Estimation of how a row of synapses will be split into delay sub-rows

sub_row_estimation.ipynb

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   "source": [
    "import math\n",
    "import numpy as np\n",
    "import scipy.stats as stats\n",
    "\n",
    "mean = 1.5\n",
    "sd = 0.75\n",
    "\n",
    "mean_row_length = 100\n",
    "\n",
    "dist = stats.truncnorm\n",
    "params = {\"loc\": mean, \"scale\": sd, \"a\": (0.1 - mean) / sd, \"b\": (np.inf - mean) / sd}"
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     "text": [
      "Average upper bound:3.354976, average lower bound:0.166187, average delay range:3.188790\n"
     ]
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   ],
   "source": [
    "mean_upper = dist.ppf(0.5 ** (1.0 / float(mean_row_length)), **params)\n",
    "mean_lower = dist.ppf(1.0 - 0.5 ** (1.0 / float(mean_row_length)), **params)\n",
    "delay_range = mean_upper - mean_lower\n",
    "print \"Average upper bound:%f, average lower bound:%f, average delay range:%f\" % (mean_upper, mean_lower, delay_range)"
   ]
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     "text": [
      "Num sub-rows:4\n",
      "Sub-row bin edges:[ 0.16618661  0.96618661  1.76618661  2.56618661  3.36618661]\n",
      "Sub-row bin edge cdf:[ 0.0069075   0.21396265  0.62712406  0.91994663  0.99337614]\n",
      "Sub-row probability mass:[ 0.20989532  0.41882873  0.29683921  0.07443674]\n",
      "25\n"
     ]
    }
   ],
   "source": [
    "num_sub_rows = int(math.ceil(delay_range / 0.8))\n",
    "print \"Num sub-rows:%u\" % num_sub_rows\n",
    "\n",
    "sub_row_delay_bin_edges = np.arange(mean_lower, mean_upper + 0.8, 0.8)\n",
    "print \"Sub-row bin edges:%s\" % str(sub_row_delay_bin_edges)\n",
    "\n",
    "sub_row_delay_cdf = dist.cdf(sub_row_delay_bin_edges, **params)\n",
    "print \"Sub-row bin edge cdf:%s\" % str(sub_row_delay_cdf)\n",
    "\n",
    "sub_row_prob_mass = sub_row_delay_cdf[1:] - sub_row_delay_cdf[:-1]\n",
    "sub_row_prob_mass /= (sub_row_delay_cdf[-1] - sub_row_delay_cdf[0])\n",
    "print \"Sub-row probability mass:%s\" % (str(sub_row_prob_mass))\n",
    "\n",
    "print mean_row_length // num_sub_rows \n",
    "\n"
   ]
  },
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