updates to repo

README.md

@@ -1,57 +1,126 @@
 # Overview
-This repository contains all raw data (spike times), and modelling code for the manuscript *"Macaque ganglion cells responses to natural scenes: time, not space is what they really care about"*
+This repository contains all raw data and modelling code for the manuscript *"Macaque ganglion cells responses to natural scenes: time, not space is what they really care about"*
 
-For further inquiries, don't hesitate to get in touch with us
-
-Barry Lee <blee@sunyopt.edu>
+For further inquiries, don't hesitate to get in touch with us<br/>
+Barry Lee <blee@sunyopt.edu><br/>
 Manuel Schottdorf <mschottdorf@princeton.edu>
 
-Funding for the project is through HHS, NIH, and the National Eye Institute EY13110
+Funding for the project: HHS, NIH, and the National Eye Institute via EY13110
 
 # Organization of the repository
 The repository is organized into five folders:
 
-- `\data\` contains the raw data of individual cells, organized into plain-text tables of spike-times.
+- `\data\` contains the raw data of individual cells, organized into plain-text tables of spike-times, together with some meta information.
 
 - `\stimuli\` contains the visual stimuli, and details for calibration, such as gamma correction and gun spectra.
 
 - `\retinatools\` contains the python-code of the model. It is used as a python-library in the following two folders:
 
-- `\run_model\` Contains jupyter-notebooks to to run the model, and reproduce the model-figures from the paper. This can be run on any laptop without problems. It also contains a subset of the data, obtained by averaging across the 6 1min repeats, for measuring model performance.
+- `\run_model\` Contains jupyter-notebooks to run the model, and reproduce the model-figures from the paper. This can be run on a laptop. This folder also contains a subset of the data (N=15), obtained from the spiking raw data by smoothing with first order kernels of 2 ms or 4 ms width (depending on cell type, see paper) and averaged across the 6 x 1min repeats, for measuring model performance.
 
-- `\run_model_HPC\` Contains the code to run the RGC model for whole arrays of cells, and was used to compute the supplemental movie (REMOVE BRODY).
+- `\run_model_HPC\` Contains the code to run the models on large arrays of cells, and was used to compute the supplemental movie. This requires ipyparallel and the slurm engine.
 
-All code was tested on mac os 11 with Python 3, numpy, scipy and cv2. 
+- All code was verified to run on macOS 11 "Bug Sur" from jupyter notebooks (6.1.5) with:
+  - Python 3.9.1
+  - Numpy 1.19.4
+  - Scipy 1.5.4
+  - cv2 4.5.0
 
 # Details of the data files.
 
 ## Organization of spiking data
-The time resolution on these data is 0.1 msec. 10 min files. These ASCII files are records of spike occurrence. Total spikes are given and then the video start time (an internal control; it varies from cell to cell).  The spikes/5 sec column are a check on firing rates through the video. In 0 are the total spikes before the video starts; time period is variable. The following 120 bins are spike counts in subsequent 5 sec epochs. Finally, bin 121 is spike count following the live video termination before recording was switched off. The spike time list that follows is referred to the begin of the live video (and have been corrected for a slight difference in clock rate between video and data acquisition computers). 6x1min files Format is similar. The first 1 min of the 10 min video is repeated 6 times. There is a variable delay (blank frames) before the beginning of the first live video repeat. The each live repeat is preceded by 5 sec of blank frames, and there are ca. 5 sec of blank frames after termination before the next repeat starts. After completion of the 6 repeats there is a further period of maintained activity (column 7).
+There are two types of files for the 6x1min and 10min records. Both contain ASCII records of spike occurrence, and some meta information. The time resolution on all data is 0.1 msec. 
+
+* 10 min files. Total spikes are given and then the video start time (an internal control; it varies from cell to cell).  The spikes/5 sec column are a check on firing rates through the video. In 0 are the total spikes before the video starts; time period is variable. The following 120 bins are spike counts in subsequent 5 sec epochs. Finally, bin 121 is spike count following the live video termination before recording was switched off. The spike time list that follows is referred to the begin of the live video (and have been corrected for a slight difference in clock rate between video and data acquisition computers).
+
+* 6 x 1min files: Format is similar. The first 1 min of the 10 min video is repeated 6 times. There is a variable delay (blank frames) before the beginning of the first live video repeat. The each live repeat is preceded by 5 sec of blank frames, and there are ca. 5 sec of blank frames after termination before the next repeat starts. After completion of the 6 repeats there is a further period of maintained activity (column 7).
 
 ## Cell List
 
-| File name     |  Cell Type      |  Record Duration | Cell Key     |
-|---------------|-----------------|-----------|--------|
-| Lss01071.txt	|     -M+L off    |   10 min  |	67#4   |
-| Lss01078.txt	|       MC off    |   6x1min  |	67#6   |
-| Lss01181.txt	|       MC off    |   6x1min  |	68#3   |
-| Lss01221.txt	|       MC on	  |   10 min  |	68#10  |
+| File name     |  Cell Type      |  Record Duration |   Cell Key   |
+|---------------|-----------------|------------------|--------------|
+| Lss01071.txt	|     -M+L off    |   10 min         |       67#4   |
+| Lss01078.txt	|       MC off    |   6 x 1min       |       67#6   |
+| Lss01079.txt  |       MC off    |   10 min         |       67#6   |
+| Lss01086.txt  |       MC on     |   6 x 1min       |       67#7   |
+| Lss01087.txt  |       MC on     |   10 min         |       67#7   |
+| Lss01110.txt  |       +L-Mon    |   6 x 1min       |       67#14  |
+| Lss01112.txt  |       +L-Mon    |   10 min         |       67#14  |
+| Lss01130.txt  |       +M-Lon    |   6 x 1min       |       67#21  |
+| Lss01131.txt  |       +M-Lon    |   10 min         |       67#21  |
+| Lss01141.txt  |       +M-Lon    |   6 x 1min       |       67#26  |
+| Lss01142.txt  |       +M-Lon    |   10 min         |       67#26  |
+| Lss01159.txt  |       MC off    |   6 x 1min       |       67#33  |
+| Lss01160.txt  |       MC off    |   10 min         |       67#33  |
+| Lss01167.txt  |       +L-Mon    |   6 x 1min       |       67#34  |
+| Lss01168.txt  |       +L-Mon    |   10 min         |       67#34  |
+| Lss01178.txt  |       S on      |   6 x 1min       |       68#1   |
+| Lss01180.txt  |       S on      |   10 min         |       68#1   |
+| Lss01181.txt	|       MC off    |   6 x 1min       |       68#3   |
+| Lss01183.txt  |       MC off    |   10min          |       68#3   |
+| Lss01221.txt	|       MC on	  |   10 min         |       68#10  |
+| Lss01229.txt  |       S on      |   6 x 1min       |       68#12  |
+| Lss01231.txt  |       S on      |   10 min         |       68#12  |
+| Lss01256.txt  |       M off     |   6 x 1min       |       69#6   |
+| Lss01257.txt  |       M off     |   10 min         |       69#6   |
+| Lss01258.txt  |       M on      |   6 x 1min       |       69#7   |
+| Lss01259.txt  |       M on      |   10 min         |       69#7   |
+| Lss01284.txt  |       +M-Lon    |   6 x 1min       |       69#7   |
+| Lss01285.txt  |       +M-Lon    |   10 min         |       69#7   |
 
 # Details of the stimulus videos
 
-1) 1x10_256.mpg  This is the 10 minute video. It begins with  751  blank frames (approximately equal energy white) followed by the 10 min live video (starting at frame 752, 90000 frames) followed by 750 blank frames. These frames were played at 150 frames/sec, 3 times acquisition rate. Timing pulses are provided on the audio channel beginning at the first frame of the video. They have a complex pattern, repeating every 5 sec. They were provided to ensure syncing with the data acquisition system that recorded spike trains.
+1. 1x10_256.mpg  This is the 10 minute video. It begins with  751  blank frames (approximately equal energy white) followed by the 10 min live video (starting at frame 752, 90000 frames) followed by 750 blank frames. These frames were played at 150 frames/sec, 3 times acquisition rate. Timing pulses are provided on the audio channel beginning at the first frame of the video. They have a complex pattern, repeating every 5 sec. They were provided to ensure syncing with the data acquisition system that recorded spike trains.
 
-2) 6x1_256.mpg.  This is 1 min video. Structure as in 1x10min, except only 9000 frames.
+2. 6x1_256.mpg. This is 1 min video. Structure as in 1x10min, except only 9000 frames.
 
-3) Gamma correction. The r,g,b, values (0-256) from the mpeg decompression can be converted to intensity values by
+3. Gamma correction. The r,g,b, values (0-256) from the mpeg decompression can be converted to intensity values using the following equations, where I is the r/g/b bit value:
+```
         red = 0.01451 + 0.9855*pow(1.0*I/256, 2.3122);
         green = 0.005123 + 0.9949*pow(1.0*I/256, 2.2752);
         blue = 0.02612 + 0.9739*pow(1.0*I/256, 2.2818);
+```
 
-where I is the bit value. 
+4. Gun spectra. The spectra measured for the 3 display guns are tabulated in GunSpectra.txt  Based on the gun spectra and luminance estimates, the gun values were calibrated to deliver the same luminance. However, based on reverse correlation analysis on M cells, it was estimated that, relative to the red gun, the green gun luminance was overestimated by ca. 5% and the blue gun underestimated by ca. 20%. The gamma-corrected intensities can be converted into l/m/s cone signals using the following equation:
+``` 
+    lcone = 2.74*rgamma + 3.4*ggamma + 1.34*bgamma
+    mcone = 1.21*(1.06*rgamma + 3.58*ggamma + 2.07*bgamma)
+    scone = 0.212*rgamma + 8.28*ggamma + 285*bgamma
+```
 
-4) Gun spectra. The spectra measured for the 3 display guns are tabulated in GunSpectra.txt  Based on the gun spectra and luminance estimates, the gun values were calibrated to deliver the same luminance. However, based on reverse correlation analysis on M cells, it was estimated that, relative to the red gun, the green gun luminance was overestimated by ca. 5% and the blue gun underestimated by ca. 20%.
+5. These parameters were considered constant and not further optimized.
 
 # Model details
 
-A codel for primate retinal MC/PC/KC responses to natural scenes
+1. The averaged 6 x 1 min responses were best for modeling, while the 10 min runs gave better results for the reverse correlation. 
+
+1. For rates, using filters employed in the previous paper (van Hateren et al.,
+2002); 8-stage low pass filters were used with time constants of 2 msec for MC-cells
+and 4 msec for PC- and S-cone cells.
+
+1. The model provides additional data files in /data with the averaged 6x 1min into firing rates. The format of these files is 2xT, where the first column is the cells activity, and the second column is an estimate of luminance. For modelling, we only use column 1.
+
+2. A codel for primate retinal MC/PC/KC responses to natural scenes, produces fits to the following cells:
+
+| File name     |  Cell Type      |  corr. with model |  Notes     |
+|---------------|-----------------|-------------------|------------|
+| 078	        |     MC off      |       0.804       | ok         |
+| 110	        |     PC Ron      |       0.836       | ok         |
+| 130	        |     PC Gon      |       0.777       | ok         |
+| 167	        |     PC Ron      |       0.835       | ok         |
+| 178	        |     S on        |       0.815       | ok         |
+| 181	        |     MC off      |       0.805       | ok         |
+| 194	        |     PC Ron      |       0.842       | MISSING    |
+| 225           |     PC Roff     |       0.798       | MISSING    |
+| 229	        |     S on        |       0.780       | ok         |
+| 254	        |     MC on       |       0.802       | MISSING    |
+| 256	        |     MC off      |       0.794       | ok         |
+| 258	        |     MC on       |       0.803       | ok         |
+| 284	        |     PC Gon      |       0.778       | ok         |
+| 287	        |     PC Gon      |       0.764       | MISSING    |
+| 299	        |     MC on       |       0.826       | MISSING    |
+| 302	        |     S on        |       0.732       | MISSING    |
+
+And in addition reproduces the pie charts.
+
+We also provide one example for a PC Off cell, where the fit seems to work well. The model is identical to all PC fits.

data/lSS01178.txt

@@ -0,0 +1,924 @@
+Total spikes	5169
+Video Starts	55461
+
+Spikes per rpt
+	847	884	900	895	809	809	25	
+Spikes/5 sec
+0	18	2	4	9	2	3	0	
+1	73	76	79	88	72	71	5	
+2	76	89	92	97	88	84	4	
+3	56	59	83	76	55	63	8	
+4	54	61	62	64	53	57	8	
+5	85	86	88	86	80	76	0	
+6	57	57	58	55	52	54	0	
+7	55	58	59	58	52	54	0	
+8	69	64	66	66	65	58	0	
+9	87	95	90	88	84	81	0	
+10	63	72	74	65	65	70	0	
+11	73	80	73	72	70	70	0	
+12	79	82	68	70	70	68	0	
+13	1	2	4	1	1	0	0	
+14	1	1	0	0	0	0	0	
+
+Spikes times
+0	-50291	-10849	-46202	-48939	-33836	-46292	6296	
+1	-50204	-445	-38065	-46173	-12897	-41727	17390	
+2	-50130	2578	-37400	-42952	2512	-8095	23106	
+3	-49931	2624	-1229	-41794	2542	2496	25145	
+4	-44445	2675	2588	-31574	2601	2545	48122	
+5	-43313	2722	2633	-24858	2640	2600	55079	
+6	-38469	2772	2674	-14466	2679	2655	79530	
+7	-36707	2821	2724	-8397	2754	2694	90418	
+8	-35302	2857	2767	-5182	2808	2747	95722	
+9	-33724	2896	2817	2561	2849	2790	118317	
+10	-33066	5551	2855	2636	2921	2827	119062	
+11	-14435	5663	2890	2683	5746	2940	129312	
+12	-12857	6493	2923	2726	6083	5676	134525	
+13	-10257	7693	2959	2786	6910	6040	142549	
+14	-10160	7811	4037	2834	7759	7704	143243	
+15	-8995	8202	5706	2875	7922	7778	144495	
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run_model/.ipynb_checkpoints/Piecharts-checkpoint.ipynb

@@ -0,0 +1,264 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Make sure retinatools is in the python path\n",
+    "import sys\n",
+    "sys.path.append('../') "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pylab as pl\n",
+    "from retinatools.library import filter_video_PC, get_PC_response, filter_video_MC, get_MC_response, filter_video_BO, get_BO_response, compare_to_file"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Note: all reliabilities rounded to 2 significant digits, and rounded up"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pl.rcParams['figure.figsize'] = [15, 4]\n",
+    "def make_plots(cell_name, RpcF, RpfS, filename, dt, shift, column, retinal_reliability): \n",
+    "    \n",
+    "    # Calculate the R^2 values / variaces explained\n",
+    "    rF = compare_to_file(filename, RpcF, dt, column, shift)    \n",
+    "    rS = compare_to_file(filename, RpcS, dt, column, shift)\n",
+    "    spatial_factors = rS**2\n",
+    "    temporal_factors = rF**2 - rS**2\n",
+    "    missing_in_model = retinal_reliability - rF**2\n",
+    "    \n",
+    "    #Get the data for the plot\n",
+    "    cell_activity = np.loadtxt(filename)\n",
+    "    time_cell = np.arange(0, len(cell_activity))*dt + 5000 - shift*6.66 \n",
+    "    time_model = np.arange(0, len(RpcS)*dt, dt)\n",
+    "\n",
+    "    #Make the plot\n",
+    "    pl.figure()\n",
+    "    pl.subplot(1,2,1)\n",
+    "    pl.plot(time_model, RpcS/np.std(RpcS), label=\"Model without surround\")\n",
+    "    pl.plot(time_model, RpcF/np.std(RpcF), label = \"Full model\")\n",
+    "    pl.plot(time_cell, cell_activity[:,column]/np.std(cell_activity[:,column]), label=\"Cell data\")\n",
+    "    pl.xlim([10000,11500])\n",
+    "    pl.ylim([0,8])\n",
+    "    pl.xlabel(\"Time [ms]\")\n",
+    "    pl.ylabel(\"Activity [Z-scored]\")\n",
+    "    pl.legend();\n",
+    "    pl.title(\"r_model=\" + str(rF))\n",
+    "    pl.subplot(1,2,2)\n",
+    "    labels = 'Center-contribution', '\"Surround\"-Contribution', 'Missing in Model', 'Individual variations'\n",
+    "    sizes = [spatial_factors, temporal_factors, missing_in_model, 1-retinal_reliability]\n",
+    "    explode = (0.1, 0.1, 0.1, 0.1)\n",
+    "    pl.pie(sizes, explode=explode, labels=labels, autopct='%1.1f%%', shadow=True, startangle=-60)\n",
+    "    pl.axis('equal')\n",
+    "    pl.title(\"Cell: \" +  cell_name);\n",
+    "\n",
+    "    #Save figure + data\n",
+    "    pl.savefig(\"./\"+ cell_name + \".pdf\")\n",
+    "    \n",
+    "    # Select data for cell and model, and save it\n",
+    "    t0 = 5000\n",
+    "    time_cell = np.arange(0, len(cell_activity))*dt + 5000 - shift*6.66\n",
+    "    cell = cell_activity[(time_cell>t0) * (time_cell<64980), column]\n",
+    "    time = np.arange(0, len(RpcF)*dt, dt) \n",
+    "    model = RpcF[(time>=t0) * (time<64980)]\n",
+    "    time_file = time[(time>=t0) * (time<64980)]\n",
+    "    model_data_save = np.transpose([time_file, cell/np.std(cell), model/np.std(model)])\n",
+    "    np.savetxt(\"./\"+ cell_name + '_time_cell_fit.csv', model_data_save, fmt='%1.5f', delimiter=', ', newline='\\n', header='time [ms], cell, model', footer='', comments = '', encoding=None)\n",
+    "\n",
+    "    pl.close()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# PC cells"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for cell in [\"110Ron\", \"130Gon\"]:\n",
+    "    \n",
+    "    if cell == \"110Ron\":\n",
+    "        fitparameters = {'xoff': 2.9, 'yoff': -1.8, 'rcenterM': 20, 'rcenterL': 5.0, \\\n",
+    "                         'lum_factor': 1200, 'tau1': 6.9, 'tau2': 60, 'k1': 0.017, 'tau3': 400,\\\n",
+    "                         'alpha': 0.68, 'k2': 4, 'o1': 0.07, 'dt': 1000/150.}\n",
+    "        filename = './data/File110Ron.txt'\n",
+    "        celltype = \"Ron\"\n",
+    "        fitparameters_s = fitparameters.copy()\n",
+    "        fitparameters_s['rcenterM'] = fitparameters_s['rcenterL']\n",
+    "        retinal_reliability = 0.91**2 # From Barry for 110Ron\n",
+    "\n",
+    "    elif cell == \"130Gon\":\n",
+    "        fitparameters = {'xoff': 2.9, 'yoff': -2.2, 'rcenterM': 4.9, 'rcenterL': 32, \\\n",
+    "                         'lum_factor': 290, 'tau1': 7.8, 'tau2': 100, 'k1': 0.018, 'tau3': 2000,\\\n",
+    "                         'alpha': 0.71, 'k2': 3.0, 'o1': 0.06, 'dt': 1000/150.}\n",
+    "        filename = './data/File130Gn.txt'\n",
+    "        celltype = \"Gon\"\n",
+    "        fitparameters_s = fitparameters.copy()\n",
+    "        fitparameters_s['rcenterL'] = fitparameters_s['rcenterM']\n",
+    "        retinal_reliability = 0.80**2 # From Barry for 130Gon\n",
+    "\n",
+    "\n",
+    "    # full model\n",
+    "    lum_signalM, lum_signalL = filter_video_PC('../stimuli/1x10_256.mpg', 9750, fitparameters)\n",
+    "    RpcF = get_PC_response(lum_signalM, lum_signalL, fitparameters, celltype)\n",
+    "\n",
+    "    # no-surround model\n",
+    "    lum_signalM, lum_signalL = filter_video_PC('../stimuli/1x10_256.mpg', 9750, fitparameters_s)\n",
+    "    RpcS = get_PC_response(lum_signalM, lum_signalL, fitparameters_s, celltype)\n",
+    "    \n",
+    "    make_plots(cell, RpcF, RpcS, filename, dt = fitparameters['dt'], shift = 1, column = 0, retinal_reliability = retinal_reliability)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# MC cells"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for cell in [\"299Mon\", \"078Moff\"]:\n",
+    "    \n",
+    "    if cell == \"299Mon\":\n",
+    "        fitparameters = {'xoff': -2.5, 'yoff': -5.5, 'rcenter': 4.0, 'rsurr': 13.0, 'ksurr': 0.45, 'surround_delay': 3, \\\n",
+    "                         'lum_factor': 60, 'tau1': 7.3, 'tau2': 38, 'k1': 0.019, 'c1': 0.016, 'tauplus': 110, \\\n",
+    "                         'tauA': 20, 'k2': 50, 'dt': 1000/150.}\n",
+    "        filename = './data/File299Mon.txt'\n",
+    "        celltype = 'on-cell'\n",
+    "        fitparameters_s = fitparameters.copy()\n",
+    "        fitparameters_s['rsurr'] = fitparameters_s['rcenter']\n",
+    "        retinal_reliability = 0.92**2 # From 299 MC on cell\n",
+    "\n",
+    "    elif cell == \"078Moff\":\n",
+    "        fitparameters = {'xoff': -8.4, 'yoff': 2.5, 'rcenter': 4.5, 'rsurr': 20, 'ksurr': 0.34, 'surround_delay': 8, \\\n",
+    "                         'lum_factor': 1000, 'tau1': 5.0, 'tau2': 16, 'k1': 0.00048, 'c1': 0.0024, 'tauplus': 22, \\\n",
+    "                         'tauA': 33, 'k2': 560, 'dt': 1000/150.}\n",
+    "        filename = './data/File078Moff.txt'\n",
+    "        celltype = 'off-cell'\n",
+    "        fitparameters_s = fitparameters.copy()\n",
+    "        fitparameters_s['rsurr'] = fitparameters_s['rcenter']\n",
+    "        retinal_reliability = 0.89**2 # From 78 MC off cell\n",
+    "\n",
+    "\n",
+    "    # full model\n",
+    "    rates_center, rates_surround = filter_video_MC('../stimuli/1x10_256.mpg', 9750, fitparameters)\n",
+    "    RpcF = get_MC_response(rates_center, rates_surround, fitparameters, type = celltype)\n",
+    "\n",
+    "    # no-surround model\n",
+    "    rates_center, rates_surround = filter_video_MC('../stimuli/1x10_256.mpg', 9750, fitparameters_s)\n",
+    "    RpcS = get_MC_response(rates_center, rates_surround, fitparameters_s, type = celltype)\n",
+    "    \n",
+    "    make_plots(cell, RpcF, RpcS, filename, dt = fitparameters['dt'], shift = 2, column = 0, retinal_reliability = retinal_reliability)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Blue ON "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cell = \"178Bon\"\n",
+    "\n",
+    "fitparameters = {'xoff': -7.6, 'yoff': 9.5, 'rcenterB': 10.0, 'rcenterY': 14.0,\\\n",
+    "                 'tau1': 10.2, 'tau2': 86, 'delay': 29, 'beta': 0.0, 'alpha': 4.9,  \\\n",
+    "                 'tau3': 280, 'kL': 0.01, 'kM': 0.051, 'kB': 0.12, 'offset': 0.025, 'dt': 1000/150.}\n",
+    "\n",
+    "filename = './data/File178Bon.txt'\n",
+    "fitparameters_s = fitparameters.copy()\n",
+    "fitparameters_s['rcenterY'] = fitparameters_s['rcenterB']\n",
+    "retinal_reliability = 0.91**2 # From barry 178 Bon cell\n",
+    "\n",
+    "\n",
+    "# full model\n",
+    "lum_signalL, lum_signalM, lum_signalB = filter_video_BO('../stimuli/1x10_256.mpg', 9750, fitparameters)\n",
+    "RpcF = get_BO_response(lum_signalB, lum_signalM, lum_signalL, fitparameters)\n",
+    "\n",
+    "# no-surround model\n",
+    "lum_signalL, lum_signalM, lum_signalB = filter_video_BO('../stimuli/1x10_256.mpg', 9750, fitparameters_s)\n",
+    "RpcS = get_BO_response(lum_signalB, lum_signalM, lum_signalL, fitparameters_s)\n",
+    "\n",
+    "make_plots(cell, RpcF, RpcS, filename, dt = fitparameters['dt'], shift = 2, column = 0, retinal_reliability = retinal_reliability)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

run_model/Piecharts.ipynb

@@ -2,7 +2,18 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Make sure retinatools is in the python path\n",
+    "import sys\n",
+    "sys.path.append('../') "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -13,7 +24,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -22,7 +33,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -86,7 +97,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -114,14 +125,14 @@
     "\n",
     "\n",
     "    # full model\n",
-    "    lum_signalM, lum_signalL = filter_video_PC('../1x10_256.mpg', 9750, fitparameters)\n",
+    "    lum_signalM, lum_signalL = filter_video_PC('../stimuli/1x10_256.mpg', 9750, fitparameters)\n",
     "    RpcF = get_PC_response(lum_signalM, lum_signalL, fitparameters, celltype)\n",
     "\n",
     "    # no-surround model\n",
-    "    lum_signalM, lum_signalL = filter_video_PC('../1x10_256.mpg', 9750, fitparameters_s)\n",
+    "    lum_signalM, lum_signalL = filter_video_PC('../stimuli/1x10_256.mpg', 9750, fitparameters_s)\n",
     "    RpcS = get_PC_response(lum_signalM, lum_signalL, fitparameters_s, celltype)\n",
     "    \n",
-    "    make_plots(cell, RpcF, RpcS, filename, dt = fitparameters['dt'], shift = 2, column = 0, retinal_reliability = retinal_reliability)"
+    "    make_plots(cell, RpcF, RpcS, filename, dt = fitparameters['dt'], shift = 1, column = 0, retinal_reliability = retinal_reliability)"
    ]
   },
   {
@@ -131,20 +142,6 @@
    "outputs": [],
    "source": []
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -154,7 +151,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -182,14 +179,14 @@
     "\n",
     "\n",
     "    # full model\n",
-    "    rates_center, rates_surround = filter_video_MC('../1x10_256.mpg', 9750, fitparameters)\n",
+    "    rates_center, rates_surround = filter_video_MC('../stimuli/1x10_256.mpg', 9750, fitparameters)\n",
     "    RpcF = get_MC_response(rates_center, rates_surround, fitparameters, type = celltype)\n",
     "\n",
     "    # no-surround model\n",
-    "    rates_center, rates_surround = filter_video_MC('../1x10_256.mpg', 9750, fitparameters_s)\n",
+    "    rates_center, rates_surround = filter_video_MC('../stimuli/1x10_256.mpg', 9750, fitparameters_s)\n",
     "    RpcS = get_MC_response(rates_center, rates_surround, fitparameters_s, type = celltype)\n",
     "    \n",
-    "    make_plots(cell, RpcF, RpcS, filename, dt = fitparameters['dt'], shift = 3, column = 0, retinal_reliability = retinal_reliability)"
+    "    make_plots(cell, RpcF, RpcS, filename, dt = fitparameters['dt'], shift = 2, column = 0, retinal_reliability = retinal_reliability)"
    ]
   },
   {
@@ -208,7 +205,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -219,501 +216,20 @@
     "                 'tau3': 280, 'kL': 0.01, 'kM': 0.051, 'kB': 0.12, 'offset': 0.025, 'dt': 1000/150.}\n",
     "\n",
     "filename = './data/File178Bon.txt'\n",
-    "column = 0\n",
-    "shift = 3\n",
     "fitparameters_s = fitparameters.copy()\n",
     "fitparameters_s['rcenterY'] = fitparameters_s['rcenterB']\n",
     "retinal_reliability = 0.91**2 # From barry 178 Bon cell\n",
     "\n",
     "\n",
     "# full model\n",
-    "lum_signalL, lum_signalM, lum_signalB = filter_video_BO('../1x10_256.mpg', 9750, fitparameters)\n",
+    "lum_signalL, lum_signalM, lum_signalB = filter_video_BO('../stimuli/1x10_256.mpg', 9750, fitparameters)\n",
     "RpcF = get_BO_response(lum_signalB, lum_signalM, lum_signalL, fitparameters)\n",
     "\n",
     "# no-surround model\n",
-    "lum_signalL, lum_signalM, lum_signalB = filter_video_BO('../1x10_256.mpg', 9750, fitparameters_s)\n",
+    "lum_signalL, lum_signalM, lum_signalB = filter_video_BO('../stimuli/1x10_256.mpg', 9750, fitparameters_s)\n",
     "RpcS = get_BO_response(lum_signalB, lum_signalM, lum_signalL, fitparameters_s)\n",
     "\n",
-    "make_plots(cell, RpcF, RpcS, filename, dt = fitparameters['dt'], shift = 3, column =0, retinal_reliability = retinal_reliability)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 47,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "from scipy import stats"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 48,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Summary Statistics"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 49,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "bon = [0.7802624476726236, 0.8153849801352536, 0.7320091829732691]\n",
-    "MCoff = [0.8049267745650888, 0.8036790439827152, 0.7935184997426455]\n",
-    "MCon = [0.8019790948348724, 0.8262426182591949, 0.8027547139233095]\n",
-    "Gon = [0.77803242901496, 0.7773717115038241, 0.7643298886394182]\n",
-    "Ron = [0.8356837630544626, 0.8421023207571601, 0.8631643469665483]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 50,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0.8055167908846377"
-      ]
-     },
-     "execution_count": 50,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "np.mean([MCoff, MCon])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 51,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0.009978215489268237"
-      ]
-     },
-     "execution_count": 51,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "np.std([MCoff, MCon])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 52,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0.004073589415373797"
-      ]
-     },
-     "execution_count": 52,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "np.std([MCoff, MCon])/np.sqrt(6)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 53,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[None, None, None]"
-      ]
-     },
-     "execution_count": 53,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "a = []\n",
-    "[a.append(s) for s in MCon]\n",
-    "[a.append(s) for s in MCoff]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 56,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[0.8019790948348724, 0.8262426182591949, 0.8027547139233095, 0.8049267745650888, 0.8036790439827152, 0.7935184997426455]\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "Ttest_1sampResult(statistic=180.5122672859438, pvalue=9.899823797921202e-11)"
-      ]
-     },
-     "execution_count": 56,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "print(a)\n",
-    "stats.ttest_1samp(a,0.0)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 57,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0.8101140766560623"
-      ]
-     },
-     "execution_count": 57,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "np.mean([Gon, Ron])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 58,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0.038054509734225"
-      ]
-     },
-     "execution_count": 58,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "np.std([Gon, Ron])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 59,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0.015535688543437792"
-      ]
-     },
-     "execution_count": 59,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "np.std([Gon, Ron])/np.sqrt(6)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 60,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[None, None, None]"
-      ]
-     },
-     "execution_count": 60,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "a = []\n",
-    "[a.append(s) for s in Gon]\n",
-    "[a.append(s) for s in Ron]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 61,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[0.77803242901496, 0.7773717115038241, 0.7643298886394182, 0.8356837630544626, 0.8421023207571601, 0.8631643469665483]\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "Ttest_1sampResult(statistic=47.60198351217366, pvalue=7.729070905370263e-08)"
-      ]
-     },
-     "execution_count": 61,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "print(a)\n",
-    "stats.ttest_1samp(a,0.0)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0.8014294544016898"
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "np.mean([bon, MCoff, MCon, Gon, Ron])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0.03193849279442697"
-      ]
-     },
-     "execution_count": 14,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "np.std([bon, MCoff, MCon, Gon, Ron])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0.008246483379718748"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "np.std([bon, MCoff, MCon, Gon, Ron])/np.sqrt(15)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 63,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[None, None, None]"
-      ]
-     },
-     "execution_count": 63,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "a = []\n",
-    "[a.append(s) for s in bon]\n",
-    "[a.append(s) for s in MCoff]\n",
-    "[a.append(s) for s in MCon]\n",
-    "[a.append(s) for s in Gon]\n",
-    "[a.append(s) for s in Ron]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 65,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Ttest_1sampResult(statistic=93.88904032952797, pvalue=5.283140568700127e-21)"
-      ]
-     },
-     "execution_count": 65,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "stats.ttest_1samp(a,0.0)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0.7758855369270488"
-      ]
-     },
-     "execution_count": 15,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "np.mean(bon)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0.034178442512351186"
-      ]
-     },
-     "execution_count": 16,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "np.std(bon)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0.019732932984988107"
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "np.std(bon)/np.sqrt(3)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 62,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Ttest_1sampResult(statistic=32.104091600278146, pvalue=0.00096883034904349)"
-      ]
-     },
-     "execution_count": 62,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "stats.ttest_1samp(bon,0.0)"
+    "make_plots(cell, RpcF, RpcS, filename, dt = fitparameters['dt'], shift = 2, column = 0, retinal_reliability = retinal_reliability)"
    ]
   },
   {
@@ -740,7 +256,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.8"
+   "version": "3.9.1"
   }
  },
  "nbformat": 4,