predicted + responses into the relevant pRFs. + ''' + # if not given an image number, we want to iterate through all images: + if image_number is None: + return np.asarray([cortical_image(datapool, visual_area=visual_area, image_number=ii, + image_size=image_size, n_stds=n_stds, method=method, + subplot=subplot) + for ii in range(len(datapool['stimulus_images']))]) + if subplot is None: + plotter = plt + fig = plotter.figure() + else: + plotter = subplot + fig = subplot + if method == 'triangulation': + # deal with pyplot's interactive mode + maxecc = float(datapool['max_eccentricity'][image_number]) + labs = datapool['pRF_v123_labels'] + (x,y) = datapool['pRF_centers'].T + z = datapool['predicted_responses'][image_number] + + (x,y,z) = np.transpose([(xx,yy,zz) + for (xx,yy,zz,l) in zip(x,y,z,labs) + if l == visual_area]) + + plotter.tripcolor(tri.Triangulation(x,y), z, cmap='jet', shading='gouraud') + plotter.axis('equal') + plotter.axis('off') + return fig + + elif method == 'pRF_projection': + # otherwise, we operate on a single image: + r = datapool['predicted_responses'] + maxecc = float(datapool['max_eccentricity'][image_number]) + sigs = datapool['pRF_sizes'] + labs = datapool['pRF_v123_labels'] + + (x,y) = datapool['pRF_centers'].T + z = r[image_number] + + (x,y,z,sigs) = np.transpose([(xx,yy,zz,ss) + for (xx,yy,zz,ss,l) in zip(x,y,z,sigs,labs) + if l == visual_area]) + + img = np.zeros((image_size, image_size, 2)) + img_center = (float(image_size)*0.5, float(image_size)*0.5) + img_scale = (img_center[0]/maxecc, img_center[1]/maxecc) + for (xx,yy,zz,ss) in zip(x,y,z,sigs): + ss = ss * img_scale[0] * size_gain + exp_const = -0.5/(ss * ss) + row = yy*img_scale[0] + img_center[0] + col = xx*img_scale[1] + img_center[1] + if row < 0 or col < 0 or row >= image_size or col >= image_size: continue + r0 = max([0, int(round(row - ss))]) + rr = min([image_size, int(round(row + ss))]) + c0 = max([0, int(round(col - ss))]) + cc = min([image_size, int(round(col + ss))]) + (mesh_xs, mesh_ys) = np.meshgrid(np.asarray(range(c0,cc), dtype=np.float) - col, + np.asarray(range(r0,rr), dtype=np.float) - row) + gaus = np.exp(exp_const * (mesh_xs**2 + mesh_ys**2)) + img[r0:rr, c0:cc, 0] += zz + img[r0:rr, c0:cc, 1] += gaus + img = np.flipud(img[:,:,0] / (img[:,:,1] + (1.0 - img[:,:,1].astype(bool)))) + fig = plotter.figure() + plotter.imshow(img) + plotter.axis('equal') + plotter.axis('off') + return fig + else: + raise ValueError('unrecognized method: %s' % method) + +def _image_plotter(imgs, **kwargs): + """simple function to plot images from the images object + + kwargs must contain data, a dataframe with only one unique image_idx value, which specifies + which image to plot + + imgs: 3d numpy array containing all images + """ + ax = plt.gca() + _ = kwargs.pop('color') + try: + data = kwargs.pop('data') + except KeyError: + return + cmap = kwargs.pop('cmap', 'gray') + if data.image_idx.nunique() > 1: + raise Exception("Didn't facet correctly, too many images meet this criteria!") + ax.imshow(imgs[data.image_idx.unique()][0], cmap=cmap, **kwargs) + +def plot_images(images, image_value, img_restrictions={}, facet_col=None, facet_row=None, + cbar=True, **kwargs): + """plot stimuli in an easy to arrange, easy to parse way + + images is the dictionary created by util.metamers.create_image_struct, which has a 'label' key + containing a DataFrame that labels images by various properties, and other keys corresponding + to different image types ('stimulus_images' and 'normalized_stimulus_images'), most likely. + + image_value: key in images which contains the values to plot (e.g., 'stimulus_images' or + 'normalized_stimulus_images') + + img_restrictions: dictionary, optional. keys must be columns in images['labels'], values are + the value(s) you want to plot + + facet_col, facet_row: strings, optional. how you want to split up the facetgrid + + **kwargs: will be passed to sns.FacetGrid + """ + tmp_df, facet_col, facet_row = _facet_plot_shape_df(images['labels'], img_restrictions, facet_col, + facet_row) + # we just use this to determine the max and min values; we still need the full one to plot + imgs = images[image_value][tmp_df.image_idx.unique()] + size = kwargs.pop('size', 2.3) + margin_titles = kwargs.pop('margin_titles', True) + try: + min_val, max_val = imgs.min(), imgs.max() + except ValueError: + # if imgs is not a proper 3d array (which happens when some of the images are different + # shapes, we can't use a simple .min() and .max()) + min_val = min([im.min() for im in imgs]) + max_val = max([im.max() for im in imgs]) + cmap = kwargs.pop('cmap', 'gray') + with sns.axes_style('whitegrid', {'axes.grid': False, 'axes.linewidth':0.}): + g = sns.FacetGrid(tmp_df, col=facet_col, row=facet_row, size=size, margin_titles=margin_titles, **kwargs) + g.map_dataframe(_image_plotter, imgs=images[image_value], vmin=min_val, vmax=max_val, cmap=cmap) + g.set(yticks=[], xticks=[]) + if cbar: + make_colorbar(g.fig, cmap, vmin=min_val, vmax=max_val) + return g + +def _cortical_image_plotter(x, y, z, **kwargs): + plt.tripcolor(matplotlib.tri.Triangulation(x, y), z, shading='gouraud', **kwargs) + +def _facet_plot_shape_df(plot_df, restrictions, facet_col, facet_row): + """handles much of the shaping and restricting of dataframes to plot them using FacetGrids + """ + tmp_df = plot_df.copy() + for k,v in restrictions.iteritems(): + if isinstance(v, basestring) or not hasattr(v, '__iter__'): + v = [v] + if k in tmp_df.columns: + tmp_df = tmp_df[(tmp_df[k].isin(v))] + if facet_col not in tmp_df.columns: + facet_col = None + else: + # this annoying bit will make sure there's not a None in the variables we're facetting on + tmp_df[facet_col] = tmp_df[facet_col].replace({None: [i for i in tmp_df[facet_col].unique() if i is not None][0]}) + if facet_row not in tmp_df.columns: + facet_row = None + else: + # this annoying bit will make sure there's not a None in the variables we're facetting on + tmp_df[facet_row] = tmp_df[facet_row].replace({None: [i for i in tmp_df[facet_row].unique() if i is not None][0]}) + return tmp_df, facet_col, facet_row + +def plot_cortical_images(plot_df, plot_restrictions={}, plot_value='predicted_responses', + facet_col=None, facet_row=None, xlabels=False, ylabels=False, + set_minmax=True, **kwargs): + """plot the predicted responses on stimuli ("cortical images") + + plot_restrictions: dictionary, optional. keys must be columns in plot_df, values are the + value(s) you want to plot + + plot_value: column in plot_df which contains the values to plot, defaults to `predicted_responses` + + facet_col, facet_row: strings, optional. how you want to split up the facetgrid + + xlabels, ylabels: boolean, optional. whether you want to include labels on x and y axes. False + by default + + set_minmax: boolean, optional. whether all images should have the same vmin and vmax values + (True) or whether they should be allowed to have separate vmin and vmax values + (False) + + **kwargs: will be passed to sns.FacetGrid + """ + tmp_df, facet_col, facet_row = _facet_plot_shape_df(plot_df, plot_restrictions, facet_col, + facet_row) + size = kwargs.pop('size', 2.3) + margin_titles = kwargs.pop('margin_titles', True) + aspect = kwargs.pop('aspect', 1.25) + if set_minmax: + min_val, max_val = tmp_df[plot_value].min(), tmp_df[plot_value].max() + else: + min_val, max_val = None, None + cmap = kwargs.pop('cmap', 'gray') + with sns.axes_style('whitegrid', {'axes.grid': False, 'axes.linewidth':0.}): + g = sns.FacetGrid(tmp_df, col=facet_col, row=facet_row, size=size, margin_titles=margin_titles, aspect=aspect, + **kwargs) + g.map(_cortical_image_plotter, 'pRF_centers_dim0', 'pRF_centers_dim1', plot_value, + vmin=min_val, vmax=max_val, cmap=cmap) + g.set(yticks=[], xticks=[]) + if not xlabels: + g.set_xlabels('') + if not ylabels: + g.set_ylabels('') + if set_minmax: + make_colorbar(g.fig, cmap, vmin=min_val, vmax=max_val) + for ax in g.fig.axes[:-1]: + ax.set_aspect('equal') + return g + +def plot_cortical_images_diff(plot_df, diff_vals, plot_restrictions={}, + plot_value='predicted_responses', facet_col=None, facet_row=None, + xlabels=False, ylabels=False, set_minmax=True, **kwargs): + """plot the difference between two specified categories of cortical images + + note that the two versions you want to plot have to have the same number of rows in the + dataframe (probably the same voxels, to be safe), so this will almost certainly fail if you're + trying to compare V1 and V2, for example. + + diff_vals: dictionary, should be of the form {'a': ['b', 'c']}, where a is a column in plot_df + and b and c are two values of that column. then we will plot the plot_value of b minus + the plot_value of c as a cortical image + + plot_value: string, optional. column in plot_df which contains the values to plot, + defaults to `predicted_responses` + + plot_restrictions: dictionary, optional. keys must be columns in plot_df, values are the + value(s) you want to plot + + facet_col, facet_row: strings, optional. how you want to split up the facetgrid + + xlabels, ylabels: boolean, optional. whether you want to include labels on x and y axes. False + by default + + set_minmax: boolean, optional. whether all images should have the same vmin and vmax values + (True) or whether they should be allowed to have separate vmin and vmax values + (False) + + **kwargs: will be passed to sns.FacetGrid +""" + tmp_df, facet_col, facet_row = _facet_plot_shape_df(plot_df, plot_restrictions, facet_col, + facet_row) + new_df = tmp_df[tmp_df[diff_vals.keys()[0]]==diff_vals.values()[0][0]] + new_df['diff'] =tmp_df[tmp_df[diff_vals.keys()[0]]==diff_vals.values()[0][0]][plot_value] - tmp_df[tmp_df[diff_vals.keys()[0]]==diff_vals.values()[0][1]][plot_value] + size = kwargs.pop('size', 2.3) + margin_titles = kwargs.pop('margin_titles', True) + aspect = kwargs.pop('aspect', 1.25) + if set_minmax: + min_val, max_val = new_df['diff'].min(), new_df['diff'].max() + else: + min_val, max_val = None, None + cmap = kwargs.pop('cmap', 'RdBu') + if min_val < 0 and max_val > 0: + norm = MidpointNormalize(min_val, max_val, 0.) + else: + norm = matplotlib.colors.Normalize(min_val, max_val) + with sns.axes_style('whitegrid', {'axes.grid': False, 'axes.linewidth':0.}): + g = sns.FacetGrid(new_df, col=facet_col, row=facet_row, size=size, margin_titles=margin_titles, aspect=aspect, + **kwargs) + g.map(_cortical_image_plotter, 'pRF_centers_dim0', 'pRF_centers_dim1', 'diff', + vmin=min_val, vmax=max_val, cmap=cmap, norm=norm) + g.set(yticks=[], xticks=[]) + if not xlabels: + g.set_xlabels('') + if not ylabels: + g.set_ylabels('') + if set_minmax: + make_colorbar(g.fig, cmap, vmin=min_val, vmax=max_val) + for ax in g.fig.axes[:-1]: + ax.set_aspect('equal') + return g + +def _hemi_check(hemi, mode='pandas'): + """make sure the hemi string is correctly formatted + + mode: {'pandas', 'surfer'}, determines whether our response should be 'R' / 'L' (pandas) or + 'rh' / 'lh' (surfer), based on the conventions of SCO's `model_df` and pysurfer, + respectively. + """ + try: + if mode=='pandas': + hemi_dict = {'R':'R', 'RH': 'R', 'L': 'L', 'LH': 'L'} + elif mode=='surfer': + hemi_dict = {'R':'rh', 'RH': 'rh', 'L': 'lh', 'LH': 'lh'} + return hemi_dict[hemi.upper()] + except KeyError: + raise Exception("Don't know what to do with hemi %s" % hemi) + +def _flat_cortex_pivot_table(hemi, mesh, model_df, df_to_pivot, piv_val='predicted_responses', + piv_idx=['model', 'image_type']): + """ + This is a separate function because we only need to run it once per hemisphere. + + hemi: the hemisphere to create the pivot table for, either 'L'/'LH'/'l'/'lh' or 'R'/'RH'/'r'/'rh' + + mesh: a neuropythy projected mesh, constructed by calling e.g., + subject.LH.projection(method='equirectangular') + + df_to_pivot: "long form" dataframe containing the values you want to map onto the cortex. Probably + plot_overall_df or SNR_overall_df + + piv_val: the name of the column in df_to_pivot that you want to use as values (and so will be plotted) + on the cortex + + piv_idx: str or list of strs, the name(s) of the column(s) in df_to_pivot you want to use as indexes in + the pivot table. These will probably be something like 'model' or 'image_type' + """ + hemi = _hemi_check(hemi) + hemi_df = model_df[model_df['pRF_vertex_indices'].isin(mesh.vertex_labels) & model_df['pRF_hemispheres'].isin([hemi])] + piv = df_to_pivot[df_to_pivot['voxel'].isin(hemi_df['voxel'])] + piv = piv.pivot_table(values=piv_val, columns='voxel', index=piv_idx, aggfunc=np.mean) + return hemi_df, piv + +def _flat_cortex_shape_data(mesh, piv, piv_idx_vals, hemi_df): + """ + based on export_predicted_response_surface, need to get this in right shape for plotting + + mesh: a neuropythy projected mesh, constructed by calling e.g., + subject.LH.projection(method='equirectangular') + + piv: a pivot table (as created by _flat_cortex_pivot_table) containing the values you want to + shape + + piv_idx_vals: str or tuple of strings, the specific values you want to grab from piv. + """ + preds = np.full((mesh.coordinates.shape[1]), 0., dtype=np.float32) + idx = np.array([np.where(mesh.vertex_labels==i)[0] for i in hemi_df['pRF_vertex_indices'].values]).flatten() + preds[idx] = piv.loc[piv_idx_vals].values + return preds + +class MidpointNormalize(matplotlib.colors.Normalize): + def __init__(self, vmin=None, vmax=None, midpoint=None, clip=False): + self.midpoint = midpoint + matplotlib.colors.Normalize.__init__(self, vmin, vmax, clip) + + def __call__(self, value, clip=None): + # I'm ignoring masked values and all kinds of edge cases to make a + # simple example... + x, y = [self.vmin, self.midpoint, self.vmax], [0, 0.5, 1] + return np.ma.masked_array(np.interp(value, x, y)) + +def _flat_cortex_make_cmap(mesh, plot_val, vmin=None, vmax=None, + v123_cmaps = {1: 'Reds', 2: 'Reds', 3: 'Reds'}): + + """This will only plot the corresponding values on V1, V2, and V3 (as specified by mesh property + + 'benson14_visual_area'). The other parts of the cortex will just show the sulci / gyri pattern. + + mesh: a neuropythy projected mesh, constructed by calling e.g., + subject.LH.projection(method='equirectangular') + + plot_val: numpy array or string. if an array: must be the same shape as mesh.coordinates for + the corresponding hemisphere, and contains the values you wish to make the colormap + for. If a string: the name of a neuropythy mesh property that you wish to make the + colormap for + + v123_cmaps: dictionary, optional. Keys must be 1, 2, and 3 and values are the colormaps to use + for V1, V2, and V3. by default, all three will be matplotlib.cm.Reds. + """ + + for k, v in v123_cmaps.iteritems(): + if isinstance(v, basestring): + v123_cmaps[k] = plt.get_cmap(v) + + def _get_color_vals(properties_dict): + if properties_dict['curvature'] > 0: + blend_col = np.asarray(sns.xkcd_palette(['dark grey'])[0]) + else: + blend_col = np.asarray(sns.xkcd_palette(['light grey'])[0]) + if properties_dict['benson14_visual_area'] == 0 or properties_dict[prop_name] == 0: + return np.concatenate((99*blend_col/100, [.1])) + else: + return np.asarray(v123_cmaps[properties_dict['benson14_visual_area']](norm(properties_dict[prop_name]))) + + if isinstance(plot_val, basestring): + if vmin is None: + vmin = float(mesh.prop(plot_val).min()) + if vmax is None: + vmax = float(mesh.prop(plot_val).max()) + nuniq = pd.Series(mesh.prop(plot_val)).nunique() + merge_dict = {} + prop_name = plot_val + else: + if vmin is None: + vmin = float(plot_val.min()) + if vmax is None: + vmax = float(plot_val.max()) + nuniq = pd.Series(plot_val).nunique() + merge_dict = {'summary_vals': plot_val} + prop_name = 'summary_vals' + + if vmin < 0 and vmax > 0: + norm = MidpointNormalize(vmin, vmax, 0.) + else: + norm = matplotlib.colors.Normalize(vmin, vmax) + + map_colors = np.asarray(mesh.map_vertices(_get_color_vals, merge=merge_dict)) + cmap=matplotlib.colors.ListedColormap(map_colors) + + return map_colors, cmap, nuniq, v123_cmaps[1] + +def flat_cortex_plotter(x, y, color_idx, hemispheres, cmaps, **kwargs): + + l_idx = hemispheres == 'L' + r_idx = hemispheres == 'R' + + # we use these x_mod, y_mod to consistently plot these patches so they are oriented in the same way + # as the pysurfer cortex plots + xmax = None + if l_idx.any() and r_idx.any(): + x_shift = x[l_idx].max() + abs(x[l_idx].min()) + x_shift = x_shift + x_shift/10 + r_cmap = cmaps[r_idx].unique() + if len(r_cmap) > 1: + raise Exception('Too many cmaps! Probably faceted wrong...') + plt.tripcolor(matplotlib.tri.Triangulation(-1*x[r_idx]+x_shift, -1*y[r_idx]), color_idx[r_idx], cmap=r_cmap[0], + shading='gouraud') + xmax = x_shift + x[r_idx].max() + x_shift/10 + idx = l_idx + x_mod, y_mod = 1, 1 + elif r_idx.any(): + idx = r_idx + x_mod, y_mod = -1, -1 + elif l_idx.any(): + idx = l_idx + x_mod, y_mod = 1, 1 + else: + raise Exception('Nothing to plot!') + + cmap = cmaps[idx].unique() + if len(cmap) > 1: + raise Exception('Too many cmaps! Probably faceted wrong...') + plt.tripcolor(matplotlib.tri.Triangulation(x_mod*x[idx], y_mod*y[idx]), color_idx[idx], cmap=cmap[0], + shading='gouraud') + if xmax is None: + xmax = x[idx].max() + (x[idx].max() + abs(x[idx].min()))/10 + xmin = x[idx].min() - (x[idx].max() + abs(x[idx].min()))/10 + + ax = plt.gca() + ax.get_xaxis().set_visible(False) + ax.get_yaxis().set_visible(False) + ax.set_xlim((xmin, xmax)) + +def make_colorbar(fig, cbar_cmap, nuniq=None, vmin=None, vmax=None, plot_vals=None): + """ + either plot_vals or all of nuniq, vmin, vmax must be set + """ + if plot_vals is not None: + vmin= plot_vals.min() + vmax = plot_vals.max() + nuniq = pd.Series(plot_vals).nunique() + + if vmin < 0 and vmax > 0: + norm = MidpointNormalize(vmin, vmax, 0.) + else: + norm = matplotlib.colors.Normalize(vmin, vmax) + + cax, _ = matplotlib.colorbar.make_axes(fig.axes, shrink=.5) + cbar = matplotlib.colorbar.ColorbarBase(ax=cax,cmap=cbar_cmap, norm=norm) + + if nuniq is not None and nuniq < 10: + ticker = matplotlib.ticker.LinearLocator(numticks=nuniq) + elif fig.get_figheight() < 4: + ticker = matplotlib.ticker.MaxNLocator(nbins=5) + else: + ticker = None + + if ticker is not None: + cbar.locator = ticker + cbar.update_ticks() + + return cbar + +def plot_flat_cortex(model_df, meshes, data_df=None, plot_val='predicted_responses', hemi='both', + facet_col='image_type', facet_row='model', facet_col_vals=None, + facet_row_vals=None, set_minmax=True, + v123_cmaps= {1: 'Reds', 2: 'Reds', 3: 'Reds'}, **kwargs): + """Plot the specified property on a flat cortex, averaging across specified categories + + This function plots the property specified by `plot_val` on a flat cortex. `plot_val` can come + from either `data_df`, in which case it must be the name of one of the columns (e.g., + 'predicted_responses'), or from `meshes`, in which case it must be the name of a property of + the mesh (e.g., 'benson14_visual_area'). + + If `plot_val` is from the mesh, then only one subplot will be made. Otherwise, we will create a + seaborn FacetGrid, facetting along `facet_col` and `facet_row` (both of which must be columns + of `data_df`), with one subplot for every combination of the values of `facet_col` and + `facet_row` (e.g., if each have two values, four subplots are made). Within each subplot, each + voxel/vertex will have all of its `plot_val` values averaged together. + + For example, with the following options: `facet_col='image_type'`, `facet_row='model'`, + `plot_val='predicted_responses'`, let the unique values of `'image_type'` in `data_df` be + `'original'` and `'V1-metamer'` and the unique values of `'model'` be `'full'` and + `'dumb_V1'`. Then the first plot will show the predicted response of each voxel in the full + model, averaged together across all original images (and so on). + + If you don't wish to plot all values of `facet_col` and `facet_row`, use `facet_col_vals` and + facet_row_vals` to specifiy a subset. + + To change the colormaps used, set `v123_cmaps`. + + Arguments + ============== + + model_df: dataframe summarizing model performance. must contain pRF_vertex_indices and + pRF_hemispheres + + meshes: a dictionary of neuropythy projected meshes. Keys should be 'L' and/or 'R'. constructed + by calling e.g., subject.LH.projection(method='equirectangular') + + data_df: "long form" dataframe, optional. contains the values you want to project on the cortex + (e.g., SNR_df, plot_overall_df). If None, it's assumed that `plot_val` is a property + mesh and only one subplot will be made (thus, `facet_col` and `facet_row` will be + ignored) + + plot_val: str, optional. Default: 'predicted_responses'. what you want to plot. Must either be + a column in `data_df` or a property of the neuropythy mesh. + + hemi: string, optional. the hemisphere to plot, either 'L'/'LH'/'l'/'lh' or 'R'/'RH'/'r'/'rh' + or 'both' (default). if 'both', then both will be in the same subplot + + facet_col: str, optional. Default: 'model'. name of the column you want to display as the + column of the pivot table. Must be a column in `data_df`. Ignored if `plot_val` is a + property of the mesh + + facet_row: str, optional. Default: 'image_type'. name of the column you want to display as the + row of the pivot table. Must be a column in `data_df`. Ignored if `plot_val` is a + property of the mesh + + facet_col_vals: str or list of strs, optional. subset of facet_col that you want to visualize + on your columns. Probably values of the model type ('full' and 'dumb_V1'). If + None (default), will use all + + facet_row_vals: str or list of strs, optional. subset of facet_row that you want to visualize + on your rows. Probably values of the image type (e.g., 'original', + 'V1-metamer', etc). If None (default), will use all + + set_minmax: boolean, optional. whether all images should have the same vmin and vmax values + (True) or whether they should be allowed to have separate vmin and vmax values + (False) + + v123_cmaps: dictionary or matplotlib colormap, optional. If a dictionary, keys must be 1, 2, + and 3 and values are the colormaps to use for V1, V2, and V3. by default, all + three will be matplotlib.cm.Reds. If a colormap, same colormap will be used for all + three areas. + + col/row_order can be specified in **kwargs + """ + + if hemi=='both': + hemi = ['L', 'R'] + else: + hemi = [_hemi_check(hemi)] + + if not isinstance(v123_cmaps, dict): + v123_cmaps = dict((i, v123_cmaps) for i in range(1,4)) + + if data_df is not None and facet_col_vals is None: + facet_col_vals = data_df[facet_col].unique() + if isinstance(facet_col_vals, basestring): + facet_col_vals = [facet_col_vals] + + if data_df is not None and facet_row_vals is None: + facet_row_vals = data_df[facet_row].unique() + if isinstance(facet_row_vals, basestring): + facet_row_vals = [facet_row_vals] + + vmin, vmax, nuniq, hemi_dfs, pivs = 0, 0, 0, {}, {} + if data_df is None or plot_val not in data_df.columns: + try: + for h in hemi: + vmin = min(vmin, meshes[h].prop(plot_val).min()) + vmax = max(vmax, meshes[h].prop(plot_val).max()) + except AttributeError: + raise Exception("Neither your mesh nor your data_df contains plot_val %s!" % plot_val) + if set_minmax: + vmin, vmax = float(vmin), float(vmax) + else: + vmin, vmax = None, None + flat_cort_df = [] + for h in hemi: + map_colors, cmap, nuniq_tmp, cbar_cmap = _flat_cortex_make_cmap(meshes[h], plot_val, + vmin=vmin, vmax=vmax, + v123_cmaps= v123_cmaps) + nuniq = max(nuniq, nuniq_tmp) + flat_cort_df.append(pd.DataFrame(data={'hemi': h, 'color_map':cmap, + 'x_coords': meshes[h].coordinates[0,:], + 'y_coords': meshes[h].coordinates[1,:]})) + flat_cort_df = pd.concat(flat_cort_df) + flat_cort_df = flat_cort_df.reset_index().rename(columns={'index':'color_idx'}) + g = sns.FacetGrid(flat_cort_df, aspect=len(hemi)) + g.map(flat_cortex_plotter, 'x_coords', 'y_coords', 'color_idx', 'hemi', 'color_map') + if set_minmax: + make_colorbar(g.fig, cbar_cmap, nuniq, vmin, vmax) + return g, flat_cort_df + else: + # we do this first, because it will be the same for all facet_col, facet_row pairs + for h in hemi: + hemi_dfs[h], pivs[h] = _flat_cortex_pivot_table(h, meshes[h], model_df, data_df, + plot_val, [facet_col, facet_row]) + # that obnoxious second term means that we grab the entries in the corresponding pivot + # table that we're looking at in this call (the appropriate model, image_type pairs) + # and determine what their minimum values are (same for vmax) + vmin = min(vmin, pivs[h][pivs[h].index.isin([i for i in itertools.product(facet_col_vals, facet_row_vals)])].min().min()) + vmax = max(vmax, pivs[h][pivs[h].index.isin([i for i in itertools.product(facet_col_vals, facet_row_vals)])].max().max()) + if set_minmax: + vmin, vmax = float(vmin), float(vmax) + else: + vmin, vmax = None, None + + flat_cort_df = [] + for h, c, r in itertools.product(hemi, facet_col_vals, facet_row_vals): + summary_vals = _flat_cortex_shape_data(meshes[h], pivs[h], (c, r), hemi_dfs[h]) + map_colors, cmap, nuniq_tmp, cbar_cmap = _flat_cortex_make_cmap(meshes[h], summary_vals, + vmin=vmin, vmax=vmax, + v123_cmaps= v123_cmaps) + nuniq = max(nuniq, nuniq_tmp) + flat_cort_df.append(pd.DataFrame(data={'hemi': h, 'facet_col': c, 'facet_row': r, 'summary_vals': summary_vals, + 'color_map':cmap, 'x_coords': meshes[h].coordinates[0,:], + 'y_coords': meshes[h].coordinates[1,:]})) + flat_cort_df = pd.concat(flat_cort_df) + flat_cort_df = flat_cort_df.reset_index().rename(columns={'index':'color_idx'}) + g = sns.FacetGrid(flat_cort_df, col='facet_col', row='facet_row', aspect=len(hemi), **kwargs) + g.map(flat_cortex_plotter, 'x_coords', 'y_coords', 'color_idx', 'hemi', 'color_map') + g.set_titles("{col_name} | {row_name}") + if set_minmax: + make_colorbar(g.fig, cbar_cmap, nuniq, vmin, vmax) + return g, flat_cort_df + +def _pysurfer_shape_predictions(hemi, model, image_type, model_df, plot_df, subject): + """based on export_predicted_response_surface, need to get data in right shape for pysurfer + + hemi: the hemisphere to plot, either 'L'/'LH'/'l'/'lh' or 'R'/'RH'/'r'/'rh' + + model: the model you want to visualize predictions for, must be one of the model values in + plot_df (probably 'full' and 'dumb_V1') + + image_type: image type to visualize predictions for, must be one of the image_type values in + plot_df (probably, 'original', 'V1-metamer', etc) + """ + hemi = _hemi_check(hemi) + hemi_df = model_df[model_df.pRF_hemispheres.isin([hemi])] + piv = plot_df[plot_df['voxel'].isin(hemi_df['voxel'])] + piv = piv.pivot_table(values='predicted_responses', columns='voxel', index=['model', 'image_type'], aggfunc=np.mean) + if hemi=='L': + preds = np.full((subject.LH.vertex_count), 0., dtype=np.float32) + elif hemi=='R': + preds = np.full((subject.RH.vertex_count), 0., dtype=np.float32) + pidcs = hemi_df['pRF_vertex_indices'].values + preds[pidcs] = piv.loc[model, image_type].values + return preds, pidcs + +def pysurfer_plot_predictions(subject_id, hemi, model, image_type, model_df, plot_df, + mode='data', save_path='brain.png', cmap='Reds', subjects_dir=None, + **kwargs): + """plot average predictions on pysurfer brain and save to image + + this is a little limited, since I don't think it's as useful a visualization. but it will + average across all predicted responses for the specified model and image (similar to + `plot_flat_cortex`), allowing you to visualize how the predicted responses are distributed + across the visual cortex. + + this takes a bit of time to run. + + This requires the optional library pysurfer to run. + + KNOWN BUG: this function may not properly save the image by itself. If this happens (the image + will be saved but will not show the overlaid brain), call `brain.save_image(path)` with the + returned Brain object. + + hemi: the hemisphere to plot, either 'L'/'LH'/'l'/'lh' or 'R'/'RH'/'r'/'rh' + + model: the model you want to visualize predictions for, must be one of the model values in + plot_df (probably 'full', 'dumb_V1', etc) + + image_type: image type to visualize predictions for, must be one of the image_type values in + plot_df (probably, 'original', 'V1-metamer', etc) + + mode: {'data', 'overlay'}, whether to use surfer.Brain.add_data or surfer.Brain.add_overlay to + add the data to the Brain object. 'data' allows a colormap to be specified, while 'overlay' + does not, but 'overlay' will only show relevant values on the brain, plotting all vertices + without values as transparent, while 'data' will show them as the minimum value on the + colormap, which is distracting. + + returns `brain`, the pysurfer Brain with data projected on top. It is recommended that you + close the object (call `brain.close()`) when you have finished with it + """ + hemi = _hemi_check(hemi, 'surfer') + view_dict = {'lh': {'azimuth': 290., 'elevation': 120., 'roll': 0.}, + 'rh': {'azimuth': 260., 'elevation': 120., 'roll': 0.}} + subject = neuropythy.freesurfer_subject(subject_id) + preds, pidcs = _pysurfer_shape_predictions(hemi, model, image_type, model_df, plot_df, subject) + brain = Brain(subject_id, hemi, 'inflated', views=view_dict[hemi], subjects_dir=subjects_dir) + vmin = kwargs.pop('min', .0001) + if mode=='data': + brain.add_data(preds, min=vmin, max=preds.max(), colormap=cmap, vertices=pidcs, **kwargs) + elif mode=='overlay': + brain.add_overlay(preds, min=vmin, max=preds.max(), **kwargs) + else: + raise Exception("Don't know how to add data to Brain with mode %s!" % mode) + brain.save_image(save_path) + return brain diff --git a/sco/util/__init__.py b/sco/util/__init__.py index 361c270..7202141 100644 --- a/sco/util/__init__.py +++ b/sco/util/__init__.py @@ -3,101 +3,5 @@ # Utilities useful in work related to the sco predictions. # By Noah C. Benson -import numpy as np -import matplotlib -import matplotlib.pyplot as plt -import matplotlib.tri as tri - -def cortical_image(datapool, visual_area=1, image_number=None, image_size=200, n_stds=1, - size_gain=1, method='triangulation', subplot=None): - ''' - cortical_image(datapool) yields an array of figures that reconstruct the cortical images for the - given sco results datapool. The image is always sized such that the width and height span - 2 * max_eccentricity degrees (where max_eccentricity is stored in the datapool). The following - options may be given: - * visual_area (default: 1) must be 1, 2, or 3 and specifies whether to construct the cortical - image according to V1, V2, or V3. - * image_number (default: None) specifies which image to construct a cortical image of. If this - is given as None, then a list of all images in the datapool is returned; otherwise only the - figure for the image number specified is returned. - * image_size (default: 200) specifies the width and height of the image in pixels (only for - method equal to 'pRF_projection'. - * n_stds (default: 1) specifies how many standard deviations should be used with each pRF when - projecting the values from the cortical surface back into the image. - * size_gain (default: 1) specifies a number to multiply the pRF size by when projecting into - the image. - * method (default: 'triangulation') should be either 'triangulation' or 'pRF_projection'. The - former makes the plot by constructing the triangulation of the pRF centers and filling the - triangles in while the latter creates an image matrix and projects the pRF predicted - responses into the relevant pRFs. - ''' - # if not given an image number, we want to iterate through all images: - if image_number is None: - return np.asarray([cortical_image(datapool, visual_area=visual_area, image_number=ii, - image_size=image_size, n_stds=n_stds, method=method, - subplot=subplot) - for ii in range(len(datapool['stimulus_images']))]) - if subplot is None: - plotter = plt - fig = plotter.figure() - else: - plotter = subplot - fig = subplot - if method == 'triangulation': - # deal with pyplot's interactive mode - maxecc = float(datapool['max_eccentricity'][image_number]) - labs = datapool['pRF_v123_labels'] - (x,y) = datapool['pRF_centers'].T - z = datapool['predicted_responses'][image_number] - - (x,y,z) = np.transpose([(xx,yy,zz) - for (xx,yy,zz,l) in zip(x,y,z,labs) - if l == visual_area]) - - plotter.tripcolor(tri.Triangulation(x,y), z, cmap='jet', shading='gouraud') - plotter.axis('equal') - plotter.axis('off') - return fig - - elif method == 'pRF_projection': - # otherwise, we operate on a single image: - r = datapool['predicted_responses'] - maxecc = float(datapool['max_eccentricity'][image_number]) - sigs = datapool['pRF_sizes'] - labs = datapool['pRF_v123_labels'] - - (x,y) = datapool['pRF_centers'].T - z = r[image_number] - - (x,y,z,sigs) = np.transpose([(xx,yy,zz,ss) - for (xx,yy,zz,ss,l) in zip(x,y,z,sigs,labs) - if l == visual_area]) - - img = np.zeros((image_size, image_size, 2)) - img_center = (float(image_size)*0.5, float(image_size)*0.5) - img_scale = (img_center[0]/maxecc, img_center[1]/maxecc) - for (xx,yy,zz,ss) in zip(x,y,z,sigs): - ss = ss * img_scale[0] * size_gain - exp_const = -0.5/(ss * ss) - row = yy*img_scale[0] + img_center[0] - col = xx*img_scale[1] + img_center[1] - if row < 0 or col < 0 or row >= image_size or col >= image_size: continue - r0 = max([0, int(round(row - ss))]) - rr = min([image_size, int(round(row + ss))]) - c0 = max([0, int(round(col - ss))]) - cc = min([image_size, int(round(col + ss))]) - (mesh_xs, mesh_ys) = np.meshgrid(np.asarray(range(c0,cc), dtype=np.float) - col, - np.asarray(range(r0,rr), dtype=np.float) - row) - gaus = np.exp(exp_const * (mesh_xs**2 + mesh_ys**2)) - img[r0:rr, c0:cc, 0] += zz - img[r0:rr, c0:cc, 1] += gaus - img = np.flipud(img[:,:,0] / (img[:,:,1] + (1.0 - img[:,:,1].astype(bool)))) - fig = plotter.figure() - plotter.imshow(img) - plotter.axis('equal') - plotter.axis('off') - return fig - else: - raise ValueError('unrecognized method: %s' % method) - - +import metamers +from core import (create_SNR_df, save_results_dict) diff --git a/sco/util/core.py b/sco/util/core.py new file mode 100644 index 0000000..30cec59 --- /dev/null +++ b/sco/util/core.py @@ -0,0 +1,115 @@ +#################################################################################################### +# sco/util/core.py +# Utilities useful in work related to the sco predictions. +# By William F Broderick + +import itertools +import pickle +import neuropythy +import pandas as pd + +def _bootstrap_voxel_df(df, extra_unique_cols=['image_name']): + """bootstrap the rows of `df` + """ + try: + df = df.groupby(['voxel']+extra_unique_cols).mean().reset_index() + except TypeError: + df = df.groupby(['voxel']+[extra_unique_cols]).mean().reset_index() + sample_num = df[df.voxel == 0].shape[0] + boot_idx = df[df.voxel == 0].reset_index().sample(sample_num, replace=True).index + gb = df.groupby('voxel') + resampled_df = gb.apply(lambda g: g.iloc[boot_idx]) + return resampled_df + +def _create_bootstrap_df(bootstrap_num, plot_df, bootstrap_val, diff_col, extra_unique_cols): + """bootstrap `bootstrap_val` `bootstrap_num` times, getting the difference between each pair of + `diff_col` values + """ + bootstrap_df = [] + df_dicts = dict((k, plot_df[plot_df[diff_col] == k]) for k in plot_df[diff_col].unique()) + for i in range(bootstrap_num): + bootstrapped_dfs_dict = dict((k, _bootstrap_voxel_df(df_dicts[k], extra_unique_cols).set_index(['voxel', 'image_name'])) for k in df_dicts.keys()) + + tmp_dfs = [] + for name1, name2 in itertools.combinations(bootstrapped_dfs_dict.keys(), 2): + tmp_df = bootstrapped_dfs_dict[name1][[bootstrap_val]].mean(level=(0)) - bootstrapped_dfs_dict[name2][[bootstrap_val]].mean(level=(0)) + tmp_df = tmp_df.rename(columns={bootstrap_val: '%s - %s' % (name1, name2)}) + tmp_dfs.append(tmp_df) + + tmp_dfs = pd.concat(tmp_dfs, 1).reset_index() + tmp_dfs['bootstrap_num'] = i + bootstrap_df.append(tmp_dfs) + + return pd.concat(bootstrap_df).rename(columns=lambda x: x.replace('-metamer', '')) + +def create_SNR_df(plot_df, bootstrap_val='predicted_responses', diff_col='image_type', + extra_unique_cols=['image_name'], bootstrap_num=100, file_name='SNR_df.csv', + extra_cols=[]): + """Create a dataframe with bootstrapped signal-to-noise ratio values. + + the signal-to-noise ratio is a measure of the difference in a given value between + conditions. `plot_df` will be split into the unique values of `diff_col`, then averaged so + every combination of voxels and values of `extra_unique_cols` have one row, then bootstrapped, + taking the difference in the averages of `bootstrap_val` between each pair of `diff_col` + values. This will be done `bootstrap_num` times, then the average over the standard deviation + of these `bootstrap_num` values will be recorded as the SNR. Each voxel will therefore have n + choose 2 values, where `n = plot_df[diff_col].nunique()`, one for each pair of `diff_col` + values. + + This will also save the returned `SNR_df` at `file_name`, since this takes a while to run. + + extra_cols is a list of strings corresponding to columns in plot_df that you would also like + to add to the SNR_df + """ + bootstrap_df = _create_bootstrap_df(bootstrap_num, plot_df, bootstrap_val, diff_col, extra_unique_cols) + + gb = bootstrap_df.groupby('voxel') + + SNR_df = plot_df.drop_duplicates(subset='voxel')[['v123_label']+extra_cols] + SNR_df.index.name = 'voxel' + for col in [col for col in bootstrap_df.columns if '-' in col]: + SNR_df[col] = gb.apply(lambda g: g[[col]].mean() / g[[col]].std()) + + SNR_df = pd.melt(SNR_df.reset_index(), id_vars=['voxel', 'v123_label']+extra_cols) + + SNR_df.to_csv(file_name, index=False) + + return SNR_df + +def save_results_dict(results, file_name='results.pkl'): + """we want to save the results dictionary but we can't pickle functions. We need to do this + + because our results dict contains both functions and arrays of functions. So we go through and + save those entries in our dictionary that are not callable. + """ + save_results = dict() + for k, v in results.iteritems(): + # we can't pickle the free surfer subject + if isinstance(v, neuropythy.freesurfer.subject.Subject): + continue + # we can't pickle functions or lambdas + if _check_uncallable(v): + save_results[k] = v + with open(file_name, 'w') as f: + pickle.dump(save_results, f) + +def _check_uncallable(x): + """if this is an iterable, we want to call this again (we don't know the structure of this + iterable, so x[0] might be another iterable). + """ + if hasattr(x, '__iter__'): + # we can have an array that just contains None, in which case v[0] will throw an + # exception + try: + return _check_uncallable(x[0]) + except IndexError: + return False + # if this is a dictionary, it may return a keyerror, in which case we want to check the + # first value. + except KeyError: + return _check_uncallable(x.values()[0]) + else: + if not hasattr(x, '__call__'): + return True + else: + return False diff --git a/sco/util/metamers.py b/sco/util/metamers.py new file mode 100644 index 0000000..171bb96 --- /dev/null +++ b/sco/util/metamers.py @@ -0,0 +1,143 @@ +# metamers.py +# +# code specifically for evaluating SCO model performance on "texture metamers", as discussed in +# Freeman2013 and Portilla2000. See Metamers.ipynb for more information. +# +# References: +# - Freeman, J., Ziemba, C. M., Heeger, D. J., Simoncelli, E. P., & Movshon, J. A. (2013). A +# functional and perceptual signature of the second visual area in primates. Nature Neuroscience. +# - Portilla, J., & Simoncelli, E. P. (2000). A parametric texture model based on joint statistics of +# complex wavelet coefficients. International journal of computer vision. +# +# by William F. Broderick + +import re +import os +import neuropythy +import pickle +import pandas as pd +from .core import save_results_dict, create_SNR_df +from ..model_comparison import create_model_dataframe, _create_plot_df +from .. import calc_surface_sco + +def _search_for_label(x, regex, none_val): + """search filename x for label using regex, returning none_val if nothing is found + """ + tmp = re.search(regex, x) + if tmp is None: + return none_val + else: + return tmp.groups()[0] + +def create_met_df(df, col_name='image', type_regex=r'(V[12]Met(Scaled)?).*', + seed_regex=r'im[0-9]+.*-([0-9]+).*png', name_regex=r'(im[0-9]+).*png'): + """for each image, determine its type, name, and seed + + These are all based on regex matching with the image name, so the assumption is that images + have been named in a straightforward manner. If you used createMetamers.m / Makefile to create + your stimuli, then the default values should work. Otherwise, you'll have to set them by + hand. + """ + if 'language' in df.columns: + df = df[df.language=='python'] + df['image_type'] = df[col_name].apply(_search_for_label, args=(type_regex, 'original')) + df['image_type'] = df['image_type'].apply(lambda x: x.replace('MetScaled', 'SclMet').replace('Met', '-metamer')) + df['image_name'] = df[col_name].apply(_search_for_label, args=(name_regex, None)) + df['image_seed'] = df[col_name].apply(_search_for_label, args=(seed_regex, None)) + return df + +def create_image_struct(results, regex_dict={'image_type': (r'(V[12]Met(Scaled)?).*', 'original'), + 'image_seed': (r'im[0-9]+.*-([0-9]+).*png', '1'), + 'image_name': (r'(im[0-9]+).*png', None)}, + replace_dict={'image_type': [('MetScaled', 'SclMet'), ('Met', '-metamer')]}): + """given the results dict, return all the images with labels (for easy facetting) + + This returns a dictionary with the following keys: + + - stimulus_images: the images array from the results dict + + - normalized_stimulus_images: the normalized images array from the results dict + + - labels: a pandas DataFrame containing labels of the images. The main two columns are filenames + and image_index. filenames is the filename of the image, without any folders and + image_index is the appropriate index into stimulus_images and normalized_stimulus_images + to find the corresponding image. It will also contain a column for each entry in `regex_dict`, + storing the result of searching filenames for the value in a column labeled with the key. + + this plays well with plot_images, allowing one to easily find and plot images in an easy-to-parse + way + + each entry in regex_dict should have two values: the first a regex to search filenames with, the second + the value that should be inserted in case the regex comes up empty + + replace_dict allows one to optionally replace values from one column. After a column is created, if its + name shows up as a key in replace_dict, we will call label_df[key].apply(lambda x: x.replace(*replace_dict[key])) + (so the value should be a tuple or a list of tuples, in which case each is called one in turn) + """ + filenames = [os.path.split(name)[1] for name in results['stimulus_image_filenames']] + label_df = pd.DataFrame({'filenames': filenames}) + for col, (regex, none_val) in regex_dict.iteritems(): + label_df[col] = label_df['filenames'].apply(_search_for_label, args=(regex, none_val)) + if col in replace_dict: + if hasattr(replace_dict[col][0], '__iter__'): + for v in replace_dict[col]: + label_df[col] = label_df[col].apply(lambda x: x.replace(*v)) + else: + label_df[col] = label_df[col].apply(lambda x: x.replace(*replace_dict[col])) + label_df = label_df.reset_index().rename(columns={'index': 'image_idx'}) + images = {'labels': label_df, 'stimulus_images': results['stimulus_images']} + if 'normalized_stimulus_images' in results.keys(): + images['normalized_stimulus_images'] = results['normalized_stimulus_images'] + return images + +def main(images, output_dir, model_name='full', model_steps=['results', 'model_df', 'SNR_df'], + **kwargs): + """Run the SCO model on metamers + + NOTE that you will likely need to set type_regex, seed_regex, and name_regex so they correctly + parse the names of your files. The defaults will work if your original files are named + 'im##.png', where ## can be any number of digits, and you used createMetamers.m to create your + metamers. This results in metamers being called V1Met-im##-#.png, V2Met-im##-#.png, and + V2MetScaled-img##-#.png, where the final -# specifies the random seed. If your filenames are + formatted differently, you'll need to set these regex expression by hand (can be done + SCO_KWARGS or if main block in cluster_submit.py). + + Three outputs are created as part of this call: results, model_df, and SNR_df. Each requires + the previous one, but they can be created in separate calls (loading in the previous ones). To + specify the ones you wish to run in this call, use model_steps + """ + if 'subject_path' in kwargs: + neuropythy.freesurfer.add_subject_path(os.path.expanduser(kwargs.pop('subject_path'))) + if isinstance(model_steps, basestring): + model_steps = [model_steps] + subject = kwargs.get('subject', 'test-sub') + max_eccentricity = kwargs.get('max_eccentricity', 7.5) + bootstrap_num = kwargs.get('bootstrap_num', 1000) + type_regex = kwargs.get('type_regex', r'(V[12]Met(Scaled)?).*') + seed_regex = kwargs.get('seed_regex', r'im[0-9]+.*-([0-9]+).*png') + name_regex = kwargs.get('name_regex', r'(im[0-9]+).*png') + print("Running %s" % model_name) + if 'results' in model_steps: + results = calc_surface_sco(subject=subject, stimulus_image_filenames=images, + max_eccentricity=max_eccentricity, **kwargs) + save_results_dict(results, '%s/results_%s.pkl' % (output_dir, model_name)) + else: + with open('%s/results_%s.pkl' % (output_dir, model_name)) as f: + results = pickle.load(f) + if 'model_df' in model_steps: + images = [os.path.split(i)[1] for i in images] + model_df = create_model_dataframe(results, images, '%s/model_df_%s.csv' % (output_dir, model_name)) + else: + model_df = pd.read_csv('%s/model_df_%s.csv' % (output_dir, model_name)) + # extra_cols is a list of strings corresponding to columns in model_df that you would also like + # to add to the plot_df + plot_df = _create_plot_df(model_df, extra_cols=['Kay2013_output_nonlinearity', 'Kay2013_SOC_constant']) + plot_df = create_met_df(plot_df, type_regex=type_regex, seed_regex=seed_regex, + name_regex=name_regex) + if 'SNR_df' in model_steps: + SNR_df = create_SNR_df(plot_df, bootstrap_num=bootstrap_num, + file_name='%s/SNR_df_%s.csv' % (output_dir, model_name), + extra_cols=['Kay2013_output_nonlinearity', 'Kay2013_SOC_constant']) + else: + SNR_df = pd.read_csv('%s/SNR_df_%s.csv' % (output_dir, model_name)) + return results, model_df, plot_df, SNR_df