From e0b580d1f9ccba8f70b12d761c22a6768d3f07ca Mon Sep 17 00:00:00 2001 From: MoiColl Date: Mon, 20 Jun 2022 15:56:53 +0200 Subject: [PATCH 1/5] create functions linked_depth and independent_depth --- notebook/simGL.ipynb | 243 ++++++++++++++++++++++++++++++++++++++++++- simGL/simGL.py | 58 ++++++++++- 2 files changed, 295 insertions(+), 6 deletions(-) diff --git a/notebook/simGL.ipynb b/notebook/simGL.ipynb index e0f8eba..395d95c 100644 --- a/notebook/simGL.ipynb +++ b/notebook/simGL.ipynb @@ -18,7 +18,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 5, "id": "a3c58dad-95fa-4fe1-8971-521842ea4182", "metadata": {}, "outputs": [], @@ -38,7 +38,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 6, "id": "966418dd-9400-405c-8983-a4714ad51704", "metadata": {}, "outputs": [ @@ -4316,8 +4316,247 @@ { "cell_type": "code", "execution_count": null, + "id": "326f8a0e-61f5-42fb-871f-aaeb60b9b33a", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "53c5c073-a47e-4d08-99e3-91ee91bb5f64", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "007e3796-3095-42b7-b90b-6c536722a719", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "f1f616b5-1187-40c5-8736-cd1c8f5eb554", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np" + ] + }, + { + "cell_type": "code", + "execution_count": 2, "id": "d298a22c-d9fe-44d4-897f-e763d35cb7d9", "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([9148, 9149, 9150, ..., 1937, 1938, 1939])" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "seq_len = 37498\n", + "n_reads = 9527-12\n", + "read_length = 151\n", + "\n", + "\n", + "df_sim = np.array([int(x) for x in np.random.uniform(low=0.0, high=seq_len, size=n_reads)])\n", + "pos = []\n", + "for s in df_sim:\n", + " for i in range(s, s+read_length):\n", + " pos.append(i)\n", + "pos = np.array(pos)\n", + "pos" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "id": "35643811-795f-4387-9f9c-d99bfc17e3a8", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[15.37810676 13.95450312 14.17387291 10.11706523]\n" + ] + } + ], + "source": [ + "def depth_per_haplotype(rng, mean_depth, std_depth, n_hap):\n", + " if isinstance(mean_depth, np.ndarray):\n", + " return mean_depth\n", + " else:\n", + " dp = np.full((n_hap, ), 0.0)\n", + " while (dp <= 0).sum():\n", + " n = (dp <= 0).sum()\n", + " dp[dp <= 0] = rng.normal(loc = mean_depth, scale = std_depth, size=n)\n", + " return dp\n", + "\n", + "gm = np.array([[0, 0, 1, 0], [1, 1, 0, 1]])\n", + "mean_depth = 15\n", + "e = 0.05\n", + "ploidy = 2\n", + "seed = 2\n", + "std_depth = 2\n", + "\n", + "err = np.array([[1-e, e/3, e/3, e/3], [e/3, 1-e, e/3, e/3], [e/3, e/3, 1-e, e/3], [e/3, e/3, e/3, 1-e]])\n", + "rng = np.random.default_rng(seed)\n", + "#1. Depths (DP) per haplotype (h)\n", + "DPh = depth_per_haplotype(rng, mean_depth, std_depth, gm.shape[1])\n", + "print(DPh)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "566fd8ee-ed0c-49c4-b326-83c6cd7e9aa0", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([15.37810676, 13.95450312, 14.17387291, 10.11706523])" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "DPh" + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "id": "259f5a19-f129-4251-beb6-3a9eaac155c4", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(4, 10)" + ] + }, + "execution_count": 57, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "def linked_depth(rng, DPh, read_length, sites_n):\n", + " '''\n", + " Simulates reads in a contiguous genomic region to compute the depth per position.\n", + " \n", + " Parameters\n", + " ----------\n", + " rng : `numpy.random._generator.Generator` \n", + " random number generation numpy object\n", + " DPh : `numpy.ndarray`\n", + " Numpy array with the depth per haplotype\n", + " read_length : `int`\n", + " Read length in base pair units\n", + " sites_n : `int`\n", + " number of sites that depth has to be simulated for\n", + " \n", + " Returns \n", + " -------\n", + " DP : `numpy.ndarray`\n", + " Depth per site per haplotype\n", + " '''\n", + " DP = []\n", + " read_n = ((DPh*sites_n)/read_length).astype(\"int\")\n", + " for r in read_n:\n", + " dp = np.zeros((sites_n,), dtype=int)\n", + " for p in rng.integers(low=0, high=sites_n-read_length+1, size=r):\n", + " dp[p:p+read_length] += 1\n", + " DP.append(dp.tolist())\n", + " return np.array(DP)\n", + " \n", + "linked_depth(rng, DPh, read_length = 2, sites_n = 10)" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "id": "08b04ef0-d54f-45f8-bff1-640916061ea3", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([5, 4, 1, 1, 1, 3, 7, 9, 4, 3])" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.random.randint(low = 0, high = 10, size = 10)" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "id": "a03db2fd-3480-47fd-aa80-6c41a0f41cc6", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(10,)" + ] + }, + "execution_count": 38, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.arange(10).shape" + ] + }, + { + "cell_type": "code", + "execution_count": 58, + "id": "bc2d50fd-39e0-48fd-8088-31dd9c42bbb8", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "numpy.random._generator.Generator" + ] + }, + "execution_count": 58, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "type(rng)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2a474f22-7871-42cd-8459-ea7197b1284a", + "metadata": {}, "outputs": [], "source": [] } diff --git a/simGL/simGL.py b/simGL/simGL.py index b4f3bf5..1526045 100644 --- a/simGL/simGL.py +++ b/simGL/simGL.py @@ -55,7 +55,43 @@ def refalt_int_encoding(gm, ref, alt): refalt_int[refalt_str == "T"] = 3 return refalt_int[gm.reshape(-1), np.repeat(np.arange(gm.shape[0]), gm.shape[1])].reshape(gm.shape) -def sim_allelereadcounts(gm, mean_depth, e, ploidy, seed = None, std_depth = None, ref = None, alt = None): +def linked_depth(rng, DPh, read_length, sites_n): + ''' + Simulates reads in a contiguous genomic region to compute the depth per position. + + Parameters + ---------- + rng : `numpy.random._generator.Generator` + random number generation numpy object + DPh : `numpy.ndarray` + Numpy array with the depth per haplotype + read_length : `int` + Read length in base pair units + sites_n : `int` + number of sites that depth has to be simulated for + + Returns + ------- + DP : `numpy.ndarray` + Depth per site per haplotype + ''' + DP = [] + read_n = ((DPh*sites_n)/read_length).astype("int") + for r in read_n: + dp = np.zeros((sites_n,), dtype=int) + for p in rng.integers(low=0, high=sites_n-read_length+1, size=r): + dp[p:p+read_length] += 1 + DP.append(dp.tolist()) + return np.array(DP) + +def independent_depth(rng, DPh, size): + ''' + Returns depth per position per haplotype (size[0], size[1]) drawn from the "rng" from a Poisson + distribution with a lambda value "DPh" per haplotype + ''' + return rng.poisson(DPh, size=size) + +def sim_allelereadcounts(gm, mean_depth, e, ploidy, seed = None, std_depth = None, ref = None, alt = None, read_length = None, depth_type = "independent"): ''' Simulates allele read counts from a genotype matrix. @@ -117,14 +153,18 @@ def sim_allelereadcounts(gm, mean_depth, e, ploidy, seed = None, std_depth = Non if ref is None and alt is None: ref = np.full(gm.shape[0], "A") alt = np.full(gm.shape[0], "C") - assert check_mean_depth(gm, mean_depth) and check_std_depth(mean_depth, std_depth) and check_e(e) and check_ploidy(ploidy) and check_gm_ploidy(gm, ploidy) and check_ref_alt(gm, ref, alt) + assert check_mean_depth(gm, mean_depth) and check_std_depth(mean_depth, std_depth) and check_e(e) and check_ploidy(ploidy) and check_gm_ploidy(gm, ploidy) and check_ref_alt(gm, ref, alt) and check_depth_type(depth_type) #Variables err = np.array([[1-e, e/3, e/3, e/3], [e/3, 1-e, e/3, e/3], [e/3, e/3, 1-e, e/3], [e/3, e/3, e/3, 1-e]]) rng = np.random.default_rng(seed) #1. Depths (DP) per haplotype (h) DPh = depth_per_haplotype(rng, mean_depth, std_depth, gm.shape[1], ploidy) #2. Sample depths (DP) per site per haplotype - DP = rng.poisson(DPh, size=gm.shape) + if depth_type == "independent": + DP = independent_depth(rng, DPh, gm.shape) + elif depth_type == "linked": + assert check_positive_nonzero_integer(read_length, "read_length") + DP = linked_depth(rng, DPh, read_length, gm.shape[0]) #3. Sample correct and error reads per SNP per haplotype (Rh) #3.1. Convert anc = 0/der = 1 encoded gm into "A" = 0, "C" = 1, "G" = 3, "T" = 4 basepair (bp) encoded gm gmbp = refalt_int_encoding(gm, ref, alt) @@ -289,7 +329,17 @@ def check_gm_ploidy(gm, ploidy): if not (gm.shape[1]%ploidy == 0) : raise TypeError('Incorrect ploidy and/or gm format: the second dimention of gm (haplotypic samples) must be divisible by ploidy') return True - + +def check_depth_type(depth_type): + if not isinstance(depth_type, str) and depth_type not in ["independent", "linked"]: + raise TypeError('Incorrect depth_type format: it has to be a string, either "independent" or "linked"') + return True + +def check_positive_nonzero_integer(read_length, name): + if not isinstance(read_length, int) and read_length <= 0: + raise TypeError('Incorrect {} format: it has to be a integer value > 0'.format(name)) + return True + def check_ref_alt(gm, ref, alt): if not (isinstance(ref, np.ndarray) and isinstance(alt, np.ndarray) and len(ref.shape) == 1 and len(alt.shape) == 1 and ref.shape == alt.shape and ref.size == gm.shape[0] and ((ref == "A") + (ref == "C") + (ref == "G") + (ref == "T")).sum() == ref.size and ((alt == "A") + (alt == "C") + (alt == "G") + (alt == "T")).sum() == alt.size): From 642453b680bcea41c083d5209470140c05b14ed0 Mon Sep 17 00:00:00 2001 From: MoiColl Date: Tue, 21 Jun 2022 10:55:44 +0200 Subject: [PATCH 2/5] correct transposed issue in linked_depth() and assert dimentions between DP and gm --- notebook/simGL.ipynb | 364 +++++++++++++++++++++++++++++++++++++++---- simGL/simGL.py | 3 +- 2 files changed, 337 insertions(+), 30 deletions(-) diff --git a/notebook/simGL.ipynb b/notebook/simGL.ipynb index 395d95c..4b4b640 100644 --- a/notebook/simGL.ipynb +++ b/notebook/simGL.ipynb @@ -18,10 +18,19 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 63, "id": "a3c58dad-95fa-4fe1-8971-521842ea4182", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The rpy2.ipython extension is already loaded. To reload it, use:\n", + " %reload_ext rpy2.ipython\n" + ] + } + ], "source": [ "import time\n", "import numpy as np\n", @@ -29,6 +38,7 @@ "import msprime\n", "import tskit\n", "import simGL\n", + "import matplotlib.pyplot as plt \n", "\n", "%load_ext rpy2.ipython\n", "\n", @@ -38,28 +48,10 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 60, "id": "966418dd-9400-405c-8983-a4714ad51704", "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R[write to console]: ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.1 ──\n", - "\n", - "R[write to console]: ✔ tibble 3.1.7 ✔ dplyr 1.0.9\n", - "✔ tidyr 1.2.0 ✔ stringr 1.4.0\n", - "✔ readr 2.1.2 ✔ forcats 0.5.1\n", - "✔ purrr 0.3.4 \n", - "\n", - "R[write to console]: ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──\n", - "✖ dplyr::filter() masks stats::filter()\n", - "✖ dplyr::lag() masks stats::lag()\n", - "\n" - ] - } - ], + "outputs": [], "source": [ "%%R\n", "\n", @@ -4441,17 +4433,46 @@ }, { "cell_type": "code", - "execution_count": 57, + "execution_count": 195, "id": "259f5a19-f129-4251-beb6-3a9eaac155c4", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "(4, 10)" + "array([5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5.,\n", + " 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5.,\n", + " 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5.,\n", + " 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5.,\n", + " 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5.,\n", + " 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5.,\n", + " 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5.,\n", + " 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5.,\n", + " 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5.,\n", + " 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5.,\n", + " 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5.,\n", + " 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5.,\n", + " 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5.,\n", + " 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5.,\n", + " 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5.,\n", + " 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5.,\n", + " 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5.,\n", + " 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5.,\n", + " 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5.,\n", + " 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5.,\n", + " 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5.,\n", + " 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5.,\n", + " 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5.,\n", + " 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5.,\n", + " 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5.,\n", + " 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5.,\n", + " 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5.,\n", + " 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5.,\n", + " 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5.,\n", + " 5., 5., 5., 5., 5., 5., 5.])" ] }, - "execution_count": 57, + "execution_count": 195, "metadata": {}, "output_type": "execute_result" } @@ -4484,9 +4505,12 @@ " for p in rng.integers(low=0, high=sites_n-read_length+1, size=r):\n", " dp[p:p+read_length] += 1\n", " DP.append(dp.tolist())\n", - " return np.array(DP)\n", - " \n", - "linked_depth(rng, DPh, read_length = 2, sites_n = 10)" + " return np.array(DP).T\n", + "\n", + "DPh = np.array([5] * 500)\n", + "linked = linked_depth(rng, DPh, read_length = 100, sites_n = 300)\n", + "linked.shape\n", + "linked.mean(axis = 0)" ] }, { @@ -4554,9 +4578,291 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 196, "id": "2a474f22-7871-42cd-8459-ea7197b1284a", "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5.,\n", + " 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5.,\n", + " 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5.])" + ] + }, + "execution_count": 196, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "rng = np.random.default_rng()\n", + "DPh = np.array([5] * 50) # 500 haplotypes each with depth 5\n", + "linked = linked_depth(rng, DPh, 100, 300)\n", + "linked.mean(axis = 0)" + ] + }, + { + "cell_type": "code", + "execution_count": 197, + "id": "b87456c3-77bd-4886-ab9e-8545da8a2c77", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([6.24362391, 6.22134131, 6.3227217 , 5.62047026, 6.01859957,\n", + " 5.09101895, 4.75162037, 3.50866578, 5.35176806, 5.17917373,\n", + " 4.49449315, 6.64999401, 5.7870754 , 5.18628934, 4.15691961,\n", + " 4.03752717, 4.93745233, 4.73877702, 5.76580129, 5.86630209,\n", + " 7.26625988, 2.41767582, 4.57193522, 5.54650625, 8.15204092,\n", + " 3.91883976, 5.53601392, 4.1392852 , 4.97307886, 5.34080056,\n", + " 5.64398088, 6.50211826, 5.16538773, 5.1446952 , 5.19940298,\n", + " 4.96726068, 5.58953678, 4.1571701 , 3.8272178 , 5.78357617,\n", + " 6.32684022, 3.80844625, 4.03159482, 5.67242941, 7.94770987,\n", + " 7.10795932, 7.09427356, 7.20178838, 5.24779528, 4.11999149])" + ] + }, + "execution_count": 197, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "rng = np.random.default_rng()\n", + "DPh = rng.normal(loc=5, scale=1.0, size=50)\n", + "DPh" + ] + }, + { + "cell_type": "code", + "execution_count": 199, + "id": "57ca520c-2abc-4419-8c13-e34ab902b5f8", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([6.24333333, 6.22 , 6.32 , 5.62 , 6.01666667,\n", + " 5.09 , 4.75 , 3.50666667, 5.35 , 5.17666667,\n", + " 4.49333333, 6.64666667, 5.78666667, 5.18333333, 4.15666667,\n", + " 4.03666667, 4.93666667, 4.73666667, 5.76333333, 5.86333333,\n", + " 7.26333333, 2.41666667, 4.57 , 5.54333333, 8.15 ,\n", + " 3.91666667, 5.53333333, 4.13666667, 4.97 , 5.34 ,\n", + " 5.64333333, 6.5 , 5.16333333, 5.14333333, 5.19666667,\n", + " 4.96666667, 5.58666667, 4.15666667, 3.82666667, 5.78333333,\n", + " 6.32666667, 3.80666667, 4.03 , 5.67 , 7.94666667,\n", + " 7.10666667, 7.09333333, 7.2 , 5.24666667, 4.11666667])" + ] + }, + "execution_count": 199, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "linked = linked_depth(rng, DPh, 100, 30000)\n", + "linked.mean(axis = 0)" + ] + }, + { + "cell_type": "code", + "execution_count": 216, + "id": "326b222a-e518-4c91-9576-bbdd01d0db6c", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.scatter(DPh, linked.mean(axis = 0))\n", + "plt.plot(np.arange(10)[2:], np.arange(10)[2:])\n", + "plt.xlabel(\"Input\")\n", + "plt.ylabel(\"output\")\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 166, + "id": "51014df3-f5fb-4543-aeb6-aa7eef5d574b", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "500\n" + ] + } + ], + "source": [ + "def linked_depth(rng, DPh, read_length, sites_n):\n", + " '''\n", + " Simulates reads in a contiguous genomic region to compute the depth per position.\n", + " \n", + " Parameters\n", + " ----------\n", + " rng : `numpy.random._generator.Generator` \n", + " random number generation numpy object\n", + " DPh : `numpy.ndarray`\n", + " Numpy array with the depth per haplotype\n", + " read_length : `int`\n", + " Read length in base pair units\n", + " sites_n : `int`\n", + " number of sites that depth has to be simulated for\n", + " \n", + " Returns \n", + " -------\n", + " DP : `numpy.ndarray`\n", + " Depth per site per haplotype\n", + " '''\n", + " seq_length = sites_n+(2*read_length)\n", + " DP = []\n", + " print(sites_n+(2*read_length))\n", + " read_n = (DPh*seq_length/read_length).astype(\"int\")\n", + " for r in read_n:\n", + " dp = np.zeros((seq_length,), dtype=int)\n", + " for p in rng.integers(low=0, high=seq_length-read_length+1, size=r):\n", + " dp[p:p+read_length] += 1\n", + " DP.append(dp.tolist())\n", + " DP = (np.array(DP).T)[(1*read_length):(-1*read_length), :]\n", + " return np.round(DP-((DP.mean(axis = 0)-5).repeat(DP.shape[0]).reshape(DP.shape)))\n", + "\n", + "rng = np.random.default_rng()\n", + "DPh = np.array([5] * 500) # 500 haplotypes each with depth 5\n", + "linked = linked_depth(rng, DPh, 100, 300)" + ] + }, + { + "cell_type": "code", + "execution_count": 182, + "id": "9d9d18de-3ab6-4b01-85b8-9223f37d1c63", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "500\n", + "(300, 500)\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "rng = np.random.default_rng()\n", + "DPh = np.array([5] * 500) # 500 haplotypes each with depth 5\n", + "linked = linked_depth(rng, DPh, 100, 300)\n", + "print(linked.shape)\n", + "plt.plot(np.mean(linked, axis=1), label=\"linked\")\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 183, + "id": "b3d3c1bf-dcbb-4844-b86e-0b05c0f733a1", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([4.876, 4.486, 5.898, 4.906, 5.092, 5.288, 4.89 , 5.084, 5.266,\n", + " 6.27 , 5.486, 5.278, 5.488, 4.846, 4.874, 5.284, 5.292, 5.318,\n", + " 5.324, 5.532, 5.716, 5.316, 5.13 , 4.928, 4.716, 4.52 , 4.9 ,\n", + " 4.892, 5.096, 5.318, 4.718, 5.13 , 4.288, 5.256, 5.056, 4.862,\n", + " 4.872, 5.064, 4.64 , 5.212, 5.222, 4.638, 5.24 , 5.24 , 5.246,\n", + " 5.25 , 4.642, 5.238, 5.216, 4.818, 5.61 , 4.802, 4.992, 4.578,\n", + " 4.2 , 4.576, 6.14 , 5.16 , 4.572, 4.55 , 4.74 , 4.328, 5.134,\n", + " 5.132, 4.3 , 4.68 , 4.686, 4.262, 4.464, 5.666, 5.082, 5.078,\n", + " 5.074, 4.272, 4.672, 5.056, 5.052, 5.054, 4.424, 4.998, 5.016,\n", + " 5.028, 4.854, 5.262, 5.462, 5.272, 5.058, 5.072, 5.08 , 5.068,\n", + " 5.106, 4.906, 4.706, 4.71 , 4.332, 4.556, 5.568, 5.772, 4.768,\n", + " 4.786, 5.598, 5.188, 4.558, 5.36 , 6.166, 4.79 , 5.402, 4.82 ,\n", + " 5.204, 4.422, 4.79 , 5.182, 5.182, 5.192, 5.188, 4.958, 4.764,\n", + " 4.54 , 4.74 , 5.532, 4.756, 4.33 , 5.71 , 5.106, 5.09 , 4.488,\n", + " 4.684, 4.5 , 4.896, 4.686, 4.696, 4.09 , 5.13 , 4.538, 5.134,\n", + " 5.126, 4.54 , 5.162, 4.56 , 4.978, 4.764, 5.144, 4.342, 4.722,\n", + " 5.106, 5.106, 5.122, 4.122, 4.326, 5.148, 4.16 , 4.974, 5.16 ,\n", + " 5.164, 5.194, 5.202, 4.178, 4.356, 5.114, 6.112, 5.518, 4.926,\n", + " 4.914, 5.924, 4.536, 5.162, 4.746, 5.568, 5.556, 5.342, 5.726,\n", + " 4.532, 5.116, 5.124, 4.522, 4.536, 5.136, 4.742, 4.94 , 4.526,\n", + " 5.122, 4.3 , 4.686, 5.102, 4.9 , 4.688, 4.706, 4.89 , 4.478,\n", + " 5.08 , 5.266, 5.474, 5.68 , 5.704, 4.512, 4.482, 5.06 , 5.678,\n", + " 4.698, 4.704, 4.704, 5.088, 5.512, 4.128, 4.734, 4.9 , 4.484,\n", + " 5.066, 5.066, 5.042, 5.65 , 5.066, 5.08 , 4.882, 4.666, 5.698,\n", + " 5.074, 4.478, 4.488, 4.694, 4.898, 5.13 , 5.148, 4.732, 5.534,\n", + " 4.946, 5.358, 5.164, 5.48 , 4.974, 4.174, 5.606, 5.198, 4.808,\n", + " 4.806, 5.002, 4.586, 5.176, 4.964, 4.138, 5.36 , 4.986, 5.182,\n", + " 4.902, 4.376, 4.768, 4.732, 4.728, 4.734, 4.516, 4.514, 4.528,\n", + " 5.95 , 4.752, 4.122, 4.536, 5.17 , 5.182, 4.602, 5.826, 5.23 ,\n", + " 5.228, 4.842, 5.646, 5.25 , 4.848, 5.044, 5.234, 5.646, 5.456,\n", + " 5.254, 5.27 , 5.276, 5.662, 5.252, 4.246, 5.244, 4.656, 5.29 ,\n", + " 5.716, 5.538, 4.746, 5.354, 5.56 , 5.146, 4.748, 5.336, 5.326,\n", + " 5.946, 4.766, 5.358, 4.77 , 4.776, 5.358, 4.714, 5.346, 5.962,\n", + " 4.938, 4.93 , 3.732])" + ] + }, + "execution_count": 183, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "linked.mean(axis = 1)" + ] + }, + { + "cell_type": "code", + "execution_count": 184, + "id": "2fb36f4b-4efc-48f7-91a0-1fcda7f67e0d", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.hist(linked.mean(axis = 1))\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "594bdaab-ef8c-4762-84fb-286996d135ac", + "metadata": {}, "outputs": [], "source": [] } diff --git a/simGL/simGL.py b/simGL/simGL.py index 1526045..16803a3 100644 --- a/simGL/simGL.py +++ b/simGL/simGL.py @@ -82,7 +82,7 @@ def linked_depth(rng, DPh, read_length, sites_n): for p in rng.integers(low=0, high=sites_n-read_length+1, size=r): dp[p:p+read_length] += 1 DP.append(dp.tolist()) - return np.array(DP) + return np.array(DP).T def independent_depth(rng, DPh, size): ''' @@ -165,6 +165,7 @@ def sim_allelereadcounts(gm, mean_depth, e, ploidy, seed = None, std_depth = Non elif depth_type == "linked": assert check_positive_nonzero_integer(read_length, "read_length") DP = linked_depth(rng, DPh, read_length, gm.shape[0]) + assert DP.shape == gm.shape #3. Sample correct and error reads per SNP per haplotype (Rh) #3.1. Convert anc = 0/der = 1 encoded gm into "A" = 0, "C" = 1, "G" = 3, "T" = 4 basepair (bp) encoded gm gmbp = refalt_int_encoding(gm, ref, alt) From 35c3c3f949ddd0efacb2706e65d810291da02c03 Mon Sep 17 00:00:00 2001 From: MoiColl Date: Fri, 15 Jul 2022 19:28:32 +0200 Subject: [PATCH 3/5] generalization of functions and to include different error rate per haplotype --- notebook/simGL.ipynb | 3039 ++++++++++++++++++++++++++++++++++-------- simGL/simGL.py | 119 +- 2 files changed, 2588 insertions(+), 570 deletions(-) diff --git a/notebook/simGL.ipynb b/notebook/simGL.ipynb index 4b4b640..f7ebc20 100644 --- a/notebook/simGL.ipynb +++ b/notebook/simGL.ipynb @@ -18,7 +18,7 @@ }, { "cell_type": "code", - "execution_count": 63, + "execution_count": 4, "id": "a3c58dad-95fa-4fe1-8971-521842ea4182", "metadata": {}, "outputs": [ @@ -48,7 +48,7 @@ }, { "cell_type": "code", - "execution_count": 60, + "execution_count": 5, "id": "966418dd-9400-405c-8983-a4714ad51704", "metadata": {}, "outputs": [], @@ -58,7 +58,8 @@ "#.libPaths(c(\"/maps/projects/racimolab/people/qxz396/simGL/notebook/renv/library/R-4.1/x86_64-redhat-linux-gnu\", \"/tmp/Rtmp9Hi1cZ/renv-system-library\"))\n", "\n", "library(ggplot2)\n", - "library(tidyverse)" + "library(tidyverse)\n", + "library(cowplot)" ] }, { @@ -3281,195 +3282,167 @@ "id": "0adff1f0-603f-4d29-8e21-a4b56bd24c6a", "metadata": {}, "source": [ - "## 9. Linked depth" + "## 9. Linked depth\n", + "\n", + "### 9.1 create function" ] }, { "cell_type": "code", - "execution_count": 295, + "execution_count": 5, "id": "97d5b6bf-2832-4887-86f4-5e23648ad0e5", "metadata": {}, "outputs": [ { - "ename": "NameError", - "evalue": "name 'gm' is not defined", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", - "Input \u001b[0;32mIn [295]\u001b[0m, in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 21\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m linked_depths(DPh, read_length, start, end, rng)\n\u001b[1;32m 23\u001b[0m rng \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mrandom\u001b[38;5;241m.\u001b[39mdefault_rng(\u001b[38;5;241m1234\u001b[39m)\n\u001b[0;32m---> 24\u001b[0m \u001b[43msimulate_depths\u001b[49m\u001b[43m(\u001b[49m\u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43marray\u001b[49m\u001b[43m(\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m30\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrng\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mread_length\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mstart\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mend\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msimulation_type\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mindependent\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n", - "Input \u001b[0;32mIn [295]\u001b[0m, in \u001b[0;36msimulate_depths\u001b[0;34m(DPh, rng, read_length, start, end, simulation_type)\u001b[0m\n\u001b[1;32m 17\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21msimulate_depths\u001b[39m(DPh, rng, read_length \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m0\u001b[39m, start \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m0\u001b[39m, end \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m0\u001b[39m, simulation_type \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mindependent\u001b[39m\u001b[38;5;124m\"\u001b[39m):\n\u001b[1;32m 18\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m simulation_type \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mindependent\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[0;32m---> 19\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mindependent_depths\u001b[49m\u001b[43m(\u001b[49m\u001b[43mDPh\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrng\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 20\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m simulation_type \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mlinked\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[1;32m 21\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m linked_depths(DPh, read_length, start, end, rng)\n", - "Input \u001b[0;32mIn [295]\u001b[0m, in \u001b[0;36mindependent_depths\u001b[0;34m(DPh, rng)\u001b[0m\n\u001b[1;32m 14\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mindependent_depths\u001b[39m(DPh, rng):\n\u001b[0;32m---> 15\u001b[0m rng\u001b[38;5;241m.\u001b[39mpoisson(DPh, size\u001b[38;5;241m=\u001b[39m\u001b[43mgm\u001b[49m\u001b[38;5;241m.\u001b[39mshape)\n", - "\u001b[0;31mNameError\u001b[0m: name 'gm' is not defined" - ] + "data": { + "text/plain": [ + "array([[0, 0, 0, 1, 0],\n", + " [0, 0, 0, 1, 0],\n", + " [0, 0, 0, 1, 0],\n", + " ...,\n", + " [1, 0, 0, 0, 1],\n", + " [0, 0, 0, 0, 1],\n", + " [0, 0, 0, 0, 0]])" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" } ], "source": [ - "def linked_depths(DPh, read_length, start, end, rng):\n", - " DP = []\n", - " rng = np.random.default_rng(seed)\n", - " sequence_length = end-start\n", - " num_reads = int(round((sequence_length*DPh)/read_length))\n", - " for num_read in num_reads:\n", - " read_5p = rng.integers(low=0, high=end-start-read_length+1, size=num_reads)\n", - " depth = np.zeros(sequence_length, dtype=int)\n", - " for r in read_5p:\n", - " depth[r:r+read_length] += 1\n", - " DP.append(depth.tolist())\n", - " return np.array(DP)\n", - "\n", - "def independent_depths(DPh, rng):\n", - " rng.poisson(DPh, size=gm.shape)\n", - "\n", - "def simulate_depths(DPh, rng, read_length = 0, start = 0, end = 0, simulation_type = \"independent\"):\n", - " if simulation_type == \"independent\":\n", - " return independent_depths(DPh, rng)\n", - " elif simulation_type == \"linked\":\n", - " return linked_depths(DPh, read_length, start, end, rng)\n", + "def linked_depth(rng, DPh, read_length, sites_n):\n", + " '''\n", + " Simulates reads in a contiguous genomic region to compute the depth per position.\n", + " \n", + " Parameters\n", + " ----------\n", + " rng : `numpy.random._generator.Generator` \n", + " random number generation numpy object\n", + " DPh : `numpy.ndarray`\n", + " Numpy array with the depth per haplotype\n", + " read_length : `int`\n", + " Read length in base pair units\n", + " sites_n : `int`\n", + " number of sites that depth has to be simulated for\n", " \n", + " Returns \n", + " -------\n", + " DP : `numpy.ndarray`\n", + " Depth per site per haplotype\n", + " '''\n", + " DP = []\n", + " read_n = ((DPh*sites_n)/read_length).astype(\"int\")\n", + " for r in read_n:\n", + " dp = np.zeros((sites_n,), dtype=int)\n", + " for p in rng.integers(low=0, high=sites_n-read_length+1, size=r):\n", + " dp[p:p+read_length] += 1\n", + " DP.append(dp.tolist())\n", + " return np.array(DP).T\n", + "\n", + "def independent_depth(rng, DPh, size):\n", + " '''\n", + " Returns depth per position per haplotype (size[0], size[1]) drawn from the \"rng\" from a Poisson \n", + " distribution with a lambda value \"DPh\" per haplotype\n", + " '''\n", + " return rng.poisson(DPh, size=size)\n", + "\n", + "gm = np.array([[1, 0, 0, 0], [1, 1, 1, 0]])\n", + "DPh = np.array([5]*5)\n", "rng = np.random.default_rng(1234)\n", - "simulate_depths(np.array([1, 30]), rng, read_length = 0, start = 0, end = 0, simulation_type = \"independent\")" + "linked_depth(rng, DPh, read_length = 100, sites_n = 300)" ] }, { "cell_type": "code", - "execution_count": 294, - "id": "5821d5bd-1c1f-4f2c-a9ee-1b9526e4b416", + "execution_count": 9, + "id": "9da33c87-f111-4c51-95fc-2c4a1179a311", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]" + "array([[5, 6, 2, 4, 6],\n", + " [6, 3, 2, 2, 2],\n", + " [6, 3, 3, 4, 4],\n", + " ...,\n", + " [6, 3, 4, 4, 8],\n", + " [2, 3, 4, 2, 2],\n", + " [7, 5, 5, 5, 3]])" ] }, - "execution_count": 294, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "np.arange(10).tolist()" + "independent_depth(rng, DPh, size = (300, 5))" ] }, { - "cell_type": "code", - "execution_count": 4, - "id": "16b03419-a0b2-4c42-ae12-9e505eb6ae86", + "cell_type": "markdown", + "id": "0a67f95f-da21-4475-b698-54f669addfe2", "metadata": {}, - "outputs": [ - { - "ename": "TypeError", - "evalue": "'float' object cannot be interpreted as an integer", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", - "Input \u001b[0;32mIn [4]\u001b[0m, in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 43\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m depth\n\u001b[1;32m 45\u001b[0m start_time \u001b[38;5;241m=\u001b[39m time\u001b[38;5;241m.\u001b[39mtime()\n\u001b[0;32m---> 46\u001b[0m depths \u001b[38;5;241m=\u001b[39m \u001b[43mread_depths_from_reads\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 47\u001b[0m \u001b[43m \u001b[49m\u001b[43mnum_reads\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m10_000\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msequence_length\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m100_000\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mread_length\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m150\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mseed\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m1234\u001b[39;49m\n\u001b[1;32m 48\u001b[0m \u001b[43m)\u001b[49m\n\u001b[1;32m 49\u001b[0m \u001b[38;5;28mprint\u001b[39m(time\u001b[38;5;241m.\u001b[39mtime()\u001b[38;5;241m-\u001b[39mstart_time)\n\u001b[1;32m 50\u001b[0m \u001b[38;5;28mprint\u001b[39m(depths\u001b[38;5;241m.\u001b[39mmean(), depths\u001b[38;5;241m.\u001b[39mvar(), depths\u001b[38;5;241m.\u001b[39mmin(), depths\u001b[38;5;241m.\u001b[39mmax())\n", - "Input \u001b[0;32mIn [4]\u001b[0m, in \u001b[0;36mread_depths_from_reads\u001b[0;34m(num_reads, sequence_length, read_length, seed)\u001b[0m\n\u001b[1;32m 5\u001b[0m num_reads \u001b[38;5;241m=\u001b[39m (sequence_length\u001b[38;5;241m*\u001b[39mread_length)\u001b[38;5;241m/\u001b[39msequence_length\n\u001b[1;32m 6\u001b[0m \u001b[38;5;66;03m# 5' positions of reads that overlap the interval [0, sequence_length).\u001b[39;00m\n\u001b[0;32m----> 7\u001b[0m _5p \u001b[38;5;241m=\u001b[39m \u001b[43mrng\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mintegers\u001b[49m\u001b[43m(\u001b[49m\u001b[43mlow\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m-\u001b[39;49m\u001b[43mread_length\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mhigh\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43msequence_length\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msize\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mnum_reads\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 8\u001b[0m \u001b[38;5;66;03m# Left and right positions on the interval.\u001b[39;00m\n\u001b[1;32m 9\u001b[0m left \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mmaximum(\u001b[38;5;241m0\u001b[39m, _5p)\n", - "File \u001b[0;32m_generator.pyx:540\u001b[0m, in \u001b[0;36mnumpy.random._generator.Generator.integers\u001b[0;34m()\u001b[0m\n", - "File \u001b[0;32m_bounded_integers.pyx:1256\u001b[0m, in \u001b[0;36mnumpy.random._bounded_integers._rand_int64\u001b[0;34m()\u001b[0m\n", - "\u001b[0;31mTypeError\u001b[0m: 'float' object cannot be interpreted as an integer" - ] - } - ], "source": [ - "import numpy as np\n", - "\n", - "#def read_depths_from_reads(num_reads, sequence_length, read_length, seed):\n", - "# rng = np.random.default_rng(seed)\n", - "# num_reads = (sequence_length*read_length)/sequence_length\n", - "# # 5' positions of reads that overlap the interval [0, sequence_length).\n", - "# _5p = rng.integers(low=-read_length, high=sequence_length, size=num_reads)\n", - "# # Left and right positions on the interval.\n", - "# left = np.maximum(0, _5p)\n", - "# right = np.minimum(sequence_length, _5p + read_length + 1)\n", - "# depth = np.zeros(sequence_length, dtype=int)\n", - "# for a, b in zip(left, right):\n", - "# depth[a:b] += 1\n", - "# return depth\n", - "\n", - "def depth_transition_matrix(depth: np.ndarray, max_depth: int = None) -> np.ndarray:\n", - " if max_depth is None:\n", - " max_depth = np.max(depth)\n", - " depth = np.minimum(depth, max_depth)\n", - " # Count transitions.\n", - " M = np.zeros((max_depth + 1, max_depth + 1))\n", - " for j, k in zip(depth[:-1], depth[1:]):\n", - " M[j, k] += 1\n", - " # To avoid absorbing states, add an epsilon to each count.\n", - " M += 1e-6\n", - " # Make each row sum to 1.\n", - " T = M / np.expand_dims(M.sum(axis=-1), -1)\n", - " return T\n", - "\n", - "def read_depths_from_transition_matrix(\n", - " T: np.ndarray, sequence_length: int, seed: int\n", - ") -> np.ndarray:\n", - " n, m = T.shape\n", - " assert n == m\n", - " rng = np.random.default_rng(seed)\n", - " p0 = np.sum(T, axis=0) / np.sum(T)\n", - " dp = rng.choice(n, p=p0)\n", - " depth = np.zeros(sequence_length, dtype=int)\n", - " depth[0] = dp\n", - " for i in range(1, sequence_length):\n", - " dp = rng.choice(n, p=T[dp])\n", - " depth[i] = dp\n", - " return depth\n", - "\n", - "start_time = time.time()\n", - "depths = read_depths_from_reads(\n", - " num_reads=10_000, sequence_length=100_000, read_length=150, seed=1234\n", - ")\n", - "print(time.time()-start_time)\n", - "print(depths.mean(), depths.var(), depths.min(), depths.max())\n", - "\n", - "start_time = time.time()\n", - "T = depth_transition_matrix(depths)\n", - "print(time.time()-start_time)\n", - "#print(T)\n", - "\n", - "limit = 1000 # just plot first 1000 bp\n", - "x = np.arange(limit)\n", - "#plt.step(x, depths[:limit], linestyle=\"--\", label=\"from reads\")\n", - "for i in range(3):\n", - " start_time = time.time()\n", - " depths = read_depths_from_transition_matrix(T, sequence_length=100_000, seed=i)\n", - " print(time.time()-start_time)\n", - " #plt.step(x, depths[:limit], label=f\"markov chain {i}\")\n", - "\n", - "#plt.legend()\n", - "#plt.show()" + "### 9.2 create function to truncate edge effect" ] }, { "cell_type": "code", "execution_count": null, - "id": "efb18a65-bd95-45d6-aa99-c4b87c0d671e", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "486629e1-bfae-49a4-9fe9-ee427efa04c7", + "id": "e6e70bc4-fa9f-4bcc-aaa2-b557015ca6c0", "metadata": {}, "outputs": [], - "source": [] + "source": [ + "def linked_depth(rng, DPh, read_length, sites_n):\n", + " '''\n", + " Simulates reads in a contiguous genomic region to compute the depth per position.\n", + " \n", + " Parameters\n", + " ----------\n", + " rng : `numpy.random._generator.Generator` \n", + " random number generation numpy object\n", + " DPh : `numpy.ndarray`\n", + " Numpy array with the depth per haplotype\n", + " read_length : `int`\n", + " Read length in base pair units\n", + " sites_n : `int`\n", + " number of sites that depth has to be simulated for\n", + " \n", + " Returns \n", + " -------\n", + " DP : `numpy.ndarray`\n", + " Depth per site per haplotype\n", + " '''\n", + " seq_length = sites_n+(2*read_length)\n", + " DP = []\n", + " print(sites_n+(2*read_length))\n", + " read_n = (DPh*seq_length/read_length).astype(\"int\")\n", + " for r in read_n:\n", + " dp = np.zeros((seq_length,), dtype=int)\n", + " for p in rng.integers(low=0, high=seq_length-read_length+1, size=r):\n", + " dp[p:p+read_length] += 1\n", + " DP.append(dp.tolist())\n", + " DP = (np.array(DP).T)[(1*read_length):(-1*read_length), :]\n", + " return np.round(DP-((DP.mean(axis = 0)-5).repeat(DP.shape[0]).reshape(DP.shape)))\n", + "\n", + "rng = np.random.default_rng()\n", + "DPh = np.array([5] * 500) # 500 haplotypes each with depth 5\n", + "linked = linked_depth(rng, DPh, 100, 300)" + ] }, { - "cell_type": "code", - "execution_count": null, - "id": "af2dac64-370b-4f2f-bb53-c8ead88afa97", + "cell_type": "markdown", + "id": "f5fd5e79-bd6f-4eb4-b810-817457c619f9", "metadata": {}, - "outputs": [], - "source": [] + "source": [ + "### 9.3 Explore real data" + ] }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 20, "id": "5ab6b6ab-76ea-49fb-9893-94a37a429d92", "metadata": {}, "outputs": [], @@ -3483,7 +3456,7 @@ }, { "cell_type": "code", - "execution_count": 102, + "execution_count": 21, "id": "f1ec1dfb-5b91-4462-b800-cd84f20237cc", "metadata": {}, "outputs": [], @@ -3516,7 +3489,7 @@ }, { "cell_type": "code", - "execution_count": 103, + "execution_count": 22, "id": "1aa158af-c202-4f8f-a824-6e32508c5abc", "metadata": {}, "outputs": [], @@ -3528,7 +3501,7 @@ }, { "cell_type": "code", - "execution_count": 104, + "execution_count": 23, "id": "7b98e547-4dbd-4ebd-8c67-6a3984c57ead", "metadata": {}, "outputs": [ @@ -3549,7 +3522,7 @@ }, { "cell_type": "code", - "execution_count": 105, + "execution_count": 24, "id": "690c2c53-c345-4e9c-b8bf-e23d779decdc", "metadata": {}, "outputs": [], @@ -3559,7 +3532,7 @@ }, { "cell_type": "code", - "execution_count": 112, + "execution_count": 25, "id": "cfbc07d1-3d76-4c8f-ba49-ea9827751de0", "metadata": {}, "outputs": [], @@ -3572,7 +3545,7 @@ }, { "cell_type": "code", - "execution_count": 113, + "execution_count": 26, "id": "0e4a70d9-9812-4bc8-bd33-f6f096bd50b7", "metadata": {}, "outputs": [ @@ -3597,47 +3570,17 @@ " head()" ] }, - { - "cell_type": "markdown", - "id": "f79835db-5132-4789-9239-70f3c89f2a43", - "metadata": {}, - "source": [ - "## Base Pair seq quality" - ] - }, - { - "cell_type": "code", - "execution_count": 114, - "id": "800b1fab-5881-496b-aaa3-78cb5a492ac9", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n" - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "%%R\n", - "\n", - "df %>% \n", - " ggplot() +\n", - " geom_histogram(aes(x = quality), bins = 30)" - ] - }, { "cell_type": "markdown", "id": "2ce4fcdf-00f5-45c7-98c7-c23959144906", "metadata": {}, "source": [ - "## Read length" + "#### check read length" ] }, { "cell_type": "code", - "execution_count": 237, + "execution_count": 19, "id": "0e3ad833-45b3-4c14-b01b-6591475e6cca", "metadata": {}, "outputs": [ @@ -3682,14 +3625,14 @@ "id": "5126f220-af54-4504-9e70-a5721eaa7f26", "metadata": {}, "source": [ - "## Depth\n", + "#### check depth\n", "\n", "mean depth" ] }, { "cell_type": "code", - "execution_count": 117, + "execution_count": 20, "id": "209addda-cb89-4221-8849-3cdb9871ff1b", "metadata": {}, "outputs": [ @@ -3720,7 +3663,7 @@ }, { "cell_type": "code", - "execution_count": 125, + "execution_count": 21, "id": "5de28d50-7d2b-475d-a8f8-9b26e5a5f9f0", "metadata": {}, "outputs": [ @@ -3745,7 +3688,7 @@ }, { "cell_type": "code", - "execution_count": 119, + "execution_count": 22, "id": "f7faf341-8a26-46fd-bdca-0bc1f8904556", "metadata": {}, "outputs": [ @@ -3779,7 +3722,7 @@ }, { "cell_type": "code", - "execution_count": 122, + "execution_count": 23, "id": "5ae2b982-fa06-4a22-bfe0-9df8f05e2d63", "metadata": {}, "outputs": [ @@ -3789,7 +3732,7 @@ "37498" ] }, - "execution_count": 122, + "execution_count": 23, "metadata": {}, "output_type": "execute_result" } @@ -3803,12 +3746,12 @@ "id": "bee7963a-aaaf-4a4a-98fe-0ec8ec36c398", "metadata": {}, "source": [ - "Expected under poison and observed" + "Expected under Poisson and observed" ] }, { "cell_type": "code", - "execution_count": 238, + "execution_count": 24, "id": "8fe62eb3-0d1d-4dd6-8899-f18954ce9724", "metadata": {}, "outputs": [ @@ -3830,9 +3773,19 @@ " geom_point(data = data.frame(x = 0:100, y = dpois(x = 0:100, lambda = 37.70846)*37498), aes(x = x, y = y), color = \"red\")" ] }, + { + "cell_type": "markdown", + "id": "dff7440a-356b-4b0c-a25d-e072234b7370", + "metadata": {}, + "source": [ + "Measure of autocorrelation as the difference in depth from each position to the next one\n", + "\n", + "a) per position" + ] + }, { "cell_type": "code", - "execution_count": 135, + "execution_count": 25, "id": "f81cb819-5792-4739-b604-074ee8e45124", "metadata": {}, "outputs": [ @@ -3857,6 +3810,14 @@ " geom_point(aes(x = positio, y = diff))" ] }, + { + "cell_type": "markdown", + "id": "1b68883e-6ebe-4a4c-a817-aa21a6ba1a58", + "metadata": {}, + "source": [ + "b) as a histogram" + ] + }, { "cell_type": "code", "execution_count": 208, @@ -3912,6 +3873,16 @@ " summarize(mean = mean(diff), var = var(diff))" ] }, + { + "cell_type": "markdown", + "id": "6f9fcd4d-820f-45e7-93e0-3abc9162607f", + "metadata": {}, + "source": [ + "### Independent depth\n", + "\n", + "Same measurments if position depth was determined by Poisson distribution (independent depth)" + ] + }, { "cell_type": "code", "execution_count": 128, @@ -3990,19 +3961,29 @@ " summarize(mean = mean(diff), var = var(diff))" ] }, + { + "cell_type": "markdown", + "id": "c629dfc7-890b-4c8d-bff4-77c50ab1015e", + "metadata": {}, + "source": [ + "### Linked depth\n", + "\n", + "Same measurments if reads were simulated and the depth per position was calculated from that" + ] + }, { "cell_type": "code", - "execution_count": 197, + "execution_count": 26, "id": "8e09a60f-8bf2-4de7-bc47-48e351d1e588", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "array([19103, 19104, 19105, ..., 29248, 29249, 29250])" + "array([ 3018, 3019, 3020, ..., 20943, 20944, 20945])" ] }, - "execution_count": 197, + "execution_count": 26, "metadata": {}, "output_type": "execute_result" } @@ -4022,7 +4003,7 @@ }, { "cell_type": "code", - "execution_count": 198, + "execution_count": 27, "id": "75be1eb1-8559-4bff-a3f9-8450718df9ec", "metadata": {}, "outputs": [ @@ -4053,71 +4034,71 @@ " \n", " \n", " 0\n", - " 19103\n", + " 3018\n", " \n", " \n", " 1\n", - " 19104\n", + " 3019\n", " \n", " \n", " 2\n", - " 19105\n", + " 3020\n", " \n", " \n", " 3\n", - " 19106\n", + " 3021\n", " \n", " \n", " 4\n", - " 19107\n", + " 3022\n", " \n", " \n", " ...\n", " ...\n", " \n", " \n", - " 1427245\n", - " 29246\n", + " 1436760\n", + " 20941\n", " \n", " \n", - " 1427246\n", - " 29247\n", + " 1436761\n", + " 20942\n", " \n", " \n", - " 1427247\n", - " 29248\n", + " 1436762\n", + " 20943\n", " \n", " \n", - " 1427248\n", - " 29249\n", + " 1436763\n", + " 20944\n", " \n", " \n", - " 1427249\n", - " 29250\n", + " 1436764\n", + " 20945\n", " \n", " \n", "\n", - "

1427250 rows × 1 columns

\n", + "

1436765 rows × 1 columns

\n", "" ], "text/plain": [ " pos\n", - "0 19103\n", - "1 19104\n", - "2 19105\n", - "3 19106\n", - "4 19107\n", + "0 3018\n", + "1 3019\n", + "2 3020\n", + "3 3021\n", + "4 3022\n", "... ...\n", - "1427245 29246\n", - "1427246 29247\n", - "1427247 29248\n", - "1427248 29249\n", - "1427249 29250\n", + "1436760 20941\n", + "1436761 20942\n", + "1436762 20943\n", + "1436763 20944\n", + "1436764 20945\n", "\n", - "[1427250 rows x 1 columns]" + "[1436765 rows x 1 columns]" ] }, - "execution_count": 198, + "execution_count": 27, "metadata": {}, "output_type": "execute_result" } @@ -4129,7 +4110,7 @@ }, { "cell_type": "code", - "execution_count": 199, + "execution_count": 28, "id": "68077b07-005c-4356-bb64-873058f3a515", "metadata": {}, "outputs": [], @@ -4139,13 +4120,13 @@ }, { "cell_type": "code", - "execution_count": 200, + "execution_count": 29, "id": "f2bfe1ec-cbbf-4eeb-aea9-50350b36221b", "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n" + "image/png": 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++4zl8/DDDx9zyw6ZA5kDmQOZA+05kCfg9rxasz6tDW9lwpBu+9Ey5WfmQOZA5sA8OZAn4Hlye0XTetWrXjWWc2uYY+xjdsgcyBzIHMgcaORAnoAbWZQ9cNHG//zP/xR/93d/V/z1X/91cfzxxxcPetCDMmMyBzIHMgcyB6bgQFZDmoJ5ayno9a53veIDH/jAWipyLmvmQOZA5sBMOZB3wDNl7+pFzpWRN7/5zcPfHe94x6oA6qbPU045pfqWXzIHMgcWx4HHPvaxxZ/+6Z+GPrvDDjssLiM55c4cyBNwZ5YNNwB3/f72t7+tCnjRRRcVT3va00LHrhz/8PLEJz7RO+XfmQOZA3PmwBve8Ibik5/8ZFGWZUj5G9/4RrgKds7ZyMlNyIE8AU/IuCEG485mT1/5yle8U/6dOZA5sCQciJmE/eY3v7kkucvZaOJAnoCbOLSGvt/znvccK+3BBx9cXOc61xlzzw6ZA5kDi+fAIx/5yLFM7LfffmNu2WE5OZAn4OWsl4Xk6thjjy3ufve7V2k/+clPLnbaaacCM5Se8irbcyT/zhyYPwfud7/7FUcffXSx1VZbFdtss03xohe9qHjwgx88/4zkFCfiQEZBT8S24QY68cQTo4X74Q9/GHXPjpkDmQOL5cCuu+5a8Adtu+22i81MTr0TBwa5A/7gBz8YVoHs3n75y192Ysha83y3u90tgKz+3//7f8Xll19evP/97y/ucpe7hOsHzzvvvIodO+64Y/AHCjqvsCu25JfMgYVz4B3veEdxq1vdqrjFLW5RMPZlWh0ObCHouf+Dzy0wzxj7/93vftdLDr761a8GsanGhxnFc845Z+VXhqxsf/WrX/XCI42EO34vvvhi/Rl9nnHGGcUznvGM4qyzzhr5frvb3a741Kc+NeLW9ceNbnSj4te//nVx5ZVXdg26tP6vcY1rBHHgFVdcsbR57Jqxrbfeuthuu+0K+umQaBZ9at78+eIXv1g85CEPGUn2rW9965jbiIcV/LHqdXXd6143OgcNbgf8ile8YmQyR60GmH6mcQ40Tb6EOOaYY8YmX9yxjJUpcyBzYLEcOPDAA8cywBiYaTU4MLgJ+GY3u9kI59kJ3+QmNxlxyz/ac+CP//iP23vOPjMHMgfmyoEb3OAGY+nF3MY8ZYel4MDgJmBWfzTALbfcskB0tvfeexcx9Zql4P6CM/GmN71pJAec81772teu3G5961sH8fNpp51WuelLtoSlnMjPzIHFcQDQJAhoJd5j/VW/5+dycWBwKGgmEBC7TBDI3e9whzssF8eXKDcPf/jDC0BYLFruf//7V+dG//7v/x4m4gc84AEht9tvv31QRdprr72CxZ23v/3txY1vfOMlKknOSubA2uXABRdcUGAR6/e//31x6KGHrl1GrGDJB7cDpg5+/OMfhwaJflymeg6wCz711FPDDUf4BGhz0kknjZ2b/+u//muBmTvO1LfYYov6SPPXNc2B17zmNcXznve84rLLLlvTfJhX4Zl43/Oe9xTvete7wiQ8r3RzOtNzYHAoaFRpEJ0qMVl8//vfHxHT6LdVes4CBcj1gt/73veSbECa8J3vfKd40pOeVLArtvSf//mfxV/8xV9Yp87vGQXdmWULCdAFBc0FHtgQVwJF/+d//uf6c6mes+hT8y4gSixcxGAJgCS3lw2JVr2u1gwKmvtqLdFAuS0k0ygHPvOZz9ROvvhGleYFL3jB2OTLt3333ZdHpsyBigOov9jJlw+77bZb9T2/9M8B1AE9oV6YaTU4MDgRNOIYT9kYh+dIUVxyySXjjhGXlKrS0HRCI0XPTh05ELvMAz3vTLPjwG9+85uxyO2NZmMfs8NScWBwE/D+++8/xmBsHGca5cBDH/rQUYfEr6OOOioqvn/Ws56VCJGd1yoHnvnMZ47hA7JO6mxbw4tf/OKxBN72treNuWWH5eTA4CbgjRs3Fky4WCTiFp+TTz654Kwx0zgHQItztgKhtvVf//VfBaAs+AbP+M05MChLy8M3v/nNWQQ9zs4170K7Of/88wvMmqI/fthhhxW77LLLmufLLBnwuMc9rjjhhBOqJDZs2FDsvvvu1e/8stwcGBwIixtBMD/585//fLk53zF3qw5CiBU3g7BiXFk+ty4grOXLfTpHQ+xTuqDu22xtmovz+bLqdbVmQFg0B1biXBrAH6vxTHEOoM+rfOJ5m9vcZuQ3NqChH/3oRwFpqX6xrZ0pcyDGAS4F0HbCc9WIievZz352gQ484t0YpmTZynSnO92p4nm+C3jZaqc+P4MTQd/whjccKTGmKLndJ9MoBwBvICK05PU2uRnp61//euCfvbPjQQ960KAuULA8yO+Tc+De9753cdVVV41EsKwqSCOZND8ow/HHHx/a/aZNmwr+lpkwpmMBke9973uL1772tcuc5Zw3w4HBTcCmbNUrO7hMoxxoe7/v+973vtGAf/jVNnw0cHYcJAfs9ZVawFW7FQrpmS42uaULffdlprPPPnsseywgMq0GB9bEBHz9619/NWpjjrlse8kCK+wY3fSmN405Z7c1zAE9f7Qs4Px4lehP/uRPquwC5PRGLqqPS/LCHcCeuAc902pwYHATcEwX8dxzz12N2phjLlnpNxkoQaH/rne965hZSlDmhM+UOWA5gKlST1ihWyXC5CqLU+yf77PPPsURRxyx1NnH0tg1r3nNKo/3uMc9ihe+8IXV7/yy3BzIKOjlrp8qd6uOAqwKYl4yCtowY4lfMwp6iSvHZU2lEBkF7Riz4J9rCgXNuSUmKe985zsX82yIX/ziF4vnP//5xatf/eoFV3f75C1iFb3fPfbYIyDHb3vb244Aaqy/L3/5y+0TyD47cwCAHFdD7rDDDgUmQ1eJvvnNbxaYR8ROOAC+VSNso2tbR8d2Feh+97tflefTTz99FbKc8/gHDgxuB3zmmWeO2Z/97Gc/W6AeMUsCDPHgBz+4AnAwgW3evLm3JGexA26jJoJh95i9WYxxPOxhD5uqfHkHHGefrxfQ6k95ylPinufg2nYHjNlJ2r0lDLukcATW3yLefZ+69NJLxy4YAevwpS99aRHZa5Umhjc+/vGPj/jFGBEXqAyJfF2tWtnWzA740Y9+9FjdxMxTjnma0uHpT396NfkSFbcIsSNedXrCE54QLcIq7fKjBVhSx5hpwVe96lVLmtvRbD3wgQ8cdZBf6NSuCj3ykY8cyypXmy4z+cmXvDIBZ1oNDgwOhAVy0ZMFKfhvff32+scYRF81BGiMF1gVi9HPfvazmHN2m5IDsba6CsYgKPZWW201VvqY25inJXGI8X5JspazMVAODG4C/rd/+7exqjrxxBPH3Pp2+Od//ucQJfcP83ef+9ynuMMd7tB3MnOPj4u+Y/SWt7wl5pzdpuQAF9n7RaS/i3nKJGYW/FOf+tRY3P/93/895rasDjH92Z133nlZsxvyteuuu47lL6YbPOYpOywFBwY3AXPJPAYBUCNgAvz2t78dLhqYNbdJ76tf/Wpx6KGHBks0KQMWs85Hl/gxpnGzm92sCvLRj360ePnLXx4uaODMXFVI8Kc7YSYHgEHwOdNsOAAQ6P73v39QAWPx6O+4nk2q08fKDpK28md/9mcByPfpT3+6WCUdfOzI/+AHPwhtnUX0XnvtVaCWtMx09NFHF2984xurLCKSXjXrY1Xm1+DL4EBY+TKG1WnFGYS1GnXVFoS1GqW5OperDuy5uiRXv1EmaJ7aH1enPru3Va+rFAhrtczUtKhfkIzbbbddBYjCDvQ8RHiI3x7zmMdUOWQhEDPNV3lY8Mu97nWvABRrygY7Xm/f98ADDyz460r/+7//G4zcU0eckaPOtNYuy2BgBNh24YUXhr/Pf/7zI1c9wlN2XmoCkTNUAH1eLN2F9x5VPUszoj6tb33rW8Uf/dEfdclub34vvvjiwGuM8/zyl78MkpvrXe96tfH7/L/0pS8tQBpPSlyUYG01azxIBvoyEISxHNqTEv0qZumOfnzAAQeEdLk2kjbW9275a1/7WsG90GBEiJsjrGnarpZpqM/BiaBvfetbV5MvlQYSmVt/Zk128iWtyy+/vHjnO98562Qnih+UJIN6G/KTL2FAQE+ywr7nPe9Z/OIXvwiTL/H84z/+I481RZzZIcKH/7SR5zznOSPlf9e73lVNvnzgMhEuv5iUbn/7248Fvdvd7jbm1oeDn7yIc5Hic8T4nEF/97vfLS655JLiBS94QW0xY5gN9PonpSc/+cnRyZf46Ad9qAodddRRI5MvcTPpx4hFM8dMbAxoV+SvT2KRAxKeSZhFHryfx9jbZxnmHdfgJmB2Vp44m10EoZO8jEQHmZbobF3Jo8IZFNca6Vm6ltubb/zKV76in6qn3d1Uji1fkDZ4Ymc4L+JCg0URkjAlLlhoavd9t8cmCVjTd8173ZNz9rZEXdjxse8701lUcP6vRHqraIxF8z+P5+AmYMQxnhali7isdmQRQ01LFrzVNq6//du/HfFqDd+PfBjwDwzlq7oLkzHW2iw98YlPtD/De5PN7rEAxoGjBk9PfepTvVMvv2984xuPxYM2wKIIC1Eq/oTX69evr81K35avmna4TTvy2sz+4eNLXvKSMW8pMTuGgrTtsRhO+RuLsKUDYwJ/qnpGGstqhKVlkWbubZAgLDoe1mtoCKAYpxHhdakBK4I7+OCDC73QvkscKb99gxA+/OEPN4qgQHZzlsNKltWtEhIFu7tQ96Yn+qy77757wbngHe94xwKEL2YXF7lLaspz1+8M+LS71DV87MQQG7LTvfvd715wgfqWW46ugzEJuttuuwV1NkR604rxdtlll4KzZkjvu+1Sri4grL/8y78sdGfFTUKLVEOivWHY5IILLgimPZlgQTcrxfoUpljtrhJNAC+50fBtnsccc0wQfZMux2NoZfD+rGc9a+z4oU18MT+nnHJKoQs3sCecOaeOiFDT/MhHPlL81V/9VYHxoGtf+9qxKCd241hFNzy0u76OmWJ1NXEmFxAwBcIa5ATMalcHgQXweiZJrnoDjDElo6BjXFk+ty4T8PLlPp2jIfYpygSlJuA0N5b7y6rXVWoCHl16L3cdtM4dOwau0mMHZ1ezrSOY0OO6detWxig6hkPYsfs/9H/VjR2avtsnO5vb3OY2AWHahVXs+mw8sfPOLvGtql90xf/+7/++eNSjHhXAML4c7FCUT32gxDGaAiL1lre8ZXHqqaf65Hr9zVV4mneeMRBfrwnWRMZ5J2JXLoZosxMDmMQRgeYf3tsz05qkkp8AcWl8AOL0vU9DNvRFjfdDH/pQMi98YFxUv2ABOBbS332cgRMnUhbyxA57EcQu/HWve12QQH7hC19YRBZapzm4HTAoP2+/GLFL7CysNZdaeGTyRZxqicEOUU8f1OcKEPF8XxZ+HvCAB7RCe8MbeOQJNHDfYjCfxjx/N4mgUWmx9nvBLJx88slVFgFceRwDImoMRExCsctJUA25733v2zq6tjvgd7zjHcEQjY94lmpPPi37mwnPHp0gHv/EJz5RefF9iqOqc845p/rOC8cJiLAnIa+a6OMAI+LHKu+n6Xds3GESjiHdPT9icVNWPcONfa9z43jlFre4xYgWCmJoayikLnzdN19XKb/kgbYN8p3FH22X8Z97khdJa2YHHLsk4AMf+MDMee8nXxLsA2Qxi4xz0Xhf1FbCwIo0Rl4NJ+ZnSG6eXx6hryZNbZmnsQWNypmnPgZEHye/X/va18acF+ZmJ18ywc1edeQnX/yyK56UmtR8Un2iS3qxcSdlvcvzI5bONFIp9I+ZAC3ZxaZ1n9U7CwiQ/yp5QYIxDzsQk5ZncCJogA6eYkrp3s8sfiP6WkZildoX+Q6XipfjgBh5FHDMz5DcuN7Okh9AY1IC67/r+01ucpOxIJMg2MciiTjMKt5IUq2cFAGtnpva6jRgK03DPmOocPsdDMQs6DrXuc7E0U4zVlJeC3IjE/73xBlrGZCdpiK9CUIbmIYfLZOd2NvgJmDuH7WWdzjHwcD9rIn7cS0BBIvtZqyfRb2fdNJJvSSNqOqMM85oFRd3B3u1B3i0yHtuW2W8Z0/vfe97Q4zwjoHigx/84EgKWBECyWppmvPCd7/73ZUdb+Lk1q7Xv/71Nvre3k844YSxuOZxFehYon9wAIGsxETA2XodeekEfrGENSlhcCVFHCvELq9I+U+5v+1tbxv5xASUai/HHXfciF9vBYuzW86DJyXGWtDdSrRx7mKfJ6Gd8YpXvCJoFqCTvH79+gKNlGWlwZ0BM3ix6qGz8Zyn7B/jFKgDYIlmGgs6scbS9gwkFjblhroAZyWI2TiL5QIJysAkAUho7733DioFdOrLLrus+OY3v1n8wz/8Q8FZGmo26HjyrQu99a1vDZa0mJABy3GJ+1pSQ4JXgEQQdyKtSe2SEE9u3ry5eOUrXxkALV14HPPLOTM7PEBGXantGbDG+5rXvKY47bTTwnkwQL5FEio5TAJYYfO8jvUp2jnHIlyIgbpYSnLTpUyHHHJIsMRF22dRzsKThVZfRJ8lz0iTeDK5p1DQlA/VLC5T4XyWfsx905wZ07f7IIzLMK5wFktZ+6BYXdXFixYMZUUqM+9deCxfjJOUwdPgdsAUEKAFDZyGj/4j5iinOUfzTEv9xsAFO0JWoPNIL5WPlDs73xe96EXBFixnI+gCf+5znyvoMIjLAcsgImXyZXXMOQoNmMbDAM75ELsEVrnEA5irK3EmyeDQ9YpI0mZxgzk9Ju15EOXfIKCpPs/MyTc6vvyp7masLJxbMYj1YbWKumbSnwffAJExAYM+ffzjH18cdNBBseLNze25z31uWPDFzndjmUCnFvAkdponPTsEWPWQhzwkgLcQe2MNigXuRRddFNo9Jmqpj74IG+ssjgHBsfgHgZzafdPHkRDqBE0/BLS1adOmvrJToG+NZCt2NWxTImwCHvnIRxYsVuqIRQQ62x5DoWEY2+BHH/1H45zFc3A7YBqTF2sidkM0QceaFeIWtLO/pB7Td6TdB3VdAfo0EcNgZ7iJEBsxaLCAYOXYdG6GqgELnTYUE2/RQZp2wB/72MfCYG7TYCEQO++3fqZ5p9yoW1liV+TPFe133vkOD1OGOFjoWPOQsUs7PJ+YxKxoz6dZ95t6pG0y4PKO9TGMY5DHttR2B/zjH/94zLIXadCOmID62g21zTdYB7sQ9m3V9yl47O8ERk2HwbwtocanAKCmMEg4bnvb2zZ5q/3O7p4JK0beGBCTld3lklfatBJnpRgKmYZ82yW9tpM7k6oVn4NKVyM0tq6oExYdSuzgkdYpsfhh06X0H//xH8Hwj/5exDO1A16KCZjbSmxHmYZBHuSicTHxctctN83MgmLpMhhPqj7i88iq1Q7c/nvT71j+msK0/c7A20QgMw877LAxb5ydxy4Vtx6ZNPxCgIms7fmzjavtO2dH/rYabtaiM9cRkxWTW2pREasHyz8GzdiAZf3Upe+/0ebZkSoxGTLQYYGrLVEeFpL00zpiMkkhbRFHzvOO7MMPP7zgrlxPlo++T8XqhvA2jI/P/sYs5Bve8AbrVPvex/jAIrFOV9nmfZ2oATbtvDm622GHHWrznfpIX0Yy5snmwX+zv2P817BaV0hY/GUTiNzVXjoSz4c+9KE22iDFA6G9SGKhHRNBb73ITGnaDFqzJtCm7EosQm7WabKo6Cs9BsFp4mqzm52UH23y5aUDmhbnNE3hGag8Wphzr6ZwmsYkz9gE2qb9UE8MCH7BUJcHWw7KFSPrJ/Y95eZ34uSrTTlsfJQHasoDbSxFDPxN4VNhJ3FPLVZtHtr2KRumLi+pxUcqDNiLtnGn4mDsrJuAbfz0o6YJuE1/TOWl77ZLOpp/ravYRs2Os7F+S71oPKm8z9o91TeWYgcMUGIafTvLvEc84hEFtnQtMYAgbkGU2RU0ZOOpe/eiF/ymFOLr4kl9syKYlJ869x133DHYYK7zM8k3rnDD+EkTxUS6hGGF21T3nOOB5rXEbmqWhv4RcXPGZOmss84Kq2nr5t8Z5Bgs/MSn/mLtxBqqQFTsgT8AZGhLkxC2jKl7HZgYCLDF3UU1o60ImiOOFOIUvEBshzNJmdqEYTK43e1uN+K1yRAHOyt/dy/1acW0IxG6H/DY3gbkPo/95JwTcek0xI4zpfdLeTjTVgLQZ6Uh6m6fti1a97bvvn13ufcYwCztVYmxmrNtyI5/oK3tmMGO14quvcERyuz7sqYxr2dKBD04EBYiQgBYDDRMvI9+9KOLl73sZQXGOGY1+VKJNFwGXiXMDcas0ej3eT+5fBvTh9zPCn88KIXzOS6xQIzEmRKTHiJezlvsOZWuJOEvFpvaTL6UFf++czPYtZF+oFbAHzqtdHBUzWY5+ZJfVDJYyKGrCXqWc8A+9FzhgZaZ9ul5wkDDgK9+ppl8KQfmJxG/cQkGxy9MhF0mX+JoS9zaZK16EY4JiT45z8mXdFF5A8TGBAAvqU9rBQs/nuATdwgrcfTRdvIlDH0IQxDgTRA5btiwIRxj8E4e4D/9gD9upJp28iVNjhNQK7NjD+4AKe3kixtYAlSzWDRzvspkByqe/NAmzj//fLxNRbRn4oPor/4Ypy5y+hv5oe7Io06+Pgw8BiRK3dKu7eSLX9LkEhMufMEw06InX59/+3twO2AaOx0hX8Zgq3k53/NlDMtZLz5XTB5MKn536P2t2m+7q1q1vKfyS5kgRTmn/K2a+6rX1ZrZAYNABKDDTsn+9WGTGbu6Nk5r4pJ3+83uGrs2duw0g+Akvq7qOqm0WF1qnIjjWWHa/PKOHqF1Y/ekvwF76Lt9ps7aYvkAnW7DslBqY6wEpDR51rC8dzljjeWljdt6AWJpmvCu7qwN3Wn8supnskJMxm94aM/duBRA4+QZM4/o2xJSnK4EAp/6hFe0RU0TcXRbwogG4bCORJ9qIs7pNR37XJRRfqQHNh/+GMOX52lPe9qIf8IquMf7Tf1mR2rTpH0jdaIe2LXZb7HzzFS8KXfyZ/smExV/pLNRVP4sUT/2QggMp9j8xN6beGbj591eDEF8P/rRj7yXkd9IKVBbQkKBSB6wHlITmxdQzpYwXqNl5pjB8xEJng2/zNb2BrcDBsHnxXpaedwEwjnwpESletK0Yt/ano/aOEGnejEe6lOcj0y6qmWSbDOA2ny0fUeM2gbpzaTCABejpvPcGG+ZFGaJbASxrSoQmmdEY7EJE5AHA1uKmJBRAQGRHLNCpW2I8Km64pahtlalYuefNm/cEOQtKNnvvOs9ttadBYla8rLu+h6rJ/2GYRrErvMi8hmzM255bXdViDVjRkO6nAF7NZ82ZbX5aePf+6njOX7R/VdUMwuxSXTBOWZiLGsidNvZpHhKlREpJefybej9739/MKZCXFoeDcfYxhEbFGu3uE97lEMc09Ca2QHXickAn8yTYoN1U/qx67M++clPNgWr/T5JPmojNB/96tN8Gnm1ZgFHPsiPJtUe75/fGDWYJcXOtlMoT4yZ1JFe89Z0BkkcqbpqE1bzgNESPUNWN/sETNZEVrqjfj1uQN3bPGMDc5twk/pJAZNS8cXMaOK3rU4vfr2pR9wWTdYYRhdplc13W93groZ5OF9nJ9uGVIUtNhbas+uUAQ8kQstIgwNhIQJJ0ax2gan0ED11JXRNPQEomIbqeDJNvF3CsjpOURMYJWY8xduVTsU9qXvMhGksH8TfJOJS4Fpsh+Xzl6qrNvfZalzsKjwoR7/xbJMPrpn05K9J9N8VfOPd+T1r0JxPs669eb/89rqjMT9Nbk367E3hJ/leV8/EZ+0exC7maJOmR+WnwnQdXznWsYQ0LUUA/CBMinqyJkb33HNP/zn8nuZIMBphT47pEveUwLyjwQZtDOWJIYFpxM+Uw5+H2PNLL1okD5OYsmPlrg2ZXQxXvNWJN9vwF7Sg3dGg1H6DG9xgLCioT0t1HUL9pZCK+l2fmOcDeemJc+FYp7L+vFoZExrm/WZJGFRYJ4YLlKiLVFkZ2Pw5lYajHSgS9Mgjjxyz6+zbJHXlr6kD0brvvvtqlI1P2jrHGAzOLBos+p9zSH9xSCxC7IRbRDBn2ccee2zMa+UWkzAxKWP3u634vIpsyhfMwnrxLGL8FLHw8ZM2efdtLxUed0SjmN+0RL1RD/Q5xNmW2hzdWP+x9zrJAtgBuzikLCoZoW+jHVJHlJ8z7bb4GaRGutjUeOukLYAwOSoAL0E+ub41xieOLrQt0n7tWEscWHZTwpqZF1GjvdB0EYeGn/dzcGfAGQU97yY0eXoZBT057+YZkkE7o6DnyfHJ0+JcG5oULzJ5yrMNac/rZ5vSbGJfM2fAsA8bqx5xGDs7mITV7NY4t/Dm0IiL1S7fWWlb5GvXdDhr0wsP+kL7AhRiR8AfO2wANfqbJzzjPBfQBsYUACABGuLWFETEnCV55LQ1u8fZCyA3/GNNJ0acldo0QUHr+aj6R3dS/ViD8iAl1d2u6jXcLJ6A3zTNut0TaXP5On7ZDYP61XA87Xk1uw52k6z6/+Vf/iWabcAp7Mg0jtjOMhrQONJuHv7whwfwHjsCjQuwUVtip0E4AG/s5K193VQcXP0JMlXT4+l3+amwfbuz67H5AGFbR6CEacOEwc54naTgvPPOC3r16NZbYJPtZxwnUZfs7OhTtH/qlfFjklupUnlH15U65o+Jij/KgFjdSh44A1ZtAsqJMQtF6+PfIqSVb13sYJM/vdJQeWgNZsTyz0UwmpZt8+rG0+NMMPmp32lrHuSFf45Q1A92A9rQM57xjHA800Xa1CbeOj+D2wEzSKTOFGFwylJPHZP0m0cRWgQwDcGCdNg1AEpgl9eFUI6/173uVam8IJIEXYnIeJpVLY2xiRh0UAvwDb4uHIMaf36COvvss8NkpGExRZkSZYFgZFGwadOmsWscQc+iiqViXI0P8Zg1yK7ufT2JGyMklsg/k7In2lvT5MTRCGJhrrizhPETe7QB6IdB0hM3u3Q5ByaOFIAIwBFi7Tqi/mKTBMcueh7nw8cuM1A/oPtjdoL1e99PJn0vDiYNJGRMnpDfVcX6COJzPxHH2jJiUMK3BRWRPuLWSRZXhFXiqEABSurmnxhBYSyKlc/7jf1GlI0VrSai7LENA2MKi21PjDP+HNj70d8s1LEuh3EgjG94om8pRoE4/RjGGPWe97zHB6t+U88WALtOjp/qxPtVwJYvqR3w4CZgBsmU3WF45VdLLfkXvMUaMPGhp8qK0tMkJtC41YPdqBLnR2984xvDbR+TTsDsKj/+8Y9rlL0/WWx4HVmv6sJuIKUTyO4KpGWMv3WZnaYu6+LlGwuCGGo0lmabfAPIYyHlyS8kwADEJio6cOoM2sfZpA7DAIXR+jpKlakuH6kwmk6Md/qt7yeLyZQUSvNhJ2AkP3a3qPmJqSFxjaS3MoUJXPoq6jJdSPPSJYz1m5r0rB/ekSBxDeKk1JRP2iZStRhx7zBjmCf6hJeAeT/6m93x6aefnhwjdCFb1/bryhBru3X+NV9tn6kJeHAgLHaMKWKwmwUpsMHHHVv1eT/+t0f3IsJh1T4N+TiniSsWNlZOn2cPzrDxeHCK/bao97r8TpInpCUM0J78jiHGS8J04ZHnvU8zheb2/mK/Y2VQf21Ae+p31k8WdV0oxTO/sCTOWB3hxiA7b4rlJZaHunEx5r+rW10+Unzp0g7bjmGT9ttZzQ1NfBzcBFxnOQr9yGnIq6Yo/J3G7dUssOaCWKMrHXHEEcGGNQ2CAY2z5phKSJd4LWqwLhziXtLs0hi5PxXxqCXi8Oeb3FuaIhUvx+4rRseP81VP0yLDfXz+N5cxePLoZP2Obe0m4gw9dha10VkrYqcS438XoyPs/hRJH8tXTIzu/aV2yFZM58OA8k5RSr855X9ad6wlxSjFF1T9EAl7il2hyTGCXYiwOOIcNlb+1OKcdGI4Ep9+0+9Ymj4Mxw2czU9KKrKvC8+RB2NejLDjHqO6tuT9q6GNmFgYHusxAceEtH9Pqfag/nwfBj8xDxqcCJqVLCAgRBKcyQKAoYMggo1VTFcm09EQ5SBS9RMPvwFaAJlHJ7Cu89Wly60qwPNZ9ell21ZcVhe27pueCwJiYnEAyIq0GExQUUCchXoP50WAM1BnYdJB/MMigPMX7UyseDGCrhd8o1TPBQ6E0c4QywsG0i0oCfG93tSDf+6cxQ/Eggn0LcQZj95hS16mEaeFCFv8Y/fDRM9ZKmefdUbdMZTBQoGzJ3iEWhI8YXHG+aDuAjgPQ1cRf/hJmYZEHEqZmRSYfLvu6CgeeeaMEVEf9UgeGMiUp00soF6oe6QwPDlrjInqbDy0HUTotA/aFIsJ0kwNzjZs3++IN7GAxHjAbgudWDthxfoUdcgREOUE7JM6K0dyQf+hfLR7u/sHrEjaTOqAGRn8Ob8EFMUCi+MW2hLgrD4InqOuyHjB2TegMOoY0S/1tkGOoJToO6QPWBLDIfhhsQUvAJSxCKYs2idpu13OtdXaG3zhOLDN0ZeOS/QFxhj6Bm2WsYjxHPG2rSvAcuvkjBZiU4SRH8YdS+QDYCg7YjYgOk5ZP/4d3qlVvhh+wPvv8pu+Rxk8be0dhvCbgnLgTqUAUuB3H5MvAzGDJ3ppMZELAxWTC52vzeTLmS7nVKBMLRE31/R58aT10/Wd80w6Ix2LyYuFBBMvaHH0c9mdsWhg8iT/AB1okHRODDeAsGTFzrV4TCwMJiAelehwAHdifFE/PJFQcM4N2AzwEmnZnQfh0RnmaScKztjRU8bSGYsDCD1KBhsAcLMg6hBEM2dBXkfUpwf6mUmGvDHQg35lIsYohp08KSv5ZkKI4QY0XgYMrPewULLh9XubJwMaPKOe0AJg92f1v1lgsNMnz7QNT9QBetqcq4Huh/e01bq2TZloS6CBuREM/759+3Rm9Zu2zgRMv2RyqtvVsBAk35SNvkL+mQBSEzB4BhYYTBR28qUsjA8A3TC9Sr+A99hsps8AykTLoWlSI+9MRtQLE1QdUX+UjbRYbHzqU58KC2YmKlvfxMECgB08O0XqH8kd+WGxwDk27ZYFMH2fxae3DVCXD76xMWHipX+TnzZE+oAYkZIx7kHs2FlYxG7RYkPFuEX8LPb85MsYzLjAood+tt6cTbPAZ1K2Im00AxgTKSu8bELLtylTWz+D2wHTqBjgmSS8ysU0h+rspkF/UoHEzcrQXk+HtRgPkvrOd74TGkCsMlBQP/DAA0Nnp+IxlEHD8ChYGhvp2RVgLL46N8Kz4p2GyEcMVcuOn4n3SU960kj0DNp2AuUjE2vMxCPiH3YRqB2xCrfEZMUOImbQnzwxuLLLpAM3Tf423jbvHuBCWemgnlAt8iJ374dBGGQ1A6AlVud+cgftbI9LGOC7Gm1I8Zq02VGzs2Dy0DpFOwAtAUux3S55QXQYG5gZ/ClfTExI+Wd9bGDzHrvPme+0Lz1K0D7FQM8FKDHimIkJwhLl83WmY0sTCNTGE4ub70wgIHoZyyB27akdGeMIE3sdMTaBwkfypRNcnX//LdaXvR9+w5NY3adQ0ITZLNIG1H5igEe+Q0yi8ITxFX/+GIH+T5um/xMf478nJlhM3tKHmRc2icYFEy07dCsh0HB92y5P7YAHNwHTCFhJxXaPVJSeNyqj2z7poHRUJXYXavuVDnL00Ufrp+rJBETj9cQq0xohZ+JlAmK3gY6odjwNR0MBpekneP3e9IwNpE1h2n5nx+Dzq2F1UOI3vGLBESMGdS4HYNLwxAoefjURq3erl9zkv+l7bDBJTYRt+MtqPdYmyQc7Ld2pMEDETBo2qVHY8tDm6kRuDAZIP9jlWQJlqjvh2IJS/SJxYJfl6TGPeUzUHX+0E3ZF86K6OtF2qRNwnV/y67UZYv5ZXDCQd1VzZDHj40NaZxHctJ2Uyp0Pm+IvC9i2WBAfB22TNlpHjE11JiuV5zYOpAKqOmTdY++Mv2gIpMrLUR2L4NR34kSSxCIRYiHNmFpnUyCW5xB4gn+pCXhwICx2RamBzirMd+Uhk6QldrdKfoWs7qn0cLdnYoiKVB8wNpnZtDTuZXnG8hvLG50tRZz5pBp7ioc+rr55FJssyOeklGqTxGcnwhSfUipcsfyw46gjBqFYvdkyI75LUWo3XidyjKWXin/Z3NsY8meH1sT3WLli9e3biv9t46njufVnNw/Wvc17mz44yeaAds+mqA018bZN/9fJl/SQnNWpq7bJUx9+BjcBsxPyk6UyivPgSYmzOCZ3CFGHNdJw+OGHR6ONiTbwyCoN8aYiKXmq6DV27Vdsdx1NMOGYUrFIeO/k7MVBGtgbIGkSk/Fdd4EaB8/UFYbWDztTLwK33yd5j7WVac6a61bmVqxtz9VtvhGJtaWmyzvYTfsdMgM5wEWlOqMfapdX/eqTc0jqIkZIduZJ2rfapNk0CSCdaiIAWykDJXVhOTP15DUqUjwlXFu+gvmYlNDpb6LYkYSGsWOluvFkE2KP8ew3/66791S9qqZE3YUTOn4TBwtCxq5Zjo2+DLHf8d4S87kibpxBITJiwkTEpAQYA0s9kxJnMBz4gxpENUEbBPEhevHWaDhz3ehUTDRtOhTWjwB08ce7gj04I7XgHERasU6qcbV5okZQB5xpEweIXQuWIgzgEM72/I6IwdyeYeKXxo5fT7hzZEDH4RzXTkaAvw466KCo6JIBhQ6MuA6D8dRLn0S9WOteDDAxsStpegMZFuDBdxYWiBpju3wQqXaAZeECuMcSiOjUpGf96TtnZqQX2x1x9AHSlUUdom78Moj7HRIIUn95BgtPjgli6lSkjdgeNK4XK9JOrFlRzecsn5zz+cEafsQslnEs5etM8wbCFvGhJV+PLJ5ZfNAe7aUnhAG8mCKQ87E64nwStDJ1s07QvikJG/EC1GoCaYGhwPiKqvJofvxEr+72ydhTp9WgfmnDMd4yBqfMWdKeuLAmZblQ42bxqTd4Ua+WZ7xzBKhx0O499oR4qDMAoMwLXNQBRoD2Qf/zkzATdZsdteZvmufgzoBhJhXb5txwGsbNO6yeV8073Vmmx2SDeEtVHmaZ1rzipvPSsetEuPPKS1/psHhjUKu7a7uvtOYZzxD7lG46JhEJz5P3XdNa9bpKnQEPUg2Jg3WMfFtix4R6CGovrIJ0hcMqExEUenmHHHKIDTL2DpKO1ZieBXI2pBB4DzJiZwfy0O5ubISkj8iP81/on/7pn6rrwRDZKOiCnZi3H2zjafMeQw62CdfGDytOdofWpisTq98BExdnjKgXwBdLLJgQebKSt2hiLTuTtL/PE9G0+mUHZ3eNXJuHDiyLMOLGPGDdToQzNqQQ7NJQb3jOc54T0MEAkjgrgkC3pgxYYPS/yYA7SHQmMq9+wkrcq8jFxNWs9FFv8QYVKCO2w3kizgOdz04dQFSK2A3Qhm2dgchFdc9KXzwYiPhYYHBMws6BhRM7ZXZrSjHwGmHwz5N+CSCIyZydSxvxpsbtn5SBuJCgUBZ2n1bSA8qb/h4j+rxKboiH9mXPCDUM4Cq/U6XvwnMl1PPUNKU3ackOMrYzJGwduhkeo3EBgXSuE9XGbFOHgH/4R9uhjetTvzEptDnfpb0AoGQHTd+mvVs+a3zs1L3UBPW1OvOcSBia9KHZsaLXq0SdWNwE471eN8jCgzz4M2N06unXOi9gGOelL31pAJfpGKzx88SfLmase9/vg9sB28nLM2uTiHYQoekE6r/XXdaAMXJE254QidCRY2o+dOrNAov3xPlD7OwGG7NM6gwoljjfZAExyarWD7Q23lm+M5nacjBBWOR327QR/XuDJ7GwiP+YQBksvaoP/tFrtJOLjYMzfNoGu1YmCTo8olRPTOIMsJaYoK3Y3H5r+247ex36mPg4RkEsr+RVpRioY8Ae9V/3ZIBm8iYPiPxo222IxSdaAZwt6wDnw7HQZSJhIWMJQxV+0Lbf696ZvK2FMI6Z0FOH2qjcsHimv60TMW9sENa0bb2n+i5lRx3NW8vTOFJP8ku+LfmLXfjGAo62GaPYgi3mry83FjbewEZd//Z24TUfIKvt4k3dY09drLDw0c2T9cdCB3F2bNFo/XV590cNXcJ6v6kd8ODOgHXn6BnAb3a4qcmX79p5effEAB4jzo+8/qT6Y9UfI1ZfMSJ9O2mpH3tbjrq1fZ4vphwXQQwYljDo4c/k7PfUe93q2YbRFS+qCjHCsliK2JGryBi92FRdx3Yy/swvlUadu11gNC2yMPBgySNkJ518iZO4GMSgtpMvftk5Q4rkDz/cP/hL3Ezylur6nPUXe/eqMeijK7XRd1UUbN3kS3wsHJRigz/faEOxCwc0XOrpTSDiz96qpuFQEVsW8pgH8pXqM3yL6f7jDnajLSnuIMV/rXurvtU27kX6G9wE7A/ULXPZkfoBwH5Poafx41fuGg4rPymbrn5w1DApNC0ij9gqV8XcGr7LM4Ys7hK+L7+Irur4m0qnbdlV1M9OOEaIllPkd+ZWvGXDxNpOne6jDVv3nmo/sTCztio1yW5ewzSJ7Nitex5aMXisvHVuHvGqRkXqwkzyTdsWYVP9iR1+XRtLpRure88jwjYBrVLxz8I9xmd/tGLTTY3JHNO1pbr4iUMX97Hxs20ai/A3uAkYEWSMUMIGqRw7T+U8A5Nndas4Vmt0MksbRPGeAXGjoJ19x6QTcb4XI8Ql/oyOs0rOWGI6h5zrTUqAZ9rc5Tlp/IgeOV/x5EWRIGPrUOixgZiBD8MUdSYENV3dAXOZhVeJAGFed0aLCgkiIkSRiC5pBxgG8RTbAWO2r80igXzFjjAQP1o0qt1t+fRpf57Xvm7ZiTWJJBEZI2b3BJ/1rlWPirZ+7YTEYlLP8HT3bP3qO0hYMA96PECfQ5QZu+xAwzQ9VZJEXPRvi1Jvg7pWaVkKpavpWxQy/T02cbAz46goZjpR44k9Y7tmz0cQvmogJRaH2kiPfevDDQkc4xl8ZjLV824bN6LhlPpbSjJCn/CaFTZOfSdtHddj/YPvOm7GLnQhHr04R+OMjTf6jeeseappDe4MOKOgtWqX/5lR0MtfR+SQgTejoFejrlQK0XSUsRqluTqXlGuVy7RmzoCpMhBxgEjYCfDHaseeq2D0Qd3Rr8SvRdFeXe2jb+xaCbdOdkpesZ2dBytjvgMEAHHXRBhDwD/iFW4WUgKMpekoCle/YRGJ3Vns2kVW9HzzuyRWpsTHH2IydtTwhN88EcFiRADUsfpbv359WNHrb3ZMaupN3ewqEj6qe5NBAs5j8cuun0mYXSS7KM5vMESBuI34bIcjTvgEb9HBhi8A2XDT8zEAeIhDNR8829ofVpSk1VUmLY1Lzzm1HvQJUIO6B+CFX8SiILBBdWtY2xbwS/n4lkJmUz+UDT/syJEc7LPPPiP80PThG7sPdFHt7gCDNJo+iFp95yiF8zTwCSD/OZYBQBjbOYBHQGOAPJAfdrv4xWQqbZ2wXoUM0CG7B/Sy+U59eNu86DUTpxpP0LJM8mR3yi4y1n+pU9q7lp060b4AHywCGKCQ+qM96jt2o0H6ozdt+YsONWXDr22n7IKRCtG3FamvceGX9kq/QQpWRxtFqga/d9ppp4KLItD3Te0kKTt9l35Mv6GONE12zvwmr9S3tlO+A3BEgkJ++I7BEW2bGl5R96Dd4ZEf9ygDl44g2UDixxGQhqX/NFlAQ5wNv7S9a1jqh3zRhpCcANaivdCu6EPwX/1SLktI2/QbTz0m0vGNOoCw60D8Pu2YMSQbf6/vck65cBLARCkDWS9/Ai0vhUHRP2nEpZxDRr8RRoxeJPMgjWksnIjQgn8R6Y19Iz6p3GR8sTzKpFfKoDwWlwyWpXTyUkSgpZxxlCICDH5kMKniF1FtKTuV6psMnOFbLJ2+3cSIwVieReRT5c3WbYyPdfmhzkRMNRZ/XZjYN0GqR/ND3sTq1Uj8tINYHNL5R+KQI4OScsb8ejcZ2EpRmYn6FcBTFa+I2qJ+ND5ZbFR+RRw65lfUNSbmF/HJIi7EL4uksbg1D/4pALsQRoy1JMPIQiv4EXHhiB/asm0fXd5FNFoSn8YpSNkqLrELPpKOz7P+pn3JoqnRr6YhE3LoZxpenyL+boxD/dqngCSrPGvZZaIbi0vrWlQhR/xT3za+Wbz7/i1HJlUeZJHQmL6Atir/Wkae9Mm+8kseibOv+IjH5nXad9kARufZwYmgWfGkCFFa04pMGB0NnoqXFT+7xRTF4mNHVIfK9XGxkkTdgrNMq19LeVjls7L28Hy+oa6CasSiyJedXXvs7LEuf6xW9ayuzl/TN3Zs3hKQhknVrX63T1smVuZ9XDSv+s6k05QXpC/cOMPNLuyKPQFG8VIT7yf1WyaZApUs4oXvMmKkvI64A3wB9d4UBhVA1I48oTtsLcv576nf7G4VvY4fzhO6sa5FAABAAElEQVR1l9jER40TCQw7zLbEObxNU8PBgxg4Sb+nnuw8wTlYqss7O0Orj84usU6zw8bb57v2g7q8anqMTXper248wRxMg2+xcfEORqNJsuDD1P1GO6HOJGtdWP9tzYigmXhSNOnAlIoP90k6XUzhvy4N7fAe4Ud59JtHGvKtazp1eejjm+a1S1x9qRV4UanNgwUVWfemdwA5fVCXelIeWvGpzcM0bZwJV1VyFFVq40696wTQFCZVl1qmVPwpd9/mU/GnwuPetCD3YVPtaFK+K799OqnfXr3KA0NT4RbpbkX0Nh+T1ruNw753rUsbNvbepV/GwrdxGxwKWm2CxgqPTdQ6YiWeotggzYqbFWnKDjFnnDHizCRGrGZjCwhd8Xqj8BhdUBup1jgDcbNT8v5jac7Kjd2UJ/gbc/f+7G+v92q/dXm3CFkfzksw/G/1z5mcJc7q2xIqWCmUqFr0Ii6Pjvfx61k0Z74xta7UvbE+nthv2p6WKba7joXBjd04xLlziqh3UK8xmmT3Szwe2W77vr9sIpYubl31kNF8iJHWS+xbnVvMUA+7ck+MP1jv8ulzDd+8ydpabrrIgryleDPtJTO+3IyffVLsWtA+4yeuwU3AiA1QK/GEZSMmK0QnihTED4MYEyUNuW4gwNqVrWBUR1TcBQgE4I2dXFCFUdUYnxfEEXL2MwLBB/yAsQrMNVpdVkBBquJEmnJmFQZpzBJa0RUDDn4f/ehHB9AOgAjyQ3n9zjkmBmbCtioUhLHqNfAoNsAC/ycN1ECUMGwfExsTJwYeVBVF/ZNPxOiojuiuhpU96jJMesRvRV0AtawFJHQpY+XU+AFt2Pypuz5Rs+D2I0BRAIw4HvD5Z6HlJ3GAIahHYNLOEuJuOzlSJuobE5mqsoN/ys2Rgq0fFmdeNQUgGWAa2rYFlREnwDN4xaCIWUYsBsELbTM2X/pOP2AxqmAU0kc8bPMCQIX7lZkM2NnqwpCJwOq7kl/UiSD6GH0P0IttO/Q3+AkfEFXDH+JE/AqwadJdHEYsMGWI6hRqgvbSgE1i2czfIEUZbL1gWpa8wq9UHpBysEAiDY5zYvzlO0AiVJGIj3KyOKeu7KJA+c8TnmOgIjaBUQ+YcKR9EA9HDlyGQvn8LV2MO9QB/QP/1Lv2IU1P686WnW+4WxW4WJtBfQf+MBnRxrD0Ze84xxARImbGLMzpegLIZyds+502TVwp3qtfygO4kTGS+mCMs+2LsYk8QvrUsPpkLLbmPAFu6uZF/dinNyRkv/X5PrgzYCqLCsE02pBo1WH4sbpgcEeUmhLrxcIsuxsDK5NL3+K1RZabgZpBNF/GsMhaaJe2bi5SYt92sSyfr1Uf/1JnwAAtFk59oqBlRVZKYcfQcLJDDKg2WSWOfZPmVrnJ5F3K6mcMASer0sqP+pfVZ/AHmk8mk7HvoKNlRRb9k0FtzL/G659iGCKgoCmbrBarcCLaqeLmmw0n4r7wrS1KUlbHI+FtXLzDF9khj/kRkWcpqglj7rLLrvKmPBB1iDF/Ph37W1b1IQ7Kb91j77K6bfSjSEnNjz5lJzUSVtR0okhi2cGOlElEryPhYvlSN5DCopYy5l92lCNxCqhrzI/GwVPzzFNUVMb8NrWrdevWlbJrjiJ5RTWoil92DGNx23zou82/GBpJhpHdaIhb0bwaXhYsVZq2bHXvsruNpiM71CoukLeaRt1Tdu0VirrOn36jHDIZjMUNkt33QcJQPg3rnyJJqPJry4vWg+2PIk0L/U8WdqWAlkbCyO44Gb9PL/a7DYof5LUNC/JZ88sY1DSmynWolX/Cya5+JD4b96TvIkELaYhhm97i1jL28VwzKOg6VCDiIW+hSSp8jNAL83qFVgRqAyD2RfSCiDpGUnljzpOgZxF3k38FvGikWCxCdBzLHyJT0loUoWuqol/O/7xotU2+EJf3eVaEyNZaPkKvugty0tZnjOdtyuT90GY5foCa4mQ3yjEFZ5eIy/smRNHUVVM+bLr4RVe2KQw6qlbPWuNApM3RSluqS0frp85P23S6+EP8KzuZLkGCX5DAaudYAyOaT4G6kERYEfC8y6l55CgFvdw26cMbPdLh4g1rWUzj6+PZ91jB8YAsqvrIWrC0p9IJG+HgzoDrOgETQhvSTtzGL2eAXUVzMWPmTWlx3ucnX8LYzujjUPCWd5/Xb4vYTJnlbMpLzOxdU5i675yvWeoy8Ntwfb7r4NQmTkV6cn45C2pjwtGn27a/qMEUH562vepUN+7Ulc0v9PGbmnz5Zg0K8XtR5I391OXD8qbNBqgurrpvMWMydf6bvqVswjeF6/J9cBNwDNSgDMHqSRvqol4COIGdaRfyxv/bhAW4YoEHGgarLxDnjp523nln7zTX33Z1DEhlEvJWlCaJw4ZhF2YJQNeiKXVNYixfgHKgGOAl5r+rmyL6AVu1JQBqbSiFTE5ZBGsT57L4YZc3CSm/bdgYGEq/+0W4Spj0+7yeXVDyljcWRNh3XgGm9klIQmdN7XvZrHPSU/yIwmKTEeodiL9iYgCbNJOvigOtu7/6jG+IRhEfgmaMIf30kmgbD+/cpakDqf8W+83uEcASqGdEkBBl5Co87azeIAQm4xDtWWRoLG51i/FMv/EE3GYRk/qNqxhj1yWCSgUMpwQitatSOwM7YqXUzknj5tlmUQMy3V9XiPgekaul448/vkIIW3d/F3BMnGr923dQ2zF0PghQe11bk5RGkfeILmMmP6mn2EKNvDAQwqfU4AmqW6/WTCH4bZl4J/8qaQHlnSKQ4vDeIvzxSz/oqgrk60HTtEccbfOPJMG2U40r9aQcVltA/SHFiNVdXT/HHCdoc08YlNB+zjeQyCCYQQEzvlmaVoJTt2HRdOyRDW4goVX9qY2Eyko48B8bKzWtSZ8gsZmAU6jzSeKNSScmiacuTEZB13Fnib6tOgowxsqMgo5xZfncmAwyCnr56iWWI91gZBR0jDuLc1szKGi5ZLsUPdMRJBxIP7nYvZRdWSkikJFvUiUjv1PIRDnnKmWXWPnFpjFu+iciweobcYJW1m/+KSv+gGq0aZNH0NsgUa27fT/wwAMrhLeInoItVY0b5LbNH+EEgFXK5QzR+CxqWzpt5QfUpuyUxlCysjsqQbmKqcKA+CavYgS9FEBQyIfNN+E1X/Ypu6sSBKWsUqv0bPlk9zbiDmIW9LXsIiob17KDK0Gfi4GOyq9MEKWsyKvfNk77jh1fmx99l11lSdrk+773vW/wE0N8i0RjJLycOTWmCZqV+iYt2bGO+Ke+QEdrPvQpBvJD3Yl0ZcQ/dqLVD0/4actn32PtXCQYpezuQxygl61/bFXLDm4kfjHuMuLH+td3wSBUYUACg3S37Un9oSlAnVH/2k7huUhFxpC9YnK1SlfOGqv4tezUo8ZLe6WdEEa/6xO3OhSyouLlcpYqPo1XdMsD6h+b6urGk7gF/Dbihv153LENbv3yDkpYJBKVu0gAQrygdmU3W4oxkVJ2oSV1LeCkqgy4Ed7Xk0V6kyb24RmLaLs+7dhv1aKgDkD/MyamwsI7vstFDCNxE1akHiGvsvMv5agrjKsaN+kyPuEuVytWZdJ6Ef3zKj61523zSp+BP9aNd8YCnhYhru4atxwxjYXDD3zjqX8e2a3uPEX0PJZnjX/SZwoFPTg1JC5IsMyc5J0B3TNaBw0bHw0Ufykj+/j18Ygocur82TyoypR1m+e7iMCTg5wtuyjP91ruScso1rjG6qRLXCJGDuGZeLqEs5OKD8fiUHl1yCGH1MYrRh+CX7GtW+vPp6G/6yYk/DCgk5e6yV3j0icTD2FYKKlblyd9S8TKIQ6xkz4Wh/KcNOpU0mSXHuLAn1+8dMnPJH5Ti8qucXGpQ2yssfHQlygjfd+6z/udCy/sQj6WPgsIu/Ct6wex8F3cDj300N74Qb5ZNMLnPv5SE/DgRNAW+COVNzEJ00fCpuLFX+obEfh4ACPpOd5IAhP+wCoM1yti0H/ZCPUKziohzso9gGRR+bV1wjWQnFe3JRkcgzUvAH0pe8xt41J/WPk5++yzw0/Adpdeeql+GntyFsiZWtPFB2MBWzoACuIsta5N+6gAq7zvfe8Ll4X4b21/c9UdV2nG0pUdWqXGgtWtSy65JBmt1m0snmSgJfrA9YNq2jOVLc7dsboFrkJ0olPeZu7e9liMNq0XoaxSvWB9rC8QaEoEPTgQFgPkvKlLmlREn8TZHKYfl5EUIEbeAActI9k8tsmf1jWDYF9kedMEUJEdbEhWn33lQeNR0FAXdC3tb9r81AGhZDei2esEXqwCrdCL8r8uy6qKtuh+z2KQxVETdb1woim+eX3vOjZMkq+rW/YkoZcwzAtf+MJkrtog/ggcQ4mK6GQsXrVpDGo2RrEKTCGjY+Gb3Bj0uDAbW8ixtJrCd/0eWzyQNhd+e6JjWtWjPo1pxPLh00/9tohM/HCZtx/8uf4xRew8oK7GBFJthLgs2tIiefnmSaUnk6JfQUin2gp84IJ2qA7R7PMEipsdeVeUu8ZDfkRMGX7GLt/AfrhSCgHNd3aPSm2NlIAw7oOadq1t0gDshj16UMZ1JOfK4bMcVwQDD3V+Z/mNPtAkeaNMaGsoHXbYYfra65PdeFc0fV0GkOoglZk1DW4CxiA9k5wd/JlQMcCAyoG/fxLxH+JRdjastAXoFFUXYWDQwYlKkfOGQsw8hvphcsYiFQM3K1h2NNx8E1NdIgBisvXr1wd1ESYTjITvv//+Ic+oqggQKMTLP/LFHw2ZwRd1KgYNBjs5A6r8kRaXJahfdlJMeqTlV8qEx5A9ajnr1q0r5AL6kAfUllCN4GIEJla+Ex96zqg10cBRpN9rr70K9GkFnFGgVkAHQ52CixMYxFEf8sYl0AFFnQrrOYTzuq/wHt1W3VmyuMDaDnrOdHLUZygTgw51geUx8ok/dHnthEi+lfCjBP9YtXtCZQV+ouYhQLOQTyYEu/OCD1igsm4q7vTx8Rte0A5oGxhLIY+okFCPEO2OMnkLapSXCwpoW9z2w28WM7QTy1ParFe1oV7JH+XnIgUGJQgeMUHSflGdoq3YgZNyk5aNj8sAyK8tb4jsD/+YyGkvlidcFsGFDKio2J087/AKNR3aGmXCED/tHH5bvsAvFjnUE23B8xgVIBZRtGnyRj9DF5/2ycUMSgz0m0Q8i+ocqk9M8rQFu2NjAU27oX60XgiPX/oht3fZCZ86Iz82T+QTnhI37lixUiJ/8JRxh/hpD7R/jo24vYz+i7U62h5jlPZnLspgYU0Z6Y/UD0Qb4GIRq7uOUR/GDtRvyIuXXFB2Fsjkhbzhj7qj7bFwonz0RfLm9V4Jh8ok5bI7c8YGFkXwEr6j6oi6IG2I/k+9sRjiGypVVuzMwghxNPkhv7Rp+rQS4xHW6gjLMQ9tmnLT3mkv5JGx3V6swFjEuCRmKMcsHZJHykYd04ch0uUSGfLrifxTR3ZR7P30+XurjUJ9RjhJXJx5WWspk8ShYRhsfvrTn4YGAhQfpu+3336hAi+88MJQgVg4obHTmVEMx5IVgxWdBJk/ZxYxYtBgEqFC2anYzsxAysqU9PmjEXmjDzZOdjAMhJSdtDdv3hzyRMOkoaGLzF3D8IU/3NWQAR2CTuwNgKBrSnxcBCBo8IL7UTH9JsCVUEbOz1D9oTEzADAAceaHIQQ6Je8MXAyUDIp0KtJhcsXyDfzkBhEGDDoKEy+NFb7SsSgz56J0EDo1ccAjXQBcdNFF4dyKzrJeFiAsilSchj/4jj4f8cFrJnp4ytkxeWViYWfGgMagzlkg5aV++cYEgF8GC3YRTDjkFR1peGd3SFgUwlIXnRvekib5pdPTkfnGYMdvOix5o23gh3tC4SNEPVIf+GHSpS7JGzsZBgniYQCC55wpcfEE9SDo2jApMpjCA9oPbZUBgLiInwtFmCBwY1KDZ5Zor+ysKQvp6sKHiZHJQmzwFne4wx1Cu8IP7ZyBSOub70zM/DHxkS9L8IWJhT/aInkkX/CZd8qJqT7aBfVFHTKg4pc+QP1QbiZ74qdd0k7gNfmj/WN5C/1N0lKivZ8vkyz9gPYAMSkJAj+UhbpGp5i6pi8ymNJfuKKR35awKsVkQ9sjDfhBG6VeqQ/qmMmEOqUe4Sn61SxQWVRTNiYiTJYKQCzwmPiVx7QpFgE6uLMIp+xc64hONXmFiJ/2gX/Kx0TJQgR38kGfQBqDOwsJ2jlp0I65UQgxLu2JtOzkQ9xMFiyESIu2pjxgB0dbpv7o06RN3ZBHbho76qijQn+nv9Bf8Q9fKDP1TP8hD4x1jHvkm3aGeVv6DTYT4Bd69Ey03BjFpA4vyAe8J33qirZMG4Foz7RD+Ep5mLAZq8gHbZi2QT4EaBjaNuMHfYSFCGMS/IBfpMO3jTKF6SKFvkM44tuwYUNoI7QTscMe6hB+k38WbdTpZhl3reUxykc90Gb6JPJFucdImLpwkkGqF6SZdKAyhqKUQgd0nDSWVig5Wf2P5Qd1EY1HnzIoBH9A9dXNPqVhjcVDHq2fad6lkVTxS6ebKF7paJ3CSYMvpbG3CqP8RhUqpQ41Tfm7hpUJIqhNoUIhg1ApO4JSBq2xSyakYyfLp2WSATbpp2u+ZIAsUU8hHKpO/I7FIRPIyEUhMT/TuKH6QvvkDzS0DBjRfMTSwK9Msq39+zhkoRXSlR3jSBzwWwbmETcf1v6mL5J/Llmw7n29y8RQqQLaOGUSDCpW1o13GXg75YP26OPwv2ViqeqpSx35eKb9TX/qEocs0EsxJtQpTNv4ZXFayuKvt7ipN+0LfTwzClpqsgsJ00e8WzGK/YC/1Df8cVenvWSd1Sir8b4I+6eIVtkdLCuxImdFugzEDp6dzbICQ9gFsKtPETtCVvh1bS4Vto07Eg92PkiG5mEL1+aJvsSOmR3LNASinH42T2IXN88rKOEVx2XsZDPNhgNIMPuyL43EgN21p6vlPv5L/t0LBxC1WOpLdUXjRBTY140dGmffz2W675eBy5919V3eaeJDRFhHFpBU52/Sb7QnSEWqk8YzabhpJ1/S7buPtSnLItq4nhm3yV/2050DHGXOmgY3AXP+MgviDMsTAIEmwnasJc6G+iTOK7rosfaZdtu4OF9eFkLvl3MpbSeLmmgsP/T8k10UO886UrQw4JxZEBIbCN3yeZKeW05bLup1neAT2mo89FVGzqvnTZx9rhr58+s+89/3ua23Ed9nXjWuwU3AgEAYyDwhugMRyCF/EwFo8CTWikbUbThUV+RtzD/hCaODq8YHuhGk9rQE4AUAC2UFtQmYIEapgQhRH6AVkJ6AcuSsLcRDni0BxgA8AlEWwCEAYgDuWAKgpIMo7gC64DfADpDn7DznTfDIEiI7EJxMMoDHANtsEqQsImlLqUsWmBwAsIAQBvCmk6ENG3uHt6kBmgsQWBQAOAMAJ6b7AoI5NgGCzGXxAIH0n1TMGhsEWYgA/lEUr5juK570pCdVC5VYudSNSQ8gEWCo1IIGdLSC8TScfQIIgygXxwRKiMPbqkTRJwHcUe8AvADz1BH+PWpY/Suf9bc+zzzzzBGxJGkB8ONCEoBK2t9xB/AFkl95ShwKxKFN0B4tv3DzbVHTtU8AahCguUlVb+TMNMRR94+8PetZz4p6YVxgHGuapOAxvGBsgD8g/C0/aPfTLtCJn/7I1bD07zZEP64jNk6A5GZNg7OERUXTyEHdDYk4PwCdOCSiEyMuXIT4blZ8ZDJi4JrneeCsyqLxMjGAeO5677WGX9bnEPuUnjMObaxY9bpKnQEDLV849YmC9ka3pfN3RsaJaskYAg5UqKy0RuICESxi6FIGpxF3UYsJ4WW1FxC2MoCVIABlFxj+mpDHsqsdia+uDLL7DnGKLmDrMMQnu60qP9I4WocVvcASxGFdnvw32WWWsvrtFMbH0cdvEKPUAXZ7tS5lF1GKVKJV3mRybfRH3VHf2C+W3VCjf18uzZdM4mNhQSmD9NR25MP2+VtUg8bSb4pfJAvJMLITTH6DX4JjCOV673vfO+JPAIylSHhG3OryoRdK0F9l0G4dri5O+436od+L/m3J5Rb0ZVEVKkXVJlwKoX5F3aoUCVVt+qKGM/addlPX/+GF1j9PAWE12o7WPPGUHWkpUqmxdK0ffcevvvfx5EIX2b2P2I6PtfOuadGvRTWpFOlEFImu8Vl+1/GYfiuqdiN8tjyf5H1NoKBBj6Z0eKUSOpEwecR/V9QphhSsgjmRIfZhN4GopC9C5LVZxM+xu3qb0sBQByIk9DLXCsGvPoA+i+IXupeI29bJOeeyIrkn4Q39gvuu+xD7AU5SvdBJ8tImjEzEQS+2jd++/QAiRPcXbYpJjyH6ztOi45tFfYAwtwadpiljagc8qDNgDAQsC8VML9Jp+px8KSuTSZ35zTp+YCxiLU2+yq86niz7NxUDD2nyhecYWpBrJnth/6yR4mRSxIa95HWSSBRzAn4h0/9xYBb10Yd50ab6GdQEDJhoWQhLLqzKLLHKVxCGdZ/2fdJyA+LweZw2Lzl85sCkHADc1gdxXj1k0j4LyCvT1RxgfO2T5qGuOKgJGLNtavptmorATKCn1OocwI0nJlkUuLFzqwQ4DHNt1t6ufpvmiVGGZz/72SNoyjbxgRCVu2UDWrSNf/WDCTfEuF0IcSmI62UgENk6gJEfUJqIh/qmGNK4jzQwhQmdfvrpfUSXjKNrHRNRXd+zCPlYopjUpI34xWTXfGAeFa0A2vasiPaju09Ad6B4MatoCdAQ5g+7koKo6sJhhhHCzKqap63zvyzfMDIjZ/wzyQ4aBEceeWRvcWOuErOps6aMgp41h3uKf9VRgDE2ZBR0jCvL58bOIqOgl69eYjnSCTyjoGPcWZxb6gx4Jihosf5UIavFsk8poJESpHOK+kRByyUEpZy/lhvEXqromJWgXC36Taog2P+NIVpFtzXYkE2h3AQgMoYKFD3CERQ0tlwFbBWQhnLrT+VfdpwlSFDiFt29yp382D8QlviR1XOF1LXf7Ts2mWWnFVCQpFuHZpZVeym6qKWAyUoRrQQ0opZTLkUoQSiC6pSLDkpZSVaIThl8A5qU/Ivea8gb9WmR38THH+mLHl4pEoRy48aNAc1pUZuKeCQsacuuaKTslA1UOWlqOYn3Lne5Syngulr0NShz7DsTlj/4qHHwJG34TrqgVvWb3LoSkMViqL5y02/6BOkq4KdSjJ5UdSLiv1Cf6ocnfkBYU/8ilSjFOH2w8SxG6Efixh/8BL0pO7yRbxofKE3LY9xFtzrYaBb95VJujipTNsiJFz7yx7vGaduH8pg0cCdPovcb+gpP+KTh2jxB72p7El3cwGPsW2MDXN3hSyou9cNTgEWBL/AdVLNcFFCF03zH4qEv2nioZ3hA29a2Fwvn3Uh35513rtIUPfgRJDY8pf41L/CO+rDx0A6e//znj9hNp67lAoSgGSG75lIuSSnJs4ajnZB/sVlQuek3fdIutIyiOx36mn6zT7m8IxoH+aSuUrbZZQIPfU2kViVIbgGSRuPRtMi/bd/Y7RbgUugb2KrHdrb6hSfk3bZDj46nH5OHVL/QuHiKjnL5+Mc/Pow1cu4fkOk6rlNntB1t/+SLcYTfaBOI6d6SOtD4SI82Qt6YN+RGrorPyu9pnnNDQYPIfdOb3hRuaeFgHMMT3LSBAjui0pgCNMASeyOFMGVi4tYYbsuYhojDi7BAVTaZCWyTJmjlWYsP2+QDP9jMZcXMjSdtiduVuPFkVUk62EJMFa4qv7rkG6MiGPPwCGRuFlIjDHXxYUQHIw3TotRloAw373DzzVAJk6EY08n0fxyQibV3ewIcGdYdq3ThPeOOSidsuF5F0NjO5G5IGgdPrNJwFR7XcmElBwQw7xATNdZqIKwmxTIXPnb4h5WVlMWhDtEEr1zfpsSdneRxaMTZrKzew1VuQytbLs9iOCA7rDHLVZyZshhvItmB9LIQ5xxZz8qb0lzV76jHcK9uptlxgKsSwRT0QfQBJmFP3dA0PrT5TQdD9YaDaxKDUHFRMAp374qouQrBGQUTNn+seEUEMPUfqgwiFqrSmObF5qev3fk0+ZlFWPg+7W5jFvnKca4uB+iDntpMvoTpqy32IanyZVi232uhjIvmOTy288A076my9DNbSezYB2a7zqSL6AddNTKsnYpJDDGBErZu1d4tImi741Q/XZ/YeUbErSjBruHVPytomx+PcFR/kzxBH/d5HeEkedAw6LnB+y5SA+xCI9lYVbJtclXLsKz5pp9gO1ju9h3JIkc6YkGt0ZSqWEkaE1+PRNTyB+1asANBt7hlkJXzhoQxqyHNttre8IY3jMwD06QW2/0SX287YCYWJljUPLDvyyTIWZCq3dC56BSzJoxdAEnnEgBuMELEKiCBkWQFyDDyW3/gzm1FJ510kjpVT86VYtawGNBRRVBi989CgEkcg/ZKiNf23HPPcO6Kwf0UEZ+ABwruZW1DWrFNah7ExeIEUT8XNHDhAOoT22+/fcGViVjU4SYeLGMdeuihIxcrkHd4s88++xSI4zEYj/qOEvUuoKegCiWgnnBhAEboKQv+KA/8U8KddsHTk12k8Y10ORcEMU1eU0T5KU8dgUHgeMTemoJaFWePdRZvyJOATUasjSHZEQDISHLwSUAkwUi+AE4KzsvhB3+WKDflipVf/fGN+Cxh5e20004L6ieooDz3uc+1n0feid+3iVR6SI3wS5z0FfLd9ao7cB6I6+gj9H1wBZQbiRh4CtxSFxyQcS4WoR/Zfkbb4dhKpWgjBYz8oO/R1iA2AFwGQr35M+lI0BEn+GFvP6Nd2T6OZ3uhAP65O9YS57NcZkG/sCTgrsBr6ofjOFsn9E3aZ5N1K3gk4LKCCyzsxRU2nXvf+972Z/VOvaAexQQeI3hIvVFm8rrvvvvGvFVu+KdvKvEbflPvtAXb9nEn71ZKyThtiTqzPLHf/DsLPsKLGd5CgKSFmAetvKDyJCaFq9+8kD75oy5R3/J1ih/63Pr164vNckRq8863WVCvZ8CaQW6aAYgFiY3UwHREzdwwETvr7ROExUDCgJkvY9DaWN4nHTdfxrC89aM5Y8BkMKKfDokYi4aorkMdDbFcq1ymFAhrJmpIcuYzQnLbzchv/6NPNaQTTzxxzAg7sHNpk63/ZLAZ8ytXh43A1rvEZ/1+/vOfH4vbfp/3O8bvUTtqm65cO9eoIuCN56MugxqATQM1Hft7Xu9WpaUpTcohi7lkPmU1XftNdkHJ701p132vS1fDyep+Jmlr/P5JeiLlCqob/hu/ff17P7IjTIb1fut+i/QpxEO7npQHoqXQyLuUClhd3vSbLGhKVB5j6kZNfCIO2aGVXPSi8cWeIsmLfpedbdQ9Fsequ8luf6SsTWWnTrTMqK/Jbr23v7mpIUkBOlNfO2DEG96STufMrLEAiGRkQbTGSp2LOwsOcERwySWXTIxxQOTch41r7h5G9SlT5sA0HEBl9H3ve980UVRhUzvg3kBYVUoLfOEsE3FZDIm5wGwtddJ58l3q6lmpzInxgqnUiPqYfGHYySefvFJ8y5ldTg7YM+VZ5XAcBTOrlOYQL7vftgf4c8hOTiJzYE1x4M53vnNnwJNlEIDJPujpT3/6GICtj3hzHGuLA4ASZ02DmoABiqCcDggL0SqTMWhDbv3pQjExNpcegKiMgcjaxk3YeayquuQHVLNXG6kL/5GPfCSKBtcw8Ag0tRKoQi6msG58q7s6krqbFR1wwAGtoiYPIDI9gt4Gxuh/itAK6NruUnFZd/LFH3z1KGnrT86z7M+Zv4MKRhURNDMIVE+or9URvOaeY48ajiFV6+LhQgeQ6KDF6f/8WcR+XVi+4f9b3/pWk7dOfcZGRp0xDnDzU0xMTt6biDasaO+U3xTKuQ7tr3HBc/gwKTH+LgN540lq1SpVNi5gUBKzlSNaLOre93MmKOiumezrDJh0Mwq6K/cX5z+joBfH+y4pZxR0F24t1q9uEFYZMRzjIOVa5TKlzoDngoL2qGf/u08UNAhGRT/KSqdCtUmltnoXi13BqLfojFX+iYcLCISJlVssPtmZlBgBj33DjcsCRLk7IOtkJZz0lwo/jTuXE4hu4EiasjMIeRH1sMqdCxm4AEHTsihgjM6DDKy7TGK9IDRFFFmFFx29EEZ2vAGdLjvkUs7oghvoeDFKEozla3pi/i2JPAY9qnVKPREX4eAl6HKM22s8sSft4pxzzhnxQzzUi/dPWvxhuF4kIiWISi7jABkNuvmggw4KdQkPNSyXXSi/QFs//OEPHzFUr/7e8pa3VGHUrc2T9mX9kZeDDz44GL63yH3KuU6M6Xv/NmyXd+U5YWw6Ng60D2TnWMoONKB0qQuQuhjghy9iVzfwzIax77IbLDdt2jSmwUD5rL/UO/VAfVGf5557bmhfimIVlchWcRA3F6gQTuykh3Ysi8QoH7ngIZUX767jEe7UCW1DdqLlcccdNxIH+Rf95apd+3j4ThsUvfWQR5EaVOFl41FdDsE7iHQ/xtAvKZvtn6RBO9K0CMNlOnb802+xJ3wHwW6/MYY0tT3LExu2y7u9MMOHI33KymU1+o26hIegnS0iGr8ioStFalDKtbPhEg0uqNH208dzTaCgL7300gJjBZmWiwMYYJAJciRTmC3F6An1hdGETKvPARmMKxSzDGqhQIsA+cmCIdiCRuQIMEzUfToxV24AKrj/exF5b5NRdlMY2sDiV6Z+OSATdADxYrSDO6r7Mh6V2gFPLujvt9y9xCbXtPUST46kXw74yZfYGeCgPPkGNgzin0UxM3ktagLD/K1eVLDbbrt15q1cu7mwvLfJLMZr8uTbhlPd/agGDePSqaee2j2CjiEGNQFjni3TanAAsFamzIFZcUDbVwwQ1pSmiGGbvOTvA+cA4F3+Zk2DmoDl/Ka6iWkaxsUYryK1aeIlLCIO7OYuC8XsW9flDRvPXSl2pRe2qCGxrNU1uqj/vuonGnnEcVkGaZD/y0Jc3wbBGxDichl79Rv74/MibEfrRS/cQ9yVuAd21kdZiMljpAuH2Dd1w0bzhz/8Yf0ZfabiX0sXOHRdfDGGMPZjxxuU+l577RXlbZ+O8VbQZwpzjgtLPAJ+CRciPOIRjwhG9lEl8TpdXEyggwJPDKCjAsAEw7nRCSecUOUco/sY7OcyAgEZFVasBZJXKxqD7KecckrVeRmE9BsD5ctf/vKCO4u5I1nAPUFdCuPoAkio0mLwohPSENCr5BzLf688/+GF/HOjEf5Ij07GeZySAHKCcXTKJCAVdS6e+tSnhrMyARmEiw5Ii1trmDDhDwbpX/Oa1wSVBBonFoaw841/NUaPO+ck5Jv0EY2hAsIZCg2ZwQyj6fCPCya4kOKCCy6oFkrwljBcYEF5BeBVvOIVr6jyqC/wA+P1XBSASgsTN5cRsJjBIDtG7MnXne50pxAENSBbT6Djn/CEJwQ/6s4gddRRR4U8WvUXVGKYTGgz3PyESPKFL3xhIeYNwwUML3rRiwKfaCe8o3bBGQ91T6flzPuNb3xjEGERjnSVmBxQISHOGFFv8BKdWOpHifogXnuRxzvf+c6QJ+6+ffvb3x4M6NMWHvOYx4QLNbiUhPj447tS3aILnsArJcpGnyBe2hFtmHZtid9ctnDssceGczPswJOnzWLQHjd+CzAqqBlp/djwlBdeYlCfczclVKk4vmDCUdL+BL81n+QRXlFn1Ie/C5h2YRe9tFPCeOKSDu4tJ25E2K9+9avDxS6MI/RzJSwkoebDRQU60dHWOWsGrYvKHe0Poq0KkCz0S8rJJR2E5Y+2JWA+jbbgCI22AQ/8gpK+KcCv4phjjgn29Rmr4JcS7Zh6p27pF/QH+ip1z6IfvmOX/6Mf/Wi4pEDj1z7AuEY9MnbSV1FPJC7KQt/gW6zu4DuXbeimhXFOAHkhbfjMnxL1SZ0xTnIJyj3ucY+Kf6j9EAf9gzqsI+qH8Zp65R2+UleUFaJs3MCFOhl9lsUUvKG+qEv4Y0X4e+yxR2jX8JPxDxVJ/rRu6/Iy7bdBWcJSZsBsJjqtICZlKkEQsOG6RBhLhdHpMTX2i1/8ItxSRAVSGVyphl8mMjqSNggaE+dLukql4um0wONp8IDAmJiYuKh8eyEEeWAgYwJELxadR4iJigmKSYvwEJMRjZ9bibDqgzsD2Y9+9KPwnYbMtY/kBbE7jZeOTgPifAhdY4wR0OG4GpIOiP4f+WIg1XuZrZ4q+eAmKzo4EyUgD9zoEAx6lN0OQmILN5SH9JlUSYuyPe95zyu4RxOekhcWG1zSjhsLAHjGYGGJzkTeuL2EW5LomNSFninSoag/1NWYFNFb1jjoVPCfJ7fPMLnCW9JXi0jEz0CiEy+8Ix8sfhg0KTe3v5DHyy+/PNwEBI+0vhlw4aPYAy+4bQtiQKJ+3v/+9xfUBwMrAxRxatpcaSlI73DzFHExKPBHPZEnJgG+C7I1tDfS1zLjnzLDX9ordU+81PmGDRtCG4TfgNnY7WmbIZwuIBjQ4Cnhuf2FvJEn2i9tAnd4Sx6UKAsD5KZNm4ITfYABmPbIgE6alBNdeW7jIT/ERfzEzQBHuvADeve73x34ym/aEe2PJ2Wjrinz3nvvHcKgQ4y+PQtd4mIw54/Jm3wSjjZFvlms0T7PPPPMUBcMmNTdZz/72VAeyvS5z30u8BP9ZOqJBQxtZePGjWGCZbCFyP/DHvawMCbYiZlFLXFSZtLURTkW91iYUk7CUmf0f/4g8k5YzhC5rYizcPLA9XbkH14RFiL/9Cv6m7ZP2iILOW5lg78sNll40jdph0oWW0E907Zoz/CTdOAR4UmfHR2TNosheEo9wIv99tsvtB8mJMqqi2PeGb8IS7y0d/oJ7Yt6wJ26o6wXX3xxaA8swtDDpm3gn/6nPKF9sriBSH+dLOboB4xbjG+0aZ6Mp5QbHpBHJnh4RL9jPIfg+XpZMEC0FeqWy3607zBm77TTTuE7YyvjE/2IfHHL1/nnn1+87W1vC3mEryzwGO+Jl8UEPGDiZoyYNQ1OD5hVsFZ0n8xj0LKNv8+4FxkXkze71LbEypVOyEDZlhBFegMkrP6ZKBgomOS187SNc57+6NBt7nBmdc9gTQfPtJocYKBHooMkht3+rECCHMH4KwzhGGk3pYk0hJ2qLkI9p1ksEPc8QEQ+7WX6zaIPqRSLkEkIaRPjYx/ETl11tG18g5qAWdUg8su0Ghxgl8IgpxKG1ch1zuWQOcBCG/EzEhl2eJnWNgd01z0tF1IT8JbTRrxM4RHzZlotDiA6ypQ5sCwcYFGIpAupTKbMgVlzYFATMGe/iF8yLT8HOKeCOMMZEnGOlGl1OUC75GgEMe8szwCJP9Nyc4Dz41nToGYrgCJf//rXW69e207WACkADLQhQAR9EUAFCwqZJl5EazGVDMrFeZclwA+AXgBC2UGIODjLBaTQlnfEy5m8oqb5zTmXqh/hDlhGiXgt2Evd2zwtkjnmH4ARgA1LtJm6ssD/Zz/72QGAZMP5d+LgogoQlqBVOSeHX54Y3AHExL55v7Hf1At/TPSItSCelr/UG+ApxKhKqhKkvyd9app14Sk7Vs7ExGnlDaQ6yP8UHX744WM8oe3FkLepOFLuxKGgnJQf3AH4YSAG3m4SEBoAIM5aAR1NQ7QhQEBK5Ie++MpXvlKdwhOkNu7+AhDqG4CWxoOfmGiU9s/O/YgjjoieIwP0UvCXTZgyWgKNPc248/znP3+kvDbuuneAV11uxFJVxliclBOQIMBX3gGFARADz+F5wG/wG3bCBbAYU5+MpTWN26DOgGEEjKTxWATyNAxalrCrbow8xkcmWkBYFoUb87dKbkw+TBxXXHHFKmW7Nq9MSCBXQaEPiYbYpxToA8J5SLTqdZU6AwZOvnDq8zKGn/3sZ6XA2UtRkwmXD2y//falrDirCxqkUVbGubu8cxmDrDpLUZWYKHwqLVnd9hpfKh3cX//614+lhdF5EQOPuYuKSCivj4/LJPjz7vo7VR7ZAVRhMLwPL2XiLUWFIRhB1/BNT9k5VvE0+Y19l51ip/Cif9loWJ50uGBCdDhLLp+QwSKahuySw6UElJ02Gctf324bN26M1mOXdGRBW+VVdlnVe10cXNwhamG1frlYgEsNaGuiUlPrN5aWoFyTl0NY/7L7KUX1pzF+Lt0QtZnAL+rRxtH3u6jajcQvUovQbkQ9bcRd0xU1nqodijQhXBCi3/yTuETqFI3H++U39WDdqWMuDKEOrbtIkKo8WHf/LqpFpaiZjYT1fmK/U2MHfuk7PkzdZQz45YIQWRBX4WQhGY1H4+W7qEyW1I2o94V2QF/t40/UtaLz7CB3wEceeWQhk02vqi2IgVBQR2S5Fohdj9pFnUV5Eb+hq4w6EjqxQ6B1cmRwfoMKEqJq9GnR350XkeakqhizziN5Q2SvOrmzTq8pfnR09RihyW/+PlwOIEWVRUjQ2e6jlKkd8KDOgJVRnJH0rVcqyxeNfk08Zzn5wkCsOEGctQ2F2rQ5JkKMlsyTlnXyhQfkjbO6ZSH0gDG4kmltc4BjMayBzZoGOQFjWciCh2bNxBx/dw6oFS7qaijUFjxmAVNDKfs05cAi1rIQ4wbWkTKtbQ6A5cCozqxpkBMwaEtg/gx0IEJBwE1LXPQAWWTntHHOO7xOejZdRMEA1zwhEsQEnCdMynmUpvfT9BtELqZAIdCpXe9rbYp/Ed9BWGIq0CMsfV5oR0ho5kWY+sN04iIIxGkdAazB7CgoeAyydKVYu03FwZFKE4HAxZQlNse7oHGb4o19x7azJ8aqFCGqV2JyABRXR5jYnYY8MrpLXG143SW+lF+OBeuIPtmVaFOMTxs2bAjtoGv4rv4HeQacUdBdm8Fi/GcU9GL43jVVBtSMgu7KtcX4zyjoxfC9KdXUGfDgUNBiaD8gK0WnqxSd4HL9+vUVCk6YVL3LTTGlgGFK0fcqRexUuYOa80hbMbwekJqyqy5FLFH5tfHpu1y0kPwOOlaM05egMklT9PJKWflX/olf4+Epun+l6CCWuIteZQgDUhA0ouwuKr+yOq7ebXj7LhcKBDSf2Iet0oc3ivDTOCi/SBCq+EBCkh55hl/qX3SHSzFuX4rB91L0eksZpEsxjj/CS9InPtFvLHfffffwDd4q0lRR0AJuq9IjDOnhDzSnRWKKjmOJX+tGPcNX+Cl6ryE/IJdFF7EUvc5QX/APPgpIqgT1LZNJSI+8gS6lbB6BKSvhkjai/uGPAPBG8qn8JW7li7YP/Pt2RD7Vn4DPQp6Ig7zLznkkbvgp+sfBP09NC3980/h5t4haeIZfkKwHHHBAlR7IdS23xkW6xIPfGLpZdgKlAOVK2vRznvOcUmwPV/mAxz4+4gUBq2UUffFS9JFLeCk2dcunPOUpAY0MX0Go4k9uESplIVaKbfHgV/NGfYByTaHFybtc9BHikNumqnwRXnStR35TdtKSW7bKpz3taaVIdsJvUdUpxVZwKZKLUM+ab32KHnAU0U550K6QSzBG0oH38FMkRKVIHUqRdpRyKUPox/BLy0Z4ymc1A2SALkX/vpRLRUJdwBfKQRsS4yClXMYS+Ig/0dMN7Zr+J1Kt0B6IS3SpQ7uXS2ZC+eRCgRCGPGna+tS6FzvwYZyUC1hKkUSVcotX6GOnn3566BfkibIoT3iSH42HJ37os9QjYwEIbJEkjPmxYXjXPkf+GIf1O3njm+xCSwE1hrQpJ/4Y9wQ/Uoo+f3C3Y4GGB7Uvkr3wXW7GKuWCkFAHgjcp5Xy/tO2FtOSijBCnXKBTCoC3REsDvtgyT/u+ZlDQ0hDCTR1SGZkcB2SQDbeXWGcMBHQxB8mNKlwXyG0v0xAG5zljQQ+47dnpNOktU1gMmbQ9B8YgALd2TUqy+AgGH7ilaF4kC5tw60ydxoAsHHpB2XOrEjePNREiX264UjAll4nwGzvkAA651Wfz5s3VHcbcgMXVgKtKHC3Zqw7ryuHrglumuDXIErcloa2AkQ+u2pwXIZbHEIqol44kKYuYcHvTiKP7gfEXLmNoQ0gO0J2Wib9qIxjW4UapPii1Ax6UCFpW/OEuyj4YluNIcwBLWVyNNw0xwHFbC2ofGL9fS9Rm8OiTH3ZQ6TPeuri4A9neQVzndxHfZAcbDIvYgZ1r97iODjewDpmu5gATCFbw6hZVV/sexhuTL5NwH5SagK8+2e8jlRzHmuAAq9K+iMlhrdFaKPOyl5EFpJ9M9L7ttdYe25R32euzTRm6+kEqMmsa1ASs9oVnzbRVjZ+dq6euCHFQtYi3piUuvIbmYW912rz2HV7OpVpH2YQkbopIzhLnft0jC7Q6u8/kGbFiH0R7bENy/j/ijYvp5Tw4iBzJC8czquGATWJE26tK8P/QQw+dOPuI6z3JmXBwwp70PIm6QDzsCVBgE3Fc1pbYocoZ84h3DHHMmkZTnHVqM44f0R7nmYg30S9FkfqBD3xgVCeYW0/EVF64+UQO4quc8e5VSbhjGCtYXCLAmWkdYYg8RQKoCNZVmAipbNSCBIxSefdnoeSRMjGIMlhoPhmUY52kiijywgB07rnnFmJKMww2pI/Bfi6vEIBBFQJ3e9E3HYBzdQYojLTDU8TGqNJwWTUG5ImbvGEI3jdifjOYcTzAOwh1AVaM+LPpkxEWBZSbsyk7AdHpqAfrRp0wgGo6t7/97UMdiUm5UPfUF2daxEc5OC/UhQh55ow01vk4x3z84x9fYJiB/BC/hquY9YcX4qEM/Nn24dsRv/FDfOi+kieI3YWfkIhz1113LQQEVjBZKMETvhGGgZZ3zomVdKdC3NwOhooP7fzDH/7wmDoefvmjXSoPNR6enNHDAwEAFQJMGWkX+I/xAzfqF6KsiHKpc1Q7Hve4x4WL4rnkQAAx4TvtGH5wFkt7tLR+/fqCi1BiRL6xeEd7FHDXiBffN+ADKnWcX2KFjIvWiVeAR8VJJ51UCMiyEEDXyKTLsQgXStA2PGlb8vrCWi/0A8S1DOCo2D3sYQ8biZu2Rf4ptxJtg7T22GOPUBfwnh06ad3vfvcLlsLgI78ZF2hn8EuAWFXbZNIVsFwoK/2NchIvbdcT6eP+nve8J5x1o7IDT1iE0GZQXSM//MEj8gMdf/zxhR/jlB+MFZSdoyXGGkuxSVS/kw/6phK/yd/973//oBb2jW98I4znuMMzjjawJOf7m4anj9A2aPOMbwKWC5dqvPnNbw7tmOMRS1zQguVEMTdcbNy4MYxVn/jEJ4o+L9ax6dn3QZ0BUzAaAw01X8Zgq3k531lw5MsYlrNubK4Y0Fj85MsYLFeW810nunwZw3LVDwsZrZuRnEUtRM/Zsc/LGIDmSwHDn6yqApRcVuWVm34DMo8BeBlcxr6pn3k8UZGR1V6nPKAe0iZvsmIc8Se7jqDSpOnJLqb80pe+VMqVhEl1D9IRcEoUko9aUpt8eD+oCMnqtbqMYccddxyL54QTTijlKrEx95i6jI9ff6PqgsoTagfwTDpAiTqWfu/ziaqGAHga40Y1jrLzJyvsRv+yim/0U1cOyq3pUc91flPfZCdeClK4U9hddtklqJ+l4hSUbSlX/3WKMxYXZSOt2Dd1Q91MeWCfXDigfugT55xzTuWPy1dk0Ky+q79FPWUX3JgX+gZj3gZR36GdTZpX+gzqZD48qoennXbamLv6Y7yBj2JLO+lH/bZ9ijQl1AkqY7bueBcTpmE8i8WF6ih+/BioY58No37FRGzlX6RLJWpJPs1Jf68JNaQYRJ6V+yWXXCL8zhTjACIsxEVNK2ZEUvbcBbGU3BgTi7KVmzTkoH6ESAvR5Kxov/32CyKrq666alZJhHilY1fqC3UJ4Q8xOqJCPQev89/HN6xMwWMPOuoSN2pTqE8tGyE+v+yyyxqzhQoNqjRKSMi8iBHxJiJLCLH+ql4piRTw8ssv16Ku9FP7C7tHP0YxdsliIVk+xOFiFyL53X4466yzgpjbunFssllU0/qg1A54/HCgj9QWFIfs5sZSHpooeqyAUzqgCynK7I2xXHTRRSN+dKAacZzgx6xvwUFsOuvJl2KrfmkTC9SfPyNrCjfNd86zpiUWDctIbSZf8u11hWVnNVYcq1pnz/LHPC65w1AmX9is/SXG8qZydjkyEenQWBLYKpg1DWoCRo/Pkwd22O8eJGO/rZV3Vph1PFI+rHMo0vXr1weghH6f9AlAaJaEwRAwAbMmAFFtSPMiVrraeO/FzyGHHDJ1PPPMb5fMiqWsVt7lzusRf6CnafuWLLhvlfWAp108sINeFgL8maImG/Jyv3Mq6Ji7WNSrQK76cR5tYFATMOjAgw8+OPAPxBzIPiyhgIr2BBIOdF30YNx7lt9W/Br5XDlZNF/lWPMi5zUBjQjQpS21vS7NN0B2XTQ0UJh0sr322qtgdwTqWcw4JpPn+jydONQTRvRBI8LnriSm36ogNHK9mKFylBeuqIvtjqnjFCHmscSuR8x5Fs973vMKeAGKG9FvF1L0Z10YeACqGcR0HTHg680/3IVM3TcRZQDZnSKQt3VEW1FRa524LhUHi9TXve51xcknn1ybDxsecB11qmo99pu+cwQx7c6cvkv/FlOZGm30yQKTNmCJclmJGf3W/gZ5PumlI9TJJP3C5s++M/a87GUvs07Rd9D+aCigJgh/2xA8tGh28k3fAwFuCXfGj5jkQP0xRrBQ51ipraU3DRt70l+4LzxFWORiHIrRGWecETRM0HJQogxe24BvIOThA5od1B1jMeMFbX7WlFHQs+ZwT/HTQPwZSE9RLyyajIJeGOs7JZxR0J3YtVDPjBPQ0MaKVR//UmfAyNgXTn2ioGUXUorubyk6bcGg9gMe8IARRJ6szMLFAHKmFRBuovdVis5wKSueEX/Shkd+i45bMAR+z3ves0LKeT+yoy5BCooe4khY609WWCPfLKoOo/sCBAmG+T3qUVaXIRxPAUCNxGHjt++iiziG4hORb3VhAmlp+hZJKuCFkfjVuLv61Sf5wFg8aVIueGPTj73LzqNKUy9jID4uDlD/slKt/NiLBvhOvVkD7BjFl918QF9SHtFbDYhujK5rPjG2DzpU4089tSz2uwCXRi47sN/0HWPvmhYG7dXdP+Wau8of/kGLgrakvKK3XIrt4VLOnco999yzFNBQuDwAf1wm4OPS35RddsjR77Rp0MaaN56iPxkuyCA8bQktANkhRsODAiaMnKUFFLS9aAQtAtoJCGPNiz65oAMj+LLbH/uGHy4pIF5QpiL2jfrRuOyTNqL9R66xDPwiHgzni655Mh7R6R3hAWH4E3xIKcZ7QtvTix30m+z0wkURNn34xFhh3WiPoMTVjfyJrn1tm6Gdyu4utFuRsIT6ox41Dn1yGYTmR6QJjdoS5Fkm3vBHONEDrvilceqTstCPxMZBKTaTq7RFDzmkyWUhIqGp3AmneSHPGo8+KQ/tmPbEBSaMwfoNbQu+6+/Yk7bERQ46zuGHsVrTpFz6bp+ySw0aDjZO+jEXQuBP9MRD26At23GDetI5QuOjr1B+LgARveBoeuq36zOFgh7UBIwqiK2IuneYb2+ZqfM7629UpohWW+e9S37olNpYBAk7lgYDKJ2wKU5dsGhcDHpNYVLfdYLVCRiVo5jfuoVMzL91o8Nza4xY2orGbf328c6tTNxu1RSXiP1DfRx33HFjfplEmPA1DvjkB0H91vYp4sswqVNvdSokqfgYUJmAL7744ipfKb/WPTYx2+/cTGR/T/LORIBqVdPgTtxi07fqB9qGmcRZEPKdwZl2qN8o9yR56juMHKOVgt5unRexYx0mYBYdk+aFhXRMdZP4uiyYJk3fhtNFYGwCblLRE5FyIw9YAP//9s4D6I7quuOXapCokkI1IAHGInRMd8ASYMoI0YibEAAAQABJREFUY4ploiGUjIFgTMcQF2VAxEMSgW05Gcd2gKE6howJCBSbYpqpptoYY7plCAhEDYjesr+bnOfz7ne3vbf7yn7nzHzf7tu9e8v/9tMuCybqXZeNNoUZpLSHbq+jYgLmGCldeVn3rLTSGlnWd3W8o3JlIKg6flbO0nhobLH4Gehjz/WzRAbViof42PXp92XviUMmYFbIZb8vEp765RizImG7DVNkEpA0KHtsF8GgH8YjOz75tuyV+DhujTSL2o+HaTABZ+0ww/BFfsd2fEW+02EoG8f36Wdp90wqYKD/Qk4PR2bK+7R4+vG8zEKU4xWZrD7/+c8XwiWtPIOyACF/1ElsAua4yrT887yInT3hhDMVjoPsyKU9dHtNm4DLa9AkOR5UQpkg6ZSFspcM/oXMbwpFVkGgbjUX07KQrOxbr9I0ClHKyqNQuQysqyIUYuqgd99917vGqyPuWJzJZBl73PZMwmC/GtKHH37oFeTC5938TuRL3o0ocYh8sJP4wvrvJA79Taz8+n2Re8qGHkERioXTbTgZfHtirlYkrzoM+UJ+WJQS9rYPivvabkjaaTdx1P0tCl9ZFKvzWHhpB8mio+31Bx980Pa7jh+5EzAOGC655BKXHCTf+sN2dBAJpwtovTEJ04BCn7CSZ97hS1SOS6uysZUd5KQRJXJKyV5lVxoUZ2IKJWxPuW1dEzac1wJsPYjcEE8iS297g6/VmEZhW6CUH4m8ue1NzAYvkYNGtaOL1BVhWFSgxUg9hx2rLfGKfuA/N4ZvGL1oQcc0vPH3LZrD5Dnh0ni/vGEcZX7jE1u0oDvR6sRHM1RWYxlHFllaxNR54gGtTFFGhMURDNrkOGTII/wAhyTa92gPo2muDxnBB3YRii1MipqkFYmfM4nlIIS88Ey6+GaHEvGa9xWe903sfSIL9T6zw3fgjFOXPKLdVkX4c04j+gv+p2OUcJi8NnbC5Yi99s+YJxLOTsuh0MyZM/1z+h6bgqqOIkzNQPIiUwsaR+c4B0e1Xa9YOegZLyFVEQbTVa02EtaWn1DwMczJHXhCwYwGp+OsJDmlhbNART09YW149XMGC1T98fYTczKBuQODLA7YcdzNIoRDznEsD2lPOxz2fc4553iH5jRGwsyZM8dPWAy85AvnEHRyJjIhPHZxOhDOQ8A8kbs57BdR6adD4dydAZXJicGHjqkdjbD6xQwL5/J0xJh5RsIK8Qdq4+SfeBJWlU+eQ8lxfv7GG294rMgvecU8KRaP5JmBDTV+vCxh08t5qpghUG4WIxyOQb4wB6I9Jewebx7G96xQtS9ozt8Eg0Q+6NsdYTBDYJDFZIxFAAsVDqgnPjrJeeed5+svcSvqMeMEFBaNXGX3z86SPN1xxx1+YqaOcbqAoT3ejuCGEBf4YiKlOz0ev8g/GJA2cdFx2X1BTG44c5eBgHhx/k4eGbC40r5ZFDD5houBRM7k2KlzSIAsxijz/PnzvYmF7Ghwrq8P7qAtc1gD7w899FDfLnGWQrtmRc+VwwZikxMmOTgxYJKkf9A+ceSvFwXk8/zkwAQOAuAeUxjKQZrSp8CcASxhA3utWyYe0j3ttNN8GwcfMKWNw30BAzg9TObCqUrkrr798I4+CmFqxqRIP8IkBuLQDcy8aN/0C66kLXT11Vf7xQsY67EkYT37+pFw+kr7JH76EOmFEwf5PP30031fB0dMF8GdeuCwBuoZG2vGEMYF8KGeOLgCByFsCOQgAfJEHskPY2qirOnwW8ARiLR7xhw2OuSFuidvHA7AeCZEn6efYkqIaRg40A6xXyYuDo2RDQB5oq3SHzmUgTFHFiGkhzke6dD/qVsOYiCP0kdIk/eYcpJH2qccwgBm/KafQezSqWPGAdqUmIKSJuMNJl2Mn7xnLGNRQT6Jn77Hs0TR0CWuQP0YySTPd7QNscUlPGUKCTzAmDYMHhzCQ1m0lz7GVMY12hx1RDr0W3AMTVQT5S0/BzDupXEMwzwU+U27kbrR4dv33PpNcs+pFkw22IsOE7ESFK82DFDsAgGTwUHvCCkToMizE044odV4eMdAxuDDt0xqdDpW7omsyA/cNKoY8Z6GwE6PDsLEzWRAJyYeJl8GK3afmnCHxo6N9zQAGjCdlp0mOxHsOGWgp8PqyVfyictBJuk0oiw4VWDiY5Woic7DYeSsGomfBktjziJctdGRmBDBXSY9OhZ/EOkxuCdyFD+ApMVHRwFfbZOMwwQmKQZyFggQ+WRQoY7pqDIYYcfJooJ4GPwEByYbFkRMbNQHi0d2PLLzpnMwaDIAJcplvtzUD6c9MQAwkO21116+zhlsmKx5BlaJHKpVJ+SN9yzOeE84Jinqnbhl0iacUOzYPgZEwutFL4tDPQGDLTtasCL/iWax4zhOykY6/PE8RrRHJghOCWKBRJ1h9wl+fMMiiH4Rq3udB9ltsdihHTBZMLjTboVou7RNiLoi3zL58ow2QT2AGxMJEzELICZX4RbwPYsJiEUcgylhNbFrIxy4acpzQ0s7x76VST+cgEkHfGg/tAfpc4RnohWbdMojeRXuIHUPMSnwjomQRToTMG2CIynh0tDu4NRRF+AjRJwShzyj/xOGMY36IX+MFdSZjAsSliuYsshlkUlf4Tv6f7izo63KQk3qiu+Z3IibOOhXQoyj9GfaH/lnUcXCjAUqi1uZgFkcQ2xu2PTQnljUMK7Sl1lQ33333X6sxFabPwiuAn2ryFGTLDpZXLOYZiHLQogFjiYWJ0zeTOikTXsFx5hrVcZbNjYsCmTy13FVfp901FRKVqAfJbvd1PdVvUgabWXC7qQhe3X2pLH5QwSSxsJWpfWHinlMoJ7sAFphdPiy98SNNmXR7zjQgG8SdsqIb5KG7VXxi8Yl4ZIOHy0j6SRON9rSSVbC/sAC+TZ2RTM3hlksbJFnSQfw8SWDsdeuJe5EHOBNJpIB2OdPtFZPS8zEdJyYNyTH43kTkaQztb3T4bjn4IVksMwME34T+601k2PvUfZIBkevkRt7r5+JxmUMT55h2pUsZLzZVDIA+ziTCarjMsyaNaut7nReuE8G6cy4UeBKdg3eDCmW5zA+fqNUR1hMOmLveYaZC6ZWae+LPE98aft00PTOCx/LOyZr+rtk8G1hhUKWfhe7p30lk/KIcMkkNuJZ7HueJRNtZthkt+nzlPa9fo5JXjLRtBSWkh16atx6jNBxcI/ZYbIZGfFtsglo4ZMscEe81/GAXzKJebPKZPEaNVfT4cVsMJks/ThAu0STnv5L3VGusA4TZxvRPFAvOixxpNUJY4iETRb2bfH1XQs6WeV8lKys/ISWnGn5kfwxIFRJVU3AmHYku4YWiFSgrmS5T1gxLdAFfHnX7TVhl0bTzIqXPGS9L/sODVMpV3iNxZWs9HLTT3agbXGykInFVfQZ+dITcDgQoaGahkuyUymcdlrHK5rPouGSHXaqzWUYR1gn+rduv3wXMx0L48v6nezc/CROGizossKmvUubgLMmWOyN0+LjuSy0ssIUeVe0vyXy3bb2G2tbyU6vFaZI2r0Ik+xYSy3CE+6Gn6ywg83KH+M6GISTDt+wAEz7Vtpq2vtOn9MeGJfD7xPupM9nbAJOG9+JQ/LJNdR2D9PgtDTChc/DeHScZe+THXd0ysxkQcPOQi4RUkzxIAzTj9+wWjQLBdZKjJADhAQ7CHZOt5TYy3YbRdffly0H7Jk8Ck8VKfJNXpz6Pew/ffpMFusQebOw/HQcsfu0NhAL280z8l5FWvQtfXAA8rJuCDYi7GZhRXYTV/itsFvD5/yO9TEdLmQV63dl7mERF6Gw/ca+0Szg2Pt+PIOtWqavSRnyDiqQvgYbOySewb6uoj2Hcaf9Ji3Y9YggdNtA/twtJTNfZhRalJcZsIaXmVrQ4lsUhSD9F5M31JC30lHC22cAo/HIX6ipi2xFZIM6AeSKVRB+lbO0P8M0RM6TJq8Lwxf5jTwwjcJyIktK2LxpwVvPUbTRlJzlqn92fY9ChCbRlEYeGhKypLBewzDyG9lat0RbyqOENd4mJ0sLn6ddSzzUCcSiBGWXKVOm+N+d/EOejzwLQkZclpB9p1FWG8grp9Y4Tos/7zlyS5QTi1DMB3jYtnRbYZzII8KEehR535R9jwITY0pREt/ILFKzSBTeRF6rw5JmOE7wPpS762/Ce+YOGYOL9B/mFGTq4dyC/kcaJRzZtFdtz0O/1m0vkx8or8UI2XbdlD+y1J2DCuOnQyUu5bzSDirlKNSgocogQsWiNIJmIbvdkBIvQa5ohTJZxEwoUNIiboT4oXZdmB6/yRNaslDiNrI1UPKbCTmRF3olB60pjTJImvIX3zFRooiQRjgpR3GHCQw80CAkPDgJaeUDBhk0psMBiTiuv/56+cQrd+g4Wi8iNwn7ZsRTBoRDE21eFlFo2aOsBqFpiUIOnRi84DBgPoImLwcNaE1YHSk4kQ4alaEjfh1O7lEgkolKnnFFyQQlDxRGWJ3DYSFu8sP9pEmTvEINkyb5Iu+a0OAWQslE16U811faLX8oBGLWhLIK5wZzkESMyAeKYKKwwm/aIHlEGQWtViHaN4ooQiikoBTEgo2JOiTym7WYAy+UiTSRPtrKgn+sr7HIQKmK+om91/Hpe6wZhMgvmsRMNLTPcOCWcFxp8zFCcYsysmg/6qij2uqGdgY+mhiQaQMQ79AGRnEtPJHppkQhDeVVTSwWULai7GAjhLYu55jHCKsCFBjpnygWZhFtkfxoogyxhT1tUiZqlKvoe5SLuqB9MnahLKXbHBwUcBai7mTzwDONARr1KPDSF7jnO+LVh4rwLQce0IbQJmesZsFJe6KvcdIRSlwxRUDJA4ptWFWEFI4v1C3KrbQV2gl/pM/YwVgqiqN8JxxUtO7Rlq6bMs2Q6k5c4oddpdkO8ryTKwMpFdlPtkIn+c77hgG+DCsqL75BeE+n1mZIg5CnbvPAIMZgJiy+buMbhO8ZlFjc5rGVByGvZfLQxD4l3JOmjRXDXlcshKRudBvNlAHrgMNyz0oKsxRUzGG5iemK5J+VnTgmkGdcWU2zUipC7HoYYLVciR0iJhKo20PsIlGlzyNMMJiIkNmwSkYOArGQYNWI2Qeq9uxURZaBUxTNLtNpwDpKW/ETDvlO4p6xZSKESQTmQ2CiFy2UR9JDDqlNYiQ92KPaZlae513hEJRh02fFF7LaKAtl0hSG0e/0PZwNzBmQO7HbQeyCmRBmNdpkgV0hizzYd8jcYGFh/oW8LkwL20l24ci4piSsZHaznRA2sezG04i2i6kVedCU+BH25i/y7Pzzz/c7bPLD7oL2xeIXG1d2riwe2DWHuz/5PryCDyxLIXbA9A9sTicmnopiHApMqhAD0VezWNwSJ1d2SphPxYjdFvbyyLtjBNdLdjmx97Fn9EMxs+I9CxCOv9PmODzHJAkzTdKAWKyws4Y1DudEiB1X2UVZosHuOVrEcVoiJsriRmBelLVjo66lDWufBRI37QL8cARDW56fmBZqc0DChTtL2MwxfRPaDrgIYVsv/hLkWXilfbPrRfeDNgVHEJa0mJ+F4eV3cqCJdxAlv7lSTuFU0M5oH5owlwpNQHmPqRYmpHAl2QHDTZUdsf6+yvtG7YCxkxN7tiyQZNLTYcKBU78res+kRdw0YAaXIsQ3sIrSGnOROMIwDGppnRV2m0ysfEf6sLmwf0sjGiETkyYGQ2z/OiU6c7c74LAskhfKIzaI3dQrnZiBM6aoImnJFfZoKCeXd/rKYC0emPTzrHvY8CwGOiVZQMUmPBwx0G+wQReCvat/p+2AEZHgqCFGiDhQ/tJtTYfDFhXWeRnCtj705oUyHmz0PAonD8Jn7aq6aTewmMOFUF7+0t6zuGFsEF8FaeF4ziIFW2VI74BZnLAQ0wT2tNnYwg6nGLEJkzoVBUEWVln9AnYzIiom4nATpPOh71m48B3tBqLvkRfKHqsrFij4BwhJxitY66FISMLG5gDsm7XyGmLGIt7tJM6sa9oOuFEyYOQxsZ1aCAwymjqIwYbdXdHJlzzIABVbSXaax3DFp+OR9OQZv7MmX8LFdhZ61yNx9foalkXSz+IASJgiV3aGWYOMjoN6L0KdtL28+slLVzwWietVHR6sZFCV50U1i7MWErTntPohHfQlyhKDZkh5stEwfC9+VzX5klfaC7vfIsRCPkZaV0PeSzvUOgHyjkk5Rrov6PtYWJzeQGlci9g37Fy14hMcg+REo1hQ/wy9nRjJeJWl6Ae3MqRwDNaL0DBsVb8bNQHDfoQ1mEd466mLiDvUriySVpGFQ5F4CBNj+8m3sAhDSvOZHYbTv7W3I/18EO47KU8s33AH0pSEeKdJlKD0s9h9J6x3rbwSizPvmWi0hixFvmPXFLaXcCBKix83lWnEJBRipMMiBilLMTZyEY5X2XQGKTyiIXEXm5evNLxjmtrs9qBY20rDVI8deaxZvG5B7N6LElrWur9xz5ieRrDSsyi0rNBh4aaEJHmW5zov8qzq68jRuOoUehgfkx8ahXRUtBvFsb3OAtqJMiDp54cmGrhFiYYYmzCREdHgsmQxYRrCUgt3BHQmTHFgs4b51X5Ow/hgm2qfvuH7cDUMmyZvxw6rMSTYWsidOqEs85Uy8cHxCImBQe9GYWsVJeTuQtzj6xu5aEiwkdEkpY5gk6FtjNlNbLWuFyosDLRcMIw37TfsQjRFswi3mTHCjEN2FcjUNNuX52iBwmaDRSasZtkdxeLTz2BTis6Dfs6AjwhE14N+j4gATdcsExMdnnsGw1C2z3M0drWmOc9CStvRheH079BdI3UdGyPQKaENaIKjUMUikIkXsQZtS8ujdVpyz3iU1o/ZherJk7DCVYGNKxMZZaTNI7+OYYaoQkjLeOWZvuJTGZo6dWohV8aYCsFmRhaNDJl2xTiXpZcDByYcG0kT+TEEly6mHY9cOfYdft/BgLY2MdFf0BYEPsIa/jVKBgw+pgVdQyupKcpuZcA1ZauraOm8LILKKtx0lWjNH8vEbFrQNQNdQfSiaatlwBVE2/coYjLgvmeqRAZGhQwYPBCis3pFgw1NT3j9aLShVIHdWRbBiiMcf7AKUaaaMmWK13yU51wxnYFQeGDHjY1ZSDidx9aO1SWyBHYZeBaDpQL7B/YIu09NaE5LOrKKk/es2tg5iA0w9m+s/lmt6nxneZCSuGJXdnqwu8gD5WOHwqpb216G3yEjlfxyZcWI/IUdIYoTlFfeoxlLPvNkR2Eaeb9xUiBpcA13LnwPR0CH4R7OgsabFXGazJIdr3wvLDHkctQjnBatPY5CECIIwpMu7Fx2iuxiuvHqg7Y1msrkExypG/ICC1FkvJSVVb/kNcZKJD/ki/4hsjK+Q9kFRS8OGeiE2O2SJ/LHrk3koGEb4Qg5jTOar5JffcVeVn5TTokvLW+kT3+Sb7hSF2jzdkq6fYgGMaxzSYM+AoEbz2hTQtjESzh2+mjWssPi8AHGDHZYhEc7+cILL2yFlW8Ys2QXKXGCg7yXK3GI9ysJF7siL6WtkP8ii0PGL8pFPsFRFKN03JRH8sGOFXtlbTssYZEzSzi56rEBnDVRl4xzRcrFThyLEIkX2+mQ6KO8Bz84lBIWjJkj+kmN2gEzoMDyY0Dknh1WqL0LiyGmrECllCGOp2OClsGERqSVn4rGB1sLVjkdSeKSfMDGQ6M5NBGC9ZvVOGFnM0gXJfDAAUYa4cBBjjLTYWJlBN+wHPob7mGJIXvsdgfMwMVxcCEJpjzHPKSIBqnEoU0YeIbZSRorVb7hCuZMkKEzBFh/upOzQCirg0BbZuDIwhXTO7g/4QBE+sI6JB8MqmgEMxEj86JssPIQnwixWNJih7wdMJMLE2lITCDh4EoYdgMsXlmcFjHVk3hjmqu8w9kDpi5yApqElyvudGOOcbJ2VQzWut4kriJXFv2hfJx6gFUdm8iy4mSBSRuM9TX9HUf9MVnGdsAxkyLENzHZsMQZSw8Wt7DWYWHT3mMEi1tcqFJnbByKEO2SxQqmcSwSwErMJGN1xSZlSrJBCklr8cfKEYZnjqCN10lpO+CBmIA73bWFgDG4ITOQHWr4Xn6zIkM2oYnJoBuiAlm1IdynYRa1j+U7fOrG0qfSkGHG3mXlFXmgXgxkheUdA27WhE4YsU/mHkImSafvhOj4DMAsJOBYFFX6CdPKwkXymxUmjI/fDHjaVWnR71nwhNrEsfiZdGIamLGw8ow8YROeRVmmL5igsBtGtkvblImKQY2dry6vpCH48ZvJg7rSO30Jx5Vdr8Spn9Ou0nx20z+KKhdJnNJu5DdXBm0m16yJHOUe0crV37Jg0WYn+l3Retff1HGPOIOJT+S0aWmwS2VHSpkgXS4Wb+GYyGSdpuWL57iYGSO7deHA5eHD4hh9A7AvupCBk8MGStcltu9MyrG6wkxIc380NrRf3I+KOaJ+F94z4ccWaGG4bn4jmpLFkY6n3mlfp5Rzn7W6z/m09ZrdFw02j0irivR0OsLOKxsv36V9A/su7Z1OO7wvsgvV3xA+j8J8FPkmLU7JH3HKX1rYTp9LfsMdaJH45NsiYSVMUTyy6lvi6uSalWcGQN6TRwYCIRkY6TO0NU2x+GLP+AaMY1QUk9i3sWfSbsJ3LA70oB2+Z4GXlve052Ecg/6bOpCycJV78h2rBx0+LFssvMQj8bJxkDEv/F6HTYsr9g1xo4Cl65JFn6QpV/mWDUoaEbZs2mlx1fk83nPqTDESNxWJjKfbP9hgsB+oRFZoMTYslcJqMUyrLAtCVoIUhzjxhYp8hXjL2MiywuMb7R9WICIN3iGr0AR7LIuQN4fly/qd58MZrkL4fdo3RRZAyMGIj07STd3jMzlGiAckv7HTvGLfyDPYffIt16IrY+pKTDskLq7aLI52Qn50/EXu0TjXE6eOX+6R6cbsHvkOljfpIGZgt8pKHDk1Yg+cJCCX1UQYnS+ZwPQzfZ+cz6w/9/cM8Gna1PRR8lLGuT+RsmPT6XJPP+fA9Sx88HIXfsdvFh2x5zzT9SaFiz2Td/qa5u2McSnWz/W34T16FUVwOv/8831ZKBMLK10u+kNIiB50GH2fnPsbBve/9biCH+Y0YmKkXRFnzPY89h3jBnWJJypwRjzCH7tY4onVFbL0GMHt4Zu09+E37Ox1+eu4D9OU3wMxAUtmur1ScciucNWIYhSsPjqtrJQYWGLyX9JFDgCrRogBChYZ7Fw5mUfewRJiwsXciAYCe1BPyISDNYZtKJMnAxRKEJh9MFDQEVHygrWIKzUIOY3YRjJ4YZ4kJh4oSCFvpqFQLuQiqO3DlkIBQWxQGeBxZi7l9REX+Ee6sIRh8TE44jydtJCfwI5Km+gooybKiQwVmSuLHL1wQC4EpmBWZJLW8abdszAQMy4Jc/rpp7d53mFRFjNfYSBEDi0Ei0tkpfKMKxMm7iSFqBvKTZ1TjyyGWFBQPsyQtItQzK1gwcLiZZLgfR7rTtLRV2SHxJMcgu7rlnZE3mlLmNxhMkIbQFFPm6DRnrUZFrjDboO9SDti0qY8tEHc8zEpHproJJR1ZIJyI20GLJjc8Y6F61QWtWClJy7YkuAF8U3Yt6Tc2l6aPBIf7TtG9EEwYBLShNIRZnco/JQl8Na2ovPmzfN1oFnBsNgpn5jKSNuYksgltYkcbYKyIqeHTcxERnsDH/KG60UUhTTRPgkni23SiclsqVP0HIT1rOOQeyYk0qet0KdRXCKvWUR6sHjxfkXbYnzQ9cjYyJgmfZk2idIp3vG0AinKdKEzDtImvBCmQqLkJoefIBqhHTJmpBG6MciYtbtK8syCS4hy0FdIk7zqsZHyxPq8fNuL60DIgO0whvyqZmBrmmkBkxGyKTQum0JMinT0Ipqmw1JmJgoGQjNDGvwaEzlj08aKYR//mPilbnQrGhgZsM5UN/comQjrlpURu2FNaCeGz3iP3CHU2GRlpuUREg87EjlGUJ6FVxQHYL/AZmXw4hrGxQ5J1PbR+NOrfuJjdZa3UiUcKzwtw2OXn7ZbIHyMQm1BGgt5YvBlNR96iSGOOXPmuNmzZ7dFx86BlSXawKx+w4mIXVmVBu5wCfRgA17hqhbTGnZ+eQTrcEqye9EEOw+TMVmh8w6HFprNyK4RTXbkVXBGYGFBTMbgUaQO/QcZ/1Cqwcwn1KIN23NYj+xusw5xyEiy1KswXeoejkj4nEipDzSX87TvdQZoh6FFg7z/whe+kKoQSD+ACybau/JN3pV2C8dMiEUVZjahmRtjDdwpcd/JEZn8wTnTZmeYDcrZu3BD8vpA2I45njMmYiB/lC3NCYfkv+wVU0LtWCaGP7v3ULmLdPDPrB3HsJuVA0nS8sF7cCxLYfsK+wM7deG4xOLW/sUpL+UWgpskXEl5VvW1UTtgToNJO1xZAxczBQkrUoeP3cN2g50UI7ROkdsVIVgkUCx9JkFYb1DaChBtbgnjAyb/iiwQJCxX2GqcuJRGTCohix2NX9g9IcEyg+UbK4+EpaPR4brdATPIh7aSpAHrW3uwysqL5IkrgzUDmWb1xU5b0d/IPfjAfgt387AWxeuQhO3kyuSrNZN1HLDDsTMOF2ISRtqY/C57zdsBp7WfkAWu0yVPRetFvsPuFu1aTZhP5cn6YHfGvNOl9SniT8NSp512j9iGE59CoszkVZt8hWH0b/q2+ArIwwrRi3iTIg69KNVxFrlHHMEGIiQmIxFxMKnGPA3yDf1IRH0s3rX3tTBO/TurncbqamKiyS2LXR0PYxOsc/oL/SaP8HVAHkOMY4uOvLjS3qftgLMFAWmxDejzvF2pZFsPzvKs7BWZbRrFnManhc16XqQTxXYF4a4zKw3epZmXyHcxXMXOT8LIFZMqSGRD8lxfYyYh+n3Re73D0N9kLSZ0uPAeLkXIZo3hG37Hb/KiuRASRu+c5Vkn16w6lUVILP1O0ir7TVr7CTkRZeMNw8uRf/p5TL6v33Mfcp7C97Hf3WCJe8o0ivWltLBldrV5O+q0NGLP0zDV9al3x2Ec9COhtD4q77u5xiZf4pP61vnNSieUUUvYLC1vCdPttVETMDuiIqSF9hI+xmKVd7FrlsegTo7pg10ZUpFjvGC/hZTmFzgMJ7+znJYTRiuj5H0j3oG0Qpt8I1dRWpHfnV7THGygSdkJsXIPFXaKxgVGMdxj9dNJ3mIHKUg8sMAhdm39IBT2YiT5ir3jWVm2cGxXyZnUeYS2d1nKar95ceFfOY1gYxclzjMvSnjEq4rSfM1rxxtpx/yFeWAHSr+qg9LqCEVSSJRT89KW8oYa6lrpLC+OTt83agKGZYPhNZMplYMGK2wETbzHbjAkDLr1ro0JMc11JQM/GqlphPkTnnlIm0rEtEU3XvlO7yK1tirvYYPGzAfkW7kiR0buIYRmLhqbZQicNKsG7VEGR1jELCaEDabjBOPQQxQNHvYVxC6AjkDnkw4Ivmis4o2pCqLsIXsLlm84UGftSCQfsKxgU4adTpzTSziuaNeL1il1jHwZUxHqXJu+oYGdNRjrOPPukadqD1pgSp5hZ4qcipV82LaLlD0v7bz3aCCjkSqE/BJNa7R7YxMOGrkQnCjaa4xCuTkHqId1zXdM/rBDY0Td0K474Xixo9QauGj1hxr3pMkuC09PtG36BOIGRDannXZaW5ZEK5oFHbjkEXHq8mLdkEb0qVB/JS1skee0s9ARB+Z4pCPE2IhcWxMYwO7XO2hEUrCt6Vd6fNXfcZ93uEMYnt/UUdhfGaskHd7lHX7C4TzCeqZdynxBOari1MXyLs8aJQOmUAyMAJ/GFpOCD9s1JgMZtjKE+e1WBhzGNwi/TQt6EGqhWB6a2KcoE1REfFUMpcEINex1NSpkwDQVVuNoE7MTQt4iwnVWObCismQ7hJE/NBlxzyi/5VrUxSSHDrAyx4YUTVg0USUOrjG2mH6PLeUwEL6YJd9gpgm5JbtpeY+jCtEW1eG6uWf3L/GDaXjcInGjOSxh5Aq3BBtU+c01bZdBm5JwsKiRDWEDKs/0dwsWLPA2v+RL7wS6KaN8i/MDSRMNXZSfwFy7gcQFKf7D2TH2chEK10HyxlUrPbFb0u+kPFzBU79DC53dX/g8S0EH2TCKd8QD52tiopyDUlK3BEtf8kZdUu86X8J6x+4UrhhjjWgFwz6Wb7lquSh1xBgl78UGGO4Ru2fqNdRFYNyS8PrKDrMOWSVtCq4GaYFtrN/CtdN5gbMFxyjMD0qCOhz3lD9UWOykvsBOx637AvFhPaDfh/ehnP2yyy7zvg9wV9wLatQOGBYCA08exTozFVOUcGKQJeelsyBXRQGBxghbMM3XsuSFBhmamGgV+UFcAcLqDp3/Y36AGQIDDgNh6OcZ1ilG/bAeu90BI4eKscdFq1Hqs0zdwkbGoYAQ/q6LyHEZhGF3Uo8y2MIKQ4wQmjZJ3GWusFEZ3GJEmmjkM2ijNyCLTFjUsKU1KzX2fd4z2HJZdsCwAjGtCYmJBvOZUIOecLT70IQs/D78jcgGhyeaWOhqtr9+h+gHZzdplNWnmHTCiQTrglAZjnbMblPcesKGPj9xCBJzMEKZ0w4QQP6olYpoOyzsRCM/rw2j7Mc3VeyAKYs4ANHYwSYWkQFtPLaJIDzY0Sbo64ipqIcYIWaYnyizCts3FkaexeqKeEMxGOG1GWYeboRHbCCiKyxpmLSpRywgDv1//QrCdUOjYgccyv66ASzr27zVNZ2MzisdOG3y1WmEky/v0pyl6+/6eX/CCSeMSF7sJJGNhZMvgem4yDOroNjkS7xZg25euqEZW1EZLrIkFiQi7yYdJkK8GVVB2M2mEQMOMkY8LcnkS1gGErxd1U2xwxxIkzzHJl/JT1k2acx8J81LG2kU5VZJfvRV+q5+Fk6+vGPHJZMvvylTTMmTd1CaC0c9+RKOekxr37wPqSrLC+KFYxcjFhZCmFqmkSw0eB8u0PU34KY5JfpdkfvY5Mt3ZeXJYp/NIpY+A8E9YjFeNzVKCUtWf3WDxuooi9h5iCJAVrhhfxfuRiiPlFuUlGJljK2uY+HynoWKOhIeByqdkuw45HvRqJTfaVd287GdZl5bSYsvfB6LW8Kw0GGFHYZhgCNfdZPsisJ00nZIYbiiv2Nar+y20kjaYtr7Op4zcWa1v6x3YX7Cthi+17/T6kCHKXof69d8q7Xss/LG4kXGYrmmpV0Gj7Q4wudlsZByaccrxNkLEU6jJmDOrNUDf2ygx4tRjDBiDylNjR0fqFnEoAB7FGUw2LCcpxsjvVPTq0sJC3tmkEk0O3Ue5Ug+tAhh4YSEfCuNlRqGzfsdswuFnRXKb4p2cr4NNc9jB2toFjV5ZKBn9wn7V+qauJgw0NSugsJ8SZy0MQ5TwI8zu3cmXBYmpE9eqtI4l/Ri15inIViqiGpgRYaE0xCoyFFx+tuYXgRcmNgiA05ENzL4kBNCPkTDX+cJ9iWLH8EcbkAsXbEyIF76RkiMKdSZECZteiedpeWMiASdhKqIRWdofoc8WPdbxGNhOcCcMpyWaIAL6zfWNiSfiPG6yXdM34PxXy/80EHJIvIsGt/SV+m3yPrDcSQrnk7fNUoGDAhUAOYCrETlIGhsKDlzGDZh1oAEG4dOTuOBnchKEHeSsBGpKGRWaSykWAUgA4a1xAqL7xmgYY9wT/zarITvRWmJwQvnCrpDxmQgsTT78Qw2EwoVyE9Ce2YcWTBIUhY8YGnTCgbOKnxBo3SEkghO748++ugoBLAqGfAxF2IRgJIMeFO/yHkxGYp5/yEy2OYcWs/AiikGzu2pR2zBGSi1vSblpx3hDCDPvjqa0ZyH5BFFL9opO1zKIoOdfEp54MLwrgqi7rJkwJIGMjlYirjzC0/UQcaGKAY2srbfxnELfRKvRdQBSmVMZoQn/+xC6L95gyF6B7Q16oO+zuSVtpOT/Ob1KfLGGMIkIa5MYa+iZ0IeMfGSyZ+FBvGJSRhpMElRV7T/0G6W8LQfuBc4tRBOCWxPFowoCYbECWTa9IeJnwWi9hcgO86y7P0wLX4jg0ZchMe7ND0IPNqxCGFhQTth0xPuJImLvkm9sNFBN4CNEGf/FqW0uqJv0laImz6Be9yQGJfQRcBHAeOA9AvGIvRFNCEKpI7pU3ntR3+Xd58mA/7TkisvhiF6T2MR9gE8fUDlysCYNQGLPIeOh3KHVAAVQnxcy1DoaEBkE0zAsZUZz2HtkFc6UNGdW5k8VR2WAQQ7PzoCi55wAmYgoTyUjVWlnoCryAvpky4dEJlQ2gSMC0C0VrHfZgBAEYyBBa1H2keWcxAWCSym6JSyE6EtISsLtS4pE5qsMXlht+VlkUMZwZMBjYGFSYDFgRDsP1byLOLSjoyUsFVfmWzoJ/jJ1hMwWJB3dush9+Dmm2/2iyfaD0pWDPoykDOBwWVJs8fX+c+yy9fhytyzwGHRqI+ZpA2wO6Q8MvkSZ2yxRdtCeY72oIl4wYI6QkFTJk3C6F2m/oZ7sSigPbKY4RrKjsNvuvlNn2FBxWIjbQLGCoK/PKIe6WdYFLBApO8wcXdLtHewp0+kucYEI8ZVFjkyL5BubJHD2EC9Uccs7OumRu6AadwATeWEMiJWPzEFArRYw0GYnU6MnYSCEY2yDIW7FL5FQUZYU0wiIouQeBlAcQAApa0AJWw/rgwkIZtf+9mOlRkOBQc1QN3ugGPpE29socRkFWMrEl4IDkcoXmCQY+GgCT/fITtS0qzLDjjWPiRPtEXaZAyPTtqqxCvXIjvgWF2DSSzfcH/YqeKQIsbil3T1Vfsh1s+7uc/qU/MT8Y/2PsZOGA4K7EkWfRCLShYNWuwl+WEhAYuVhQX1Qjk5GIDfPA/HFRaEsguWOPQ1y788E4WwUWUy73YHDBuXvAohV5UNhDwreo21Db6VdlsknlhdxcZ3jqPVbG12xTxLIywIRJmLRaPeESMqwYSqCkrbAZebRarISQ/jQC4WknjhCZ+Hky/vw04i38guSH7nXbWXIB1WWOQ8g40SEo1nkCmcfMmr7HCRx8eIleppiYyoCkrDVXsGk3TyJl/CcaiFsBrlu5h703DyJWwZdprEXeYqbLPYNwzwsPRibUgWcLHvqnoW83ZF3GASLoB5LjuLopMv3+CprpcU1juLZVjsMvmSFyYofXqOzh87KBYf1A0kLGjEY7FxJTZW6fhY9KURC5oqiUWonnyJG05P1kSWlj4LrTQCm260oGMmpzL+SJp5eWYBJXnQky/fl9VRkDTLXBs9ATPY10Fl2T5F2JFFwtRRlrrihB2ZRmmn+qSFT3uuB0MdBvFBpxTuHGQAzYsP9lqdlNc+yHesXdbVB3RZ0/CGTRoO5Pq7Qb5n1x9SzJwwrezYDGuSekgLn9aWdRy9upfDDML0EPOUpbx+Efa3MvELpvqb2DP9Pnaf17di31T1rNETcJpSTQy8kP0bCyPPyq44kSnESJ/TquV4EraIo3kJ249rzP5TtMmzdrlaaambfGv8dDwxhZ1wQNTh5R6WYug0QHYuEoZrzLwl7WhK/V0391l4Ei8sdrFn1On0Qg6cVp/spGKyQ+HsiI6Fzu+g3IumtuQHsZa2WpDnabt4yk17EhLZN3LHmJlMjKsi33JNczbCO2TJVVLMiQjx6zN+i6aXNvbJ991waGL+GEK7/dhCStLmSh2hawBpmT6/hZ3PfV3U6AkYTUhtTE1DFVldCCgeg7RQHhMOwoZsVmQCMQWqMD79G23n0OUZiiXaTIdGL04s+Bb5U5kFhE6vV/c4Ppk9e3YrOVjz0ilo2CHWNGi0PxnMqiDMb5h4hJgYMdeJLaZwS4qMV4g8oBAitMoqq3glvXAwYyDFoQODJn9ofeJ1aOLEifKpw4RMmz60XlR4g9KVzi/tEnkzeWLyR8kENuXFF1/sZWsMPJgBZQ3cVWUP5baQ1Uc9oDmN5YG0CdKjj2GyBcH+0wpOPEN+B7sd+bDoWTBRh22JsHUSfROLBzR6YaUjnsAsEWUilCv5Y9IM9QMkT5QZpSAU/dBcloUQ3zHWaM9haNZrTWaJQ19RVhMTN/2cNjA/kVdXSeCu8aYvi95G2XQYE2LmgsRD/XdDiJrAhcU1/Zk6Q2lOE5rxogyLZYBeYFNOzCZlQY1iJQpzxId4KzxnXcdb1X2jlbCqAmkQ4okpIQxCvrrJQ7dKWN2kXde3dSlh1ZXfIvEWUcIqEs+ghWlin5JdWzes3UGrJ/Iz7HU1apSwUG3HzpAVJasbdixo4clf1gHrrH4kHFfkmPo391lmAlkNF/s0HRcq/k0g3A2y+6Ns2nEAZcPGVpeZ+6p39ZhA6XpLYwmSH3Zbkh/yrL+LeVniG0i+4Srh9LMZM2b8X8Ca/7MD0+nKvWaXYpbEKp/dgThFqTlbPnqUliQ/XKl7Iexd5Z22Y+U9HAZ5x5XdPNqt+hkcDW0+IvHWfUUph10nypJibgYn66CDDnKUN8vJRFbeUNDS5Qu5AGnfoj2tv5P7NLlyWjxFnnOQjMTPNaY4ViQewmCWBsdGx8d9zJFP0TglHHbKcKPoz2kiuzBd/RsbbCHaHVwLlLvoQ5ia1U2N2gGjyi8HwgMcnlpip3ho9ooAzHm9Re3SOOEozd5U4tNXWE50npCYlOmMRWgQV4Cw1cIBFRxlcUFDj5GYZHS7A8ZZSaitSnqYZIimraQPKzBvQsJJR6gln1YGiVeulJmy17UDZnETmkhJ2lxhv2Hjrts/5aFNF3WnqePT93k7YLRFY76qEakwSYXya+TqOKfAkxGTWRGC1d6pGUxa/Fl9iskXcZMotiEyor/iLERI2P9l2Py0k5hPayaQrAkdlnaaZQEsYvyBQ1XsgLF5j2kvx8ZNwSLtmtZHJXzROGN1FTughPFI60IgIsCUMIsQi7GwxqEQ2u4oxMGWZhI+88wzsz4t/G5U7IBZndIYhcpoFpY5uByZXxkKTVvkW4y+h5m0TFLKcdNNN8lt6hWvRVVQKFeXOGMHEORNvnz7wgsvSBSlr8hb66SsyZd0cdyPrE3kpjyjL3QrZyOePErzDoftaqztw7WAythYyg40Ly9VvadtyeRLnGjuM+DjOEOIHVPZQ9vTFvl5ExF+CtKoak3zqg4QIb+XXHJJWra7fh7DBJmwprzJl7BMwBCydJkzMCHrZtfvIyzwr1FKWChwyAqQspfptDH3aWn4YVBfhrRBv/4u5j1Hvx/0ezkPVecztnLW77lPs50Mw+X9pr5jWo4xzcoiXsXY0XRKWrmj0ziyvhPWd1oYcZmIookQxxNOVMpi8rzqa4wLQRo77bRTdCcl7V4rIuXlSS+s88JW8Z4dr1YWRHSFYo4o7JAGrN8y4wbfpNmu57W9LDtw4q2SsrwFlk0nZpNfNo608LFxNfSaFipVxuKSMQttaKlf2ltRE8RYnEWfNWoCZuDFmwnyBgzbkSeFqvOxVRNgsXMLB3OtlSyAolFXVo6JHCmUHSNngGU5zIT2pnZAgYwSrVeh2MEXsPBi5kvyTZkrcaEtC9sXotPA0oxNhrDCQpLOJs9jLE5h7UkYrmE42IcxcyX9Tbf3skqPxQObDdYmExu+bmE548UN7e2YRngsjm6e4foy9OJEHVA/7HK1gxDyJU5RkA2Hp+oce+yxLccIkif6ZRZ7VsJVeUWuTr/FkQia26SPzgDmgowBDNrIucs65UE8Ei6m4FogPssi2PxpooQ0TlBWfFnvKFPYhzo96hXsqOcYxVjxsXBpz6gfMXuk7zPma417vks7WlHihMUsmuzMHXhcY1FF3GncCvm2imujZMAAgls4Vq79UNqookLS4ojJQNLCDsvzbmXAg1jOumTA/Sxrngy4n3nrJu0m9inhAJoWdDcto/pvR4UMGNiYeFEO4iBwkY2g9IPD8LwD7pETsptB8UYM47WmLIo8nbIlkBeRJ2RinXhrqb5J9CZGbHVF6zBUjOpNDv6UCg4gJC8omhQlZP7shCYm7FzxEoT/WHb4dRwCkJYvdkk4dOAPG0W4OSJPlW9QIoOFCIel1x5+2EnQf0IXgchPcZDAX0yzVOpEKzMh04Z9GXPkIWXtxxXf2pQPlr+0hbL5gHWND3jKjS/iTseUsumWDY8liewGy34bhodTBocMsQTjqPYfEIYt85uFBmxndu0xH//EhX28tDE4MnBZ4AxRNpkjJE24Rig00v56QY3aATPgwO5CgA7BTkAGoJ0EoDASkz2hcMEAqwm2RlhBsItw6lBEtiBxEQeONhg0SYc4EPCHB6hL+Nh1GFfrsBq1owzKhZE7+EG93AGzALvwwgt9uvIPEUUoM5J3csVPbOjrGNaZ9h2OowhRdqprB8zCMpQfkha7U9h84BzTXsW5ADh3Q0V2wAxwmljxP/roo34RoCcZ4gJTYVmH3xEHnrUQbwjB0cpjJUrYMteyfYqJUy8SSAvRlbAwi6TNQjzmHxsFoDJjSlpaVe2AKZNWYOpGCx2RYGyRQbstOtGl1VXYfphc9elM9FXEM1n0xz/+0Yux2Kgh0hLCqQdmrFXQqNgBs3phhc0Okz88FunJFyDl0OUQVHa6IYWTL+9pSGW149BOxZMMvmRZJKBpl7ZaC/MwzL/DyZeyUP5Ovep0g0U4+RJXmss9nU5s164nX8Li7/bSSy/Vn1V+H+oQkADtiHbOhAb3JmbSoyeyyjOVEaEM3ugE6MGXCUi4S2kasnoAJQn6tPZol5Fsra9iByKceOKJpdJMs7ZIk5OWirzCwFJ/EmUZhVb5hiv+o3X963csXrohuGshhdYIeZMv38sBDuGuXLyXhWlU+btRSlisrvkTYmAUBR15lma0rr+TsGlXbeqRFkY/R9lHr27ZWevfOuxouC+LX12YUA95FAsTexa2s7x4y77Pap8s7MhTLEzd+corR6joRnjJU+wYP97H+obWSCZMP0jyrdOOlU+/D+/T2v4glC/MaxW/08pL3LENTpk0Y/7dO4lT6lWbmJGPmLikTP6KhG3UBIwHLLQqYe3i2xcZkngJYoDiL7YTAqiYJmGMVUQlxdTfs8CGLY6GNmwInCOgRRlzzJEVxzC+izlnQJMT86FeU0xWC+spj2IHO4TcC9pabIeaF3eZ97EdNqZVpI1ZF2xe7X2KuGnvck5smbQ6CSuDmHwrfrfZ2cmhC+SH/iM7l5g5H5Ov1qQnPvqz7FIk/n5cY1iW3SWlmeXgeWqQiPakKWS963dZ94ga0kRtRx55ZNanue9iTjLCwx9iY1AYsVi7iKa3zBUhJyb8rorfjZIBAwgdGBYxslZMHiBOL0LuiOKEDAz+ReQfgnlkyLAuYOlpGcKUKVNazu4jn+Y+wiMWLDjszWKrt6wI0mQgWd8MwjvtBYxJSg80vZQBgwUsLxSn6GA4UBB5WR5OsNFYQLHbwRyFnRse1mBZMbjojspERLg6FKBoO3hOY5WPvAq5M3mR01woB+niko98wI4LJ8a8ssbes7OmnNgVZxH9g0UNk4ywmQkPC1IWB5ighXlioUvZkOdxkD2EmAKzEJS60sRGPmAX/zrpU2CPxyTKxOlIefa7sewRB2Y+t99+u98wSJljYcs+kzZdhRY03spwAIM5me63ZfNEeOodMQkTOW0U81AOGClKWXVFXOhIYIKEXDkkRIBsziBkvMikkRWzIQoVc9GZuOGGG7ySXNpiKYy/yO80GXDjJmAzQyrSHAYjTK8n4F6Uus4JuBf5j6VRdAKOfTvIz7IG9UHOd1beqpyAs9Lp9bthr6u0CbhRLGgaxWmnneYN5llpXXXVVV21E1mVszKXv0FTluiqgD34WJtxgWGaI5S6swJHROqQa572c9356SR+2h5OHHQ5uI8pB3USfzffYGqE8wI4BYhcYofXdxN/k74N609Y8oNURsQsOp/hObtl8grnSR+EIvFW4VxF4pJryIImn/JOrjg1iRGcLixn4MgQ9sEHH4wFq/RZo3bAeC5B1if+PJEHI8MretpIiCxmHzGHHnl+W8N4qvg9jCtA7Ok4nzYkwa+XO2DaQGh/jQxKvDKFeez0d107YE6UydJoZlBBxFIHFdkBs9ASFjUsfljl3/3ud+vITmVx9qNPYX8qZpK6ILChGfS7pap2wLG8SL8tm8dYXMSBghZ21UUoVlewsGEXh6TziQkhVgIh6TDyDvGkZt2jYxHzoCfhy1xHxQ74+eefb02+gIOshZV5p4QWtVHnCPRiBVk0d+yAQwpPPgrfD9JvTOqyqIqj3bLiz3unfW3T7wap7vPy3sv3scmX9GOntvUyX3WmFdPOJ70086SieSnSf4soWkp6LBw19YKL0ygWNEpXWrkJTzWwFDolPMEYdY4AShyDQltsscWIrCCuGBZCiSQcIHTeUQzqJ6H8IspVmNSgkGU0EoE00QdeoppKaY5gujW9KuIko4zIEGUzTeIsRj+r+r5RE/CGG27oXfPBduCgA2QMYgLRCXBocobeh2LmSp3EPRq+4ZQcvBoJMUDH2EHyvs4rXq9w/yeESzr9W54P6pW8Ij/HjE2bdTAp44BGP+tHGTgEApYgfY+FDWdmG41EgHaIGaIm2M8x22cdptf3nP6kCWuGTunWW2/1rh85n10Ijkm3DnkwTTv88MMlSn8NWcuc2x6euZ7mVQ3HQWg+k09cZlYho27LXORHo2TAlM+0oCO1PKCPeikD7hUEdcmAe5X/WDpFZMCx7wb9WUyuOOh5zstfVTLgvHR6/X7Y62pUyIBpFLisgxXNCqabA9algSFXRolA/tCMNiqOQKhNmeaJrHiMozckNouwolHkkfbItagiS53I0S8mTpzo84WdpfWTdLSPO+64tvoL3eWmf9m7N8hncTiERnDMTW8nOSFOlAWxwY05lukkziLfXHTRRS28qypLkXSLhGnUDhj2JgOUCPdZuWOE3Q17jgEupJDNEb6v4/cwrgBRgJDD1zUmgp/tgDUq2fcLFixoc7gRhsbJwdixY8PHlfwusgMO+wms8dhZypVkqKJI+tGncL4RO+HpzjvvjB7SULaoVe2AQ6sB+ipOKrohFh6YhuKIgzaF56kDDzywUJSd1hWsdE4P08R80GslwVGxA8Zbi0y+AM59eFybroi8+zJH1uXFNRrfF3EDNxpx6aTMF1xwQeZnHBQ/SIQmtNFIBGIHZhDq5JNPHhm4j09Ck71urEmkGHfccUfLQxwckl4csCGuiCUPXLH3HRRqlBIWfoY1MQGHCg/6fd59XTuKvHSb8h6nEUbVIIBNe5YWdDfa/tXk0GIpggBKdDEatPrLamux/Bd5hohCUzjJ63dV3eNaeJCpURPw3/3d3zkmYQy8YXGgldmNOQRsj2233bat/sqotbd9OAp/oGkcaqHji9moPAKs5LXPZx0DDgTqPgxCpxe7D52EWD+JoeQcrOYYMXYNEuEDX1PsaFH9vsg9vrMhJmLMAn/yk58U+ayrMGeffbafD3Qk/baZ13lplAyYgqEFDX+f3W+nJ3hogLjHdIBTlLBrxdVeP6hTGUg/8hqmef7553vZz/HHH+923HHH1muTAbegKHzz+9//3oc96aST/AEjHBoSkykWjrBAwCIyYKIhbz/84Q+9XE8OQikQfd+C9LNP4WMAvHbbbTdH/6iKKBOkPTp1Gje6GpwUhIe12MlwncTLrhenI3B0yphedVtX9JNHH33U/ehHP/JzRCd57+abUSEDBiBkFdhFph183QmI+LjFpWW/Jt9O8jxI38yZM8ev/Gn8Rt0hgGs8lGHYYaLAssEGG3QXYYVfc2g7zgt64UGowmz3Jarp06f7k5RQrhtUQrEOO9uqJl/KSdtlbMa6pJeEQihzQ166hMHmuVeWBY3aAeM6EnacuHuDHR0eN9XLSq8yrW5XgFXmpUxcoXYsx7fJLs52wGWQdJ4FzQCiFQ2JgWPYDjvssHKRlQhdZAf8H//xHw4Oh9CJJ56Y6btawvXz2hl5w98AACduSURBVK8+hf9xzdLl+Mo8V6NFcapyB1w0zaLhYAfLoQ60YZSwQg3ltLi6qStY3toVLQ6W9txzzxFJobXP0YbIv1kY4fimKk7OqNgB77PPPq3JF3QBMU97dEQt2IPKEMADTkjskozKI8Bh8LTncPIlJmTt/cY1dDsK18MojoCefAnBhqFKjl081f4/xUMa7VfacC9Ogfre977XNvmCQprbVs7RxvJFuBLkt25qlBKWnIKkQatCFqLjs/viCPRCy7F4boY7JNydNM1U2n2/HV+wi9Mkg6x+ZvfpCPR7AZWes+rehDJffPXXTTFchUMapq0PFOGd+CsIw1X5u1ETMMffaWLAYlVj1B8E8KtqVA0CaPSjYBgjdBNg5/eTwrpGFGQURwAxTEj7779/+Khxvz/zmc+0LSJ7sQP+2te+NgJHjqyN0SGHHOKVw3gHy/jggw+OBav0WaNkwCAzf/587+qMwQq2XayxV4pgjyLrRgbSoyymJiNy4PD8T5MBp0IWfYEHIZybsIJ/5513fFtnUPvbv/3btoEt+nEXD4vIgIme83/Rmt10002dmJx0kWztn/azT+27775e2Qds8WLW7clAAtYgy4DJIyxhFAlZsP3lX/6lZDv32k1dwY2hTcJFYqFz5plnpqb38MMPO7yVITcOF5WpHxV4kSYDbtwEbIcxFGgNAxLEJuABqYicbBSdgHOiGbjX3QzqA1eY/8/QoE/AneI27HWVNgE3igXdaeXad/UhoB2hsxNmhWnUHQKYIIGl/OFft9+EbeekSZNaeSpzEHq/827pGwIgAFdpyy23dGuuuab/g5taN9kOuG6EK4p/GFeAKAbFbAhFucF2wOUbB+YqoXc2YhFMy8eY/0WRHTCDlvb/XKVpTX4OOwsxjH0qr6S2A85DKP0958lrpS3EAmlnB6fHEn9jO+A4Lva0RgTYFRlViwAOYQaR9ORL/tI0TQcx75YnQwAEFi1a1AYEO+K6yVjQdSM8iuMP1fpHMRSVFR1HAYNIVSkRDWLZLE+jAwFRFu1laW0C7iXaoywtBmWcRGi65ppr9E+7L4nA+uuv73A4o+mss87SP/tyj+aoECzrW265RX7a1RAYCgRuuOGGljY6yrw33nhj7fk2GXDtEFeTQBPlVSYDrqZt1B1LERlw3XmoI/4m9imTAdfRUrqPsycyYOQ+ONvWzthRxPnNb37jFi5c2H0pCsRw9dVXu89+9rMOo2qj/iOAXAWbOpR08FlsVA0Cs2fPdnvttZcbtGPsqimdxWII9AcBDjhBcXSTTTbpSQYq2wEz+eIPFscA99xzjzd45vzSr3/9674wsKhw0L7eeuuNKBiTcxVKG5zzOG3atFb8Sy21lGuKOcSwrtZDucrmm2/ufvazn/k6sh1wq6mWuuHgBcGQDzlbFQcYdZHtgOtCtvp4bQfcOaYc66lFKcRUlXVB2g64sgmYU1oeeeQRt9NOO/ldMDtRFEaQ+R133HHe+wkOx7mHLrvsMnfXXXf5+6OPProSV3q4vwuPkcIjT5rrMZ/4kPxjMRHzdT3I2T/ggAPcvHnzRmTxjTfe8M+QEVOmJvkNxtsXLlCrWFCOAO7/H9CZtdYxadbp85zyLLPMMq5pvr2HsU+ltQl5TpmgYRsrJP9p117U1dixY0ckjxnSKqusMuJ52Qf011j8S5aNKC08Byzzx8Bz6aWX+p0op0pwJCDEO30WI8+xu4IYQKpoMCussIKPT/+TQV4/G8Z77CqrwKiXZeds2BhJOXDOTnvp90ECsTx2+ox6qqo9p+WBwUgfr8YCRjBN+6ab55SHAaTONLrJX6ffDmOfyisrZbK6ykOp+HsWn1W0e+olRpVNwETOQIqcb6uttnLbb7+9u/nmm1u7GwZabaqwww47OP4gWND46eyWOPB9xx13bEXDJI/P1SribkXapxsqcNjKgZzy4osvbkPs29/+dqscaBpia9cLe7u2TNT4g8mRusJvc100c+ZMx5myQjiNr7NtwIIeM2ZMrWlIWXp5HcY+lYePDPR1toe8PNTxvhd1deSRR7of/vCHrewzPrFJqAJLuFYxqmwCZhXOoIAC1NSpU31a66yzTounjluvmFekWKY6fYZ8+amnnnIM8oB37LHHdhqVfVcRAshQ0At46aWX3KmnnuqmT59eUcyjNxpkwCiJzJ071x9ovvfee49eMKzkhkBFCDB/cbIYB55g7hduHipKpi2ayibg66+/3j3wwAPem8jll1/uNttsM/elL33JTZgwwc2aNcvhFemMM85oS7yOHyghbLfddo05BakOjHodJ5wQo2oR4HSXtdZaq3V8WrWxW2yGwOhEYJdddnH89Yoqm4DZ+fIX0uGHH+7lVeFhzGG4Kn7DymRQEgWYXXfd1V144YVVRG1xGAIDgwCKhoceeqhnjaF8ddttt7mY/sPAZNgyYggYAlEEeuIJqxeTL6XDxEUmX36jdX3ddddxa2QINAYBFpYPPfSQQ6zz8ssvu29961uNKZsVxBAYTQj0ZALuFaBaM1TSZKAyMgSahMBqq63WKg4ar7///e9bv+3GEDAEhgeBRk3A7AxCggVuZAg0CQFs7cXeE8sCU8JqUu1aWUYTApXJgAcBNMyQsFnkgHLMJq644gp/HYS8WR4MgaoQOO2007ypE+Z7WBzgwcfIEDAEhg+BRk3AwH/++ed7e+NXX311+GrDcmwIFECARSYmXUaGgCEw3Ag0igVNVWALzO4XH8SPPvrocNeO5d4QiCCA5vOkSZN8G8e2vgpPPZFk7JEhMOoQQHH3mGOOcSeddFKbt7m6gGjUDnjjjTf2WqEC1pQpU9zDDz9sJhoCiF0bgQA+z4XwPicOaOSZXQ0BQ6A8Aky++K5gQYvnreeee85ddNFFXqxZPrZiXzRqB4xJRkjf//73w0f22xBoFAJN8qXdqIqxwgwVAhwQJNwkzFnxqsgkXCc1agLGcXZIHIloZAgYAoaAIWAIZCHA4UBiXUC4J5980snxjlnfdfOuURPwtdde24YF56Tutttubc/shyEw7Aj8/d//fVsRvve977X9th+GgCFQHgGOrd1oo43cmmuu6SZPnuyP0q17Aq7sPODyxf3TF5hTaA9Wf3pT/o5DGDiMAWfaTZp8aQh1nvlaHunuvxg/frz3HW6nIZXHEi9YmNnts88+buLEieUjKPEFpyGNGzfOn1pW4rOBD9rEPiUTRtPGil7W1QsvvOAVeWPn93baqDkNSepGx9GoHTAFe+KJJ1x4YLkusN0bAk1A4OSTT3Zz5sxxJ554YhOKY2UwBAYCgRtvvNHNmDHD9cqBU6O0oNF4Fm9YHI94xBFHOJwWGBkCTUIAn+dwjaA777zToRX9yCOPNKmIVhZDoOcIcKLfQQcd5NPFhTGcJThNdVKjdsAHHHCAY+LlD8Iph5Eh0DQEZPKVcjWN3Sjlsqsh0EsEZPKVNDlb4J//+Z/lZy3XRk3AsJ41iUq5fmb3hoAhYAgYAoZAiEDMiua1114Lg1X6u1ETcCgP49ByI0PAEDAEDAFDIA+B6dOnjwgyc+bMEc+qfNCoCXj//ff35/8efPDB7qtf/ar7+c9/XiVWFpchMBAIPPvssw4Ncgi3q/w2MgQMge4QQKlx9uzZXomXIz/rlv+S20aaIXFEW9MOY+ilGn53zbj412aGVByrfoY0M6R+ol8ubTF1aZpewLCPf6PGDKlcc7XQhsDwIfDWW2+5nXfe2WtprrPOOi2N6OErieXYEBgsBDbYYAN/yAmH+cyaNav2zDWKBV07WpaAITAACOywww7+kBG0NFE03GOPPQYgV5YFQ2C4Edh66629YyApBefLh94V5V1VV5uAq0LS4jEEeoTAokWL2lJ68cUX237bD0PAECiPwPPPPz/io7lz5454VuUDm4CrRNPiMgR6gAD+ajXFzCf0e7s3BAyBfATWXXfdEYEOOeSQEc+qfGATcJVoWlyGQA8QuOSSSxxamigbchbwvffe24NULQlDoNkI3HTTTW1nx59yyilum222qbXQjXJFWStSFrkhMCAILLPMMu6+++4bkNxYNgyB5iCAO+NeUuN2wNOmTXMrr7yy4yhCI0PAEDAEDAFDoCgCZ555poMVjTb0m2++WfSzjsM1agKeOnWqu/322x3H2yFQX2uttToGxj40BAwBQ8AQGD0IXH311e673/2ue/vtt702NEfaLliwoFYAGjUBhyfCVHXGcK01YJEbAoaAIWAI9B2BI488ckQejjrqqBHPqnzQqAm4SmAsLkPAEDAEDIHRg8DSSy89orArrLDCiGdVPmjUBBwexoCXICNDwBAwBAwBQyAPgSuvvLItCOZ9F1xwQduzqn80agLmAIbLL7/cfepTn3KHHXaYu+OOO6rGy+IzBAwBQ8AQaCACkydPdo899pg3Pdp1113dM888U3spG2eGhJs+Vi2YahgZAoaAIWAIGAJFERg7dqw79dRT3bLLLlv0k67CNWoC5vDkGTNmOK5PPPGE3wEbG7qr9mEfGwKGgCEwahD4i7/4C/fUU0+5999/3+25557u3HPPrbXsjWJBcw7w/fff7ydfUDvhhBNqBc8iNwQMAUPAEGgGAmeccYZ78skn/eRLia655ho3b968WgvXqAk4ZDs//fTTtYJnkRsChoAhYAg0A4EXXnihrSAfffSR9yfR9rDiH42agPfdd1/H4eEQk/H2229fMVwWnSFgCBgChkATETjggAPaisUEPH369LZnVf9olAwYAMeMGeNuu+029+d//ufur/7qr6rGy+IzBAwBQ8AQaCAC2223nbviiisc5qwTJkxw3/72t9sOZ6ijyIsls/xHdURcJs6FCxe6qrxWob3GKTGvvvpqmSwMfNjll1/evf766wOfzzIZHD9+vHf5huvQptBSSy3lllhiCe/Orillgqs0btw4Rz9tEjWxT1EmqGljxbDX1XLLLeekbnQfahQLWhfM7g0BQ8AQMAQMgTII/Od//qfDlHXTTTd1DzzwQJlPOwprE3BHsNlHhoAhYAgYAk1CgCM+jz76aDd//nz34osvejMkHHPUSTYB14muxW0IGAKGgCEwFAj85Cc/GZFPTJHqJJuA60TX4jYEDAFDwBAYCgTWXnvttnyiHjVp0qS2Z1X/sAm4akQtPkPAEDAEDIGhQ+CYY45xW2+9teMQBk5GOvzww920adNqLUejzJBqRcoiNwQMAUPAEGg0AnPnzu1p+Rq3A/7GN77hNtxwQ3f88cf3FEhLzBAwBAwBQ2C4EXj33XfdLbfc0rOT9Bq1Az7wwAPdjTfe6FvAH//4R3fvvfd6MIe7SVjuDQFDwBAwBOpG4MMPP3R77LGH14BmIuZY24suusgtvnh9+9T6Yq4brUj8MvnKK05EMjIEDAFDwBAwBPIQuPrqq/1JSJggcaLegw8+6O666668z7p636gJuCsk7GNDwBAwBAyBUYsAO2BN7733XmUeGnW8+r5RE/C6666ryxZ1/dUWwH4YAoaAIWAIGAIJAlOnTvVnCeDOGLeRa6yxRu0H+jRKBnzrrbd6TyY33HCDYzKu+yxHa7WGgCFgCBgCzUBg7NixXm/o2muv9aZIu+66a63yX1Br1ARMgc4999xGHsZA2YwMAUPAEDAE6kOAw1Tqtv3VuW8UC/rll1/2TrRXXHFFN3HiRK/Npgtr94aAIWAIGAKGwKAg0KgJGC8mTz/9tEN4jhr53nvvPSg4Wz4MAUPAEDAEDIE2BBo1AYdabM8++2xbYe2HIWAIGAKGgCEwKAg0agJef/3123BFqG5kCBgChoAhYAgMIgKNmoCvvPJKt+qqq3pV8s0228zdfffdg4i55ckQMAQMAUPAEGiWFjT2Ww8//LBpQVvDNgQMAUPAEBh4BBq1Awbt6667zv3jP/5jyyf0wNeAZdAQMAQMAUNgIBB45JFH3Je+9CV3yCGHeHeUdWeqUXbA2AAz+b7xxhtu/PjxjpORZsyYUTeGFr8hYAgYAobAkCPw0ksveW9YUowtttjC3X777V6sKc+qvg7EBPyxj32sknIxATP5QoB56aWX+tVMJZH3ORIMxMeMGdPnXFSbPKeMUPdLLLFEtRH3MTbKwoHedZ6g0uviURbK1LT218Q+RZk++ugjq6sOOsm3vvWttq/efvttd9lll7mvfvWrbc87+UH/idFATMDY7IYmRLHM5j3D+cb8+fNbwV544QUHiE2gJZdcsjFlkfpAZk/d89cUop6YhN95552mFMmXh8m3KX1JKqaJfYoyQVZXUsvFr4xHmljI8KwKLNMWr4sliXykE+3H/cKFCys5dYLjB3fccUe39tpr+wmdHfCkSZP6UaTK08Q5+Ouvv155vP2MEDHBokWLGjVZsQNhAq6i0/azbnTaDOrjxo1z9NMmURP7FGWCmjZW9KKu3n//fbflllt67ilcH+aOX/7yl5U0+eWWWy56ONBA7IArKWESyXrrreeefPJJ98wzz7iVVlrJy4GritviMQQMAUPAEGguAiw0H3jgAfezn/3ML6J333332gvbqAkYtNh94IAjbctfO6KWgCFgCBgChsDQIjB37lzPQbUJuGQV4gf6wAMP9OxsZMH33HOPW3311UvGYsENAUPAEDAERiMCnAEsxH3d7owbZQe87777uscff9z94Q9/8JqAM2fOFCztaggYAoaAIWAIpCKA2VFIG2+8cfio0t+NmoAnTJjQBg7yYCNDwBAwBAwBQyAPgbfeemtEkLqVKRs1Ae+zzz5e+QoUUR//4he/OAJQe2AIGAKGgCFgCIQIHHPMMeEjd9ZZZ414VuWDRilhHXnkkY5dMP6gN998c7fXXntViZXFZQgYAoaAIdBQBL7yla945d1vfvObvoQHHXSQY1NXJzXKDhig2PniXenVV1+tE7eex90LO7heF8rsgHuNeGfpmR1wZ7j14yuzA+4H6vlpptkBN4oFnQ+DhTAEDAFDwBAwBOII3HrrrW677bZzkydP9m4o46Gqe9ooFnR1sFhMhoAhYAgYAqMJAaxntN4QMmF2rnXaA9sOeDS1MCurIWAIGAKGQBSBefPm+UNH9EuOt62TbAKuE12L2xAwBAwBQ2AoENhggw1G5BP3xnWSTcB1omtxGwKGgCFgCAwFArCaDzvsMJ/XpZde2k2bNs19+ctfrjXvJgOuFV6L3BAwBAwBQ2BYEJg1a5bjr1fUuB3wQw895K688kr31FNP9QpDS8cQMAQMAUOgAQjgDWvPPfd0O++8s3v++edrL1GjdsDXX3+9O/bYY70faOyAmYi32mqr2kG0BAwBQ8AQMASGHwEt88U3NCcjbb311rUVrFE74K997WvulVdeaTnh+M53vlMbcBaxIWAIGAKGQHMQOPjgg0cURmTCI15U9KBRE7A+Sgp8FixYUBFMFo0hYAgYAoZAkxFYbLHFRhQv9mxEoC4eNGoCPvrooz0UY8eOdSuuuKL7h3/4hy6gsU8NAUPAEDAERgsCZ5999oiiYhtcJzVKBvzZz37W4Ups/vz57uMf/7iL2XXVCabFbQgYAoaAITCcCGB69MwzzzjmEY4hRITJPFInNWoCBqiNNtrIbbnlli05cJ3gWdyGgCFgCBgCzUEAlvMvfvGLnhWoUSzou+66y40bN84fKYU8+IknnugZkJaQIWAIGAKGwHAjsNNOOznmDvn78MMPay1QoyZgzm786KOPWoDtsssurXu7MQQMAUPAEDAE0hA4/fTT3eOPP972Wpsltb2o6EejJuAQk3fffTd8ZL8NAUPAEDAEDIERCNxwww0jnr3zzjsjnlX5oFET8FJLLdWGTfi77aX9MAQMAUPAEDAE/h8BnDiFtPrqq4ePKv3dqAn43nvvbR0ntdJKK7mHH364UrAsMkPAEDAEDIFmIrDffvu5I488slW4ZZZZxjGn1EmLJTLTPwlN60wpI+6FCxe6Dz74ICNE8VfLLrus+9jHPtY4Lejll1/evf7668WBGIKQ48ePd4sWLXJ1s3l6CQVclyWWWMKbMfQy3TrTWnLJJb1yI/20SdTEPkWZoKaNFcNeV8stt5yTutF9qFE74JdeesmtvPLKXgvabIB1Ndu9IWAIGAKGQB4CbAbOO+8878QJfxJ1U6PsgDfZZJMWXuysMKL+7//+79YzuzEEDAFDwBAwBGIIwAzGkgbRJRPxT3/6U3fhhRd63xKx8FU8a8wOmN1vSHXbcIXp2W9DwBAwBAyB4USAHS9HEIpIjLMErrrqqloL05gJGDdiRoaAIWAIGAKGQCcIoD+k5xF0idDnqJMaMwEj4EbQrem4447TP+3eEDAEDAFDwBCIIrDaaqu5U045xaFIufbaa7tPf/rT7vjjj4+Grepho2TAjz76qDvmmGPcr371K3fqqae6adOmVYWTxWMIGAKGgCHQcAQwRdpmm23cW2+95SZNmuSwAKiT6o29zpynxH3OOec00gwppbj22BAwBAwBQ6BiBFDIWnzx+hnEjZuAK64Hi84QMAQMAUNglCBw2WWXudmzZ7v333/f4Yjjuuuu82atdRW//im+rpxbvIaAIWAIGAKGQEUIPPfcc+6kk05yTz/9tEMDGhPWCy64oKLY49HYBBzHxZ4aAoaAIWAIjCIEMD/iGEKh9957zz3zzDPys5arTcC1wGqRGgKGgCFgCAwTAmuttZbbeOONHeZIEJPxX//1X9daBJMB1wqvRW4IGAKGgCEwDAigdPWDH/zAnX322d6X+2677ebqPg/YJuBhaBmWR0PAEDAEDIHaEcDxhj4Rqe4EG8WCvvbaa/2pLWPGjHGTJ0+uGzuL3xAwBAwBQ6BBCMyZM8eznmE/a3lwXUVszASML+hDDz3UyemKr732mtt6663rws3iNQQMAUPAEGgQAs8++6w3QdJFWnPNNfXPyu8bMwFzakVIAGpkCBgChoAhYAjkIXDWWWeNCCIbuhEvKnrQmAl45513HgFJ3W7ERiRoDwwBQ8AQMASGEoHPfe5zPc93YybgzTbbrM1xNsL0Rx55pOeAWoKGgCFgCBgCw4fA1KlT/QEMOue//e1v9c/K7xdLttgfVR5ryQgXLlzoPvjgg5JfxYNjw8UxUq+++mo8wJA+5bSn119/fUhzH8/2+PHj3aJFi1rnb8ZDDddTTlJh8ff2228PV8Yzcgsnady4cY5+2iRqYp+iTFDTxophrytO6pO60X2oMTtgKdRNN93krrjiisY1QCmfXQ0BQ8AQMATqQ2CPPfZwm2yyiYvJhKtOtVF2wF//+tfdVVdd5XdUb775prvrrrtc3VpsVVeIxWcIGAKGgCHQHwQ4ghCXlNB3vvMdd//997sf//jHtWWmMTtgfHZeeeWV7uWXX3ZvvPGGN0eqE7jaasQiNgQMAUPAEOgLAjL5SuI33nij3NZybcwEvNhii7Xx2PndNDlILS3AIjUEDAFDwBDoCwKNmYDxWrLffvv5MxxXXnllt+KKK7oTTzyxL6BaooaAIWAIGALDh8BKK63UlukvfvGLbb+r/tEoGfApp5zicKCNBuonP/lJx0RsZAgYAoaAIWAIFEHgoYcecgcccIB77LHH3PTp0x16RXVSoyZggNp+++0baYZUZyOwuA0BQ8AQMAT+D4FLL720Z1A0hgUNYpxiwa6XwxhgSaMJbWQIGAKGgCFgCBRBYN68eW2HMdx3331FPus4TGMm4Llz53otaI3Etttuq3/avSFgCBgChoAhkIrAEUcc0fZu7733bvtd9Y/GTMC33HLLCGxeeeWVEc/sgSFgCBgChoAhUASBDz/8sEiwjsM0ZgKOHaK85ZZbdgyMfWgIGAKGgCEwuhBYfPH2KRG3snVSe2p1plRz3Ouvv77793//91Yq66677giWdOul3RgChoAhYAgYAgECv/71r1tPxo4d67WhWw9quKldC/r99993v/vd79zqq6/uVllllRqK8Kcop0yZ4mA7N/Ewhj+V0u4MAUPAEDAE6kBgwoQJrpfnyNe6A+agpZkzZ7oHH3zQzZo1yz3xxBN1YGZxGgKGgCFgCBgCQ4dArRMw5/Gy8z3wwAPdUUcd5VDxNjIEDAFDwBAwBAwB52plQS9YsMBPwAC96qqruueff76F+b/92785cXT9T//0T2611VZrvevmBiE6f5w12yRCGWDppZduUpEc58xyRiZnZTaF8EHOH/KjJpH1qeGoTVEaatpYMezj33vvvRdtQLVOwHRaUeP+4IMPvGxWcrHTTju5T3ziE/4nAzEHs1dByH85FL2q+KrIUxVxLLvssu6tt96qIqqBiWOFFVbwbkPTGufAZLRERhgo+Hv33XdLfDXYQenH1JX1qcGuJ3LHOAE1bawY9vEvbUFU6wS8zjrruNtvv903iPnz57u1117b3/Nv8uTJ/o/7hQsXts5g5Hc3xGDBX3isVDdxDsK3VGDTysTijMm3SeVi8Ue5mlQmFsjoczSpTPTpJvYpGeitrgZh1P5THhgXYlTrBDxx4kSHVhkKWC+++KI744wzYnmwZ4aAIWAIGAKGwKhDoNYJGDQPP/xwz46TldmoQ9gKbAgYAoaAIWAIRBCoVQta0rPJV5CwqyFgCBgChoAh8H8I9GQCNrANAUPAEDAEDAFDoB0Bm4Db8bBfhoAhYAgYAoZATxCwCbgnMFsihoAhYAgYAoZAOwI2AbfjYb8MAUPAEDAEDIGeIGATcE9gtkQMAUPAEDAEDIF2BGwCbsfDfhkChoAhYAgYAj1BwCbgnsBsiRgChoAhYAgYAu0I2ATcjof9MgQMAUPAEDAEeoLAYomP1496klJGIm+88Ubr0IaMYIVecQTiM88843beeedC4YclECfsDEBVVQrXVVdd5bbYYgv38Y9/vNJ4+x1Z0+rq1VdfdT//+c/djBkz+g1tpek3rZ4A5+677/ancW211VaVYtXvyIa9rnBGxUFBIdXuijJMMPa7yqPbnnzySXfrrbe6z3/+87Gk7NkAIXDFFVe4Nddc02244YYDlCvLSogAh6Wcf/757ogjjghf2e8BQ+Cee+7xp3FNnTp1wHJm2YkhYCzoGCr2zBAwBAwBQ8AQqBmBgWBBV1nGF154wf3P//yPW3/99auM1uKqAYGHHnrIrbHGGm6llVaqIXaLsioEOFv24Ycf9uKCquK0eOpB4Omnn/YRr7XWWvUkYLFWikDjJuBK0bHIDAFDwBAwBAyBmhBoHAv68ccfd/wZ9RcBdkxC77//vvvNb37jkCUKxZ6hjHfvvfc6rkKxZ/LOrt0h8Oabb/p6ee+991oRxfCm3qi/Dz74oBUu9sz6Xgueym8WLFjgfvvb37Ypq8bwDp/F+lnsWeUZtggLIbDEaQkVCjkEgc4++2z32GOPudtuu80xuGywwQZDkOtmZRFN7R//+Mfu0ksvdZ/73Oe85vY3v/lNr5l58cUXu8mTJ7uVV17Zhc9A4Rvf+IYbM2aMO/fcc92uu+7qXnvttRHPllxyIPQGh77SYFWefvrpvi6+//3vu5122skvfMI6uP/++3190J9+8YtfuB133NFr2lJH+pn1vfqaxDXXXOO10BctWuSuu+469+lPf9rF8A6ffeITnxjRz2J9b9y4cfVl3mLORKBRoxmDxb/+6786VnjHHnusmzZtWmbh7WX1CFx++eVe3R6zAQizsNVXX90deOCBbvPNN3fz5s1zu++++4hnq6yyijvggAP8RPDhhx+6X/3qV96cLHzGRGHUPQIvvviiO+6449x6663nFzp33nmn41mI93/913+5mTNnuuWXX96dfPLJPuxPf/rTEc+s73VfJ2kxoCNx4okn+t3vV77yFR8shnf4jLot0vdoB0b9QaAxLOhXXnnFrbjiih5FdkmaXdYfaEdnqvvtt5+bPn263/GCAKwzBgFo1VVXdc8//3xXz3xE9q9rBLC/ZoCm39xyyy1ewSpWVyg0MvlCLJJgPYfPYHta3+u6SlIj2Hbbbd38+fO9GRhcpdhYF3sWq8/Ys9SE7UXtCDRmAl5iiSXa5CPGqqy97RRKYPHFF2/VC4sijNG7eVYoUQtUCAF2vIgCjj/+eDdhwoRoveiI4CyFzgR4tuyyy7bqmPDW9zRq1dx/8pOfdP/yL//i5s6d69BKh0skBN6x8a9oP5N47Np7BBozAa+wwgp+ZQ6EKJIwKBj1H4F11lnH/eEPf/AZYRW/9tpru26e9b9EzcgBk+9pifoHbOWNNtrIFypWL+x64VpAzz77rFtttdX8Tlg/w+SPXTFkfc/DUOk/ZLvoQ8CKpj7wqhTiHRv/YvUZe1ZpZi2yUgg0ygzphhtucDfddJNn0fzN3/yN23jjjUuBYYGrQ+DLX/6y+8EPfuAjZABh8GbQP+OMMzxLM3zGCv6ss87yogN2WSgDoeQTPqsuh6M7ptmzZ7v77rvPD+gg8YUvfMHhvjDEG/byeeed5+tl6623dvvvv7+3MgifWd+rrz3h3QrdCjwG4rb14IMPdjG8Y8/CfoY4IfasvtxbzFkINGoCpqCwxGC98Gc0OAi8++67fuWuc9TNMx2P3VeLQKf1Yn2v2nrQsWFdgLkYu1+hGN6xZ0XrU+K1a+8QaNwE3DvoLCVDwBAwBAwBQ6BzBGyb2Dl29qUhYAgYAoaAIdAxAjYBdwydfWgIGAKGgCFgCHSOgE3AnWNnXxoChoAhYAgYAh0jYBNwx9DZh4aAIWAIGAKGQOcI2ATcOXb2pSFgCBgChoAh0DECNgF3DJ19aAgMJgLY8uILfbPNNnObbrqpPxhDcvqjH/3I7bDDDt6h/znnnOMfv/zyy/7wCw4vIfzNN98swe1qCBgCNSLQqMMYasTJojYEhgYBJlScMjCR4szkU5/6lJ9wf/e737k5c+a466+/3i211FL+sBJ8QnN62CabbOJPO8IvNAdmfOYznxma8lpGDYFhRcB2wMNac5ZvQyADgX322ceNHz/erbXWWg5n/pwuxXGC++67r1tjjTXcn/3Zn7kZM2b43fF2223nOCry0EMPdUzeHFNoZAgYAvUjYBNw/RhbCoZAzxHAb7AQu10OwlhuueX8nzzHXzruQbfZZhv361//2p+IhKvQXXbZRYLY1RAwBGpEwDxh1QiuRW0I9AMB/Giz4+Xwdg5HWHfddd3dd9/tXn/9dX+k3S9/+UvvqnWvvfZyHG83ZswY7z/9hBNO8E7+11xzTf+bidvIEDAE6kPAZMD1YWsxGwJ9Q4DTclCoQgbMrnbixIk+L7vvvruXBzMZw5o+4ogj/Ek7sKyvvPJK99xzz7lZs2Z5GXHfMm8JGwKjBAHbAY+SirZijh4E2AEj4+VEMHax4U4Wh/049g+P7GRS5pmd5zt62oqVtL8I2A64v/hb6oZAbQjAWo4RE2xskuWoOiNDwBDoHQK2A+4d1paSIdATBGAjM8FOmDChJ+lZIoaAIdAZAjYBd4abfWUIGAKGgCFgCHSFgJkhdQWffWwIGAKGgCFgCHSGgE3AneFmXxkChoAhYAgYAl0hYBNwV/DZx4aAIWAIGAKGQGcI/C/C8KZFwjf7aAAAAABJRU5ErkJggg==\n" }, "metadata": {}, "output_type": "display_data" @@ -4162,13 +4143,13 @@ }, { "cell_type": "code", - "execution_count": 201, + "execution_count": 30, "id": "731ba750-c35a-4691-a882-c014ecfb07cd", "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n" + "image/png": 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dHtqXaLy1fXzxxRfWqlUrt101atTIba/bbbddYh3Ne+0z/PxKfJHGG83FGTNm2K677lplLm7YsMG56PUnP/lJldK0Lxw7dqyr99e//rV98sknVl5e7pzVziiS34Y07pq3mYxTFO1QGdpHaT+v/VPbtm2tY8eOKfcDMtN+Xdtnw4YNrVmzZinnX1TtC5ZTsmVybg4uyOV7BUgBRJGEp4m9ePFiGz16tN1///1ug1u3bp3b+HbeeWe75ZZb7MEHH7QffvjBVakNWBPmmWeecQE1VTsUKLQjV9l6rx31TTfdZKeddpp5vn333deeeuqpVEVkvPyBBx6wW2+91fVFO8EXX3zR+vTpYwq+apN2Svo5++yz7corr6zSTu0kghuZyvjRj35k3377rSszVcMUvLSjU/+6devmjHQwo53t6tWr3U5G5j/96U/t6quvTlVMleUKcMcdd5zbcans4cOH22233VYlX6YLZs+e7YKMxkkuF154of3yl79MWdz//d//2aWXXmpyUX/VJwWSQYMGuZ2llmsO/e///q9blqqgMWPG2D333ONslF9BVEFFSTv+o446ymbNmuU+77fffvbQQw+598n/nH766e7gyG8XCpo6mGjTpk1y1rQ+/+tf/7JLLrmk0lirbgWwdJICiw5UdQCyaNEiZ6q2aF4pWKqde++9t+u3+il3n3bccUe3c1NQTk4KXAdtOWjUGKkMbbuyVjrggAPcdpq8zhlnnOEOTOSr9RS81baa0rnnnmsKQlpPdSnQ+sCoMVf6+9//bjfccINrg9ohNwXWsKTgq/mh/moOa77oR23ySeOmIPfCCy/Y7rvv7k4GNO4+j/Yd119/vc9e7asO8i+//HLXNvXhkUcecXZaSfUfe+yx9vXXX7v6tK3rgF2eSprf55xzjnvv/1GQ0Tj5kwgFqmzSuHHj3Hamtumnb9++bv+i97UZp1Rt0PzRj58fqfJp+Y033uj2/bL3SWOh/YAMg0l2OtnQwfeKFSucpWKC3z41plElmfsxCZZZ86lfMHfE74WqCRPFj5C1s1BZf/7zn12Q1QRQ0tGflv/1r39NBF8t1wakjUlHhNW1QRuvAocGScFHAfyCCy5wA6ZylHTGoiPc6srJ5Ls77rjDHZ35yae+qRwtVzBUH9RPBeZnn322Sju1MQbrfeKJJ2zatGmVdsj/vweV/1UfV65c6epYsGCBm9T6rP4rafKqXu1gdPQYrKO693/605/czkLlqO2y1c64unVq893vf/9712Z5aeeqgyI5pSrjuuuuc2f0fkesV/VLAWbZsmXuvfqr4JqqDC2/9957nYOfc3/5y18S+Z988klnrj7r58svv7SpU6cmvvflaieqYOuDr+rVe42rz1PbV/VPzsF03333pV2eDki0g9XBp+a/LOXi55XOdLRccyMYfFWfziZ19h3W5ttvv92VpbZpZ6nty/tMmTLFeQXXk43OAjUvtY5cnn/++dCyg+vNnDnT3nvvvUrr6YBb+wv9+Lx/+MMfXB6/nVVnpANGtVXbgJJefWB1C7b84wOATgRUx1133eXWUfn60VmXDmh8/dW9agyDc1Fzzef3Z5763vu9tuXKlf/+iiuu8E1KvGqsZKi5rvHzeTN9veaaa9w25Oe+rqjVdpyqq1tzMDhWqfJqbv7tb39L2PsOayz++c9/ujkaXFd2mh+68ufHUi7aNr/66qusXYJ1pQrmVQ9Nfatz8Bo2cTOtVmX58nSErg3aJ51pawPR0bKCRTD5DS55AwrmUbnBo3jtaFq0aOF2Sj6fBtnX75dF8arL29pQfVKQV1t1hhpM2kFqkJPbqTzBvul7HajUJvkNS0fMyUltq02/dblc9fs2aUNNPntIrqM2n3XJKZj82Pv6gt/pvS4VayNMTupTMOkWQKoylE+3OYJzTgctPr/cgubaUSj5792Hf/+js0rtEHySjXaUYXl9nupe1T8FwmDSQWe65al+tT04rxT8/JjpNfhdsB5/IBFWV/L8Da7nz0aS1wtecpZpuvMueT3NZz++vg5tZxozn3SFyX/nl/nXdM66fV4/D/ylaV+v5kq67dcY+qsnKjd5LmpH75PGI1iu6g3uP3w+/xrcFv2y2r7qtpguj/vk+6jPtRknv37yq5+DqcbD59f32i8Htx//nfYDQRct12cF9+SkA5TkvMl5avs5aBJct3Z74uCaEbxXIFPgiuJHG7s6qbJ0dqpB0+TTjy4tafmIESPczkSTTt9rJ6DLZ7rnU10bBg4caF26dDEFDwV3XVrTEa1PKkuX9LQRV1dOJt/5yya+L7qMrnLOOusst+Pz9znOO+88d2k52M4ePXq4S9LBenXJRf3QT3JgUH/UF10u0SU6TWb1V5fQdI9b/fOXUbTTVbDTcjkG66ju/SmnnOKOLNUfra/P2sFUt05tvtMYK8lFbb/ssstcm1OV4X3VVyX1XeudeOKJ7rN3v/POO6ttoy5xKclC6+hM3Nd5zDHHuDZ4z6FDh7r7ZP57/9qzZ093T9wHNI2PLmOfeeaZibJ83nRfff/UNo2txr2mvgTL1nal+8bdttyG8PM/OK8O2nIZWQcf6pu/t6p69KNLuHpuIVief6/LotoONU4KaAqSfi4fccQRJgufV6/6rFsuekZBxrvssoudeuqplfIE8/v3are81Tatp1tRqlv90o/Pp3mipLFTPp2h+++SX3XbRf1T0qvGy392C7f8o+1EZd18882uHN2b1Vmc76PK0PxPLjvs88iRI12xvm3B8dPl3j322MPVJ0fd79TtDl+Ozp6DSe2UofJqng8bNiyR169T29fk9um5kNqOU3V1Jo9Vqrwy134wOBaaY+rrRRddVGU/IDv9aJvXOvqRieaLbk2mqieT5cExCL4vynvA6uBnn33m7rvofmnwrEiXHXRUqss1Ghhd79cg1ZQU3HW5VEn3e7XR6dKpLulq0uuBkziSJrIe3NCZr3Y6CoI+qQ+6vKadnw4ilNROPcyiyeTb6fP7V00g5dHRnya3HjTSDkpn0arvoC07VQVgPcigIOCNdISpZVpHSTsTX68vO51X1a/LVHrVDjrqpDMo3R/UjlQbWE1J98N1WV7jqLmhDVnBQ0f1uj+kgLPDDjvUVIwrQ2eb2niD46QVvbnOcHUvvbqky7u63Ko6999/f7eDqC5/Td+pD5onSqq7e/fuNa2S+F7bhp550FlXqnmleaGH+rRNaB5pjqgOPUBW3balKwGa25pHerhI5SsQV+cjG1210rzRXE03qf+6vKgDbu1w/ZmPv/WgcnTp0Y9f8GHHsDrUdrVX+dV29VNXUnQ26i+ZKtjr4NIn1a/260Am1f1lnzf5VduoflLNRe2bNMd0P1iXo4NnXFpPt37U75///Odunqrf/fv3T/tBtuT2JH/WGbr81D6NfabjlFyuPmte6cdfrQzLE1ymbeedd95xwVRzRGPQr1+/YJZK72WnsdE8lpH2d1EnletPXoJlF20ADnaykN9rAgXvNxVyX3zbtTEpOAYv+fnvCvlVwUM7QX+AUsh98W33AViXZIsphQXgYuifDvx0sB4MwIXer9oG4LrY31QBuOZTv7rYG9qEAAIIIIBAgQsQgAt8AGk+AggggEBhChCAC3PcaDUCCCCAQIELEIALfABpPgIIIIBAYQoQgAtz3Gg1AggggECBCxCAC3wAaT4CCCCAQGEKEIALc9xoNQIIIIBAgQsQgAt8AGk+AggggEBhChCAC3PcaDUCCCCAQIELEIALfABpPgIIIIBAYQoQgAtz3Gg1AggggECBCxCAC3wAaT4CCCCAQGEKEIALc9xoNQIIIIBAgQsQgAt8AGk+AggggEBhChCAC3PcaDUCCCCAQIELEIALfABpPgIIIIBAYQoQgAtz3Gg1AggggECBCxCAC3wAaT4CCCCAQGEKEIALc9xoNQIIIIBAgQsQgAt8AGk+AggggEBhChCAC3PcaDUCCCCAQIELEIALfABpPgIIIIBAYQoQgAtz3Gg1AggggECBCxCAC3wAaT4CCCCAQGEKEIALc9xoNQIIIIBAgQsQgAt8AGk+AggggEBhChCAC3PcaDUCCCCAQIELEIALfABpPgIIIIBAYQoQgAtz3Gg1AggggECBCxCAC3wAaT4CCCCAQGEKEIALc9xoNQIIIIBAgQsQgAt8AGk+AggggEBhChCAC3PcaDUCCCCAQIELEIALfABpPgIIIIBAYQoQgAtz3Gg1AggggECBCxCAC3wAaT4CCCCAQGEKEIALc9xoNQIIIIBAgQsQgAt8AGk+AggggEBhChCAC3PcaDUCCCCAQIELEIALfABpPgIIIIBAYQoQgAtz3Gg1AggggECBCxCAC3wAaT4CCCCAQGEKEIALc9xoNQIIIIBAgQuUxtX+jRs32pQpU6xr167WvHnzuKqpUu6HH35oEydOtEMPPdS+++4769Chgy1ZssSWLl1qO+20k5WWltorr7xinTp1sm7dutmnn35q++23n61YscKVtX79emvdurXNnTvXfbfzzjvbLrvsYnPmzHHlaHmrVq2scePGVq9ePdt+++1t8eLFtnnzZvcza9Ys23XXXW3t2rU2bdo0W7lypXXs2NHl3W233WzdunW2fPly1x6V8cMPP7hymzVrZjvuuKOtWbPG/XzzzTe2YcMGU/0NGzZ0bVI9DRo0sJYtW7rP6tPs2bNdWU2bNnWv2267rakNKqdRo0bWq1cvU59U1qJFi1yfNR7qz8yZM93y7t27W4sWLdz7yZMnO5ttttnGGRxwwAFu+dtvv+3K13jutddepno+/vhj57Bs2TL75JNPTOv86Ec/MvXl1VdfdXnUXhk1adLEpk+f7spS+xcuXOi8Nm3a5OZH/fr1bf78+VZSUuLKVFkaEy1XUvvlJiOVqb6oXxob1adx//rrr22HHXaw7bbbzvVfnzXOGht5qO1dunQx9UVzomfPnm4+qGzladeunakvcta4ffnll249jUG/fv1cu5566ik3nvqstmquyFnjoPVUh8Ze46q55ZPGR+3WmH/22WduDqmtyqd+77nnni6r5uu8efNceVqg/mveaj3NCbVxwYIFrp+qR/NR25rea1xVnubL6tWrXR9l1rt3b9cXtU3rq62ah3vssYcbM41FWVmZK0Nzqn379m7eyUJzRO3WdqP5rj6qPo2NPqv/aqPqk4f6qTwaG/VLy/v37+/8NF+0rE+fPm591Sk7tVdzVm3TPJkxY4bbdjR31Vdtb5on+tF4qmyVpbq0rWkuaN5qfc1ljaWWlZeXu76qfRp31aV+fP/9966czp07u+1A46Q61H71X+2SUZs2bazbln2Exkllyl/bjRzbtm1r48aNs7333tvNbzkpaV2Ng8pU/Wqf/DT3e/To4Yy1X1SZ2oa0LchLY66k8letWuX8NWdlrX5qXqhtmuu+H/JXueqTxl/lqg7N5wMPPNDNBY2t+qW86q9v2xdffOG2Y9Wl/FOnTnVtVf2TJk1y+w1tR6pPPxoH5dXc0H5V46R9g/ZxqkPly1DLX3/9dTe/9Fl1ykRjps9aV+MoE7VZ46n19V79VV81F1S38shd3jJS2dpPvffee27Oae5rzGWv7VH7Bq2npDJlqu/UNs0L5dV7lS97vy/W/FPdmtPafvR+8ODBru2usBj/KdnSwc1Rl69Jc+mll7pOfPDBB3bCCSe4iZpcj5A0+FEkIZ522mn20ksvRVFcnS5DfZWbNixZ5yNpY9DONd2kjUk7pDiSHGKYxjU2VTtC7UxOPfVUe/jhhxNt0A4xqnldYyNiyqDx0s5KO6k4kwKCP/jNxxjG2beayvbz1r/WlD/4vYKadwsuD77X/AyOn+rRAasOVqJImbQ7inqrK+Pcc891gXf8+PEuqAfzak5ru0x3nl1xxRV28cUXB4vI+L0OYjRmySmWAKyjXB1RDRo0yB3tv/DCCzZy5EhXt44sFXiVdFSnYBJFeuONN+z444+PoijKQAABBBAoUIEoDwx0NbXblqsg2SYdDIUF4FguQesyhX50dvboo4/aUUcdlWj/s88+a88884z7PHr0aHcpJvFlFm8U9EkIIIAAAlu3QLpnuOko6aqdLntnm3QJPizFcgasinSqP2rUKHdva/jw4WF1uzPhqC7V6Z7P7rvvHloPCxGIS0D3F/N1GyCuPlEuAoUsEOU2qecRdPaabUp1Cbr+NVtStoUnr6/7YldffbUdcsghduSRRyZ/nfise4jKG0XSwwynn3663XfffYni/H0sv0APVSjg+x2m7nEp6QEXPSCgtughAD1YoodwlDSYeghAr7o575Muc+hen870VaYupft7nL7eYN+0vh440EAo+e+CR2u6r6o2+fYpn+rROmq71tF7PXCghz9Uvx4m8mUpf1hSmb4etUP33YL3hrSOygorRw8r6LvgEZwe9NBBlR5SSm6v2ikTPcygJNu+ffu6h0P0UITyqw3B9shLy3y/fR5XwJZ/ZOCTNgY9AKOxULtUth5Smvnvh2CUV0es6ot+VJaW6UcPhOjhCj2cpaQHvWShctRO/wCPHghJTnqQTu303+mzHrAZMWKE3XTTTaaHs1SXbqvo4S49tOL7mFxW2Ge1T/NOD4pobPyBqZarv7p8pf6oDWqv+qJX5Q3Wo/zpJllqPml9WehVP9oWLrzwQlefriypTPn4Zw+C5avPQWO9D7ZHedXmww47zD3EpM+a536b03j47Uf16oEtjaUvQ3WrTPVVr3qgT2OvuaLvNNeCzyLIKbgtqj6ffBly1PtuWy4t+nnq8yS/Kp/um8pHD7QphZWjNqk92l6U1+8r1B7tU9Re9U9J72WvcvUAqLYtPeCkdgWT+quk8jSn5aa5oLHXQ1aqQ31XvuC6at/AgQPdOpqv+qxx1oOpt9xyiz322GPOV8vVDj/XgnXrvcpV3eqD9m3qn7ZT1aX1jjnmGPdQnb7T8uT9qy9D46FyNOa+HH2ndbQsVf0+j9ZVW/zYJu+7lE9JntoOhw4d6tqlB778PJLd4YcfntjHKF5oXqpc/ahvQUP1RQ9rRnH2q7bJQGbJKZYzYD0dqMvL2qEpaSd5zjnnJNcd6RmwOqgB0tNyxZS08Woj8xOpGPqmDV/BRkGqmJLfwVS3Qym0/mrnpIMC/5RuobU/VXu1v1AKHlimyltIy3UAqIOmYttfaJ/hD34LaTx8W3UApPiUnGK5B6wjLf2QEEAAAQQQQCBcgF/EEe7CUgQQQAABBGIVIADHykvhCCCAAAIIhAsQgMNdWIoAAggggECsAgTgWHkpHAEEEEAAgXABAnC4C0sRQAABBBCIVYAAHCsvhSOAAAIIIBAuQAAOd2EpAggggAACsQoQgGPlpXAEEEAAAQTCBQjA4S4sRQABBBBAIFYBAnCsvBSOAAIIIIBAuAABONyFpQgggAACCMQqQACOlZfCEUAAAQQQCBcgAIe7sBQBBBBAAIFYBQjAsfJSOAIIIIAAAuECBOBwF5YigAACCCAQqwABOFZeCkcAAQQQQCBcgAAc7sJSBBBAAAEEYhUgAMfKS+EIIIAAAgiECxCAw11YigACCCCAQKwCBOBYeSkcAQQQQACBcAECcLgLSxFAAAEEEIhVgAAcKy+FI4AAAgggEC5AAA53YSkCCCCAAAKxChCAY+WlcAQQQAABBMIFCMDhLixFAAEEEEAgVgECcKy8FI4AAggggEC4AAE43IWlCCCAAAIIxCpAAI6Vl8IRQAABBBAIFyAAh7uwFAEEEEAAgVgFCMCx8lI4AggggAAC4QIE4HAXliKAAAIIIBCrAAE4Vl4KRwABBBBAIFyAABzuwlIEEEAAAQRiFSAAx8pL4QgggAACCIQLEIDDXViKAAIIIIBArAIE4Fh5KRwBBBBAAIFwAQJwuAtLEUAAAQQQiFWAABwrL4UjgAACCCAQLkAADndhKQIIIIAAArEKEIBj5aVwBBBAAAEEwgUIwOEuLEUAAQQQQCBWAQJwrLwUjgACCCCAQLgAATjchaUIIIAAAgjEKlAaa+k5LvwXv/iFjR07Nse1Uh0CCCCAQLEJtG7d2j7//PNYu1U0Z8BPP/00wTfWqULhCCCAwNYjsGTJEhswYECsHS7ZvCXFWkM1hS9fvtzq1YvmGKBPnz42c+bMamrjKwQQQAABBGonsGLFitqtkCJ3s2bNqnyT10vQ69atsw0bNlRpVCYLevbsSQDOBI51EEAAAQRCBerXr29RBOCw4KsK83oGvHDhwsgCcMOGDa1NmzahiCxEAAEEEECgtgK6qqrYkm1SAC4rK6tSTF7PgKu0JssFOqNevHhxlqXUrdWbNm1qq1evtjzeKYgcpLS01PSAw4IFCyIvO58FNm7c2MrLyyM7qMxnX3zdukXUrl07mzdvnl9UFK9+p7p+/fqi6I/vRIcOHWz+/PlFt7/QPmPt2rW+m0XzGs0N2KLhoCMIIIAAAgjkRoAAnBtnakEAAQQQQKCSAAG4EgcfEEAAAQQQyI0AATg3ztSCAAIIIIBAJQECcCUOPiCAAAIIIJAbAQJwbpypBQEEEEAAgUoCBOBKHHxAAAEEEEAgNwIE4Nw4UwsCCCCAAAKVBAjAlTj4gAACCCCAQG4ECMC5caYWBBBAAAEEKgkQgCtx8AEBBBBAAIHcCBCAc+NMLQgggAACCFQSIABX4uADAggggAACuREgAOfGmVoQQAABBBCoJEAArsTBBwQQQAABBHIjQADOjTO1IIAAAgggUEmAAFyJgw8IIIAAAgjkRoAAnBtnakEAAQQQQKCSAAG4EgcfEEAAAQQQyI0AATg3ztSCAAIIIIBAJQECcCUOPiCAAAIIIJAbAQJwbpypBQEEEEAAgUoCBOBKHHxAAAEEEEAgNwIE4Nw4UwsCCCCAAAKVBAjAlTj4gAACCCCAQG4ECMC5caYWBBBAAAEEKgkQgCtx8AEBBBBAAIHcCBCAc+NMLQgggAACCFQSIABX4uADAggggAACuREgAOfGmVoQQAABBBCoJEAArsTBBwQQQAABBHIjQADOjTO1IIAAAgggUEmAAFyJgw8IIIAAAgjkRoAAnBtnakEAAQQQQKCSAAG4EgcfEEAAAQQQyI0AATg3ztSCAAIIIIBAJQECcCUOPiCAAAIIIJAbAQJwbpypBQEEEEAAgUoCBOBKHHxAAAEEEEAgNwKhAfjll1+2FStW2MaNG23Tpk25aQm1IIAAAgggsBUJhAbg2267zT777DO744477Pnnn9+KOOgqAggggAACuREoDavm1FNPtUMOOcTq16/vfho1alQp29NPP20DBw6stIwPCCCAAAIIIJC+QGgA7ty5s82bN88eeOAB6969uwvGwSIbN24c/Mh7BBBAAAEEEKilQMpL0JMnT7YNGzZYvXr1rGnTppV+tIyEAAIIIIAAApkLhJ4BR3EJWsFbQbxjx47Wrl27zFuY5ppt2rRJMyfZEEAAAQQQqF7gwAMPtEcffbT6TFl+W7J5SworQ08///GPf3SXoIcMGVIpiy5BV3cWrCJ/85vfWJ8+feztt9+2yy67zHr06FGpDH1YuHChO8uu8kUtF3Tq1KmWa5AdAQQQQACB6gWaNWtmX331VfWZ0vhW5ZSVlVXJmTIAV8lZiwVffvmlvfjii3bxxRe7s+Dx48e79ypC/8Xpo48+cqWddNJJFsWZa1jHatFcsiKAAAIIIBAqoP+SG0VSEE5OoZegR48ebR06dLCGDRu6B7GSV7rxxhttt912S16c+Dx37lx36VkL2rdvb/Pnz098V1JSUu3ZcyIjbxBAAAEEEChigdAAPHToUGvSpIkLlGFnl3pKurqky9P+F3jol3kE/xvTwQcfbPpR0iXoqI4uqmsP3yGAAAIIIFBbgV69ekUSo8LOftWW0AC8du1amzp1asq2KqhWl7p27eru/SrPzJkzrUuXLtVlz/q7OXPmGPeBs2akAAQQQACBfwvoAeLXXnstVo/QAKx7tBMmTHBnsU8++aTttNNO1q9fP3vvvfds1apV9sorr1R777Zbt27u+2uvvdYWLVpkumQdd1I9OltfvHhx3FXltHz9F7DVq1dbimflctqWqCorLS211q1b24IFC6Iqsk6Uo4cTy8vLI3mwsE50aEsjdDVL/4tBvxegmJJurymtX7++mLrlbh3qll+x7S+0z9CJYbGl0AA8YsQI08/YsWNNZ7PBAHrsscfaDz/8UKOD1tfk9hO9xhXIgAACCCCAwFYkUO1v1NDDVMmXj1u1amVLly5Ni4jgmxYTmRBAAAEEtkKB0DNg76D/JnT00Ue7/za0yy672BtvvGGzZ8+2QYMG+Sy8IoAAAggggEAGAtWeAffu3dv0f3h79uxp33zzjR1++OH26quvVnqqOYM6WQUBBBBAAIGtXqDaM2Dp6B7wyJEjt3ooABBAAAEEEIhSoNoz4CgroiwEEEAAAQQQqBAgAFdY8A4BBBBAAIGcCRCAc0ZNRQgggAACCFQIEIArLHiHAAIIIIBAzgQIwDmjpiIEEEAAAQQqBAjAFRa8QwABBBBAIGcCBOCcUVMRAggggAACFQIE4AoL3iGAAAIIIJAzAQJwzqipCAEEEEAAgQoBAnCFBe8QQAABBBDImQABOGfUVIQAAggggECFAAG4woJ3CCCAAAII5EyAAJwzaipCAAEEEECgQoAAXGHBOwQQQAABBHImQADOGTUVIYAAAgggUCFAAK6w4B0CCCCAAAI5EyAA54yaihBAAAEEEKgQIABXWPAOAQQQQACBnAkQgHNGTUUIIIAAAghUCBCAKyx4hwACCCCAQM4ECMA5o6YiBBBAAAEEKgQIwBUWvEMAAQQQQCBnAgTgnFFTEQIIIIAAAhUCBOAKC94hgAACCCCQMwECcM6oqQgBBBBAAIEKAQJwhQXvEEAAAQQQyJkAAThn1FSEAAIIIIBAhQABuMKCdwgggAACCORMgACcM2oqQgABBBBAoEKAAFxhwTsEEEAAAQRyJkAAzhk1FSGAAAIIIFAhQACusOAdAggggAACORMgAOeMmooQQAABBBCoECAAV1jwDgEEEEAAgZwJEIBzRk1FCCCAAAIIVAgQgCsseIcAAggggEDOBAjAOaOmIgQQQAABBCoECMAVFrxDAAEEEEAgZwIE4JxRUxECCCCAAAIVAqUVbwv33WOPPWaXXHJJ4XaAliOAAAII1BmBs88+22644YbY21OyeUuKvZYUFSxcuNA2bNiQ4tv0F3fq1Cn9zOREAAEEEECgBoEPP/zQOnbsWEOu9L5u1qyZlZWVVcmc1zPgRo0aWePGjas0igUIIIAAAgjkU2Dp0qXWq1evWJuQ1wC8bt26SM6AYxWicAQQQACBrU5AwXfFihWR9FtnwGGpKB7Cmj59eljfWIYAAggggECtBd555x0rLY3//DT+Gmrd9dqvoMvYixYtctfYFy9eXPsC6vAaTZs2tdWrV1seb9VHrqOJ3bp1a1uwYEHkZeezQM3D8vLyorqqU69ePWvXrp3Nmzcvn7SR192wYUNX5vr16yMvO58FdujQwebPn190+wvtM9auXZtP2ljqLooz4FhkKBQBBBBAAIEYBQjAMeJSNAIIIIAAAqkECMCpZFiOAAIIIIBAjAIE4BhxKRoBBBBAAIFUAgTgVDIsRwABBBBAIEYBAnCMuBSNAAIIIIBAKgECcCoZliOAAAIIIBCjAAE4RlyKRgABBBBAIJUAATiVDMsRQAABBBCIUYAAHCMuRSOAAAIIIJBKgACcSoblCCCAAAIIxChAAI4Rl6IRQAABBBBIJUAATiXDcgQQQAABBGIUIADHiEvRCCCAAAIIpBIgAKeSYTkCCCCAAAIxChCAY8SlaAQQQAABBFIJEIBTybAcAQQQQACBGAUIwDHiUjQCCCCAAAKpBAjAqWRYjgACCCCAQIwCBOAYcSkaAQQQQACBVAIE4FQyLEcAAQQQQCBGAQJwjLgUjQACCCCAQCoBAnAqGZYjgAACCCAQowABOEZcikYAAQQQQCCVAAE4lQzLEUAAAQQQiFGAABwjLkUjgAACCCCQSoAAnEqG5QgggAACCMQoQACOEZeiEUAAAQQQSCVAAE4lw3IEEEAAAQRiFCAAx4hL0QgggAACCKQSIACnkmE5AggggAACMQoQgGPEpWgEEEAAAQRSCRCAU8mwHAEEEEAAgRgFCMAx4lI0AggggAACqQQIwKlkWI4AAggggECMAgTgGHEpGgEEEEAAgVQCBOBUMixHAAEEEEAgRgECcIy4FI0AAggggEAqAQJwKhmWI4AAAgggEKMAAThGXIpGAAEEEEAglQABOJUMyxFAAAEEEIhRgAAcIy5FI4AAAgggkEqAAJxKhuUIIIAAAgjEKBBbAN64caN99tlntnz58hibT9EIIIAAAggUpkBpHM1W8L300ktt8ODB9sgjj9gJJ5xge++9dxxVuTI3bNjgyp8zZ477XL9+fVMbgqm0tNQaN25s69ats82bN9umTZtcnu22284WLVpk22yzjen9vHnz3OeSkhIbMWKEvfzyyzZ9+vREUc2aNbO1a9eaylM569evt4YNG5ryKzVt2tSWLFnivkustOWN2lRWVmYrV640tTc5ab2OHTua+rB69WqrV6+ea6Ne27dv7+pr166d66faOG7cONf+Ro0apX2QozYqv/quditpmX60TElGa9asce8bNGhg5eXl7r3/Z7fddnN9mTFjhuuLX0/fy2/VqlX2ww8/uDKbN29uvXr1si+//NIt92OiPqlsX77KkE+LFi2sZcuWJuOFCxfa0qVLXR1yVjriiCPso48+cr5q85577un689ZbbzkLLfN5lV+eixcvdm3Zd999bfvtt7cJEya4PrVu3dqN+VdffeXKUD+TDxabNGnixkzlykR982Ontrdt29a1Ud8pj/LrfdBE46o559dTu8KSylMKemuZytKP5puM9L3armVy9nM5rMzgMpl7G7127tzZtalNmzaufbNnz3YeaqfKVj3Kp/mgV815P36+3LDtzH/nX9V/zTXfL/VD25q2Bc1zlSF3zRW917grqb36fNhhh9nUqVPdmOuzxkDltWrVytT2adOmuX5oG9Q6mjuaN0Fv1acfefmyNV7J/XFf/rtubWsaS9WpbVYGcgmuM3DgQLd80qRJ7lV9k5PyKfltScv79u3r9i077LCD3XPPPaZ1brjhBtP8U9lhSW3UnNU+Qf1Rf7X9qR9ffPGFGze1z7dJ80Xbj8b222+/TcxnzdPbb7/d7rjjDvv0009dVfLy/UquW+XIS2Om/vikdfz8CNar79XHoLmWqRz1QUmv2g40RkoyUh3+s/8+WEa3bt3c99qGNa7Ks2DBgsTcdAX9+x/NH9WnueTnmr7y23CXLl3s+++/d3Z+7qlvwbzKrzJuu+02O/HEE/Ux9lSyZfDDRz+LqufPn+82mkGDBrmz4BdeeMFGjhxZpURtbEHwKhnSXLDTTjslJluaq5ANAQQQyIuAAsCKFSvyUjeVpidw33332bBhw9LLnEYuHUBo3JNTLGfAOmPTj47MHn30UTvqqKMS9Y4ZM8aeeeYZ9/nuu++27t27J77L9E3ymUum5bAeAgggELeAzqxJdVvgiSeesHPOOSeyRursPyxFFoB1aU+XRRXlf/WrX7kz21GjRrlLpvvtt1+i7uHDh9sBBxzgPuuowF8WSmTI4I0uY/jLPhmszioIIIBAzgR01U+XU2O4+JizPhR7RbrNE0Vs8k669ReWIrsErfs5uj+jiaXGX3XVVXbooYfakCFDwup1y6K6BP3555+7e0UpK0rzi+T7GrofkHyPIM2iYsmm+2m6Tyq3fLUr7oMd3Wfy96drQtR9J+3E/D2w6vIn34tNHuvq1i3W7+SnA1cOXnMzwtp2dK/y2muvtYceesg++OCDWles9Wt7xU/16qRHJ0lbY1JM0k+681y3NMePH5+4Px2FWapL0JEF4GAjdSY8evToxOVlPYAQdjofVQBW3dpx6+xbN+yLKSlw6OCmmI6WtePXQZoeqCimpIdudGAUxXMNdcVFO289kKQH/4opaX+hlO7BXqH0vUOHDqZncIptf6F9RvCBsEIZD9/OVAE4skvQviK96sxXPyQEEEAAAQQQCBeI7f8Bh1fHUgQQQAABBBCQAAGYeYAAAggggEAeBAjAeUCnSgQQQAABBAjAzAEEEEAAAQTyIEAAzgM6VSKAAAIIIEAAZg4ggAACCCCQBwECcB7QqRIBBBBAAAECMHMAAQQQQACBPAgQgPOATpUIIIAAAggQgJkDCCCAAAII5EGAAJwHdKpEAAEEEECAAMwcQAABBBBAIA8CBOA8oFMlAggggAACBGDmAAIIIIAAAnkQIADnAZ0qEUAAAQQQIAAzBxBAAAEEEMiDAAE4D+hUiQACCCCAAAGYOYAAAggggEAeBAjAeUCnSgQQQAABBAjAzAEEEEAAAQTyIEAAzgM6VSKAAAIIIEAAZg4ggAACCCCQBwECcB7QqRIBBBBAAAECMHMAAQQQQACBPAgQgPOATpUIIIAAAggQgJkDCCCAAAII5EGAAJwHdKpEAAEEEECAAMwcQAABBBBAIA8CBOA8oFMlAggggAACBGDmAAIIIIAAAnkQIADnAZ0qEUAAAQQQIAAzBxBAAAEEEMiDAAE4D+hUiQACCCCAAAGYOYAAAggggEAeBAjAeUCnSgQQQAABBAjAzAEEEEAAAQTyIEAAzgM6VSKAAAIIIEAAZg4ggAACCCCQBwECcB7QqRIBBBBAAAECMHMAAQQQQACBPAgQgPOATpUIIIAAAggQgJkDCCCAAAII5EGAAJwHdKpEAAEEEECgdGshWLRokX388ce2cOFCa9u2re2zzz7WokWLtLr/2Wef2dy5c23PPfe0Nm3a2IYNG+xPf/qTex0xYoTNmDHDvvvuO9t9992tQ4cOoWWuWLHCJk6caE2aNLEBAwaE5kleuHTpUnv77betQYMGrr3++82bN9tbb71l5eXlduCBB1pp6f8fxuR2+vzB1/fff99Wrlzp2jF79mxTWTLZtGmTq0Nt804yatmypVt90qRJJkPlV3uC9QbLz/b9Dz/8YKqrcePGlZxk/s4777j6DzjgAKtXr+qxo/ogL7kMGjTI6tevX21zVq1aZe+99541atTI9t9//0ReLZeTlg8cODCxPNM3Kkvjv99++zn3VOV88803blw3btzo2tOzZ89UWatd7tuvctavX+/m5x577FGpL36u9OvXz9q1a1dteRoP5Q/Lq7miedG9e3cbN26cm0ennXZa2ttWtRUHvpTNhAkT3JKDDz7YunTpEvg29dtZs2bZF198Yd26dbPevXuHZtSce/jhh9180fbst6ewzH7bk4fGR3NRy9599103xmpnWVmZnXPOOWGr13qZxlJla3sIm4tff/21qU55aB+13XbbJfq5Zs0au+eee0zb+UknnWTt27e36dOn20477WRdu3at1JYlS5bYyy+/7PYF6pP2Zemk1atXu/b5bUhtmTZtmpsPO+64Y5UitJ+cMmWKderUyXbbbTfX5s8//9yaNm1qa9eudduH9jvav2l71nYcTNpXffLJJ7btttua5nQw6TvNQe03W7du7fbF1e2nvvrqK5s5c6bbRvx+Wa+5TCVbJs/mXFYYrEtg2rFGkRo2bOgm/uLFi6sUt2DBAjvooIPcAGuQt9lmG/deO0ZN2OrS448/bqNGjXLtXLZsmb322mt23HHHmTZa0ZWUlLggpcmi78ePH2+77LJLpSI1SYcPH27ff/+9rVu3zm0MN910U6U8yR9Ult/gtCGdf/75NnLkSJft3HPPdQFCgUb9VtB5/vnnK7Xz9ddfdzuIYLk33HCDPfrooy4AyyE5KWBpUmtSqmy1QWWrT3fddZfrs8ZLOxhN1DfffNNtOMnl1PRZOzhtIBqXYNKBgZzmzJnjnP7jP/7Dfve73znnYcOGJdqljfeFF15wfQ+uf/rpp9unn37qxqp58+ZurLTjCkvq37HHHmvaQStIHXLIIXbvvfe6fqsuv/yII46wu+++O6yIKstUl8oNzmlvruUaR825sICnQHbKKae4sfGb5COPPOLmbZWKqlmgetT+b7/91o2fsqo8mR955JF2//3325NPPmnXXHNNpTndq1ev0FL/8Y9/2LXXXuuM/Pz3eceOHWt33nmn+04HGNoWfNt1sJS8kw+tII2FslEA0Xbky//Xv/5le+21V7VrT5482a2nddR2BVkFbyVtN0rajjXnNfeUT2OoOaSAEJZ++tOfunml7VhJ+xIdyKtty5cvT6zSo0cPe+ONNxKfM3njx9LPxcMOO8zGjBmTKEoHJL/4xS/cOKpubZfaD51wwgl2/fXXu0CsOeeT+qZ+y+Lpp5+2/v37u6+0z9QBqMbQ+2rOq5zqkua5gqW2H7VVB2gy9/tC7WsUAH1SYNbcVJK79oE333yzq1Nt0j5F67Zq1SpRpraVV1991Y2HDh4OPfRQl1/jpfXPOOMMV968efNs8ODBiXHUQpWn/cxrW/bZyYFVyy644AKXX+1v1qyZqzPV9ukqyeIfla/xSU71t2yI1yQvzNVnTWKdyWjHn+2Pzso0uTQRksv61a9+5c6q/I7Rv2qyaVIn5w9+1iTU0aHf4LQzUIAKJgUz/70ms9YJlqGd3lNPPeU2UrVP5Wki6Uw8mC/4XhuQgp82ILVXR7cKGKr7tttuc0d5qlNnOTr7+K//+q9K7dQGqfy+zPnz59ull17q1vP9D/ZB7+WherQz8f3R5HzggQfceto4lLRMZWgnoyNlX0e6rwoGOmJWOcF1br/9dnvuuecSfVaAVgDURq02aKNVu1S3jp4VCPz6H374of3xj39MuCiPvlc+nyf4+thjj5kCnDZktUM7Hx3562BDOw6deWis5KgNu2PHjqHlBMvUHFRSINJymV9yySWJNslPG6I/Ow+ue/LJJzt7V8C//1EQPfPMM2usN1iOgoz6pvYHk+rWmYH6op12cE5r56cDn2A5/v3xxx9vmtN+PgTz6oBBYyK/5CTXY445JrRMX3a6r7LRDjaY9PnUU0+ttnydiSt4+bbrStVZZ53l1tFYad+jg9oPPvggUbTmja766OA3uX0687r11lvd9uFXUH5to8kHtJo3ffv2dUEwuZx0P2t+Buei5qjmjp+LJ554optjvn8aB81ZjZfGRQfIwaS2+rw6GZCP2nLVVVdVMtA6OrP+2c9+VsUg2HYdyCmQa66pbh04B/cdsv/JT36SKEMHLwrC3kpXn+Tk26S2q40qz5ejZTqh0Y8Cpq48KL/2VRpP38bLL7/cHTgF+6t19aMrH8n7AW+n/aeS2q9tRAde2kaC/Yzivd/nBdun91Wv4yXniPGzdlTaCKL4UVmpytPlirCkS2c11a3JHkyaMFonVdKOIblMBRt/ZKn1NLmUkvMFP+sMLriOP8NQH4NHc5o0ypfczuR2qD4dWdaUVH4w6SqFLl0lJ19vsM21eR82VgpOwT5rJ658WqYJ7JM2UqVgfcqnsxGf1L7g98nvVWawLu0UlEcbW3C5dq5hbU0uT5+VL5hXbdERuE8q18+f5PU7d+7ssyVetUNLzlfTZ7VffQ9L2nGpfbqCEEzJcyVYR3V5w+aFLzedbStYT3Xvw65SpWOT3HYdXPp6/FilukLi8wVf1bdU+X2//asfg+D6tX2vNvpyVG7yXEzun69b+5fgen558DXoF7ZfSGf8VJ7amCoF61Dfk2/PKTjWlHyAVD26/RdMOtDwpsHtLJjHO/h8/jV5f6l1tH3qANPnifI1lVPqSBLsRUzv/RGZdgzZ/mgwhR1Wjs4ilPxOXK/a2f/mN78JzR8sQ5d7hacNT+vonkpwI9QgKWmZLjFcd911VcrUmajqVB5Ndt0L7LblnlSwnuT3Orvw9Wqdo48+2k3AnXfe2Z3Z6UhN7VGZOtNIbmdyO3S2rcusClL+TM01PPCPdt46WvRtVflXX321uzSq73xfdQCgetWm5Han81lHm5rsyXl1tqU6vJPaq8Cky1za+NRnHZhoY9OZQHD9Pn36uPtgyqNxUNt1dSOYJ/j+8MMPd2fh6ovOeHQmr3tjWq4rKX65Lk/qTDq4bqr3ms+ah/77oLnKU9t0FO+/D77qFkMwKb/OTIJ50nnv2y/DYNKYqi+aP8G5IivdYklVtu6J+jFJnt86A1W5wQMf1an5lTz/UpWfzvJkG/Wtujb7Mv16yq8x/u1vf5vop8ZJP8oT3Dmq7TpT82UEX3fdddfEVRdvq6tPmmvJBrp0qvkbXL+274NzVPsAXeLV9unL8f3zdetAXycbupysqxxh27ksdMn8mi0XPn05unUTTCrnwgsvTHzv8yW/Dh06tNI2pO1HydehfUdwnfPOOy/xvfrjP/v2q70aJ23jfjtWW1WPxsrfV1f5Wl/z2Jd/9tlnu7KD/6hclaOraD6ff/V22q9oq5k4AAAQWklEQVQpqc/Ke9FFF1XJ69fJ5jV4UB9s41ZxD1gd1iU3XdLVUZmOfvRAVdhZRxDHv9d9AZ0l6DKGNjgFel0G1g5XG6vK1IMOukwS9uCBytFRqe47aPIcdNBBlTZ6X0/yq856VbcmZvLDCLpUqkCmeyzaMSoltzO5PH3WvWFd4tFOR0eQOvPT5SpNcAU67TR0aUhnvtrgt99+e1eMHgTRUbHapI0iWG9YPdUt005bQTT5HrDWUdvURjkNGTIkUYzM9YCFkiz0fXLSJFceHTUrjzao6pI2KD14ooCre4P+AENnGq+88kpirPzy6srSd2qTHP0Zus/vzffee+/Q+78+ny77aY5oTNR+XcLMJPn2a+ei91OnTnXzXTsy3xc9EKh5qzmtWwmpkvLrsqEelFEASs6reaG5ouW6Ly/7H//4xzU+W5GqvlTLZfPSSy+5bU9jpbakk2ZuuWWje7qaxzoA8UljrqRtSHNO9zzVVwUFBYDqkrY9PZi2ww47uDmqAKx5pHGTk7ZH7cj9zr26smr6TmWqbAUTbQ9+/Px6eqBJNrpKoFseOivWdquk+X3LLbeYDHSLQQ+N6cEjjVWyn/YBzz77rJsTugS77777+iqqfU3ehnSfVm2STdiDXHoIS7eL1E7tb9Rm7bdkpn2kDjy1b1Gftc2rLfpO+wxZaB+k5wt0QK4TmWDS/uyf//yny6NALhOtn2o/oLFSe3VVSgcuNW2fwbpq+15zxO+ng+tuNQE42OlCeq/Jo4mZ6giqkPri21pdAPZ5CvE1VQAuxL74NmuHr7M5HYAWUwoG4GLqly7zKqgV2/7CB+BCHatUATivl6ALFZN2I4AAAgggkK0AAThbQdZHAAEEEEAgAwECcAZorIIAAggggEC2AgTgbAVZHwEEEEAAgQwECMAZoLEKAggggAAC2QoQgLMVZH0EEEAAAQQyECAAZ4DGKggggAACCGQrQADOVpD1EUAAAQQQyECAAJwBGqsggAACCCCQrQABOFtB1kcAAQQQQCADAQJwBmisggACCCCAQLYCBOBsBVkfAQQQQACBDAQIwBmgsQoCCCCAAALZChCAsxVkfQQQQAABBDIQIABngMYqCCCAAAIIZCtAAM5WkPURQAABBBDIQIAAnAEaqyCAAAIIIJCtAAE4W0HWRwABBBBAIAMBAnAGaKyCAAIIIIBAtgIE4GwFWR8BBBBAAIEMBAjAGaCxCgIIIIAAAtkKEICzFWR9BBBAAAEEMhAgAGeAxioIIIAAAghkK0AAzlaQ9RFAAAEEEMhAgACcARqrIIAAAgggkK0AAThbQdZHAAEEEEAgAwECcAZorIIAAggggEC2AgTgbAVZHwEEEEAAgQwECMAZoLEKAggggAAC2QoQgLMVZH0EEEAAAQQyECAAZ4DGKggggAACCGQrQADOVpD1EUAAAQQQyECAAJwBGqsggAACCCCQrQABOFtB1kcAAQQQQCADAQJwBmisggACCCCAQLYCBOBsBVkfAQQQQACBDAQIwBmgsQoCCCCAAALZChCAsxVkfQQQQAABBDIQIABngMYqCCCAAAIIZCtAAM5WkPURQAABBBDIQIAAnAEaqyCAAAIIIJCtAAE4W0HWRwABBBBAIAMBAnAGaKyCAAIIIIBAtgKxB+DJkydn20bWRwABBBBAoOgEYg3Ar732mo0aNaro0OhQ+gKbNm2yq6++2gYPHmw9evSwL7/8Mv2VyVlrgQ8++MCOOOII69u3r1155ZW2efPmWpdRV1dYtGiRnXTSSbbffvvZkCFDbM2aNXW1qbQLgbQEStPKlUEmbSxvvvmmtWrVqtLaWr5s2TK3rGnTplZaGk0T6tevbyUlJZGVV6nRefxQr149U98KNf3hD3+wv/3tb1ZeXu66MGLECBs/fry1bt2asYp4UOfMmWPDhg1LlPrYY4/Z/vvvX2lZ4ss032ibUopqO02z2tBsAwcOtJUrV7rvtE3cfPPNdv3114fmrWmh36bqQr9qamttv1efiunAS2Ol/WBRjlVtBzed/Br8MWPG2IUXXuiOwoPraGf85JNPukVjx461nj17Br/O6r0GSTv2YkraATZu3Lhgu7RixYpE8FUnNm7caOvWrXMbVDGOlfqYr53fN998Y127drVvv/1WzbBVq1bZ3LlzI9km6sJYde7c2aZOner6pnmk21uZtssfWORrrFwnYvon+aQnpmpyVmwxjJX2eWEpmtPPLSVPmDDBxo0bZ2VlZe5SY4cOHdzGv3r1aps1a5Z16dLF1X/ZZZeZfpQWLlxoCxYscO+z/adhw4au7sWLF2dbVJ1aX1cJZFioO4oBAwbYAw884IKuNqTly5db8+bNTZemoxr7ujJgOlDSmf6GDRvy0qTkYNSuXTvr379/Vs46qFU5dWGs+vXrZ9OmTXMHcY0aNTLNrUzbpf2F0vr16/MyVnFVqv2u9quFur8Ic9GZr37Wrl0b9nVBLGvWrFloOyMLwHvvvbftvPPO7jLwlClTTGc+EydOdJeMdNTqA3BoK1hYtAKHHXaY3XnnnfbQQw9Z79697YorrijKS0l1YQC1kT/77LN2wQUXmA7czjjjDOvTp09daFokbbjlllvcwaguQw8dOtTOOuusSMqlEATyJVCy5Ugp1qc0zj//fLv33ntD+6cjtajOFjgDDiWukwt1NKuztUzPXupkp7Y0Kt9nwHG4+DPgefPmxVF83sos5jPg+fPncwact5kVXrEOjnV1ODnF+hS0KksVfJMbwmcEEEAAAQS2JoHYA/DWhElfEUAAAQQQSFeAAJyuFPkQQAABBBCIUIAAHCEmRSGAAAIIIJCuAAE4XSnyIYAAAgggEKEAAThCTIpCAAEEEEAgXQECcLpS5EMAAQQQQCBCAQJwhJgUhQACCCCAQLoCBOB0pciHAAIIIIBAhAIE4AgxKQoBBBBAAIF0BQjA6UqRDwEEEEAAgQgFCMARYlIUAggggAAC6QoQgNOVIh8CCCCAAAIRChCAI8SkKAQQQAABBNIVIACnK0U+BBBAAAEEIhQgAEeISVEIIIAAAgikK1CyeUtKN3PU+VatWmWbNm2KpNjvv//ePvzwQxs2bFgk5dWVQkpKSorqj2vLdcmSJfbSSy/ZqaeeWleYI2lHMY7V2rVr7X/+53/s7LPPjsSIQuIV+O///m87/fTTrVGjRvFWlOPSC33batiwYeiY5DUARzmGb731lt1+++325JNPRlksZcUgMHXqVBsxYoRNmDAhhtIpMkqBxYsX24EHHmhTpkyJsljKikmgd+/e9t5771nLli1jqoFioxTgEnSUmpSFAAIIIIBAmgJFcwa8bNky++6772zXXXdNs+tky5fA6tWr7auvvrJ+/frlqwnUm6ZAeXm5ffzxx7bPPvukuQbZ8ikwceJE22OPPay0tDSfzaDuNAWKJgCn2V+yIYAAAgggUCcEiuYS9IIFC+yTTz6xjRs31gnYrbUR8+fPdw9Z+f7rQTs9HKdXn8LGKmzZtGnTTD+k6AV0FULbi85wfQobq7BlYWMVtsyXy2t2Ahs2bHBXIXQ/3qewcQlbFjYuYct8ubzmVqD+NVtSbquMvjZddhk7dqxppzJ+/Hj30Ej0tVBiTQJ6Ev3yyy+3nj172nbbbecC8W9/+1tr0qSJG5+hQ4fapEmTqoxV2Pj9+c9/tq+//tr0cJ3GtVevXjVVz/dpCsyePdtGjRplrVq1sjFjxtigQYPcAVLyWC1fvtySl6U7fmk2hWw1CCj4/vKXv3Rj9eCDD9ouu+zi/udI8rgwVjVA1tGvi+JGwRNPPGFXXXWVlZWV2ciRI02TsXnz5nWUvDibtX79erv77rsr3YN/8cUX7ZRTTnE7eP13Mz2d+dxzz1UZq7Dx047+nnvuMe2ALrroIjvqqKOKEy4PvVq0aJFdfPHF1qNHD7etvPvuu6ZlyWOlA6rkZemOH9tfNAOrs9pzzjnHdt99d3dAq2cnGKtobOtCKUVxCVoPYCn4KrVr1850iYWUWwH9P7ff//731rFjx0TFc+fOTXxu37696fJ02FglL9Nl5xYtWrhy9DAJtxUSpJG80UM6Cr5Lly61N954wz20EzZWYcuSx0rbWtiySBpKIW47UPAdPXq0vfrqq7b//vtb2LiELQsbl7BlMOdPoCgCcJBPZ0zF9p/Qg/0rpPf16tVL/KIVBdHkcQkbKy1r3LhxYj31lyc6ox91nUVdeeWVdskll1ibNm0sbKzClgVbkmr8ksc5uA7vMxPQVaDjjjvOdBk6bFzClgVrYqyCGnXnfVEEYJ316uxKac6cOdahQ4e6I7wVt6Rr1642Y8YMJzBz5kzr0qWLu0KRPFbJ47fjjju6syqtqEtwCsik6AQUfPXoh27X+P+2FzZWYcuSx0rbWtiy6Fq7dZc0a9Yse/zxx02/CUrPQeg3k4WNS9iysHEJW7Z1C+e390VxD/jMM8+0u+66y12qPOigg6xBgwb5VaV2J3D44Yfbrbfeau+88447+9X/+23WrFmVsQobv5NPPtl+97vfucukP//5zxGNUEC/rlBB+I477nClnnjiiRY2VtrhZzp+ETZ3qy5KB61///vf3e0dPYz4s5/9zFq3bl1lXBirwpwmRfX/gPUgkO5FkuqWQNi4pLNMl810aU0/pNwIpDMuakm6+XLT6uKvZd26dVVu4aQ7BunmK37FutfDogrAdY+XFiGAAAIIIBAuwKlFuAtLEUAAAQQQiFWAABwrL4UjgAACCCAQLkAADndhKQIIIIAAArEKEIBj5aVwBBBAAAEEwgUIwOEuLEUAAQQQQCBWAQJwrLwUjkBuBfT/dvVbk/r27et+f/Cjjz6aaMD9999vAwcOdL/O8C9/+YtbvmTJEtMfydD/I9WvPHz99dcT+XmDAALxChTFL+KIl4jSESgcAQXUV155xQVS/eKGvfbaywXcyZMn25133mkvv/yy+0U1+uMW+p3Q+mtTffr0cX9FTL8X+tlnn7XBgwcXTodpKQIFLMAZcAEPHk1HIExg+PDhtu2229r2229v++67r/srVPoznfpdwp06dbK2bdvaj3/8Y9PZ8YABA+zhhx+2s846y/21Hf2ZQhICCORGgACcG2dqQSBnAi1btkzUpV/Lqj+EoV8Bqh+f9Pu19eso+/fv7/7Yu86Gb7zxRjvkkEN8Fl4RQCBmAX4TVszAFI9ALgX0h9r1d5fHjRvn/pBF9+7dbeLEibZixQr3e4QnTJjgfrXn0Ucfbcccc4w1adLE/b7tSy+91P0BjM6dO7vP/D71XI4adW2tAtwD3lpHnn4XrYD+5qseqNI9YJ3VduvWzfVVf3BBf09WwViXpvWL/ZcvX266ZP3MM8/YvHnz7Nprr+WPmRTtzKBjdU2AM+C6NiK0B4EsBHQGrHu8+gtSOotNPpPVH7goLy+v8iceFZR1WZq/vZwFPqsiUEsBzoBrCUZ2BApBQJeWw5ICbFiQLSsrC8vOMgQQiFGAM+AYcSkagVwL6DKyAmybNm1yXTX1IYBALQUIwLUEIzsCCCCAAAJRCPDfkKJQpAwEEEAAAQRqKUAAriUY2RFAAAEEEIhCgAAchSJlIIAAAgggUEuB/werW7YqFR9++QAAAABJRU5ErkJggg==\n" }, "metadata": {}, "output_type": "display_data" @@ -4189,13 +4170,13 @@ }, { "cell_type": "code", - "execution_count": 239, + "execution_count": 31, "id": "07dad14d-b547-4aae-b8b3-57c16e3ef4d1", "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n" 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\n" }, "metadata": {}, "output_type": "display_data" @@ -4215,7 +4196,7 @@ }, { "cell_type": "code", - "execution_count": 202, + "execution_count": 32, "id": "1b5c236d-dd6f-4d08-aeef-a6b07b0051e9", "metadata": {}, "outputs": [ @@ -4224,7 +4205,7 @@ "output_type": "stream", "text": [ " mean var\n", - "1 0 0.5069734\n" + "1 0 0.4994819\n" ] } ], @@ -4242,12 +4223,12 @@ ] }, { - "cell_type": "code", - "execution_count": null, - "id": "0df9566f-980a-4c5e-a72f-ad78092f082a", + "cell_type": "markdown", + "id": "aba3c70f-1658-4cc1-b63a-1deefbd14de2", "metadata": {}, - "outputs": [], - "source": [] + "source": [ + "### Real data downsampled to match polymorphisms in the human genome" + ] }, { "cell_type": "code", @@ -4306,88 +4287,1353 @@ ] }, { - "cell_type": "code", - "execution_count": null, - "id": "326f8a0e-61f5-42fb-871f-aaeb60b9b33a", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "53c5c073-a47e-4d08-99e3-91ee91bb5f64", + "cell_type": "markdown", + "id": "9c629751-01a3-4452-ba5b-3810ce9f1433", "metadata": {}, - "outputs": [], - "source": [] + "source": [ + "## 10. Error distribution\n", + "\n", + "### 10.1 check real data\n", + "\n", + "You need to run the first 9 chunks or so of the 9.3 section" + ] }, { "cell_type": "code", - "execution_count": null, - "id": "007e3796-3095-42b7-b90b-6c536722a719", + "execution_count": 30, + "id": "800b1fab-5881-496b-aaa3-78cb5a492ac9", "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "data": { + "image/png": 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\n" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "%%R\n", + "\n", + "df %>% \n", + " ggplot() +\n", + " geom_histogram(aes(x = quality), binwidth = 1)" + ] }, { "cell_type": "code", - "execution_count": 1, - "id": "f1f616b5-1187-40c5-8736-cd1c8f5eb554", + "execution_count": 53, + "id": "8c9bc317-5f45-4864-8bf1-b727998a1f52", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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\n" + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ - "import numpy as np" + "%%R\n", + "\n", + "df %>% \n", + " group_by(readnum) %>%\n", + " mutate(diff = quality-lead(quality)) %>%\n", + " filter(!is.na(diff)) %>%\n", + " ggplot() +\n", + " geom_point(aes(x = positio, y = diff)) " ] }, { "cell_type": "code", - "execution_count": 2, - "id": "d298a22c-d9fe-44d4-897f-e763d35cb7d9", + "execution_count": 54, + "id": "a49cf4d1-7b07-49fd-8f9f-3709f74e4cf7", "metadata": {}, "outputs": [ { "data": { - "text/plain": [ - "array([9148, 9149, 9150, ..., 1937, 1938, 1939])" - ] + "image/png": 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\n" }, - "execution_count": 2, "metadata": {}, - "output_type": "execute_result" + "output_type": "display_data" } ], "source": [ - "seq_len = 37498\n", - "n_reads = 9527-12\n", - "read_length = 151\n", - "\n", + "%%R\n", "\n", - "df_sim = np.array([int(x) for x in np.random.uniform(low=0.0, high=seq_len, size=n_reads)])\n", - "pos = []\n", - "for s in df_sim:\n", - " for i in range(s, s+read_length):\n", - " pos.append(i)\n", - "pos = np.array(pos)\n", - "pos" + "df %>% \n", + " group_by(readnum) %>%\n", + " mutate(diff = quality-lead(quality)) %>%\n", + " filter(!is.na(diff)) %>%\n", + " ggplot() +\n", + " geom_histogram(aes(x = diff), bins = 20)" ] }, { "cell_type": "code", - "execution_count": 30, - "id": "35643811-795f-4387-9f9c-d99bfc17e3a8", + "execution_count": 56, + "id": "2a51baea-3061-4c60-b0c5-50755b98d091", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "[15.37810676 13.95450312 14.17387291 10.11706523]\n" + "# A tibble: 1 × 2\n", + " mean var\n", + " \n", + "1 0.00104 63.6\n" ] } ], "source": [ - "def depth_per_haplotype(rng, mean_depth, std_depth, n_hap):\n", - " if isinstance(mean_depth, np.ndarray):\n", + "%%R\n", + "\n", + "df %>% \n", + " group_by(readnum) %>%\n", + " mutate(diff = quality-lead(quality)) %>%\n", + " filter(!is.na(diff)) %>%\n", + " ungroup() %>%\n", + " summarize(mean = mean(diff), var = var(diff))\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "286e99e0-7819-47cb-a38f-140a4f05f8fb", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " readnum positio quality qualityperm\n", + "1960 12 1000050 27 22\n", + "1961 12 1000051 22 22\n", + "1962 12 1000052 27 22\n", + "2106 13 1000050 33 33\n", + "2107 13 1000051 22 33\n", + "2108 13 1000052 33 27\n", + "2109 13 1000053 22 15\n", + "2110 13 1000054 27 27\n", + "2111 13 1000055 22 33\n", + "2112 13 1000056 22 33\n", + "2113 13 1000057 27 33\n", + "2254 14 1000050 22 33\n", + "2255 14 1000051 15 33\n", + "2256 14 1000052 27 33\n", + "2257 14 1000053 27 33\n", + "2258 14 1000054 22 22\n", + "2259 14 1000055 33 22\n", + "2260 14 1000056 22 27\n", + "2261 14 1000057 15 27\n", + "2262 14 1000058 27 33\n" + ] + } + ], + "source": [ + "%%R\n", + "\n", + "df$qualityperm <- sample(x = df$quality, replace = FALSE)\n", + "\n", + "df %>% head(20)" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "05501d3a-138b-4b2b-a5aa-29259e01732a", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "%%R\n", + "\n", + "df %>% \n", + " mutate(diff = quality-qualityperm) %>%\n", + " ggplot() +\n", + " geom_point(aes(x = positio, y = diff))" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "18176c08-e378-4af4-85cf-d56cf45cc12b", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "%%R\n", + "\n", + "df %>% \n", + " mutate(diff = quality-qualityperm) %>%\n", + " ggplot() +\n", + " geom_histogram(aes(x = diff), bins = 20)" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "4aca57e4-4a64-471f-a60e-bd6b5ab0ee02", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " mean var\n", + "1 0 95.2269\n" + ] + } + ], + "source": [ + "%%R\n", + "\n", + "df %>% \n", + " mutate(diff = quality-qualityperm) %>%\n", + " filter(!is.na(diff)) %>%\n", + " summarize(mean = mean(diff), var = var(diff))" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "id": "d8bee044-f5a5-4c14-88f9-2bfa4ac082a7", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[1] \"0 %\"\n", + "[1] \"10 %\"\n", + "[1] \"20 %\"\n", + "[1] \"30 %\"\n", + "[1] \"40 %\"\n", + "[1] \"50 %\"\n", + "[1] \"60 %\"\n", + "[1] \"70 %\"\n", + "[1] \"80 %\"\n", + "[1] \"90 %\"\n" + ] + } + ], + "source": [ + "%%R\n", + "\n", + "n = 1000\n", + "k = n*0.1\n", + "vars <- rep(NULL, n)\n", + "\n", + "for(j in seq(0, n-k, k)){\n", + " print(paste(j*100/n, \"%\"))\n", + " for(i in seq(j, j+k, 1)){\n", + " vars[i] <- var(df$quality-sample(x = df$quality, replace = FALSE))\n", + " }\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "57d4fdf1-4117-4039-b9ee-25bbc77f1f9e", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[1] 94.99819\n" + ] + } + ], + "source": [ + "%%R\n", + "\n", + "var(df$quality-sample(x = df$quality, replace = FALSE))" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "id": "fd3371b9-694e-41bc-9882-93a41fd5bf59", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "%%R\n", + "\n", + "data.frame(vars = vars) %>%\n", + " ggplot() +\n", + " geom_histogram(aes(x = vars), bins = 100) +\n", + " #geom_vline(xintercept = 63.50372) +\n", + " NULL" + ] + }, + { + "cell_type": "code", + "execution_count": 75, + "id": "f5d33943-b2e6-4037-9953-40aa3c8af5ee", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[1] TRUE\n" + ] + } + ], + "source": [ + "%%R\n", + "\n", + "print(100/100)" + ] + }, + { + "cell_type": "code", + "execution_count": 82, + "id": "d7468f4b-cf65-48f0-806b-5b3354350827", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "%%R\n", + "\n", + "step = 50\n", + "e = 0.001\n", + "\n", + "df %>%\n", + " group_by(positio) %>%\n", + " summarize(mean_qual = mean(quality)) %>%\n", + " filter(trunc(positio / step) > (positio / step)-e & trunc(positio / step) < (positio / step)+e) %>%\n", + " ggplot() +\n", + " geom_point(aes(x = positio, y = mean_qual), alpha = 0.5)" + ] + }, + { + "cell_type": "code", + "execution_count": 83, + "id": "3e418e6d-2e54-4aab-bc6d-02a9bd962eb3", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "%%R\n", + "\n", + "df %>%\n", + " group_by(positio) %>%\n", + " summarize(mean_qual = mean(quality)) %>%\n", + " #filter(trunc(positio / step) > (positio / step)-e & trunc(positio / step) < (positio / step)+e) %>%\n", + " mutate(diff = mean_qual-lead(mean_qual)) %>%\n", + " filter(!is.na(diff)) %>%\n", + " ggplot() +\n", + " geom_histogram(aes(x = diff), bins = 20)" + ] + }, + { + "cell_type": "code", + "execution_count": 89, + "id": "9d74e62b-3621-4258-9dc8-3aac6bee852a", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "# A tibble: 1 × 2\n", + " mean_diff var_diff\n", + " \n", + "1 0.00000165 6.97\n" + ] + } + ], + "source": [ + "%%R\n", + "\n", + "df %>%\n", + " group_by(positio) %>%\n", + " summarize(mean_qual = mean(quality)) %>%\n", + " #filter(trunc(positio / step) > (positio / step)-e & trunc(positio / step) < (positio / step)+e) %>%\n", + " mutate(diff = mean_qual-lead(mean_qual)) %>%\n", + " filter(!is.na(diff)) %>%\n", + " summarize(mean_diff = mean(diff), var_diff = var(diff))" + ] + }, + { + "cell_type": "code", + "execution_count": 88, + "id": "e11e112d-a478-4a50-b4e2-12a184faaec7", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "%%R\n", + "\n", + "df %>%\n", + " group_by(positio) %>%\n", + " summarize(mean_qual = mean(quality)) -> mean_df\n", + "\n", + "mean_df %>%\n", + " pull(mean_qual) -> mean_qual_vec\n", + "\n", + "mean_df$perm_mean_qual <- sample(x = mean_qual_vec, replace = FALSE)\n", + "\n", + "mean_df %>% \n", + " mutate(diff = mean_qual-perm_mean_qual) %>%\n", + " ggplot() +\n", + " geom_histogram(aes(x = diff), bins = 20)" + ] + }, + { + "cell_type": "code", + "execution_count": 90, + "id": "4937c8ae-c166-44cb-b03d-bd35643a4af7", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "# A tibble: 1 × 2\n", + " mean_diff var_diff\n", + " \n", + "1 0 13.5\n" + ] + } + ], + "source": [ + "%%R\n", + "\n", + "mean_df %>% \n", + " mutate(diff = mean_qual-perm_mean_qual) %>%\n", + " summarize(mean_diff = mean(diff), var_diff = var(diff))" + ] + }, + { + "cell_type": "code", + "execution_count": 93, + "id": "693f1f0d-1988-49b6-b093-f931c742e6a2", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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L2PXr14ddu3aF+PRzPKrdtGlTNu/mzZvD0qVLp6rBNgIECBAgUDuB3I6AzznnnBB/4umxxx4L5557bnZ+4cKF2Quw4tFofNrZiQABAgQIEAghtwB+NeYtt9wyfnHlypXZi63mzJkzvs0ZAgQIECBQd4HcnoJuBil8m+m4jgABAgTqKNCWAK4jrDUTIECAAIFmAgK4mY7rCBAgQIBAQQICuCBYwxIgQIAAgWYCAriZjusIECBAgEBBAgK4IFjDEiBAgACBZgICuJmO6wgQIECAQEECArggWMMSIECAAIFmAgK4mY7rCBAgQIBAQQICuCBYwxIgQIAAgWYCAriZjusIECBAgEBBAgK4IFjDEiBAgACBZgICuJmO6wgQIECAQEECArggWMMSIECAAIFmAgK4mY7rCBAgQIBAQQICuCBYwxIgQIAAgWYCAriZjusIECBAgEBBAgK4IFjDEiBAgACBZgICuJmO6wgQIECAQEECArggWMMSIECAAIFmAgK4mY7rCBAgQIBAQQICuCBYwxIgQIAAgWYCAriZjusIECBAgEBBAgK4IFjDEiBAgACBZgICuJmO6wgQIECAQEECvQWNa1gCSQkMDQ0lVa9iCRBIX8ARcPo9tAICBAgQSFBAACfYNCUTIECAQPoCAjj9HloBAQIECCQoIIATbJqSCRAgQCB9AQGcfg+tgAABAgQSFBDACTZNyQQIECCQvoAATr+HVkCAAAECCQoI4ASbpmQCBAgQSF9AAKffQysgQIAAgQQFBHCCTVMyAQIECKQvIIDT76EVECBAgECCAgI4waYpmQABAgTSFxDA6ffQCggQIEAgQQEBnGDTlEyAAAEC6QsI4PR7aAUECBAgkKCAAE6waUomQIAAgfQFBHD6PbQCAgQIEEhQQAAn2DQlEyBAgED6AgI4/R5aAQECBAgkKCCAE2yakgkQIEAgfQEBnH4PrYAAAQIEEhQQwAk2TckECBAgkL6AAE6/h1ZAgAABAgkKCOAEm6ZkAgQIEEhfQACn30MrIECAAIEEBQRwgk1TMgECBAikLyCA0++hFRAgQIBAggICOMGmKZkAAQIE0hcQwOn30AoIECBAIEEBAZxg05RMgAABAukLCOD0e2gFBAgQIJCggABOsGlKJkCAAIH0BQRw+j20AgIECBBIUEAAJ9g0JRMgQIBA+gICOP0eWgEBAgQIJCgggBNsmpIJECBAIH0BAZx+D62AAAECBBIUEMAJNk3JBAgQIJC+gABOv4dWQIAAAQIJCgjgBJumZAIECBBIX0AAp99DKyBAgACBBAUEcIJNUzIBAgQIpC/QW4Ul9Pf3h+7u/P4tEMeKP/PmzavC8lquYc6cOcnW3tfXF7q6upKtv+VmuUMhAin+7g4ODhZiUfSgvb29Yf/+/dlP0XPlPX7qj/WVCOC9e/eG0dHR3HoTQyD+7Ny5M7cx2zlQfPBJtfb4IBR/KVKtv519NtfBBVLbfwYGBsKuXbvC2NjYwRdV0WviAdDIyEj2U9ESD1pWKo/1B/vHWX6HnQclcgUBAgQIECAwWUAATxZxmQABAgQItEFAALcB2RQECBAgQGCygACeLOIyAQIECBBog0DTAB4eHg4PPfTQhDI++9nPhvvvv3/CNhcIECBAgACB1gSmfBX0U089FS666KLwz3/+M3s18WGHHZaNGl/h949//CN89KMfbW0WtyZAgAABAgQmCEwZwCeffHJ45JFHwh133BGOPfbY8Na3vjW7U09PTzjqqKNC/L8TAQIECBAgMHOBKQM4DnfMMceEq6++OmzcuDF7Gjq+T+zA6dxzzw2LFi06cNH/CRAgQIAAgRYFDhrAcZwHH3wwfPCDHwznnXdeOPA0dNwej4gFcJRwIkCAAAECMxNoGsA/+9nPws033xwuv/zymY3uXgQIECBAgMCUAk1fBf3mN785PP7441Pe0UYCBAgQIEBg5gJNj4CPOOKI8M1vfjPcfvvtYfny5eOz3HTTTeGUU04Zv+wMAQIECBAg0JpA0wA+8cQTw/e///2GEV/72tc2bLOBAAECBAgQOHSBpgH8yiuvhB07djSM9upXRDdcaQMBAgQIECAwrUDTAH7uuefCj3/842yQ+HWBzz77bIhfHRi3HX300dMO7gYECBAgQIDA1AJNA/jd7353iD+vPl122WVh3759r97kPAECBAgQINCiQNNXQU811uLFi0P8qEonAgQIECBAYOYCTY+A77777vCd73wnGz1+DnT8bOj4yVif+9znZj6jexIgQIAAAQKhaQC/4Q1vCFddddU409y5c8Ppp58e4tuTnAgQIECAAIGZCzR9CvqEE04IZ5xxRti0aVO47bbbwmOPPRbiK6OdCBAgQIAAgdkJNA3gXbt2hbe97W3hr3/9a7jwwgvD5s2bQ/wihvhKaCcCBAgQIEBg5gJNA/h73/teuPbaa8OXvvSlcMUVV4RvfOMbIX48ZfyqQicCBAgQIEBg5gJNA7irq6vhaDce/cZXQjsRIECAAAECMxdo+iKsSy+9NLzxjW8Mf/rTn8Jpp50WfvnLX2avhD755JNnPqN7EiBAgAABAqHpEXB8tfPDDz8cjj/++Ozvvx/72MfC/fffj40AAQIECBCYpUDTI+A49qJFi8I111wT5s+fnx0J9/T0zHJKdydAgAABAgSaHgH/7W9/y76G8IknnsikvvKVr4Tzzz+fGgECBAgQIDBLgaYB/KMf/Shcd9114eyzz86m+frXv549Hb1+/fpZTuvuBAgQIECg3gJNAzh+A9LWrVsnCO3cuTMMDAxM2OYCAQIECBAg0JpA078Bx1dBX3DBBSEe8Z566qnh0UcfDfEzob0KujVktyZAgAABApMFmh4BL126NDz44IPhvPPOy4L3C1/4Qrj33nsnj+EyAQIECBAg0KJA0yPgOFb80I2VK1e2OKybEyBAgAABAs0Emh4BN7uj6wgQIECAAIGZCwjgmdu5JwECBAgQmLGAAJ4xnTsSIECAAIGZC0z7N+CZD+2eBIoTGBoaKm5wIxMgQKANAo6A24BsCgIECBAgMFlAAE8WcZkAAQIECLRBQAC3AdkUBAgQIEBgsoAAniziMgECBAgQaIOAAG4DsikIECBAgMBkAQE8WcRlAgQIECDQBgEB3AZkUxAgQIAAgckCAniyiMsECBAgQKANAgK4DcimIECAAAECkwUE8GQRlwkQIECAQBsEBHAbkE1BgAABAgQmCwjgySIuEyBAgACBNggI4DYgm4IAAQIECEwWEMCTRVwmQIAAAQJtEBDAbUA2BQECBAgQmCwggCeLuEyAAAECBNogIIDbgGwKAgQIECAwWUAATxZxmQABAgQItEFAALcB2RQECBAgQGCygACeLOIyAQIECBBog4AAbgOyKQgQIECAwGQBATxZxGUCBAgQINAGAQHcBmRTECBAgACByQICeLKIywQIECBAoA0CArgNyKYgQIAAAQKTBQTwZBGXCRAgQIBAGwQEcBuQTUGAAAECBCYLCODJIi4TIECAAIE2CAjgNiCbggABAgQITBbonbzBZQIECJQtMDQ0lGsJw8PDuY5nMAJ5CDgCzkPRGAQIECBAoEUBAdwimJsTIECAAIE8BARwHorGIECAAAECLQrkGsCjo6PhySefDDt27BgvY2RkJGzYsCFs27ZtfJszBAgQIECg7gK5BXAM32uvvTY888wz4Ytf/GL43e9+F8bGxsLq1avDxo0bw5o1a8Lzzz9fd2/rJ0CAAAECmUBur4Levn17uPTSS8M555wTTjzxxHDfffeFwcHBsGTJkrBixYqwfPnysG7durBq1aps4jvvvDOsX78+O3/11VeHBQsW5NaS7u7u0NPTE+bPn5/bmO0cqK+vL8Q1pHjq7e0NXV1dydqnaK7m6QXa8Vhw+OGHT19IBW8RHyvjwdL+/fsrWF3zklJ5rI++U51yC+BFixaF+BOPhG+//fbwnve8J2zZsiUL4DhxvG7r1q3jNcTLr3vd67LL8QE7PlWd1+nADpXnmHnVdijjxPpTrT3+QsSfVOs/lP64TXoC7dgf42PfwR5oqy4WwzfFAE7lsf5gB1S5BXDcweJOfsMNN4TTTz89nHnmmeGhhx4ab2rcOefOnTu+H5511lkh/sRT/Pvw7t27x6+b7Zl4BDlnzpxcx5xtTa3cPzYrT49W5p7tbeM/plKuf7brd/9qChT9+zRv3rzsdzbFAO7v788eu9vxj5S8945UHuvjs8FTnXJ7njP+6+nzn/98eNe73hUuvvjibK5ly5aFTZs2Zec3b94cli5dOlUNthEgQIAAgdoJ5HYE/MADD4Qnnngi7Nq1K9x1113htNNOCx/5yEfCwoULsxdgxb8Rr127tnbAFkyAAAECBKYSyC2Azz333BB/Jp9WrlwZ9u3blz0lPPk6lwkQIECAQF0FcnsKuhlg/HusEwECBAgQIPA/gbYE8P+mc44AAQIECBCIAgLYfkCAAAECBEoQEMAloJuSAAECBAgIYPsAAQIECBAoQUAAl4BuSgIECBAgIIDtAwQIECBAoAQBAVwCuikJECBAgIAAtg8QIECAAIESBARwCeimJECAAAECuX0UJUoCzQSGhoaaXe06AgQI1E7AEXDtWm7BBAgQIFAFAQFchS6ogQABAgRqJyCAa9dyCyZAgACBKggI4Cp0QQ0ECBAgUDsBAVy7llswAQIECFRBQABXoQtqIECAAIHaCXgbUu1absEE6ieQ99vghoeH64doxbkLOALOndSABAgQIEBgegEBPL2RWxAgQIAAgdwFBHDupAYkQIAAAQLTCwjg6Y3cggABAgQI5C4ggHMnNSABAgQIEJheQABPb+QWBAgQIEAgdwEBnDupAQkQIECAwPQCAnh6I7cgQIAAAQK5Cwjg3EkNSIAAAQIEphcQwNMbuQUBAgQIEMhdQADnTmpAAgQIECAwvYAAnt7ILQgQIECAQO4CAjh3UgMSIECAAIHpBQTw9EZuQYAAAQIEchcQwLmTGpAAAQIECEwvIICnN3ILAgQIECCQu4AAzp3UgAQIECBAYHoBATy9kVsQIECAAIHcBQRw7qQGJECAAAEC0wsI4OmN3IIAAQIECOQuIIBzJzUgAQIECBCYXkAAT2/kFgQIECBAIHcBAZw7qQEJECBAgMD0AgJ4eiO3IECAAAECuQsI4NxJDUiAAAECBKYXEMDTG7kFAQIECBDIXUAA505qQAIECBAgML2AAJ7eyC0IECBAgEDuAr25j2jA5AWGhoaSX4MFECBAoOoCjoCr3iH1ESBAgEBHCgjgjmyrRREgQIBA1QUEcNU7pD4CBAgQ6EgBAdyRbbUoAgQIEKi6gACueofUR4AAAQIdKSCAO7KtFkWAAAECVRfwNqSqd0h9BAhUTiDvt+oNDw9Xbo0KKl7AEXDxxmYgQIAAAQINAgK4gcQGAgQIECBQvIAALt7YDAQIECBAoEFAADeQ2ECAAAECBIoXEMDFG5uBAAECBAg0CAjgBhIbCBAgQIBA8QICuHhjMxAgQIAAgQYBAdxAYgMBAgQIECheQAAXb2wGAgQIECDQICCAG0hsIECAAAECxQsI4OKNzUCAAAECBBoEBHADiQ0ECBAgQKB4AQFcvLEZCBAgQIBAg0Alvg2pq6sr9PT0NBQ30w1xrLzHnGktM7lfyrXPZL3uQ6DuAnk+/rVq2d3dnT3+jo2NtXrX0m+f+mN9JQK4r68vC8y8unmgKXPnzs1ryLaO09vbG1Ktva1QJiPQIQJXXnllriu5/fbbD3m8+Ph7IIQP+U4VuWEqj/XxoGqqUyUCeN++fWF0dHSq+ma0Le5QMcD27Nkzo/uXfae4U6Vae9l25idAILT0+NHf3x9GRkayn9TsUnmsHxwcnJLW34CnZLGRAAECBAgUKyCAi/U1OgECBAgQmFJAAE/JYiMBAgQIEChWQAAX62t0AgQIECAwpYAAnpLFRgIECBAgUKxAJV4FXewSqzf60NBQrkUNDw/nOp7BCBAgQKB4AUfAxRubgQABAgQINAgI4AYSGwgQIECAQPECArh4YzMQIECAAIEGAQHcQGIDAQIECBAoXkAAF29sBgIECBAg0CAggBtIbCBAgAABAsULCODijc1AgAABAgQaBARwA4kNBAgQIECgeAEBXLyxGQgQIECAQIOAAG4gsYEAAQIECBQvIICLNzYDAQIECBBoEBDADSQ2ECBAgACB4gUEcPHGZiBAgAABAg0CAriBxAYCBAgQIFC8gAAu3tgMBAgQIECgQUAAN5DYQIAAAQIEihcQwMUbm4EAAQIECDQICOAGEhsIECBAgEDxAgK4eGMzECBAgACBBgEB3EBiAwECBAgQKF5AABdvbAYCBAgQINAg0NuwxYbkBIaGhpKrWcEECBCou4Aj4LrvAdZPgAABAqUICOBS2E1KgAABAnUXEMB13wOsnwABAgRKERDApbCblAABAgTqLiCA674HWD8BAgQIlCIggEthNykBAgQI1F1AANd9D7B+AgQIEChFwPuAS2E3KQECBNIRyPuzBoaHh9NZfIGVOgIuENfQBAgQIEDgYAIC+GAythMgQIAAgQIFBHCBuIYmQIAAAQIHExDAB5OxnQABAgQIFCgggAvENTQBAgQIEDiYgAA+mIztBAgQIECgQAFvQyoQ19AECBAoQyDvtw2VsYY6zOkIuA5dtkYCBAgQqJyAAK5cSxREgAABAnUQEMB16LI1EiBAgEDlBARw5VqiIAIECBCog4AArkOXrZEAAQIEKicggCvXEgURIECAQB0EBHAdumyNBAgQIFA5AQFcuZYoiAABAgTqICCA69BlayRAgACBygkI4Mq1REEECBAgUAcBAVyHLlsjAQIECFROQABXriUKIkCAAIE6CAjgOnTZGgkQIECgcgICuHItURABAgQI1EGgI7+O8IorrqhD76yRAAECBBIWcASccPOUToAAAQLpCgjgdHuncgIECBBIWEAAJ9w8pRMgQIBAugICON3eqZwAAQIEEhYQwAk3T+kECBAgkK6AAE63dyonQIAAgYQFOvJtSAn3Q+kECBDoeIGhoaFKr3F4eLgt9TkCbguzSQgQIECAwEQBATzRwyUCBAgQINAWAQHcFmaTECBAgACBiQKFB/DIyEjYsGFD2LZt28SZXSJAgAABAjUWKDSAx8bGwurVq8PGjRvDmjVrwvPPP19jaksnQIAAAQL/Eyj0VdBPP/10WLJkSVixYkVYvnx5WLduXVi1alU2ezwi3rlzZ3Z+YGAg9PYWWsr/VuwcAQIECBBoItCuPCo09bZs2ZIFcFznokWLwtatW8eXfOutt4a77747u3zbbbeFY489dvy62Z655557Qnd3d9i/f/9shyrl/l1dXSE+e5DiKdYeTynXn2rtKe/zcZ9Jeb9P2T7139kU7F955ZW4izecCg3gV8OMjo6GuXPnjhdw3XXXhfgTT/FoOM+/Eff19YX58+eH7du3j8+X0pl58+aNPzuQUt2x1sHBwewfPzt27Eit9KzelO0XLFgQ9uzZE/bu3ZucfXys6O/vz+pPrvj/Frx48eLw0ksvJfkPz+geX6sTf1I7pfJYHx8XpzoV+jfgZcuWhU2bNmXzbt68OSxdunSqGmwjQIAAAQK1Eyj0CPi4444LCxcuzF6AFY9G165dWztgCyZAgAABAlMJFBrAccKVK1eGffv2hTlz5kw1v20ECBAgQKCWAoU+BX1AVPgekPB/AgQIECDw/wXaEsCwCRAgQIAAgYkCAniih0sECBAgQKAtAgK4LcwmIUCAAAECEwUE8EQPlwgQIECAQFsEBHBbmE1CgAABAgQmCgjgiR4uESBAgACBtggI4LYwm4QAAQIECEwUEMATPVwiQIAAAQJtERDAbWE2CQECBAgQmCgggCd6uESAAAECBNoiIIDbwmwSAgQIECAwUUAAT/RwiQABAgQItEVAALeF2SQECBAgQGCigACe6OESAQIECBBoi0DX2H9PbZmpySS7d+8O+/fvb3KL1q568cUXwyOPPBIuueSS1u5YkVt3dXWFCrRlRhqPP/542Lt3bzjzzDNndP+y75Sy/b333htOOumkcNxxx5XNOKP5U7a/9dZbw4c+9KHQ398/o7WXeaeU3VN5rI9fyTt37tyGNvc2bClhw8DAQK6zPv300+GOO+4IH/7wh3Md12DTCzz55JPh5ZdfDuedd970N3aLXAXuueeeMH/+/HDqqafmOq7Bphe45ZZbwpVXXhnmzZs3/Y3dIjeB1B/rPQWd265gIAIECBAgcOgClXgK+tDLPbRb7ty5M2zevNmRwKFx5Xqrv//97+E///lPWLZsWa7jGmx6gT//+c/hmGOOCQsWLJj+xm6Rq8D69evDm970ptDbW4knFXNdW5UHS/2xviMDuMo7jNoIECBAgEAU6MinoOPfBV79oq5t27aFDRs2hNHRUV1vg8AzzzwTnnrqqexnz549bZjRFCMjI9k+Hvd1p/YK2N/b6x1ne+WVV8KmTZvGJ051/+/5f/89ja+iA87Ep4I++clPhssvvzz09PSEePlb3/pWiEHw85//PJx99tkdsMrqLiH+YnzqU5/KCnzhhReyp6K9MKXYfsVXzF9//fUhvpr1u9/9bnj961/vaehiycdHt7+PU7TtTHwsv+mmm8JLL70UTj/99OwdI6nu/x31B4uNGzeGX/3qV+GEE04Y3xl++MMfhtWrV2evTvzMZz4TduzYEQ4//PDx653JV+Avf/lLOOuss8Jll10WjjzyyHwHN9qUAvEZnyVLloQVK1aE5cuXh3Xr1oVVq1ZNeVsb8xWwv+freSijfe1rXwvHH3989lqTePuU9/+OCuBTTjklxJ9rrrlmvI//+te/xt8aEF+gEp+iE8DjPLmfefbZZ0N8K9KuXbtCfEHWjTfeGPJ+m1nuRSc+4JYtW7IAjstYtGhR2Lp1a+IrSqd8+3v7e/XpT386/OEPfwi/+c1vsslT3v+TD+A1a9aE+Pz/RRddFM4444yme0O83VRvhm56J1dOK/Dtb387PPfcc9mrzt///veHCy64IMQ3nsf3Yj/44IPhve9977RjuMHMBbq7u8df8xBf52Afn7llq/e88MIL7e+touV8+5T3/+QD+OMf/3jWzsHBwSnbGo964xFBPDKIR2SLFy+e8nY2zlzgfe97X/Z0UHzg//Wvf519GER8S0b8RKyjjz565gO75yEJxLd8xU9+i6f49rulS5ce0v3caPYC9vfZG852hJT3/+QDeLoH+PjpNDfffHP2Cuh3vvOdoa+vb7b9dv9JAkccccT4lvgngC9/+cvh/vvvz556jn8LdipWIH705MKFC0N8Nmj79u1h7dq1xU5o9HEB+/s4RWlnUt7/a/M+4H379mVPi5a2l9RsYt7tbzjz9psfmJH9AYny/p9iD2oTwOXtFmYmQIAAAQKNAh35QRyNy7SFAAECBAhUS0AAV6sfqiFAgACBmggI4Jo02jIJHEwgfqJQ/PtZPL36/MFubzsBAvkICOB8HI1CoNIC8eNYD3xAzdvf/vbw+9//Pqs3fm/zO97xjvDAAw9k3+F84HylF6M4Ah0ikPzbkDqkD5ZBoG0Cd911V4hvHYufYxzfPxw/LS5+SM0HPvCB7Hz8DHUnAgSKF3AEXLyxGQiUIhA/E/rUU08NJ598crjvvvvGa7j66qtD/AafSy65JAvheER88cUXj5+P3+fsRIBA8QKOgIs3NgOBtgu8/PLL4aqrrsq+nCR+Ctz5558//nnR8RPh4tHvD37wg+xD7R999NGwe/fu8fNtL9aEBGoq4Ai4po237M4WePzxx7OvJTzppJOyryaMn9HtRIBAtQQEcLX6oRoCuQjEVzW/5jWvGR/rqKOOGj/vDAEC1RAQwNXogyoI5Crwlre8Jfz2t78N8anosbGx8JOf/CTX8Q1GgMDsBfwNePaGRiBQOYH4d9/4JSQxiOP3X8fLTgQIVEvAZ0FXqx+qIZCrQHx7UXzB1cDAQK7jGowAgdkLCODZGxqBAAECBAi0LOBvwC2TuQMBAgQIEJi9gACevaERCBAgQIBAywICuGUydyBAgAABArMXEMCzNzQCAQIECBBoWUAAt0zmDgQIECBAYPYC/wc8ouxeR7XNUgAAAABJRU5ErkJggg==\n" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "%%R\n", + "\n", + "\n", + "step = 100\n", + "e = 0.001\n", + "\n", + "df %>%\n", + " group_by(positio) %>%\n", + " summarize(mean_qual = mean(quality)) %>%\n", + " filter(trunc(positio / step) > (positio / step)-e & trunc(positio / step) < (positio / step)+e) %>%\n", + " mutate(diff = mean_qual-lead(mean_qual)) %>%\n", + " filter(!is.na(diff)) %>%\n", + " ggplot() +\n", + " geom_histogram(aes(x = diff), bins = 20)" + ] + }, + { + "cell_type": "code", + "execution_count": 94, + "id": "5783eb50-257f-4c3b-beb0-81e580525c12", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "# A tibble: 1 × 2\n", + " mean_diff var_diff\n", + " \n", + "1 -0.0146 12.0\n" + ] + } + ], + "source": [ + "%%R\n", + "\n", + "step = 100\n", + "e = 0.001\n", + "\n", + "df %>%\n", + " group_by(positio) %>%\n", + " summarize(mean_qual = mean(quality)) %>%\n", + " filter(trunc(positio / step) > (positio / step)-e & trunc(positio / step) < (positio / step)+e) %>%\n", + " mutate(diff = mean_qual-lead(mean_qual)) %>%\n", + " filter(!is.na(diff)) %>%\n", + " summarize(mean_diff = mean(diff), var_diff = var(diff))" + ] + }, + { + "cell_type": "markdown", + "id": "01c66031-6337-4945-82f6-b0558ff7df73", + "metadata": {}, + "source": [ + "### Distributions of bp quality simulating as independent\n", + "\n", + "To obtain such distributions I run `fastqc` on the fastq file that I downloaded from the HGDP\n", + "\n", + "```\n", + "fastqc --nogroup ERR757817_1.fastq.gz\n", + "```\n", + "\n", + "This outputs a `ERR757817_1_fastqc.zip` file, that after unziping it with the command\n", + "\n", + "```\n", + "unzip ERR757817_1_fastqc.zip\n", + "```\n", + "\n", + "Outputs a directory. In it, we can find the file\n", + "\n", + "```\n", + "ls ERR757817_1_fastqc/fastqc_data.txt\n", + "```\n", + "\n", + "which contains the counts of bases in all the reads with a certain bp quality" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "73203186-7675-4e03-9d0d-672f7aa66581", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "##FastQC\t0.11.9\n", + ">>Basic Statistics\tpass\n", + "#Measure\tValue\n", + "Filename\tERR757817_1.fastq.gz\n", + "File type\tConventional base calls\n", + "Encoding\tSanger / Illumina 1.9\n", + "Total Sequences\t355682661\n", + "Sequences flagged as poor quality\t0\n", + "Sequence length\t151\n", + "%GC\t43\n" + ] + } + ], + "source": [ + "%%bash\n", + "\n", + "head /Users/au552345/Desktop/fastqc_data.txt" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "71997dee-21c9-473b-968e-4220e8da563f", + "metadata": {}, + "outputs": [], + "source": [ + "def read_fastqc_data(file):\n", + " quals = []\n", + " counts = []\n", + "\n", + " printing = False\n", + " with open(file, \"r\") as file:\n", + " for line in file:\n", + " if \">>END_MODULE\" in line and printing:\n", + " break\n", + " if printing:\n", + " if \"#\" not in line: \n", + " qual, count = line.strip().split()\n", + " quals.append(int(qual))\n", + " counts.append(int(float(count)))\n", + "\n", + " if \">>Per sequence quality scores\" in line:\n", + " printing = True\n", + "\n", + " qualsdist = pd.DataFrame({\"quality\" : quals,\n", + " \"counts\" : counts})\n", + " return qualsdist\n", + "\n", + "qualsdist = read_fastqc_data(\"/Users/au552345/GenomeDK/fastqsbams/ERR757817_1_fastqc/fastqc_data.txt\")" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "8bdc976e-e1e8-4cfd-b261-b55b195ac9df", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "%%R -i qualsdist\n", + "\n", + "qualsdist %>%\n", + " mutate(counts = counts/sum(counts)) %>%\n", + " ggplot() +\n", + " geom_bar(stat = \"identity\", aes(x = quality, y = counts)) -> HGDP_plot\n", + " \n", + "HGDP_plot" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "2764476a-6949-4614-9ed7-140e1ca0d748", + "metadata": {}, + "outputs": [], + "source": [ + "qualsdist2 = read_fastqc_data(\"/Users/au552345/GenomeDK/fastqsbams/HGDP00001.cram2fastq_fastqc/fastqc_data.txt\")" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "3ff2e1d9-3572-42d8-a050-ceeb2762abec", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "%%R -i qualsdist2 -i qualsdist\n", + "\n", + "qualsdist %>%\n", + " mutate(counts = counts/sum(counts)) %>%\n", + " ggplot() +\n", + " geom_bar(stat = \"identity\", aes(x = quality, y = counts), alpha = 0.5, fill = \"blue\") +\n", + " geom_bar(data = qualsdist2, stat = \"identity\", aes(x = quality, y = counts/sum(counts)), alpha = 0.5, fill = \"red\")" + ] + }, + { + "cell_type": "markdown", + "id": "09a8b721-efe8-49c8-983a-ed456bfa4ea4", + "metadata": {}, + "source": [ + "I downloaded some other fastq files:\n", + "\n", + "- Young Yana\n", + "\n", + "```\n", + "wget ftp://ftp.sra.ebi.ac.uk/vol1/fastq/ERR335/001/ERR3351001/ERR3351001.fastq.gz\n", + "```\n", + "\n", + "- Ust'Ishim\n", + "\n", + "```\n", + "wget ftp://ftp.sra.ebi.ac.uk/vol1/fastq/ERR566/ERR566093/ERR566093_1.fastq.gz\n", + "```\n", + "\n", + "- Sunghir I\n", + "\n", + "```\n", + "wget ftp://ftp.sra.ebi.ac.uk/vol1/fastq/ERR211/004/ERR2117984/ERR2117984.fastq.gz\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "06a50150-622f-4571-a269-78191ad2a0de", + "metadata": {}, + "outputs": [], + "source": [ + "qualsdist = read_fastqc_data(\"/Users/au552345/GenomeDK/fastqsbams/ERR566093_1_fastqc/fastqc_data.txt\")" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "5dc6419b-3e0a-439a-a119-f57a3678a676", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "%%R -i qualsdist\n", + "\n", + "qualsdist %>%\n", + " mutate(counts = counts/sum(counts)) %>%\n", + " ggplot() +\n", + " geom_bar(stat = \"identity\", aes(x = quality, y = counts)) -> UST_plot\n", + " \n", + "UST_plot" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "66a684a3-d7a7-4a20-9912-3377e6b11382", + "metadata": {}, + "outputs": [], + "source": [ + "qualsdist = read_fastqc_data(\"/Users/au552345/GenomeDK/fastqsbams/ERR3351001_fastqc/fastqc_data.txt\")" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "7f0a3506-3255-4ada-90de-4244fbd1b4b0", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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CuFv2tO0kQIAAgUYJCOBG7Q7FECBAgEC3CAjgbtnTtpMAAQIEGiUggBu1OxRDgAABAt0iIIC7ZU/bTgIECBBolIAAbtTuUAwBAgQIdItAf9M2tL+/cSWl3t7e1MS6yuy7qD2GNtff19en/jI7u4Z52tz3o9/09PS0tu9E/W32D/vprL/q97jpqL1xaVc1YhXvSdOxI6qoM6+NqD2GJrrm1XvkuKi/zf7T/SZ0pN+xPm+zfQRYDG3t+8dDAMc2TJd/1eupsu/H+0De0LgAHhkZyauzo+MGBgZSE+sqgxIvgKGhoVbXf+jQodbWP2vWrLRv377W1t/mvh+1z5kzp9X28fpt63tP2E9n36/aaf/+/ZXZT/bHgc+Ay6SYeQgQIECAQMUCArhiUM0RIECAAIEyAgK4jJJ5CBAgQIBAxQICuGJQzREgQIAAgTICAriMknkIECBAgEDFAgK4YlDNESBAgACBMgICuIySeQgQIECAQMUCArhiUM0RIECAAIEyAgK4jJJ5CBAgQIBAxQICuGJQzREgQIAAgTICAriMknkIECBAgEDFAgK4YlDNESBAgACBMgICuIySeQgQIECAQMUCArhiUM0RIECAAIEyAgK4jJJ5CBAgQIBAxQICuGJQzREgQIAAgTICAriMknkIECBAgEDFAgK4YlDNESBAgACBMgICuIySeQgQIECAQMUCArhiUM0RIECAAIEyAgK4jJJ5CBAgQIBAxQICuGJQzREgQIAAgTICAriMknkIECBAgEDFAgK4YlDNESBAgACBMgICuIySeQgQIECAQMUCArhiUM0RIECAAIEyAgK4jJJ5CBAgQIBAxQICuGJQzREgQIAAgTICAriMknkIECBAgEDFAgK4YlDNESBAgACBMgICuIySeQgQIECAQMUCArhiUM0RIECAAIEyAgK4jJJ5CBAgQIBAxQICuGJQzREgQIAAgTICAriMknkIECBAgEDFAgK4YlDNESBAgACBMgICuIySeQgQIECAQMUClQbw/v3709atW9OuXbtyy9y2bVvavn177jQjCRAgQIBANwlUFsCHDh1K69evT/fee2/asGFDeuCBBw5zvPbaa9PXv/719NGPfjTdfvvth03zhAABAgQIdJtAf1UbHEe3ixcvTitXrkxnnnlm2rRpU1q3bt1486961avSL/zCL6R77rknffnLX04XXHBBNm3fvn3phz/8YfZ4aGgo9fX1jS/TlAc9PT2NrKuMz5jn2O8yyzRpnt7e3hQ/ba0/+k7b62+rfbjH0Ob62953prP+qvfzdLzvVxbAO3bsyAI4OvyiRYvSzp074+H4EOH7hS98Id1www0pjobHhoceeihdeuml2dM3velN6aqrrhqb1JjfsSNmz57dmHqOtpCof+HChUe7WKPmHxwcbFQ9ZYsJ+xkzZqQ216/vl93b9cwXByZtHKLv9/f3p+mqv+r3uDlz5lT2vj8yMpK7CysL4PhL5+DBg9lKDhw4kGbOnPmsFb7uda9LL3jBC9Kf//mfp+uvvz6bfsopp2SnpsdmjiBv2hAdaHh4uGlllaon/iqMjnnkH0SlFm7ATFF/BNiePXsaUM3RlzB//vw0Ojra2vrb3PcHBgZS+D/yyCNHv+MasETUH/1/sjfvBpRYWMKCBQvS7t27p63+qt/jnnnmmcre9yf7A7yyz4CXLVuWHnzwwWyHxIVWS5cuHd85cXHWBz/4wez5kiVLUvxlZCBAgAABAt0sUNkR8PLly7MjrbgA69FHH03XXHNN5rp27drsaDdOS7/3ve/NjgZWrVrVzea2nQABAgQIpMoCOCzXrFmT9u7dm50yHLMdO9Ucn+/GtDit4gh4TMdvAgQIEOhWgUoDOBDj87rJhqJpky1jPAECBAgQOB4FKvsM+HjEsU0ECBAgQKAuAQFcl6x2CRAgQIBAgYAALsAxiQABAgQI1CUggOuS1S4BAgQIECgQEMAFOCYRIECAAIG6BARwXbLaJUCAAAECBQICuADHJAIECBAgUJeAAK5LVrsECBAgQKBAQAAX4JhEgAABAgTqEhDAdclqlwABAgQIFAgI4AIckwgQIECAQF0CArguWe0SIECAAIECAQFcgGMSAQIECBCoS0AA1yWrXQIECBAgUCAggAtwTCJAgAABAnUJCOC6ZLVLgAABAgQKBARwAY5JBAgQIECgLgEBXJesdgkQIECAQIGAAC7AMYkAAQIECNQlIIDrktUuAQIECBAoEBDABTgmESBAgACBugQEcF2y2iVAgAABAgUCArgAxyQCBAgQIFCXgACuS1a7BAgQIECgQEAAF+CYRIAAAQIE6hIQwHXJapcAAQIECBQICOACHJMIECBAgEBdAgK4LlntEiBAgACBAgEBXIBjEgECBAgQqEtAANclq10CBAgQIFAgIIALcEwiQIAAAQJ1CRxVAO/fv7+uOrRLgAABAgS6SqAwgHfv3p3e/OY3pyeeeCJt2LAhDQ4OpksvvTQdPHiwq5BsLAECBAgQqFqgMIA3btyYFixYkPr7+9Nf/MVfpPvuuy/19vamLVu2VF2H9ggQIECAQFcJFAbw9u3b0xve8Ib0r//6r+mss85KS5YsSS960YvSU0891VVINpYAAQIECFQt0F/U4Lnnnpv+7M/+LD3zzDPp8ssvT3fccUf6+Mc/nt7xjncULWYaAQIECBAg8BMECgN4xYoV2SnnCODLLrssffGLX0y33XZbGhoa+gnNTn1ynW1PtaoZM2bUus1TravMcj09PSl+muhatv6+vr7sY5Ay8zdtnoGBgcw/PsZp49Dmvh8fl8VPW/t+1B6v3ehDbRzidTtr1qxpq7/q/Twdfb/wXSE+Az7llFPSxRdfnO3/1772temd73xnOv/889MFF1xQS58YHh6upd1jaTR2bBPrKrNN8SKYPXt2q+uPF8KePXvKbG7j5gn/0dHR1tbf5r4fwRV9p62v3ag/+s/IyEjj+nWZgqL+qH266q96P+/du7eyvhMXMOcNuQH87W9/O/vsN65+DsQ5c+Zkyx46dCg9/vjjae3atXltGUeAAAECBAiUFMgN4NNOOy270vnTn/50et7znpfOOeecrLn4a+zEE0/M/ior2b7ZCBAgQIAAgRyB3ACO+U4++eR0xRVXpHvvvTf9y7/8S5p4E47XvOY1adGiRTnNGUWAAAECBAiUEZg0gGPhzZs3pze+8Y3Z571jp6FjfBwRC+CQMBAgQIAAgakJFAbwl770pfShD30ouxvW1Jq3FAECBAgQIJAnUHgjjpe97GXpnnvuyVvOOAIECBAgQOAYBAqPgOfPn58+8pGPpE9+8pPpzDPPHF/Ne9/73nT66aePP/eAAAECBAgQODqBwgCO207eeuutz2rxuc997rPGGUGAAAECBAiUFygM4LiBQN59nydeEV1+VeYkQIAAAQIExgQKA/j+++9P//AP/5DNe+DAgezbkOKuJjHupJNOGmvDbwIECBAgQOAoBQoD+MILL0zxM3GI7wOOW3QZCBAgQIAAgakLFF4FndfsT/3UT6W4VaWBAAECBAgQmLpA4RHwP/3TP6WPfexjWetxH+i4N3TcGesP//APp75GSxIgQIAAAQKpMIBf8pKXpN/6rd8aZ5o5c2Y6++yzU/z3JAMBAgQIECAwdYHCU9DxVYQvf/nL04MPPpg+8YlPpK997WvZV6tNfXWWJECAAAECBEKgMICffvrp9IpXvCJ9//vfT69//evT9u3bU3wRw3R9v6NdRIAAAQIEjleBwgC+5ZZb0tvf/vZ03XXXpVWrVqWbbropxe0pt2zZcrx62C4CBAgQIDAtAoUB3NPT86yj3Tj6jSuhDQQIECBAgMDUBQovwlqxYkV66Utfmr7zne+kn/u5n0tf+cpXsiuhTzvttKmv0ZIECBAgQIBA8WfAcbXzv/3bv6XnP//52ee/b33rW9Ptt9+OjQABAgQIEDhGgcIj4Gh70aJF6corr0zz5s3LjoT7+vqOcZUWJ0CAAAECBAo/A/6f//mf7GsIv/nNb2ZSH/jAB9JrX/taagQIECBAgMAxChQG8Gc+85l09dVXp/POOy9bzY033pidjr777ruPcbUWJ0CAAAEC3S1QGMDxDUg7d+48TGh4eDjNnTv3sHGeECBAgAABAkcnUPgZcFwF/brXvS7FEe8ZZ5yR7rrrrhT3hHYV9NEhm5sAAQIECBwpUHgEvHTp0rR58+Z0wQUXZMH7x3/8x+m22247sg3PCRAgQIAAgaMUKDwCjrbiphtr1qw5ymbNToAAAQIECBQJFB4BFy1oGgECBAgQIDB1AQE8dTtLEiBAgACBKQsI4CnTWZAAAQIECExdQABP3c6SBAgQIEBgygICeMp0FiRAgAABAlMXEMBTt7MkAQIECBCYsoAAnjKdBQkQIECAwNQFBPDU7SxJgAABAgSmLCCAp0xnQQIECBAgMHUBATx1O0sSIECAAIEpCwjgKdNZkAABAgQITF2g0gDev39/2rp1a9q1a1duRd/73vfSD37wg9xpRhIgQIAAgW4S+IlfxlAWI76mcP369dnXFt54443pqquuSqeeeur44tddd106+eST01NPPZUGBwfT6tWrx6d5QIAAAQIEuk2gsgDetm1bWrx4cVq5cmU688wz06ZNm9K6desyz4MHD6ZTTjklXXLJJSmOkuPblcYC+PHHH0+f+tSnsvlOP/30LMCbthMGBgbS3Llzm1ZWqXp6enpS/LS1/t7e3tTX15fidxuH/v7//xJra/1t7vthHj9t7ftj/T5+t3GIumfOnJm9fqej/qr3c5V9P96D84bKAnjHjh1ZAMdKFi1alHbu3Dm+vngRRPjG8NnPfjadc84549MikB9++OHs+ZIlS6ZtZ40XUOJB1N/WF8HYjm9r/WNvom2tP/zb3H/aXnu8vNvad/T9Em/OE2apej9PR9+vLICj2DjSjeHAgQPZXz4TbLKHt956a3rggQfSu971rvFJJ510UtqwYcP48wjypg1DQ0NpeHi4aWWVqic65axZs7JT/6UWaNhMUf+MGTPSnj17GlZZuXLmz5+fRkdHW1t/m/t+HMHET3zs1cYhao/+PzIy0sbyU5z9idftdNVf9X6O121V7/vxsWveUNl5vWXLlqUHH3wwW8f27dvT0qVLD1vfzTffnB577LH07ne/O9sxh030hAABAgQIdJlAZQG8fPnytHDhwuxoNsJ2xYoVGeXatWuz09EbN25M8TlxfC589dVXdxmzzSVAgAABAocLVHYKOpqNi6v27t2bnTIcW83111+fPbzjjjvGRvlNgAABAgS6XqCyI+Axyfi8zkCAAAECBAgUC1QewMWrM5UAAQIECBAIAQGsHxAgQIAAgQ4ICOAOoFslAQIECBAQwPoAAQIECBDogIAA7gC6VRIgQIAAAQGsDxAgQIAAgQ4ICOAOoFslAQIECBAQwPoAAQIECBDogIAA7gC6VRIgQIAAAQGsDxAgQIAAgQ4ICOAOoFslAQIECBAQwPoAAQIECBDogIAA7gC6VRIgQIAAAQGsDxAgQIAAgQ4ICOAOoFslAQIECBAQwPoAAQIECBDogIAA7gC6VRIgQIAAAQGsDxAgQIAAgQ4ICOAOoFslAQIECBAQwPoAAQIECBDogIAA7gC6VRIgQIAAAQGsDxAgQIAAgQ4ICOAOoFslAQIECBAQwPoAAQIECBDogIAA7gC6VRIgQIAAAQGsDxAgQIAAgQ4ICOAOoFslAQIECBAQwPoAAQIECBDogIAA7gC6VRIgQIAAAQGsDxAgQIAAgQ4ICOAOoFslAQIECBAQwPoAAQIECBDogIAA7gC6VRIgQIAAAQGsDxAgQIAAgQ4ICOAOoFslAQIECBAQwPoAAQIECBDogEB/B9ZZuMqhoaHC6Z2YOGPGjNTEuspY9PT0pPhpc/19fX2pv79xXbUMfxoYGMj821p/m/t+b29vip+29v2oPV670YfaOMTrdtasWdNWf9X7eTr6fuPe1YaHhxvX12LHNrGuMlDxIpg9e3ar648Xwp49e8psbuPmCf/R0dHW1t/mvh/BFX2nra/dqD/6z8jISOP6dZmCov6ofbrqr3o/7927t7K+Mzg4mEvmFHQui5EECBAgQKBeAQFcr6/WCRAgQIBAroAAzmUxkgABAgQI1CsggOv11ToBAgQIEMgVEMC5LEYSIECAAIF6BQRwvb5aJ0CAAAECuQICOJfFSAIECBAgUK+AAK7XV+sECBAgQCBXQADnshhJgAABAgTqFRDA9fpqnQABAgQI5AoI4FwWIwkQIECAQL0CArheX60TIECAAIFcAQGcy2IkAQIECBCoV0AA1+urdQIECBAgkCsggHNZjCRAgAABAvUKCOB6fbVOgAABAgRyBQRwLouRBAgQIECgXgEBXK+v1gkQIECAQK6AAM5lMZIAAQIECNQrIIDr9dU6AQIECBDIFRDAuSxGEiBAgACBegUEcL2+WidAgAABArkCAjiXxUgCBAgQIFCvgACu11frBAgQIEAgV0AA57IYSYAAAQIE6hUQwPX6ap0AAQIECOQKCOBcFiMJECBAgEC9AgK4Xl+tEyBAgACBXAEBnMtiJAECBAgQqFdAANfrq3UCBAgQIJArIIBzWYwkQIAAAQL1Cgjgen21ToAAAQIEcgUEcC6LkQQIECBAoF4BAVyvr9YJECBAgECugADOZTGSAAECBAjUKyCA6/XVOgECBAgQyBUQwLksRhIgQIAAgXoFKg3g/fv3p61bt6Zdu3ZNWvV3v/vdSaeZQIAAAQIEukWgsgA+dOhQWr9+fbr33nvThg0b0gMPPHCYYUy/+eab0wc+8IHDxntCgAABAgS6UaCyAN62bVtavHhxWrlyZXrb296WNm3adJjn5z73uTRz5szU09Nz2HhPCBAgQIBANwr0V7XRO3bsyAI42lu0aFHauXPnYU1ffPHF2fPNmzcfNv6hhx5KV1xxRTbuV37lV9Lq1asPm96EJ729vdkfD02oZSo1xB89CxcunMqiHV8mao+fuXPndryWqRTQ19eXBgYGWlt/m/t+9Jvwb3Pfjz43ODg4la7X8WXCfmhoaNrqr3o/z549u7L3/b179+buj8oCOF6oBw8ezFZy4MCB0oUHWpy6jiGC+6mnnsoeN+mf2BF79uxpUkmla4n9Mm/evEa6ltmIqL+/vz9N1oHLtNHJeeLNc9++fWl0dLSTZUx53W3u+2MB0MT3lDI7JOqPn7b2/RNOOCGNjIxMW/1V7+d4zVb1vj9jxozcXV5ZAC9btixt2bIlW8n27dvT0qVLc1d45Mh4gZ9zzjnjo+NIumlDnDpv64sgXsAxtLn+OJJpa/3xR2lcnNjW+tvc9+PMQ1x70lb7ttc/3X2/6v0c9VfV5mQBXNlnwMuXL89O9cQFWHGx1YoVK7I3/rVr12a//UOAAAECBAj8WKCyI+Bocs2aNdlfDBPT/vrrr//x2n706MMf/vBhzz0hQIAAAQLdKFDZEfAY3sTwHRvnNwECBAgQIHC4QOUBfHjznhEgQIAAAQJ5AgI4T8U4AgQIECBQs4AArhlY8wQIECBAIE9AAOepGEeAAAECBGoWEMA1A2ueAAECBAjkCQjgPBXjCBAgQIBAzQICuGZgzRMgQIAAgTwBAZynYhwBAgQIEKhZQADXDKx5AgQIECCQJyCA81SMI0CAAAECNQsI4JqBNU+AAAECBPIEBHCeinEECBAgQKBmAQFcM7DmCRAgQIBAnoAAzlMxjgABAgQI1CwggGsG1jwBAgQIEMgTEMB5KsYRIECAAIGaBQRwzcCaJ0CAAAECeQICOE/FOAIECBAgULOAAK4ZWPMECBAgQCBPQADnqRhHgAABAgRqFhDANQNrngABAgQI5AkI4DwV4wgQIECAQM0CArhmYM0TIECAAIE8AQGcp2IcAQIECBCoWUAA1wyseQIECBAgkCcggPNUjCNAgAABAjULCOCagTVPgAABAgTyBARwnopxBAgQIECgZgEBXDOw5gkQIECAQJ5Af95I4wgQIECAQJHA6tWriyaXmrZx48ZS8x2vMzkCPl73rO0iQIAAgUYLCOBG7x7FESBAgMDxKiCAj9c9a7sIECBAoNECArjRu0dxBAgQIHC8Cgjg43XP2i4CBAgQaLSAAG707lEcAQIECByvAo37b0gDAwONs+7t7U1NrKsMVNQeQ5vr7+vrU3+ZnV3DPG3u+/39/amnp6e1fSfqb7N/me5Y5ftSlW1F7dNh37gAHguMMjtvuuaJF3ET6yqz/WN1j/0us0yT5om6x36aVFfZWsb6Tlv9x+ovu71Nmi9qj6HN9m32L9MXqtw3VbYVtVdpP9YXjzRpXACPjo4eWWPHn8+YMSM1sa4yMHH0GIP6y2hVP8/s2bPTvn37Wuvf5r4fR0SHDh1qrX3UH6/ftr52y7yaqty2KtuK2g8cOFCZ/WRH5z4DLtNLzEOAAAECBCoWEMAVg2qOAAECBAiUERDAZZTMQ4AAAQIEKhYQwBWDao4AAQIECJQREMBllMxDgAABAgQqFhDAFYNqjgABAgQIlBEQwGWUzEOAAAECBCoWEMAVg2qOAAECBAiUERDAZZTMQ4AAAQIEKhYQwBWDao4AAQIECJQREMBllMxDgAABAgQqFhDAFYNqjgABAgQIlBEQwGWUzEOAAAECBCoWEMAVg2qOAAECBAiUERDAZZTMQ4AAAQIEKhYQwBWDao4AAQIECJQR6C8zk3kIECBAoP0Cq1evPuaN2Lhx4zG3oYH/L+AIWE8gQIAAAQIdEBDAHUC3SgIECBAgIID1AQIECBAg0AEBAdwBdKskQIAAAQICWB8gQIAAAQIdEBDAHUC3SgIECBAgIID1AQIECBAg0AEBAdwBdKskQIAAAQJuxKEPECBAoMECbp7R4J1zjKU5Aj5GQIsTIECAAIGpCAjgqahZhgABAgQIHKOAAD5GQIsTIECAAIGpCPgMeCpqliFAgECBQBWf2958880FazDpeBBwBHw87EXbQIAAAQKtExDArdtlCiZAgACB40FAAB8Pe9E2ECBAgEDrBHwG3LpdpmACBOoQqOJzW19WX8eeOX7bdAR8/O5bW0aAAAECDRZwBNzgnaM0AgSKBRy1FvuY2mwBAdzs/aM6AsedgNA87napDZqiQKWnoPfv35+2bt2adu3alVvO/fffn+LHQIAAAQIEul2gsiPgQ4cOpfXr16czzjgj3Xjjjemqq65Kp5566rjvTTfdlPbt25d++MMfpnPPPTf98i//8vg0DwgQaLaAo9Zm7x/VtVOgsgDetm1bWrx4cVq5cmU688wz06ZNm9K6devGVe65557013/91ymOkq+88srxAN67d2966KGHsvnmzZuX+vsrK2l83cf6oKenp5F1jW3XgQMHxh7m/o7psQ1FQ19fXzZ5ZGSkaLZS02bNmpXN9+STT5aav2im5zznOam3tzft2LGjaLZS05YsWZLNd9ddd5Wav2imV77yldnk+GOzaBgYGEgHDx5MRfvo8ssvz5q47LLLipoqPe3jH/94qqqtz3zmM5X1/Spf293QVrwmo+9XMXSDV5XbGObT8b7f86Mj10NV7OA777wzPfLII+nSSy9Njz76aHr/+9+frrnmmqzpxx9/PF177bXpfe97X/b8LW95S7rhhhuyxw8++GBatWpV9njFihVZOGdPGvRP7IiKmDqyVfEijhBo69Bm/7CPvtPW/tNm++jvbe77YR9DW/tOm+3Dvcq+Pzo6mubMmRPNHjZUdrg5ETv+2p85c+b4iuIvuYkBMPEvlec///npq1/96vi8VRzpjDdW0YOhoaE0PDxcUWvT20zYL1y4cNLP5ae3mqNfW9Q/Y8aMtGfPnqNfuAFLzJ8/P8WLr631t7nvx9mH8I8DgzYOUX/0/yrOSnVi+xcsWJB2797d2vqr7PuDg4O5u6Ca8xs/anrZsmUpjmZj2L59e1q6dGn2OP454YQT0tjpyGeeeSbNnj17fJoHBAgQIECgGwUqOwJevnx5dqS1YcOG7BT02OnntWvXpuuvvz47Nf1Hf/RHKU5HxyloAwECBAgQ6GaByj4DHkOMi6rilGHeEBdgxanq+JlscAp6MpmpjR87Bb1z586pNdDhpZyC7uwOqPI03HRviVPQ0y1++Pqcgv6xR5yCjtfSkUNlR8BjDU8WvjF94me/Y/P7TYAAAQIEulFg8kPRbtSwzQQIECBAYJoEBPA0QVsNAQIECBCYKCCAJ2p4TIAAAQIEpklAAE8TtNUQIECAAIGJAgJ4oobHBAgQIEBgmgQE8DRBWw0BAgQIEJgoIIAnanhMgAABAgSmSUAATxO01RAgQIAAgYkCAniihscECBAgQGCaBATwNEFbDQECBAgQmCgggCdqeEyAAAECBKZJQABPE7TVECBAgACBiQICeKKGxwQIECBAYJoEBPA0QVsNAQIECBCYKFD59wFPbHwqj4eHh6eyWK3L9PT0pEOHDtW6jroaf+qpp9I//uM/plWrVtW1itrbbbP/5s2b03Of+9z0Mz/zM7U71bGCNts/+uij6c4770y//uu/XgfNtLTZZv9//ud/Tqeddlpavnz5tFhVvZIq7eNremfOnPmsEiv/PuBnreEoR+R9afFRNmH2CQKPP/542rhxY3rrW986YayH0yXw5S9/OZ133nnp7LPPnq5VWs//CTz44IPplltuSb/927/NpAMCn//859NznvOcdMYZZ3Rg7e1YpVPQ7dhPqiRAgACB40ygcaegjzPfjm/O6Oho+ta3vpVe9rKXdbyWbizg/vvvT/PmzUsnnXRSN25+R7f56aefTt/73vfSS1/60o7W0a0r/+53v5tOPvnktGDBgm4l+InbLYB/IpEZCBAgQIBA9QJOQVdv2ogW4y//H/zgB+O17N+/P23dujXt2rVrfJwH9QjExT/f+c53xhuPsxDf/va3x3/GJ3hQuUD082984xvpscceG29b3x+nqP3Bjh070n/+53+mgwcPZuvS94vJ+97zo6F4FlPbJnDddddlb0DxRhRv/GeeeWb6gz/4gxRX9d18883pZ3/2Z50WqmmnfuELX8iuOo83/bA+//zz0z333JM++tGPpt27d6f//u//dkFWTfZh/nu/93vZhT9/8zd/k12BG6f/9f2awI9o9otf/GK67bbbUpz6/9KXvpRe/epX6/tHGB35tHFXQR9ZoOdHJxB/eZ5yyinpkksuSfGGtGbNmvTKV74yLV68OK1cuTIL402bNqV169YdXcPmLiWwb9++9Pu///tp1qxZ6Wtf+1qKo+H77rsvXXrppenFL35xmj9/fql2zHT0As8880z6nd/5newz3//93/9N//Vf/5X27Nmj7x895ZSWiL591VVXZUe/V1xxRdaGvl9MKYCLfVo3tbe3NwvfKPyzn/1sOuecc1KcFooAjmHRokVp586d2WP/VC9w0UUXZY3GhW979+5NJ554YhbAcUHK7bffnmbPnp3e+c53Vr9iLWYXu8UFVx/84AfT17/+9XTDDTdkfwTp+9PTOc4999y0bdu29Cd/8ifp4osvzlYaAazvT+4vgCe3afWUW2+9NT3wwAPpXe96V/rqV786/pnMgQMHcv9DeKs3tmHFxynnj3zkI9kbUZz2f8c73pEFbzx+97vfnR566KG0ZMmShlV9/JRz5ZVXps997nMpTkO/5CUv0fencdfGDWf+8i//Mv3u7/5u9vGLvl+M7yKsYp9WTo3PHuMilHiz7+/vT8uWLUtxU4IYtm/fnpYuXdrK7WpD0f/xH/+RPvGJT6Rrr702+ywyao4/huKOZDHEUfHcuXOzx/6pViA+X//0pz+dXevwohe9KI2MjOj71RIXtnbTTTdl/TxORcd/P4oLsPT9QrLkvyEV+7Ruapxe/o3f+I3sL/8ofs6cOelP//RPU7w4Hn744ewzyWuuuSa541g9u/bNb35zGhwczI54Yw1XX311djV6HJHFBUHx+Xybb41Yj1p1rb7vfe/LAjgueLv88svT8573PH2/Ot7CluK0f/Tz+AMz3H/zN38z3X333dk4fT+fTgDnuxyXY+PoK+5Japh+gbiXeFwUNzAwMP0r77I1xpHXkffd1fenpxNEP48LESe+z+j7k9sL4MltTCFAgAABArUJ+Ay4NloNEyBAgACByQUE8OQ2phAgQIAAgdoEBHBttBomQIAAAQKTCwjgyW1MIdA1Ai9/+cuzb81av359ev/7359t91/91V+5d3jX9AAb2gkBN+LohLp1EmioQNylK+6mFkME8K/+6q82tFJlEWi/gCPg9u9DW9DFAvF/uk877bT0ile8Irvvd3wZRPwf2LPPPntc5VOf+lQa+86VuEnL6aefnpYvX57i1oHxfcUTh/j/4p/85CfThz70ofT9738//dqv/Vq68cYbszuqjc0Xt3iMux0ZCBA4NgEBfGx+libQMYH4xpkI17vuuiv75qVbbrkl+yaa+H+XEZ5jw/DwcHZntPiygrgpy5133pndGS1OO0fgThwef/zx9OSTT6a4nWPcQe3v//7vsxD+2Mc+lv3/zpg3lnnNa14zcTGPCRCYgoBT0FNAswiBJgh85StfSRdeeGF2h62409B5551XWFbcoejf//3f0+bNm7PvzI07F/38z/984TIxMW4rGEfUEfinnnpqdoo6vtLSQIDAsQk4Aj42P0sT6JhA3E504h2f4puXxob40o2xIb6SL4a4P/gZZ5yRPv/5z6cXvvCF6fWvf32Ko+UyQ9xWMO7rG0fZl112WZlFzEOAwE8QEMA/AchkAk0ViK98izCNW/9FyN5xxx1ZqfGVh/FFBPFdxDHEkXIEbXxLU3wd5Yc//OH0xje+MbvqOW6POdkQtxOMtmN4wxvekLZs2ZLiu6Tf9KY3TbaI8QQIHIWAU9BHgWVWAk0SeMELXpCdGo7TwXEkvGDBgqy8uIp53bp16ayzzsq+BzqOdmP4pV/6pewevRdccEGKI+T4xqD4vtbJhle96lXpF3/xF7MAjy+ROP/887MvljjppJMmW8R4AgSOQsC9oI8Cy6wEmigQF1nFUe/q1auzC6YuueSSrMwYH8E88cb4MeGJJ55IJ5xwwvh/NyrapjiyjrZjeMtb3pKdto4row0ECBy7gFPQx26oBQIdFYjPguN7n48cYvyR4RvzxPe1jv1f3yOXOfJ5hG98xWWc7v7GN76RLrrooiNn8ZwAgSkKOAKeIpzFCDRNIL6QPq6Gjp8qh/icOD5Hjv+25Hukq5TVVrcLCOBu7wG2nwABAgQ6IuAUdEfYrZQAAQIEul1AAHd7D7D9BAgQINARAQHcEXYrJUCAAIFuF/h/JNA+PhJvgrUAAAAASUVORK5CYII=\n" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "%%R -i qualsdist\n", + "\n", + "qualsdist %>%\n", + " mutate(counts = counts/sum(counts)) %>%\n", + " ggplot() +\n", + " geom_bar(stat = \"identity\", aes(x = quality, y = counts)) -> YAN_plot\n", + " \n", + "YAN_plot" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "9be30ebe-a742-41b9-a403-44b03539da13", + "metadata": {}, + "outputs": [], + "source": [ + "qualsdist = read_fastqc_data(\"/Users/au552345/GenomeDK/fastqsbams/ERR2117984_fastqc/fastqc_data.txt\")" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "96c7d105-26e4-491c-a1f4-643cffd9a283", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "%%R -i qualsdist\n", + "\n", + "qualsdist %>%\n", + " mutate(counts = counts/sum(counts)) %>%\n", + " ggplot() +\n", + " geom_bar(stat = \"identity\", aes(x = quality, y = counts)) -> SUN_plot\n", + " \n", + "SUN_plot" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "715ad34e-6c7e-4008-a5a8-9d149dd85ff8", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "%%R\n", + "\n", + "plot_grid(HGDP_plot + xlim(c(0, 65)) + ylim(0, 0.9) + ggtitle(\"HGDP (35x)\"), \n", + " UST_plot + xlim(c(0, 65)) + ylim(0, 0.9) + ggtitle(\"Ust'Ishim (40,7x)\"),\n", + " YAN_plot + xlim(c(0, 65)) + ylim(0, 0.9) + ggtitle(\"Yana Young (2,03x)\"),\n", + " SUN_plot + xlim(c(0, 65)) + ylim(0, 0.9) + ggtitle(\"Sungir I (1,16x)\"))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "624c5ef3-ce74-46bb-96a5-99fe3b9db63a", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "dc3c5078-e5f7-4ae5-a473-f793bfd546d2", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "536c5135-87ba-4fa8-8bea-7dd6f1157f62", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8bd53327-4ff2-44d0-b60c-f2419b54a0ff", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "6d368136-b61a-492a-a9fd-a1b999614477", + "metadata": {}, + "outputs": [], + "source": [ + "seed = 1234\n", + "rng = np.random.default_rng(seed)\n", + "gm = np.array([[0, 0, 1, 0], \n", + " [1, 1, 0, 1]])\n", + "ref = np.array([\"A\", \"C\"])\n", + "alt = np.array([\"C\", \"T\"])" + ] + }, + { + "cell_type": "code", + "execution_count": 68, + "id": "82fd66d8-c5fe-47f0-801f-f435e99e28dd", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "DPh\n", + "[15 20 45 65]\n", + "DP\n", + "[[14 31 44 75]\n", + " [22 19 30 68]]\n" + ] + } + ], + "source": [ + "def depth_per_haplotype(rng, mean_depth, std_depth, n_hap):\n", + " if isinstance(mean_depth, np.ndarray):\n", + " return mean_depth\n", + " else:\n", + " dp = np.full((n_hap, ), 0.0)\n", + " while (dp <= 0).sum():\n", + " n = (dp <= 0).sum()\n", + " dp[dp <= 0] = rng.normal(loc = mean_depth, scale = std_depth, size=n)\n", + " return dp\n", + "\n", + "mean_depth = 15\n", + "std_depth = 2\n", + "DPh = np.array([15, 20, 45, 65])\n", + "DP = rng.poisson(DPh, size=gm.shape)\n", + "print(\"DPh\")\n", + "print(DPh)\n", + "print(\"DP\")\n", + "print(DP)" + ] + }, + { + "cell_type": "code", + "execution_count": 115, + "id": "d535f4c8-850e-4739-8e69-63d142235638", + "metadata": {}, + "outputs": [], + "source": [ + "e = np.array([0.05, 0.05, 0.01, 0.01])\n", + "#e = 0.05\n", + "err = np.array([[1-e, e/3, e/3, e/3], [e/3, 1-e, e/3, e/3], [e/3, e/3, 1-e, e/3], [e/3, e/3, e/3, 1-e]])" + ] + }, + { + "cell_type": "code", + "execution_count": 116, + "id": "062b43b0-8a9e-441d-ba67-546c6db25f24", + "metadata": {}, + "outputs": [], + "source": [ + "a = 0.0\n" + ] + }, + { + "cell_type": "code", + "execution_count": 117, + "id": "34ad2914-3441-40a9-add1-8d7903a71470", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(4, 4, 4)\n" + ] + }, + { + "data": { + "text/plain": [ + "array([[[0.95 , 0.01666667, 0.01666667, 0.01666667],\n", + " [0.01666667, 0.95 , 0.01666667, 0.01666667],\n", + " [0.01666667, 0.01666667, 0.95 , 0.01666667],\n", + " [0.01666667, 0.01666667, 0.01666667, 0.95 ]],\n", + "\n", + " [[0.95 , 0.01666667, 0.01666667, 0.01666667],\n", + " [0.01666667, 0.95 , 0.01666667, 0.01666667],\n", + " [0.01666667, 0.01666667, 0.95 , 0.01666667],\n", + " [0.01666667, 0.01666667, 0.01666667, 0.95 ]],\n", + "\n", + " [[0.99 , 0.00333333, 0.00333333, 0.00333333],\n", + " [0.00333333, 0.99 , 0.00333333, 0.00333333],\n", + " [0.00333333, 0.00333333, 0.99 , 0.00333333],\n", + " [0.00333333, 0.00333333, 0.00333333, 0.99 ]],\n", + "\n", + " [[0.99 , 0.00333333, 0.00333333, 0.00333333],\n", + " [0.00333333, 0.99 , 0.00333333, 0.00333333],\n", + " [0.00333333, 0.00333333, 0.99 , 0.00333333],\n", + " [0.00333333, 0.00333333, 0.00333333, 0.99 ]]])" + ] + }, + "execution_count": 117, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "if type(e) != type(float):\n", + " err = err.transpose(2, 0, 1)\n", + "print(err.shape)\n", + "err" + ] + }, + { + "cell_type": "code", + "execution_count": 118, + "id": "c212e895-4ac5-4f6d-a565-8972c6c37227", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[0 0 1 0]\n", + " [3 3 1 3]]\n", + "[0 0 1 0 3 3 1 3]\n" + ] + } + ], + "source": [ + "def refalt_int_encoding(gm, ref, alt):\n", + " refalt_str = np.array([ref, alt])\n", + " refalt_int = np.zeros(refalt_str.shape, dtype=int)\n", + " refalt_int[refalt_str == \"C\"] = 1\n", + " refalt_int[refalt_str == \"G\"] = 2\n", + " refalt_int[refalt_str == \"T\"] = 3\n", + " return refalt_int[gm.reshape(-1), np.repeat(np.arange(gm.shape[0]), gm.shape[1])].reshape(gm.shape)\n", + "\n", + "\n", + "gmbp = refalt_int_encoding(gm, ref, alt)\n", + "print(gmbp)\n", + "print(gmbp.reshape(-1))" + ] + }, + { + "cell_type": "code", + "execution_count": 119, + "id": "68f74b24-6bcd-4fbe-8c8d-9b73cb5053df", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([0, 1, 2, 3, 0, 1, 2, 3])" + ] + }, + "execution_count": 119, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.tile(np.arange(gmbp.shape[1]), gmbp.shape[0])" + ] + }, + { + "cell_type": "code", + "execution_count": 120, + "id": "8908d1d6-5580-45c5-a09e-dd0fe18cef60", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([0, 0, 1, 0, 3, 3, 1, 3])" + ] + }, + "execution_count": 120, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "gmbp.reshape(-1)" + ] + }, + { + "cell_type": "code", + "execution_count": 121, + "id": "77e33ce5-231c-41c5-88bd-78c9eb2f79e5", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([0, 0, 1, 0, 3, 3, 1, 3])" + ] + }, + "execution_count": 121, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "gmbp.reshape(-1)" + ] + }, + { + "cell_type": "code", + "execution_count": 122, + "id": "709add04-88e6-40c4-a488-5a9255301038", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "False" + ] + }, + "execution_count": 122, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "type(e) == float" + ] + }, + { + "cell_type": "code", + "execution_count": 124, + "id": "43b01eca-c631-4ee1-870b-a84c40028420", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[14, 31, 44, 75],\n", + " [22, 19, 30, 68]])" + ] + }, + "execution_count": 124, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "DP" + ] + }, + { + "cell_type": "code", + "execution_count": 123, + "id": "bd7bba76-6c81-4192-924f-a8e7935e3b2b", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[[12 0 1 1]\n", + " [31 0 0 0]\n", + " [ 0 44 0 0]\n", + " [75 0 0 0]]\n", + "\n", + " [[ 0 2 0 20]\n", + " [ 0 0 1 18]\n", + " [ 0 30 0 0]\n", + " [ 1 0 0 67]]]\n" + ] + } + ], + "source": [ + "if type(e) == float:\n", + " arc = rng.multinomial(DP, err[gmbp])\n", + "else:\n", + " arc = rng.multinomial(DP, err[np.tile(np.arange(gmbp.shape[1]), gmbp.shape[0]), gmbp.reshape(-1)].reshape(gmbp.shape[0], gmbp.shape[1], 4))\n", + "print(arc)" + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "id": "a8ddf01c-9b85-41c9-b066-8077358e4bab", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([0.95, 0.95, 0.99, 0.99, 0.95, 0.95, 0.99, 0.99])" + ] + }, + "execution_count": 57, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "err[[0, 0, 0, 0, 1, 1, 1, 1], [0, 0, 0, 0, 1, 1, 1, 1], [0, 1, 2, 3, 0, 1, 2, 3]]" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "6c2e7d20-ef95-4173-84af-c881d5f92384", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[0, 0, 1, 0],\n", + " [3, 3, 1, 3]])" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "gmbp" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "4eb67402-4483-4fbf-b830-41b266595892", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([['A', 'C'],\n", + " ['C', 'A']], dtype='= 0.\n", + " If floats are provided, the decimal values will be truncated (e.g., 1.8 -> 1). The values must be sorted and the \n", + " order of these values must be the same as the first dimetion of `gm`.\n", + " start : `int` or `float`\n", + " Genomic start coordinate of the range for which monomorphic sites will be incorporated in the original\n", + " `gm` matrix. The value must be >= 0 <= min(pos). If floats are provided, the decimal values will be \n", + " truncated (e.g., 1.8 -> 1).\n", + " end : `int`\n", + " Genomic end coordinate of the range for which monomorphic sites will be incorporated in the original\n", + " `gm` matrix. The value must be >= max(pos). If floats are provided, the decimal values will be \n", + " truncated (e.g., 1.8 -> 1).\n", + " \n", + " Returns \n", + " -------\n", + " gm2 : `numpy.ndarray`\n", + " Genotype matrix with size (end-start, haplotypic samples) in which 0 denotes reference allele\n", + " and 1 denotes alternative allele.\n", + " '''\n", + " assert check_gm(gm) and check_pos(gm, pos) and check_start(pos, start) and check_end(pos, end)\n", + " gm2 = np.zeros((int(end)-int(start), gm.shape[1]))\n", + " gm2[pos.astype(int)] = gm\n", + " return gm2\n", + "\n", + "def refalt(ref, alt, n_sit):\n", + " if ref is None and alt is None:\n", + " ref = np.full(n_sit, \"A\")\n", + " alt = np.full(n_sit, \"C\")\n", + " return ref, alt\n", + "\n", + "def depth_per_haplotype(rng, mean_depth, std_depth, n_hap):\n", + " if isinstance(mean_depth, np.ndarray):\n", " return mean_depth\n", " else:\n", " dp = np.full((n_hap, ), 0.0)\n", @@ -4396,472 +5642,1181 @@ " dp[dp <= 0] = rng.normal(loc = mean_depth, scale = std_depth, size=n)\n", " return dp\n", "\n", - "gm = np.array([[0, 0, 1, 0], [1, 1, 0, 1]])\n", - "mean_depth = 15\n", - "e = 0.05\n", - "ploidy = 2\n", - "seed = 2\n", - "std_depth = 2\n", + "def refalt_int_encoding(gm, ref, alt):\n", + " refalt_str = np.array([ref, alt])\n", + " refalt_int = np.zeros(refalt_str.shape, dtype=int)\n", + " refalt_int[refalt_str == \"C\"] = 1\n", + " refalt_int[refalt_str == \"G\"] = 2\n", + " refalt_int[refalt_str == \"T\"] = 3\n", + " return refalt_int[gm.reshape(-1), np.repeat(np.arange(gm.shape[0]), gm.shape[1])].reshape(gm.shape)\n", + "\n", + "def linked_depth(rng, DPh, read_length, sites_n):\n", + " '''\n", + " Simulates reads in a contiguous genomic region to compute the depth per position.\n", + " \n", + " Parameters\n", + " ----------\n", + " rng : `numpy.random._generator.Generator` \n", + " random number generation numpy object\n", + " DPh : `numpy.ndarray`\n", + " Numpy array with the depth per haplotype\n", + " read_length : `int`\n", + " Read length in base pair units\n", + " sites_n : `int`\n", + " number of sites that depth has to be simulated for\n", + " \n", + " Returns \n", + " -------\n", + " DP : `numpy.ndarray`\n", + " Depth per site per haplotype\n", + " '''\n", + " DP = []\n", + " read_n = ((DPh*sites_n)/read_length).astype(\"int\")\n", + " for r in read_n:\n", + " dp = np.zeros((sites_n,), dtype=int)\n", + " for p in rng.integers(low=0, high=sites_n-read_length+1, size=r):\n", + " dp[p:p+read_length] += 1\n", + " DP.append(dp.tolist())\n", + " return np.array(DP).T\n", + "\n", + "def independent_depth(rng, DPh, size):\n", + " '''\n", + " Returns depth per position per haplotype (size[0], size[1]) drawn from the \"rng\" from a Poisson \n", + " distribution with a lambda value \"DPh\" per haplotype\n", + " '''\n", + " return rng.poisson(DPh, size=size)\n", + "\n", + "def depth_per_site_per_haplotype(rng, depth_type, DPh, gm_shape, read_length): \n", + " if depth_type == \"independent\":\n", + " DP = independent_depth(rng, DPh, gm_shape)\n", + " elif depth_type == \"linked\":\n", + " assert check_positive_nonzero_integer(read_length, \"read_length\")\n", + " DP = linked_depth(rng, DPh, read_length, gm_shape[0])\n", + " assert DP.shape == gm_shape\n", + " return DP\n", + "\n", + "def simulate_arc(e, err, rng, DP, gmbp):\n", + " if isinstance(e, np.ndarray):\n", + " err = err.transpose(2, 0, 1)\n", + " return rng.multinomial(DP, err[np.tile(np.arange(gmbp.shape[1]), gmbp.shape[0]), gmbp.reshape(-1)].reshape(gmbp.shape[0], gmbp.shape[1], 4))\n", + " else:\n", + " return rng.multinomial(DP, err[gmbp])\n", + "\n", + "def sim_allelereadcounts(gm, mean_depth, e, ploidy, seed = None, std_depth = None, ref = None, alt = None, read_length = None, depth_type = \"independent\"):\n", + " '''\n", + " Simulates allele read counts from a genotype matrix. \n", + " \n", + " Parameters\n", + " ----------\n", + " gm : `numpy.ndarray` \n", + " Genotype matrix with size (sites, haplotypic samples) in which 0 denotes reference allele\n", + " and 1 denotes alternative allele.\n", + " \n", + " mean_depth : `int` or `float` or `numpy.ndarray`\n", + " Read depth of the each haplotypic sample in `gm`. If a `int` or `float` value is inputed, the function\n", + " will sample random values from a normal distribution with mean = `mean_depth` and std = `std_depth`.\n", + " If a `numpy.ndarray` is inputed, there must be an error value per haplotype (i.e., the array must have size \n", + " (haplotypic samples, )) and the order must be the same as the second dimention of `gm`.\n", + " \n", + " std_depth : `int` or `float`\n", + " The standard deviation parameter of the normal distribution from which read depth values are randomly\n", + " sampled for each haplotypic sample in `gm`. This value only needs to be provided if the `mean_depth`\n", + " inputed is an `int` or a `float`.\n", + " \n", + " e : `int` or `float` or `numpy.ndarray`\n", + " Sequencing error probability per base pair per site. The values must be between 0 and 1. If a `int` or `float` \n", + " value is inputed, the function will use the same error probablity value for each haplotype and each site. \n", + " If a `numpy.ndarray` is inputed, there must be an error value per haplotype (i.e., the array must have size \n", + " (haplotypic samples, )) and the order must be the same as the second dimention of `gm`.\n", + " \n", + " ploidy : `int` \n", + " Number of haplotypic chromosomes per individual.\n", + " \n", + " ref : `numpy.ndarray`, optional\n", + " Reference alleles list per site. The size of the array must be (sites, ) and the order has to \n", + " coincide with the first dimetion of `gm`. The values within the list must be strings {\"A\", \"C\", \n", + " \"G\", \"T\"}. If an `alt` list is inputed, a `ref` list must also be inputed. If no `ref` and `alt`\n", + " are inputed, the `ref` allele is assumed to be \"A\" for all sites.\n", + " \n", + " alt : `numpy.ndarray`, optional\n", + " Alternative alleles list per site. The size of the array must be (sites, ) and the order has to \n", + " coincide with the first dimetion of `gm`. The values within the list must be strings {\"A\", \"C\", \n", + " \"G\", \"T\"}. If a `ref` list is inputed, an `alt` list must also be inputed. If no `ref` and `alt`\n", + " are inputed, the `alt` allele is assumed to be \"C\" for all sites.\n", + "\n", + " seed : `int`, optional\n", + " Starting point in generating random numbers.\n", + " \n", + " Returns \n", + " -------\n", + " arc : `numpy.ndarray`\n", + " Allele read counts per site per individual. The dimentions of the array are (sites, individuals, alleles). \n", + " The third dimention of the array has size = 4, which corresponds to the four possible alleles: 0 = \"A\", \n", + " 1 = \"C\", 2 = \"G\" and 3 = \"T\".\n", + " \n", + " Notes\n", + " -----\n", + " - The read depth indicated in `mean_depth` is per haplotypic sample, i.e. if the user intends to simulate a \n", + " depth of 30 reads per site per individual, and individuals are diploid (`ploidy` = 2), the `mean_depth` \n", + " must be 15. \n", + " - If monomorphic sites are included, the `alt` values corresponding to those sites are not taken into account, \n", + " but they must be still indicated.\n", + " '''\n", + " #Checks\n", + " assert check_gm(gm)\n", + " ref, alt = refalt(ref, alt, gm.shape[0])\n", + " assert check_mean_depth(gm, mean_depth) and check_std_depth(mean_depth, std_depth) and check_e(gm, e) and check_ploidy(ploidy) and check_gm_ploidy(gm, ploidy) and check_ref_alt(gm, ref, alt) and check_depth_type(depth_type)\n", + " #Variables\n", + " err = np.array([[1-e, e/3, e/3, e/3], [e/3, 1-e, e/3, e/3], [e/3, e/3, 1-e, e/3], [e/3, e/3, e/3, 1-e]])\n", + " rng = np.random.default_rng(seed)\n", + " #1. Depths (DP) per haplotype (h)\n", + " DPh = depth_per_haplotype(rng, mean_depth, std_depth, gm.shape[1])\n", + " print(\"DPh\")\n", + " print(DPh)\n", + " #2. Sample depths (DP) per site per haplotype\n", + " DP = depth_per_site_per_haplotype(rng, depth_type, DPh, gm.shape, read_length)\n", + " print(\"DP\")\n", + " print(DP)\n", + " #3. Sample correct and error reads per SNP per haplotype (Rh)\n", + " #3.1. Convert anc = 0/der = 1 encoded gm into \"A\" = 0, \"C\" = 1, \"G\" = 3, \"T\" = 4 basepair (bp) encoded gm \n", + " gmbp = refalt_int_encoding(gm, ref, alt)\n", + " #3.2. Simulate allele read counts (ARC) per haplotype (h) per site (s)\n", + " arc = simulate_arc(e, err, rng, DP, gmbp)\n", + " #4. Add n haplotype read allele counts (n = ploidy) to obtain read allele counts per genotype\n", + " return arc.reshape(arc.shape[0], arc.shape[1]//ploidy, ploidy, arc.shape[2]).sum(axis = 2)\n", + "\n", + "def get_GTxploidy(ploidy):\n", + " return np.array([list(x) for x in combinations_with_replacement([0, 1, 2, 3], ploidy)])\n", + "\n", + "def allelereadcounts_to_GL(arc, e, ploidy):\n", + " '''\n", + " Computes genotype likelihoods from allele read counts per site per individual. \n", + " \n", + " Parameters\n", + " ----------\n", + " arc : `numpy.ndarray`\n", + " Allele read counts per site per individual. The dimentions of the array are (sites, individuals, alleles). \n", + " The third dimention of the array has size = 4, which corresponds to the four possible alleles: 0 = \"A\", \n", + " 1 = \"C\", 2 = \"G\" and 3 = \"T\".\n", + " \n", + " e : `float` \n", + " Sequencing error probability per base pair per site. The value must be between 0 and 1.\n", + "\n", + " ploidy : `int` \n", + " Number of haplotypic chromosomes per individual. \n", + "\n", + " Returns \n", + " -------\n", "\n", - "err = np.array([[1-e, e/3, e/3, e/3], [e/3, 1-e, e/3, e/3], [e/3, e/3, 1-e, e/3], [e/3, e/3, e/3, 1-e]])\n", - "rng = np.random.default_rng(seed)\n", - "#1. Depths (DP) per haplotype (h)\n", - "DPh = depth_per_haplotype(rng, mean_depth, std_depth, gm.shape[1])\n", - "print(DPh)" + " GL : `numpy.ndarray`\n", + " Normalized genotype likelihoods per site per individual. The dimentions of the array are (sites, individuals, genotypes). \n", + " The third dimention of the array corresponds to the combinations with replacement of all 4 possible alleles \n", + " {\"A\", \"C\", \"G\", \"T\"} (i.e., for a diploid, there are 10 possible genotypes and the combination order is \"AA\", \"AC\",\n", + " \"AG\", \"AT\", \"CC\", \"CG\", ..., \"TT\"). \n", + "\n", + " References\n", + " ----------\n", + " 1) McKenna A, Hanna M, Banks E, Sivachenko A, Cibulskis K, Kernytsky A, Garimella K, Altshuler D, Gabriel S, Daly M, DePristo MA (2010). The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data. Genome Res. 20:1297-303.\n", + " 2) Thorfinn Sand Korneliussen, Anders Albrechtsen, Rasmus Nielsen. ANGSD: Analysis of Next Generation Sequencing Data. BMC Bioinform. 2014 Nov;15,356.\n", + " '''\n", + " #assert check_arc(arc) and check_e(e) and check_ploidy(ploidy)\n", + " \n", + " GTxploidy = get_GTxploidy(ploidy)\n", + " AFxGTxploidy = np.array([(GTxploidy == 0).sum(axis = 1), (GTxploidy == 1).sum(axis = 1), (GTxploidy == 2).sum(axis = 1), (GTxploidy == 3).sum(axis = 1)])/ploidy\n", + " \n", + " GL = np.multiply(-np.log(AFxGTxploidy*(1-e)+(1-AFxGTxploidy)*(e/3)), arc.reshape(arc.shape[0], arc.shape[1], arc.shape[2], 1)).sum(axis = 2)\n", + " return GL-GL.min(axis = 2).reshape(GL.shape[0], GL.shape[1], 1)\n", + " \n", + "def get_pGTxMm(ploidy):\n", + " GTxploidy = np.array([list(x) for x in combinations_with_replacement([0, 1, 2, 3], ploidy)])\n", + " Mmxploidy = np.array([list(x) for x in combinations([0, 1, 2, 3], 2)])\n", + " pGTxMm = []\n", + " #For every genotype (GT)\n", + " for i in range(GTxploidy.shape[0]):\n", + " pGTxMm_tmp = []\n", + " #For every combination of major (M) and minor (m) alleles (M and m can't be the same allele and there can be only two)\n", + " for j in range(Mmxploidy.shape[0]):\n", + " #All alleles in GT are either M or m\n", + " all_GT_in_Mm = (np.isin(GTxploidy[i], Mmxploidy[j]).sum() == ploidy)*1\n", + " #Probability of the genotype given M and m only possible alleles\n", + " p_GT = binom.pmf((GTxploidy[i] == Mmxploidy[j, 0]).sum(), ploidy, 0.5)\n", + " pGTxMm_tmp.append( p_GT * all_GT_in_Mm )\n", + " pGTxMm.append(np.array(pGTxMm_tmp))\n", + " return np.array(pGTxMm)\n", + "\n", + "def GL_to_Mm(GL, ploidy):\n", + " '''\n", + " Computes maximum (M) and minimum (m) frequency alleles in the population from genotype likelihoods. \n", + " \n", + " Parameters\n", + " ----------\n", + " GL : `numpy.ndarray`\n", + " Normalized genotype likelihoods per site per individual. The dimentions of the array is (sites, individuals, genotypes). \n", + " The third dimention of the array corresponds to the combinations with replacement of all 4 possible alleles \n", + " {\"A\", \"C\", \"G\", \"T\"} (i.e., for a diploid, there are 10 possible genotypes and the combination order is \"AA\", \"AC\",\n", + " \"AG\", \"AT\", \"CC\", \"CG\", ..., \"TT\"). \n", + "\n", + " ploidy : `int` \n", + " Number of haplotypic chromosomes per individual. \n", + "\n", + " Returns \n", + " -------\n", + " `numpy.ndarray`\n", + " Maximum and minimum alleles per site. The dimentions of the array is (sites, ) and the values per site is an integer \n", + " encoding the pair of M and m: 0 = \"AC\", 1 = \"AG\", 2 = \"AT\", 3 = \"CG\", 4 = \"CT\", 5 = \"GT\".\n", + " \n", + " References\n", + " ----------\n", + " 1) Line Skotte, Thorfinn Sand Korneliussen, Anders Albrechtsen. Association testing for next-generation sequencing data using score statistics. Genet Epidemiol. 2012 Jul;36(5):430-7.\n", + " 2) Thorfinn Sand Korneliussen, Anders Albrechtsen, Rasmus Nielsen. ANGSD: Analysis of Next Generation Sequencing Data. BMC Bioinform. 2014 Nov;15,356.\n", + " '''\n", + " #TO DO: when there are too many individuals, the numeric operation is not sable.\n", + " assert check_ploidy(ploidy) and check_GL(GL, ploidy)\n", + " pGTxMm = get_pGTxMm(ploidy)\n", + " return np.argmin((GL.reshape(GL.shape[0], GL.shape[1], GL.shape[2], 1) * pGTxMm.reshape(1, 1, pGTxMm.shape[0], pGTxMm.shape[1])).sum(axis = 2).prod(axis = 1), axis = 1)\n", + "\n", + "def allelereadcounts_to_pileup(arc, output):\n", + " '''\n", + " Writes an allele read counts in a file in pileup format.\n", + "\n", + " Parameters\n", + " ----------\n", + " arc : `numpy.ndarray`\n", + " Allele read counts per site per individual. The dimentions of the array are (sites, individuals, alleles). \n", + " The third dimention of the array has size = 4, which corresponds to the four possible alleles: 0 = \"A\", \n", + " 1 = \"C\", 2 = \"G\" and 3 = \"T\".\n", + " \n", + " output : `str`\n", + " Output file name.\n", + "\n", + " Returns \n", + " -------\n", + " None\n", + " '''\n", + " if not (isinstance(arc, np.ndarray) and len(arc.shape) == 3 and arc.shape[2] == 4):\n", + " raise TypeError('Incorrect `arc` format: it has to be a numpy array with dimentions (sites, individuals, alleles) and the third dimention must be of size = 4')\n", + " if not (isinstance(output, str)):\n", + " raise TypeError('Incorrect `output` format: it has to be a string with the path where the output is written')\n", + " with open(output, \"w\") as out:\n", + " for i in range(arc.shape[0]):\n", + " line = \"1\\t\"+str(i+1)+\"\\tN\"\n", + " for j in range(arc.shape[1]):\n", + " nreads = arc[i, j, :].sum()\n", + " line = line+\"\\t\"+str(nreads)+\"\\t\"\n", + " if nreads:\n", + " for c, b in zip(arc[i, j, :], [\"A\", \"C\", \"G\", \"T\"]):\n", + " line = line+c*b\n", + " line = line+\"\\t\"+\".\"*nreads\n", + " else:\n", + " line = line+\"\\t*\\t*\"\n", + " out.write(line+\"\\n\")\n", + "\n", + "# Functions to check input formatting\n", + "def check_gm(gm):\n", + " if not (isinstance(gm, np.ndarray) and len(gm.shape) == 2 and ((gm == 0)+(gm == 1)).sum() == gm.size):\n", + " raise TypeError('Incorrect gm format: it has to be a numpy array with dimentions (sites, haplotypic samples) with integer values 1 and 0')\n", + " return True\n", + "\n", + "def check_mean_depth(gm, mean_depth):\n", + " if not ((isinstance(mean_depth, np.ndarray) and len(mean_depth.shape) == 1 and mean_depth.shape[0] == gm.shape[1] and (mean_depth > 0).sum() == mean_depth.size) or (isinstance(mean_depth, (int, float)) and mean_depth > 0.0)):\n", + " raise TypeError('Incorrect mean_depth format: it has to be either i) numpy.array with dimentions (haplotypic samples, ) with values > 0 or ii) integer or float value > 0')\n", + " return True\n", + "\n", + "def check_std_depth(mean_depth, std_depth):\n", + " if not ((isinstance(mean_depth, np.ndarray)) or (isinstance(std_depth, (int, float)) and std_depth >= 0.0)):\n", + " raise TypeError('Incorrect std_depth format: it has to be an integer or float value > 0 if mean_depth is a integer or float value and not a numpy array')\n", + " return True\n", + "\n", + "def check_e(gm, e):\n", + " if not ((isinstance(e, np.ndarray) and len(e.shape) == 1 and e.shape[0] == gm.shape[1] and ((e >= 0)*(e <= 1)).sum() == e.size) or (isinstance(e, (int, float)) and e >= 0.0 and e <= 1.0)):\n", + " raise TypeError('Incorrect e format: it has to be either i) numpy.array with dimentions (haplotypic samples, ) with values 0 <= e <= 1 or ii) integer or float value 0 <= e <= 1')\n", + " return True\n", + "\n", + "def check_ploidy(ploidy):\n", + " if not (isinstance(ploidy, int) and ploidy > 0) :\n", + " raise TypeError('Incorrect ploidy format: it has to be an integer value > 0')\n", + " return True\n", + "\n", + "def check_gm_ploidy(gm, ploidy):\n", + " if not (gm.shape[1]%ploidy == 0) :\n", + " raise TypeError('Incorrect ploidy and/or gm format: the second dimention of gm (haplotypic samples) must be divisible by ploidy')\n", + " return True\n", + "\n", + "def check_depth_type(depth_type):\n", + " if not isinstance(depth_type, str) and depth_type not in [\"independent\", \"linked\"]:\n", + " raise TypeError('Incorrect depth_type format: it has to be a string, either \"independent\" or \"linked\"')\n", + " return True\n", + "\n", + "def check_positive_nonzero_integer(read_length, name):\n", + " if not isinstance(read_length, int) and read_length <= 0:\n", + " raise TypeError('Incorrect {} format: it has to be a integer value > 0'.format(name))\n", + " return True\n", + "\n", + "def check_ref_alt(gm, ref, alt):\n", + " if not (isinstance(ref, np.ndarray) and isinstance(alt, np.ndarray) and len(ref.shape) == 1 and len(alt.shape) == 1 and ref.shape == alt.shape and ref.size == gm.shape[0] and\n", + " ((ref == \"A\") + (ref == \"C\") + (ref == \"G\") + (ref == \"T\")).sum() == ref.size and ((alt == \"A\") + (alt == \"C\") + (alt == \"G\") + (alt == \"T\")).sum() == alt.size):\n", + " raise TypeError('Incorrect ref and/or alt format: they both have to be a numpy array with dimentions (sites, ) with string \"A\", \"C\", \"G\", \"T\" values')\n", + " return True\n", + "\n", + "def check_pos(gm, pos):\n", + " if not (isinstance(pos, np.ndarray) and len(pos.shape) == 1 and (pos >= 0).sum() == pos.size and pos.shape[0] == gm.shape[0] and (np.issubdtype((pos).dtype, np.floating) or np.issubdtype((pos).dtype, np.integer)) and (pos[:-1] >= pos[1:]).sum() == 0): \n", + " raise TypeError('Incorrect pos format: it has to be a numpy array with dimentions (polymorphic sites, ) ')\n", + " return True\n", + "\n", + "def check_start(pos, start):\n", + " if not (isinstance(start, (int, float)) and start >= 0 and start <= pos[0]):\n", + " raise TypeError('Incorrect start format: it has to be an integer value >=0 and <= pos[0] (minimum position value) ')\n", + " return True\n", + "\n", + "def check_end(pos, end):\n", + " if not (isinstance(end, (int, float)) and end >= 0 and end >= pos[-1]):\n", + " raise TypeError('Incorrect end format: it has to be an integer value >= pos[-1] (maximum position value)')\n", + " return True\n", + "\n", + "def check_arc(arc):\n", + " if not (isinstance(arc, np.ndarray) and len(arc.shape) == 3 and arc.shape[2] == 4):\n", + " raise TypeError('Incorrect arc format: it has to be a numpy array with dimentions (sites, individuals, alleles) and the third dimention must be of size = 4')\n", + " return True\n", + "\n", + "def check_GL(GL, ploidy):\n", + " if not (isinstance(GL, np.ndarray) and len(GL.shape) == 3):\n", + " raise TypeError('Incorrect GL format: it has to be a numpy array with dimentions (sites, individuals, genotypes)')\n", + " if not (len([x for x in combinations_with_replacement([0, 1, 2, 3], ploidy)]) == GL.shape[2]):\n", + " raise TypeError('Incorrect ploidy format or GL format: the third dimention of GL {} does not correspond with the possible genotypes {} from a `ploidy` value {}'.format(GL.shape[2], get_GTxploidy(ploidy).size, ploidy))\n", + " return True\n" + ] + }, + { + "cell_type": "code", + "execution_count": 252, + "id": "1d4227e9-3158-4286-8394-fcb522312217", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "DPh\n", + "[15 12 24 32]\n", + "DP\n", + "[[22 10 20 47]\n", + " [14 14 28 26]]\n" + ] + }, + { + "data": { + "text/plain": [ + "array([[[22, 0, 0, 0],\n", + " [ 9, 1, 0, 0],\n", + " [ 0, 20, 0, 0],\n", + " [41, 2, 2, 2]],\n", + "\n", + " [[ 0, 0, 1, 13],\n", + " [ 0, 0, 1, 13],\n", + " [ 0, 24, 3, 1],\n", + " [ 2, 0, 0, 24]]])" + ] + }, + "execution_count": 252, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "seed = 1234\n", + "gm = np.array([[0, 0, 1, 0], \n", + " [1, 1, 0, 1]])\n", + "ref = np.array([\"A\", \"C\"])\n", + "alt = np.array([\"C\", \"T\"])\n", + "e = np.array([0.05, 0.05, 0.05, 0.05])\n", + "mean_depth = np.array([15, 12, 24, 32])\n", + "ploidy = 2\n", + "arc = sim_allelereadcounts(gm, mean_depth, e, ploidy = 1, seed = seed, std_depth = None, ref = ref, alt = alt, read_length = None, depth_type = \"independent\")\n", + "arc" + ] + }, + { + "cell_type": "code", + "execution_count": 253, + "id": "5d784b0f-c6f2-4990-93e2-a93cea855836", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[0 0]\n", + " [0 1]\n", + " [0 2]\n", + " [0 3]\n", + " [1 1]\n", + " [1 2]\n", + " [1 3]\n", + " [2 2]\n", + " [2 3]\n", + " [3 3]]\n", + "[[1. 0.5 0.5 0.5 0. 0. 0. 0. 0. 0. ]\n", + " [0. 0.5 0. 0. 1. 0.5 0.5 0. 0. 0. ]\n", + " [0. 0. 0.5 0. 0. 0.5 0. 1. 0.5 0. ]\n", + " [0. 0. 0. 0.5 0. 0. 0.5 0. 0.5 1. ]]\n" + ] + } + ], + "source": [ + "GTxploidy = get_GTxploidy(ploidy)\n", + "print(GTxploidy)\n", + "AFxGTxploidy = np.array([(GTxploidy == 0).sum(axis = 1), (GTxploidy == 1).sum(axis = 1), (GTxploidy == 2).sum(axis = 1), (GTxploidy == 3).sum(axis = 1)])/ploidy\n", + "print(AFxGTxploidy)" + ] + }, + { + "cell_type": "code", + "execution_count": 254, + "id": "99fecd92-d730-4a46-ba9f-b2fdfd7acdd4", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[[22, 0, 0, 0],\n", + " [ 9, 1, 0, 0],\n", + " [ 0, 20, 0, 0],\n", + " [41, 2, 2, 2]],\n", + "\n", + " [[ 0, 0, 1, 13],\n", + " [ 0, 0, 1, 13],\n", + " [ 0, 24, 3, 1],\n", + " [ 2, 0, 0, 24]]])" + ] + }, + "execution_count": 254, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "arc" + ] + }, + { + "cell_type": "code", + "execution_count": 255, + "id": "e66a2d91-0a7b-4955-82ec-c54a577fd7f5", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(2, 4, 4)" + ] + }, + "execution_count": 255, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "arc.shape" ] }, { "cell_type": "code", - "execution_count": 10, - "id": "566fd8ee-ed0c-49c4-b326-83c6cd7e9aa0", + "execution_count": 256, + "id": "2d075bb4-3595-418d-b35b-2ad83199bebc", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "array([15.37810676, 13.95450312, 14.17387291, 10.11706523])" + "array([0.05, 0.05, 0.05, 0.05])" ] }, - "execution_count": 10, + "execution_count": 256, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "DPh" + "e" ] }, { "cell_type": "code", - "execution_count": 195, - "id": "259f5a19-f129-4251-beb6-3a9eaac155c4", + "execution_count": 257, + "id": "31f08bfb-48a6-4163-9dbe-dcd16c39fbc3", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "array([5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5.,\n", - " 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5.,\n", - " 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5.,\n", - " 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5.,\n", - " 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5.,\n", - " 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5.,\n", - " 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5.,\n", - " 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5.,\n", - " 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5.,\n", - " 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5.,\n", - " 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5.,\n", - " 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5.,\n", - " 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5.,\n", - " 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5.,\n", - " 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5.,\n", - " 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5.,\n", - " 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5.,\n", - " 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5.,\n", - " 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5.,\n", - " 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5.,\n", - " 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5.,\n", - " 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5.,\n", - " 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5.,\n", - " 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5.,\n", - " 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5.,\n", - " 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5.,\n", - " 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5.,\n", - " 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5.,\n", - " 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5.,\n", - " 5., 5., 5., 5., 5., 5., 5.])" + "array([[[22, 0, 0, 0],\n", + " [ 9, 1, 0, 0],\n", + " [ 0, 20, 0, 0],\n", + " [41, 2, 2, 2]],\n", + "\n", + " [[ 0, 0, 1, 13],\n", + " [ 0, 0, 1, 13],\n", + " [ 0, 24, 3, 1],\n", + " [ 2, 0, 0, 24]]])" ] }, - "execution_count": 195, + "execution_count": 257, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "def linked_depth(rng, DPh, read_length, sites_n):\n", - " '''\n", - " Simulates reads in a contiguous genomic region to compute the depth per position.\n", - " \n", - " Parameters\n", - " ----------\n", - " rng : `numpy.random._generator.Generator` \n", - " random number generation numpy object\n", - " DPh : `numpy.ndarray`\n", - " Numpy array with the depth per haplotype\n", - " read_length : `int`\n", - " Read length in base pair units\n", - " sites_n : `int`\n", - " number of sites that depth has to be simulated for\n", - " \n", - " Returns \n", - " -------\n", - " DP : `numpy.ndarray`\n", - " Depth per site per haplotype\n", - " '''\n", - " DP = []\n", - " read_n = ((DPh*sites_n)/read_length).astype(\"int\")\n", - " for r in read_n:\n", - " dp = np.zeros((sites_n,), dtype=int)\n", - " for p in rng.integers(low=0, high=sites_n-read_length+1, size=r):\n", - " dp[p:p+read_length] += 1\n", - " DP.append(dp.tolist())\n", - " return np.array(DP).T\n", - "\n", - "DPh = np.array([5] * 500)\n", - "linked = linked_depth(rng, DPh, read_length = 100, sites_n = 300)\n", - "linked.shape\n", - "linked.mean(axis = 0)" + "arc" ] }, { "cell_type": "code", - "execution_count": 26, - "id": "08b04ef0-d54f-45f8-bff1-640916061ea3", + "execution_count": 258, + "id": "df57911a-d9ce-4066-bc5c-b33705b8327b", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "array([5, 4, 1, 1, 1, 3, 7, 9, 4, 3])" + "array([[[0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05],\n", + " [0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05],\n", + " [0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05],\n", + " [0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05]],\n", + "\n", + " [[0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05],\n", + " [0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05],\n", + " [0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05],\n", + " [0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05]],\n", + "\n", + " [[0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05],\n", + " [0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05],\n", + " [0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05],\n", + " [0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05]],\n", + "\n", + " [[0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05],\n", + " [0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05],\n", + " [0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05],\n", + " [0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05]]])" ] }, - "execution_count": 26, + "execution_count": 258, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "np.random.randint(low = 0, high = 10, size = 10)" + "ex = np.repeat(e, 4*10).reshape(e.shape[0], 4, 10)\n", + "ex" ] }, { "cell_type": "code", - "execution_count": 38, - "id": "a03db2fd-3480-47fd-aa80-6c41a0f41cc6", + "execution_count": 304, + "id": "bd1d285f-6863-4b08-be45-86d8b069f860", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "(10,)" + "(2, 4)" ] }, - "execution_count": 38, + "execution_count": 304, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "np.arange(10).shape" + "gm.shape" ] }, { "cell_type": "code", - "execution_count": 58, - "id": "bc2d50fd-39e0-48fd-8088-31dd9c42bbb8", + "execution_count": 303, + "id": "405802eb-2540-4c7b-b04e-aa32d458d3c0", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "numpy.random._generator.Generator" + "(2, 4, 4)" ] }, - "execution_count": 58, + "execution_count": 303, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "type(rng)" + "arc.shape" ] }, { "cell_type": "code", - "execution_count": 196, - "id": "2a474f22-7871-42cd-8459-ea7197b1284a", + "execution_count": 270, + "id": "af47bad6-3d25-4ce1-8a36-4e4e78f2a960", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "array([5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5.,\n", - " 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5.,\n", - " 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5.])" + "(4, 4, 10)" ] }, - "execution_count": 196, + "execution_count": 270, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "rng = np.random.default_rng()\n", - "DPh = np.array([5] * 50) # 500 haplotypes each with depth 5\n", - "linked = linked_depth(rng, DPh, 100, 300)\n", - "linked.mean(axis = 0)" + "(-np.log(\n", + " ((AFxGTxploidy*(1-ex)+(1-AFxGTxploidy)*(ex/3)))\n", + " )).shape" ] }, { "cell_type": "code", - "execution_count": 197, - "id": "b87456c3-77bd-4886-ab9e-8545da8a2c77", + "execution_count": 281, + "id": "551c4ad8-e49e-412d-b28b-59b6ecb36397", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "array([6.24362391, 6.22134131, 6.3227217 , 5.62047026, 6.01859957,\n", - " 5.09101895, 4.75162037, 3.50866578, 5.35176806, 5.17917373,\n", - " 4.49449315, 6.64999401, 5.7870754 , 5.18628934, 4.15691961,\n", - " 4.03752717, 4.93745233, 4.73877702, 5.76580129, 5.86630209,\n", - " 7.26625988, 2.41767582, 4.57193522, 5.54650625, 8.15204092,\n", - " 3.91883976, 5.53601392, 4.1392852 , 4.97307886, 5.34080056,\n", - " 5.64398088, 6.50211826, 5.16538773, 5.1446952 , 5.19940298,\n", - " 4.96726068, 5.58953678, 4.1571701 , 3.8272178 , 5.78357617,\n", - " 6.32684022, 3.80844625, 4.03159482, 5.67242941, 7.94770987,\n", - " 7.10795932, 7.09427356, 7.20178838, 5.24779528, 4.11999149])" + "int" ] }, - "execution_count": 197, + "execution_count": 281, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "rng = np.random.default_rng()\n", - "DPh = rng.normal(loc=5, scale=1.0, size=50)\n", - "DPh" + "type((1))" ] }, { "cell_type": "code", - "execution_count": 199, - "id": "57ca520c-2abc-4419-8c13-e34ab902b5f8", + "execution_count": 282, + "id": "6eaf23a2-06a4-48d9-933d-6a0502f81e38", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(1, 4, 4, 10)" + ] + }, + "execution_count": 282, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 291, + "id": "e5342d0d-bdfc-4aa5-b89f-c03b0868241a", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 290, + "id": "5dc06b31-06a8-4f3d-bfd3-38e30b0757a8", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "array([6.24333333, 6.22 , 6.32 , 5.62 , 6.01666667,\n", - " 5.09 , 4.75 , 3.50666667, 5.35 , 5.17666667,\n", - " 4.49333333, 6.64666667, 5.78666667, 5.18333333, 4.15666667,\n", - " 4.03666667, 4.93666667, 4.73666667, 5.76333333, 5.86333333,\n", - " 7.26333333, 2.41666667, 4.57 , 5.54333333, 8.15 ,\n", - " 3.91666667, 5.53333333, 4.13666667, 4.97 , 5.34 ,\n", - " 5.64333333, 6.5 , 5.16333333, 5.14333333, 5.19666667,\n", - " 4.96666667, 5.58666667, 4.15666667, 3.82666667, 5.78333333,\n", - " 6.32666667, 3.80666667, 4.03 , 5.67 , 7.94666667,\n", - " 7.10666667, 7.09333333, 7.2 , 5.24666667, 4.11666667])" + "array([[[ 0. , 17.58112274, 20.94841857, 20.94841857,\n", + " 121.29153804, 121.96729347, 121.96729347, 125.3345893 ,\n", + " 125.3345893 , 125.3345893 ],\n", + " [ 46.37453531, 0. , 67.3459166 , 67.3459166 ,\n", + " 123.1925094 , 131.32453737, 131.32453737, 204.05353475,\n", + " 198.67045397, 204.05353475]],\n", + "\n", + " [[105.11933296, 105.11933296, 98.3847413 , 17.56964138,\n", + " 105.11933296, 98.3847413 , 17.56964138, 97.03323043,\n", + " 10.83504972, 0. ],\n", + " [156.91139313, 77.44780409, 148.16101652, 74.08050826,\n", + " 67.96426524, 74.08050826, 0. , 152.86834187,\n", + " 70.71321243, 63.92121397]]])" ] }, - "execution_count": 199, + "execution_count": 290, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "linked = linked_depth(rng, DPh, 100, 30000)\n", - "linked.mean(axis = 0)" + "er = np.repeat(e, 4*10).reshape(e.shape[0], 4, 10)\n", + "ERxAFxGTxploidy = -np.log(((AFxGTxploidy*(1-er)+(1-AFxGTxploidy)*(er/3))))\n", + "ERxAFxGTxploidy = ERxAFxGTxploidy.reshape((1,) + ERxAFxGTxploidy.shape)\n", + "RExerxAFxGTxploidy = np.multiply(ERxAFxGTxploidy, arc.reshape(arc.shape + (1,))).sum(axis = 2)\n", + "s = RExerxAFxGTxploidy.shape\n", + "GL = RExerxAFxGTxploidy.reshape(-1).reshape(s[0], s[1]//ploidy, ploidy, s[2]).sum(axis = 2)\n", + "GL-GL.min(axis = 2).reshape(GL.shape[0], GL.shape[1], 1)" ] }, { "cell_type": "code", - "execution_count": 216, - "id": "326b222a-e518-4c91-9576-bbdd01d0db6c", + "execution_count": 292, + "id": "1c0715ca-ec89-4b7e-960c-6b500dce15c1", "metadata": {}, "outputs": [ { "data": { - "image/png": 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VL9Kmxbp9XrecLHLat9PadkkKsU7RDKChcdgYGqZm3gZudfdVAdYmkhRiPWs34J7PDlOgS9KIdcu+3wJ/AHoDfYDngeeCKkokmTw3f11Mx6lXuySbWAPe3P037l4b+fUMusgqbcC+2roW70o94PoxBdo6T5JOrBdZX40si/wdDcH+ReBPZtYdwN13BFSfSGgWrtnJ7dNb7MhxcJclhbsko1gD/ouRr19r8vxXaAj8AXGrSCQEjW9aOq5rRwb16sQ/Vmyjd5eOnDe4J68u/2Qr69z2mTwwQd0fJXnFGvAnufshOxGYWcemz4mkoqYrZDZWVLOxopqzB/bg8RtOo1OHdoc0Ecs049rR/XTWLkkv1oB/CxgVw3MiKWXW4rKoyx8/3raXTh0a/orcP36EAl1STmv94I8D8oFsMxtJw5QjQBe0v6qkqMbTMRlmUVcLqOujpLrWzuAvpmFP1b7Ajxo9vxu4M6CaRALTdBOOllbIqOujpLrW+sH/Gvi1mV3t7tMTVJNI3MV6J+oBBur6KCkv1jn44WY2rOmT7v5fca5HJO4+86PX+GjL3piPP7D0UatjJNXFGvB7Gj3uCFwBLIt/OSLxdfeskpjCPdOMenf1j5G0Emuzscb7sWJmjwAvRjlcJHQHLqS21qcdGjbh+MFVWs8u6eeINvygYQWNbm6SpNT0QmpLMs0U7pK2Yu0mWcI/e89kAL2A+2J432oaVtzUAbXurv1ZJXAPz10eW7hnGI9+/hSFu6StWM/grwC6AecAecBsd18Y43vPc/dtR1CbSEwa32WaAdTH8J52GcYjCndJc7EG/JXAV4EZNCwyeMrMfuHuPw2sMpEYXPeLt3lz5T973bUW7mozIG1JrAF/EzDG3fcCmNkUGjb9aC3gHXjZzBz4ubtPbXqAmU0EJgIUFBTEWrcIsxaXHRLuLdGFVGmLYu4HT8M8+gF1/LNtQUvGuvso4FLgZjM7t+kB7j7V3Yvcvahnz54xliPSMNfekvy8bCzyVeEubVGsZ/BPAfPNbGbk+/HAk629yd03RL5uibz3DOD1I6hT5BDllftbXAKZacabk89PYEUiySemM3h3/xFwI7AD2Anc6O4/aek9ZpZrZp0PPAYuAkqPqloR4M8lG7nwRy2fJ1w7ul+CqhFJXjGvg3f3RcCiw/jsY4GZZnZgnN+6+5zDK0/assZdH/vkZTPx3AG8vXI7c5ZsYlifLnz5rOP52asrP7EkcuwJ3XURVYQjv9GpVe6+CjglqM+X9DZrcRmTnn+fmvqG2y/Kyqu458UltMswbr9kCF89pz/tMjPo2y3nkH8E1GZA5J8CC3iRo3Hvi0sOhntj2VmZfOPTJxz8fvzIfAW6SBSxrqIRSajyqppmn9+9rzbBlYikLgW8JJ0VW3aHXYJIWtAUjYSq8YXU3l07cmpBHq8s3YJBs5tzdMvJSnSJIilLZ/ASmgNdH8vKq3BgQ0U1s0s2MbRPF/7rymFkZR56L11WpnHPZz+x74yIRKEzeAlNtK6PW3fv44YzC+ncMUsrZESOggJeEqrxlEy0/VE3RO5Q1QoZkaOjgJeEiXUjjj552QmqSCS9aQ5eEiaWjTiyszKZdPHgBFUkkt50Bi8JsXNvy83BDDTPLhJnCngJlLvzp5KN3PPCkqjH5Odlq/OjSAA0RSOB2byrmom/Wci//3YxffKyue3iwWRnZR5yjKZkRIKjM3iJO3fnDwvWcf+flrG/tp47LxvCV8Y2NAfrk5etpY8iCaKAl7hau72SyTOKeWvldkb3786Uq0+msEfuwde19FEkcRTwEhd19c6v3lrNI3OXk5lhPDBhONeeXkBGRiw7O4pIEBTwckTunlXCc/PXUedOBtC9U3u27dnP+UN68cCE4fTuqrXsImFTwMthmbW4jNunF7Ovtv7gc/XAtj37GTvwGJ78UhGRXbxEJGQKeInZrMVlTJr2PjV1zTcZmLdyh8JdJIko4KVFjXvHZJhR59E6yNDiayKSeIEHvJllAguAMne/IujxJH6a7ovaWoBn6uxdJKkk4kanW4BlCRhH4izavqjRXDu6X4DViMjhCjTgzawvcDnwRJDjSDCi7YvanLEndOf+8SMCrEZEDlfQUzQ/AW4DOkc7wMwmAhMBCgoKAi5HWtJ4vv3YLh1jek9edhb3jhumm5dEklBgAW9mVwBb3H2hmX062nHuPhWYClBUVKSrdCFp2qt9067qqMd2y8li8fcuSlRpInKEgjyDHwuMM7PLgI5AFzN7xt2vD3BMOQyHs0LmAO2LKpI6Agt4d78DuAMgcgb/HYV78rh7VgnPzlt7cNu8lsI9Py9bzcFEUpDWwbdBsxaXHRLuLVGvdpHUlZCAd/fXgNcSMZa07uG5y2MKd/VqF0ltOoNvg1raOi/TjHp3TceIpAEFfBuyr7aOn726MurrBjz6hVMU6iJpQgHfRixeu5Pbpxfz4eY9FB3fjdKyCqobdYQ04LoxBQp3kTSigE9zlftrefTlD/nlmx9zXJeO/PLLRZw/5NhDlkhqOkYkPSng09hbK7YxeUYJa3dUcv2YAm6/ZAidO2YB2jpPpC1QwKeBpmfjN593AsXrK/jdu+vo3yOX300cw5gBx4RdpogkmAI+xTVtMVBWXsWdM0sxg699agC3XngiHbMyQ65SRMKggE9xD89dfjDcG+uR24E7Lj0phIpEJFkkoh+8BCjamvZte/YluBIRSTYK+BS2obyKDu2a/yPsk5ed4GpEJNloiiYF1dc7z76zlh/OXoY7ZGXYITsvqcWAiIACPuWs2rqHydNLeGf1Ds4e2IMfXDWChWt2ak27iHyCAj5F1NbV88QbH/Pjv3xIh3YZPPS5k/n8aX0xM/p1z1Ggi8gnKOBTwNINu7ht+vuUlu3i4mHHct+Vw+kV45Z6ItJ2KeCT2L7aOv77byv439dWkpeTxf9cN4pLhx+HmYVdmoikAAV8klq4Zge3Ty9hxZY9XDUqn+9ePpRuue3DLktEUogCPsns3VfLw3OX8+u3V9Onaza/uvF0Pj24V9hliUgKUsAnkX98tJU7ZpSwfmcVXzrzeCZdMoROHfRHJCJHRumRBCoqa7j/T0t5fuF6BvTM5fmvn8nphd3DLktEUpwCPmRzSjfx3RdK2bF3P//26RP41gWD1BxMROIisIA3s47A60CHyDjT3P2eoMZLNVt2V3Pvi0uYXbKJob278NSXT2d4ftewyxKRNBLkGfw+4Hx332NmWcAbZvZnd58X4JhJz92ZvqiM+15aSlVNHZMuHszEcweQlam2QCISX4EFvLs7sCfybVbkl0d/R/pbv7OSO2eW8vqHWyk6vhs/vPpkBvbqFHZZIpKmAp2DN7NMYCEwEPiZu89v5piJwESAgoKCIMsJTX2985t5a5gy5wMAvj9uGDeMOZ6MDN2wJCLBCTTg3b0OONXM8oCZZjbc3UubHDMVmApQVFSUdmf4K7fu4fZpxSxYs5NzT+zJgxOG07dbTthliUgbkJBVNO5ebmavAZcApa0cnhZq6uqZ+voqHvvrR2RnZfLI50/h6lH5ajMgIgkT5CqankBNJNyzgQuBKUGNl0xKyyq4fXoxSzbs4rIRx3HvuGH06qzmYCKSWEGewfcGfh2Zh88A/uDuLwU4Xuiqa+r4/3/9iJ+/voruue15/PpRXDK8d9hliUgbFeQqmmJgZFCfn2zeXb2D26cVs2rbXj5/Wl/uvnwoXXOywi5LRNow3cl6lPbsq+WhOR/w9Ntr6Nstm9/86xmcM6hn2GWJiCjgj8bfP9zKnTNK2FBRxZfPKmTSxYPJVXMwEUkSSqMjUF65n/96aSkzFpVxQs9cpn39TE47Xs3BRCS5KOAP0+ySjXzvhVLKK2v45vkDufm8gWoOJiJJSQEfoy27qvnuC6XMXbKZEfldeforoxnap0vYZYmIRKWAb4W78/zC9dz/0lL21dYz+dIh3HR2f9qpOZiIJDkFfAvW7ajkjhklvLFiG2cUdueHV49gQE81BxOR1KCAj5i1uIyH5y5nQ3kVvbt2ZPSAY5hTuokMg/uuHMZ1o9UcTERSiwKehnC/Y0YJVTV1AGyoqGbm4jJO6t2ZJ750Ovl52SFXKCJy+DSRDDw8d/nBcG+sorJG4S4iKUsBD5SVVzX7/MaK6gRXIiISP216iqa6po4fv/Jh1Nf76OxdRFJYmw34+au2M3lGCR9v28uYAd15b2051bX1B1/Pzspk0sWDQ6xQROTotLmA311dw5Q5H/DMvLX0657NszeNZuzAHoesoumTl82kiwczfmR+2OWKiByxNhXwr36whbtmlrBxVzX/enZ//vOiE8lp3/BbMH5kvgJdRNJKmwj4HXv3c99LS5m5uIxBvTox/RtnMaqgW9hliYgEKq0D3t15qXgj9764hIqqGr51wSBuPu8EOrRTczARSX9pG/Cbd1Vz18xSXlm2mZP7duWZm0ZzUm81BxORtiPtAt7d+f2763hg9jL219Zz52VD+MpYNQcTkbYnsIA3s37A08BxQD0w1d0fC2o8gLXbK5k8o5i3Vm5ndP/uTLn6ZAp75AY5pIhI0gryDL4W+E93X2RmnYGFZvYXd18a74Hq6p2n3vyYR15eTruMDB6cMIJrTu+n5mAi0qYFFvDuvhHYGHm828yWAflAXAO+orKGLz31Du+tK+f8Ib14YMJwenfVHagiIgmZgzezQmAkML+Z1yYCEwEKCgoO+7O7ZLfj+GNyuHFsIeNO6YOZztpFRADM3YMdwKwT8HfgAXef0dKxRUVFvmDBgkDrERFJJ2a20N2Lmnst0KUlZpYFTAeebS3cRUQkvgILeGuYK3kSWObuPwpqHBERaV6QZ/BjgRuA883svcivywIcT0REGglyFc0bgK54ioiERLd3ioikKQW8iEiaUsCLiKQpBbyISJoK/Eanw2FmW4E1R/j2HsC2OJYTpFSqFVKr3lSqFVKr3lSqFVKr3qOp9Xh379ncC0kV8EfDzBZEu5sr2aRSrZBa9aZSrZBa9aZSrZBa9QZVq6ZoRETSlAJeRCRNpVPATw27gMOQSrVCatWbSrVCatWbSrVCatUbSK1pMwcvIiKHSqczeBERaUQBLyKSplI64M2sn5m9ambLzGyJmd0Sdk0tMbOOZvaOmb0fqff7YdfUGjPLNLPFZvZS2LW0xsxWm1lJpHNpUu8cY2Z5ZjbNzD6I/P97Ztg1RWNmgxt1hH3PzHaZ2bfDrisaM7s18ver1MyeM7OOYdfUEjO7JVLrknj/vqb0HLyZ9QZ6N97YGxgfxMbe8RDpkZ/r7nsim6G8Adzi7vNCLi0qM/sPoAjo4u5XhF1PS8xsNVDk7kl/c4uZ/Rr4h7s/YWbtgRx3Lw+5rFaZWSZQBox29yO9KTEwZpZPw9+roe5eZWZ/AGa7+6/Crax5ZjYc+B1wBrAfmAN8w90/isfnp/QZvLtvdPdFkce7gQMbeyclb7An8m1W5FfS/gtrZn2By4Enwq4lnZhZF+BcGjbEwd33p0K4R1wArEzGcG+kHZBtZu2AHGBDyPW05CRgnrtXunstDdubTojXh6d0wDfW0sbeySQy5fEesAX4i7snc70/AW4D6kOuI1YOvGxmCyObuSerAcBW4KnI9NcTZpYbdlExugZ4LuwionH3MuARYC2wEahw95fDrapFpcC5ZnaMmeUAlwH94vXhaRHwkY29pwPfdvddYdfTEnevc/dTgb7AGZEf0ZKOmV0BbHH3hWHXchjGuvso4FLgZjM7N+yComgHjAL+191HAnuByeGW1LrIVNI44Pmwa4nGzLoBVwL9gT5ArpldH25V0bn7MmAK8BcapmfeB2rj9fkpH/CpurF35Efy14BLwq0kqrHAuMi89u9o2HrxmXBLapm7b4h83QLMpGFeMxmtB9Y3+ultGg2Bn+wuBRa5++awC2nBhcDH7r7V3WuAGcBZIdfUInd/0t1Hufu5wA4gLvPvkOIBn2obe5tZTzPLizzOpuF/xg9CLSoKd7/D3fu6eyENP5b/zd2T9kzIzHIjF9qJTHdcRMOPv0nH3TcB68xscOSpC4CkXBjQxLUk8fRMxFpgjJnlRPLhAhquzSUtM+sV+VoAXEUcf48D25M1QQ5s7F0SmdcGuNPdZ4dXUot6A7+OrETIAP7g7km//DBFHAvMbPg7TTvgt+4+J9ySWvRN4NnItMcq4MaQ62lRZH74M8DXwq6lJe4+38ymAYtomOpYTPK3LJhuZscANcDN7r4zXh+c0sskRUQkupSeohERkegU8CIiaUoBLyKSphTwIiJpSgEvIpKmFPDSJpnZntaPOuzPLDSzf4n354ocKQW8SPwUAgp4SRoKeGnTzOzTZvZao97sz0bugDzQX35KpIf/O2Y2MPL8r8zsc40+48BPAz8Ezon0TL818f81IodSwIs0dCH9NjCUhk6PYxu9tsvdzwD+m4bumi2ZTEOP91Pd/ccB1ClyWBTwIvCOu69393rgPRqmWg54rtHXpN11SaQ5CngR2NfocR2H9mjyZh7XEvm7E5nOaR9odSJHSAEv0rIvNvr6duTxauC0yOMradiZC2A30DlhlYm0ItW7SYoErYOZzafhZOjayHO/AF4ws3eAv9KwYQdAMVBrZu8Dv9I8vIRN3SRFokilTbxFmqMpGhGRNKUzeBGRNKUzeBGRNKWAFxFJUwp4EZE0pYAXEUlTCngRkTT1fxU651ADF+wGAAAAAElFTkSuQmCC\n", "text/plain": [ - "
" + "array([[[ 0. , 17.58112274, 20.94841857, 20.94841857,\n", + " 121.29153804, 121.96729347, 121.96729347, 125.3345893 ,\n", + " 125.3345893 , 125.3345893 ],\n", + " [ 46.37453531, 0. , 67.3459166 , 67.3459166 ,\n", + " 123.1925094 , 131.32453737, 131.32453737, 204.05353475,\n", + " 198.67045397, 204.05353475]],\n", + "\n", + " [[105.11933296, 105.11933296, 98.3847413 , 17.56964138,\n", + " 105.11933296, 98.3847413 , 17.56964138, 97.03323043,\n", + " 10.83504972, 0. ],\n", + " [156.91139313, 77.44780409, 148.16101652, 74.08050826,\n", + " 67.96426524, 74.08050826, 0. , 152.86834187,\n", + " 70.71321243, 63.92121397]]])" ] }, - "metadata": { - "needs_background": "light" + "execution_count": 292, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "er = np.repeat(e, 4*10).reshape(e.shape[0], 4, 10)\n", + "ERxAFxGTxploidy = -np.log(((AFxGTxploidy*(1-er)+(1-AFxGTxploidy)*(er/3))))\n", + "ERxAFxGTxploidy = ERxAFxGTxploidy.reshape((1,) + ERxAFxGTxploidy.shape)\n", + "RExerxAFxGTxploidy = np.multiply(ERxAFxGTxploidy, arc.reshape(arc.shape + (1,))).sum(axis = 2)\n", + "GL = ploidy_sum(RExerxAFxGTxploidy, ploidy)\n", + "GL-GL.min(axis = 2).reshape(GL.shape[0], GL.shape[1], 1)" + ] + }, + { + "cell_type": "code", + "execution_count": 265, + "id": "e2c4f26a-9dbb-404f-b7f5-5d2aed03d7da", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[[22, 0, 0, 0],\n", + " [ 9, 1, 0, 0],\n", + " [ 0, 20, 0, 0],\n", + " [41, 2, 2, 2]],\n", + "\n", + " [[ 0, 0, 1, 13],\n", + " [ 0, 0, 1, 13],\n", + " [ 0, 24, 3, 1],\n", + " [ 2, 0, 0, 24]]])" + ] }, - "output_type": "display_data" + "execution_count": 265, + "metadata": {}, + "output_type": "execute_result" } ], "source": [ - "plt.scatter(DPh, linked.mean(axis = 0))\n", - "plt.plot(np.arange(10)[2:], np.arange(10)[2:])\n", - "plt.xlabel(\"Input\")\n", - "plt.ylabel(\"output\")\n", - "plt.show()" + "arc" + ] + }, + { + "cell_type": "code", + "execution_count": 266, + "id": "54950ec7-864f-4dc3-aa9e-218866207fef", + "metadata": {}, + "outputs": [], + "source": [ + "arc2 = np.array([[[31, 1, 0, 0],\n", + " [41, 22, 2, 2]],\n", + " [[ 0, 0, 2, 26],\n", + " [ 2, 24, 3, 25]]])" ] }, { "cell_type": "code", - "execution_count": 166, - "id": "51014df3-f5fb-4543-aeb6-aa7eef5d574b", + "execution_count": 267, + "id": "3f36c7e2-9815-49e4-a967-db46ab23a979", "metadata": {}, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "500\n" - ] + "data": { + "text/plain": [ + "array([[[ 0. , 17.58112274, 20.94841857, 20.94841857,\n", + " 121.29153804, 121.96729347, 121.96729347, 125.3345893 ,\n", + " 125.3345893 , 125.3345893 ],\n", + " [ 46.37453531, 0. , 67.3459166 , 67.3459166 ,\n", + " 123.1925094 , 131.32453737, 131.32453737, 204.05353475,\n", + " 198.67045397, 204.05353475]],\n", + "\n", + " [[105.11933296, 105.11933296, 98.3847413 , 17.56964138,\n", + " 105.11933296, 98.3847413 , 17.56964138, 97.03323043,\n", + " 10.83504972, 0. ],\n", + " [156.91139313, 77.44780409, 148.16101652, 74.08050826,\n", + " 67.96426524, 74.08050826, 0. , 152.86834187,\n", + " 70.71321243, 63.92121397]]])" + ] + }, + "execution_count": 267, + "metadata": {}, + "output_type": "execute_result" } ], "source": [ - "def linked_depth(rng, DPh, read_length, sites_n):\n", - " '''\n", - " Simulates reads in a contiguous genomic region to compute the depth per position.\n", - " \n", - " Parameters\n", - " ----------\n", - " rng : `numpy.random._generator.Generator` \n", - " random number generation numpy object\n", - " DPh : `numpy.ndarray`\n", - " Numpy array with the depth per haplotype\n", - " read_length : `int`\n", - " Read length in base pair units\n", - " sites_n : `int`\n", - " number of sites that depth has to be simulated for\n", - " \n", - " Returns \n", - " -------\n", - " DP : `numpy.ndarray`\n", - " Depth per site per haplotype\n", - " '''\n", - " seq_length = sites_n+(2*read_length)\n", - " DP = []\n", - " print(sites_n+(2*read_length))\n", - " read_n = (DPh*seq_length/read_length).astype(\"int\")\n", - " for r in read_n:\n", - " dp = np.zeros((seq_length,), dtype=int)\n", - " for p in rng.integers(low=0, high=seq_length-read_length+1, size=r):\n", - " dp[p:p+read_length] += 1\n", - " DP.append(dp.tolist())\n", - " DP = (np.array(DP).T)[(1*read_length):(-1*read_length), :]\n", - " return np.round(DP-((DP.mean(axis = 0)-5).repeat(DP.shape[0]).reshape(DP.shape)))\n", - "\n", - "rng = np.random.default_rng()\n", - "DPh = np.array([5] * 500) # 500 haplotypes each with depth 5\n", - "linked = linked_depth(rng, DPh, 100, 300)" + "allelereadcounts_to_GL(arc = arc2, e = 0.05, ploidy = 2)" ] }, { "cell_type": "code", - "execution_count": 182, - "id": "9d9d18de-3ab6-4b01-85b8-9223f37d1c63", + "execution_count": null, + "id": "0902f69e-1d8b-4845-82f6-b47305d8be14", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 249, + "id": "a5135d3c-d120-497d-b153-fa512aad1fff", "metadata": {}, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "500\n", - "(300, 500)\n" - ] - }, + "data": { + "text/plain": [ + "array([[[ 5.68443669, 23.26555943, 26.63285526, 26.63285526,\n", + " 126.97597472, 127.65173016, 127.65173016, 131.01902599,\n", + " 131.01902599, 131.01902599],\n", + " [112.07253594, 57.03159656, 138.10895212, 138.10895212,\n", + " 268.48183046, 277.20523402, 277.20523402, 362.59675005,\n", + " 358.28258958, 362.59675005]],\n", + "\n", + " [[114.64164774, 114.64164774, 107.90705608, 27.09195616,\n", + " 114.64164774, 107.90705608, 27.09195616, 106.55554521,\n", + " 20.3573645 , 9.52231478],\n", + " [282.90240044, 171.68649385, 274.98405608, 158.49352862,\n", + " 158.84488532, 168.08230997, 51.5917825 , 278.6152674 ,\n", + " 154.88934474, 146.25282911]]])" + ] + }, + "execution_count": 249, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "(\n", + " np.multiply(\n", + " -np.log(\n", + " ((AFxGTxploidy*(1-ex)+(1-AFxGTxploidy)*(ex/3)))\n", + " ).reshape(1, 4, 4, 10), arc.reshape(arc.shape[0], arc.shape[1], arc.shape[2], 1)).sum(axis = 2)\n", + ").reshape(-1).reshape(2, 2, 2, 10).sum(axis = 2)" + ] + }, + { + "cell_type": "code", + "execution_count": 214, + "id": "7f60e504-2bea-4114-933d-a947eb72b52f", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[0.95 , 0.48333333, 0.48333333, 0.48333333, 0.01666667,\n", + " 0.01666667, 0.01666667, 0.01666667, 0.01666667, 0.01666667],\n", + " [0.01666667, 0.48333333, 0.01666667, 0.01666667, 0.95 ,\n", + " 0.48333333, 0.48333333, 0.01666667, 0.01666667, 0.01666667],\n", + " [0.01666667, 0.01666667, 0.48333333, 0.01666667, 0.01666667,\n", + " 0.48333333, 0.01666667, 0.95 , 0.48333333, 0.01666667],\n", + " [0.01666667, 0.01666667, 0.01666667, 0.48333333, 0.01666667,\n", + " 0.01666667, 0.48333333, 0.01666667, 0.48333333, 0.95 ]])" + ] + }, + "execution_count": 214, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "(AFxGTxploidy*(1-0.05)+(1-AFxGTxploidy)*(0.05/3))" + ] + }, + { + "cell_type": "code", + "execution_count": 216, + "id": "ccb6a351-76d0-43c5-a088-a425e739b71c", + "metadata": {}, + "outputs": [ { "data": { - "image/png": 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vBCRW8ASK1T/v37+An/nojbjrsTmnE2UQoIC0dapmlbvon6FT0CFmC7Bzv0KGTm0aZGISf96rVivLn61ioKbXR5Z6YOhMQ6ff2zdvSkjdrm83F8wgMcIBvXxStJOmmIgzht7Ntpg9FOvK0K02rsblslaSC+8AO2k+odZoJ0a7V2tbzFPqV7WbgYKOT4jyJehu33ME39r9BF75/uvxROYeGbzkou7d2clY71tKuSKG3raYLZDflystR0tNGAZDB9xkzk79p+vC71fXpKgO6AWXS86u5xsd3PnoEZz/7q/insfn9Tbdqin6Wi49oCqRI0mBWtinhr4O1qwyF8HKJBf1d61Kh/KAQQwdUOeRt2G1/ctKzsWwQfdcPQpLbYv84Z5r0GTxYNsx1+igHgeYjMPSYmG9gqx3fCSUs91uz5BbX6bgOshnix9W2fwZUKzHbzL03iUXPuI9vNzSJRUeZxO9Wq8vdblsMIYuhHhQCHG7EOIWIcQux+cvFkIcyT6/RQjxB4Nvqgk92+04SamUOUOvGKpLKbWG3mgna74YRr72qa2h9/9QrvWkKG8bH+I3O+YE1EpY6b75Bs5/97W4d+98ZYmH9QLdc/U4yBYslgXbokvyGAZDn6nHCANRyEPo93zR9wzJpYdR300PHcRz/+RaPHJ4ufCZDqYDlDPN0V/557qDSoqdSrOfSVEW4OcbHT0Rza2QeefhbnO3NUcHiX6WoLtESvlExefXSylfs9oG9Qo6N85FopM019Ar2MVyO0E7kZieiDDf7KCTSsThYNOzq1C21mDuQ+99X7b/dtiw3RD5w5sYtT9W0kk+eriBffNNPLB/UXfMG6k4V0cH9BBAW7lKklzyALqXpBgE5hptzEzGCAKhXS5lbPG+vfNodlKcecKsc18th7yi91nxDD1+pIkklXhivokTWD0ZgEkuA5Qzu0kuqdVm6qi47OOaFM0Ti9y2RbUvqeeOjPeta2+j25qjg8ToSi4VftRU5pJL1eQQ9bY7picArL3s0l1D78O2uMYaOr85kxQmQ0+qH7puSBjLrLKnrhdSI6BnqfZWjRFnWech2BZn6hFCwa5/CUP/0y/djT/41ztK90XkwrCc9nBPaWbv2EZLLgNk6PyRcHaa1iS6a2K2n8SiZjvV9VySNNXf4dt1Y+DdXDCDRK8BXQK4WghxkxDispJtni+EuFUI8SUhxLNcGwghLhNC7BJC7Nq/f/+KGkyoqqqWpKZt8SPffAAHF4s6H/W227OAvtbLhCUlwars/V72tVYauu2qyN0EKVabKdph7ErbFjcQQ6cHk3Id2oksXLPEEeCGxdDDQBQkN/veWWh2KgmLntx1lM2oki2rrI10nobF0KsWt2npOYGsDezZptdOhu6QXKiEbieReiKa76+bRq5dLhuofO5FUspzAbwSwOVCiBdan38PwMlSyrMA/DWA/+vaiZTyCinleVLK83bs2LHSNgPonilKtVn+7f4DePdVd+H3rAI9QO4SOHq9GXqp5NJHQF9jycVelYgCge1yWUkQ4yOXlTh+hg06VmLoHZa4IqX7mgLDsS3OTsYQItfQteRiPRfNTlrZubocLfSS6+qF71ks/tHDy3jxn38dew4t6d8bKEPvKrmYHYxLx6fnfM7F0B2Sy6aJ3AJNE9wuhl5abbFTzeAHiZ4CupTy0ezvPgBXAjjf+nxOSrmQvb4KQCyE2D7gttptAlDuRd26KQYA7DmkJmtcLIEY+tHTdQDFizlsJCXD45VMitqZi/3i63fvw80/PNTz9on1YBkul1WuWNRhbHetE6Z6AR1TPdP3WyzV3nYu8aJvgz4EPSkqGEMvuaeanbRyPqPlklysfbpg22Xv27eABw8s4f79i/r7g7QEGwy9QkPnqf+qDdWTos0Sht7qpDlDT3OGzo+pmy1xpRPVK0HXgC6E2CSEmKbXAF4G4A5rm2OFUFWUhBDnZ/stX4RvACiz/FGm4papGgBg7xFlL5py1DunC3r0DEkua8zQrZrahCqPfRlWK7m86wvfx998fXffv6deM5eLxdBX0r9QR9fmkssGCuh0fLmGLpld0dxm61SsvzdI2UhKxRZnJiPD5VImudjXxUZlpmjFyDXX3tW2tEhEu5OX8B3kc8UPwdXPFCQXlh8BqPPm9KGXaeidFJsnHBo6OyaSVMpOb6eLJDNI9OJyOQbAlVm8jgB8Ukr5ZSHEWwBASvkhAG8A8KtCiA6AZQBvkkP2AJZpq/R+PQoxGYfaLzoZFw9VT4puJsllbRl6meRSJSeVIS8WtbKH5/ByuzK9ufB7hUlRpqEbKxatgKGzB6CqiuF6QQf0KJdc7OQSOoatm2p4NCMVg3ygl1oqQM/UlctFM9OS4NHspHpE4YJLQ++l2qItuSy31TPVSlJd/31YDN2+J3inbz9bFKhVATI1ulpqJWh1UoSB0B2SK/Xf0NCzUT0f8XdzsbS6TJoOEl0DupTyAQBnOd7/EHv9QQAfHGzTurYLQPmEYhgAM5MR9s6pLL3JWvFmpuHTerlcyiY/82PrfV+6dvUKGLry47crK8rZsGuec78vDwAr6de5hp5La33vZmjQk6JccrESipI0hRAqLd/+3iBAgWVmMkYoGDMtY+idpHLdXApKLQdDr5qXsUcGy61sP1mgVL89HB+6HUCNGjsd8zzkPnP19+jpOn54cAlHlttaIwdM5p2kKuvW0NC1Dz3fv57z6uJykVI9Dxk5HgpG1rZYlnBCD1QQCMxO5sNd6mU55hptTMYhNtfVZ2vtcrF1PkI+bO/DtrgKDZ3Y3pE+Anoqc89+kkrjweHtXkkQMzT0DczQJ7nk4rAtRoHAdD2/7wYpG1FgmanHCJiGXmaRa7TTytGSq1RuL5mitvWWlmprsVWTBjk3VZUpatSEt3I88kxQ9ZeMEEeW24YkxJk3yTaGhm750F02TxtG9u2QZZeRDei2VkmgCxgFAjP1PKDXHMt/zS0rDZLsZ2vucilZyWR1tsX+j4EC+ZGlds+MupPKvLyClMaDw6/BrgcP4Wf//sa+OhquodPXNpLLxdbQO0lamBhLUonQugcHeQhHWB2XIBAFz7iLoVedQ12cq08fus3iiRS1mKtmkM9V1aQof4zsgljEzKl9NG+mAnox6Yh/Z1MtXyiaSl3TZ64O0AYvHzDsyf3RDegl+h5dwEAIzDCG7jqRcw3lEqCs0rVn6GUaev8B3bZr9QMKDq0k7XkCK01zr3+aSuPBode1KMDdj8/j+vueMCxi3aA7OsZ8NxLo+IgItJI8eHHbYhQEmJmMCt8bBOh8zkxGCIUo2FaVFJDfR6Qdu0AFvQDz/slZewVDtyZTKeDxZKtBPldVZSVckks+KWozdOVsm7MDuiMBaWpCXUO+iDYF/l7qFrlqzA8LoxvQSzJFKdArdpQ/TC6WQYkZNFm0cTT07PN+JkV70DvLwKWWw8vlhfyN35NST3rxkrnNds7QuWbbT0fDO7qN5G4h5LbFvLwEnX8+cgwDgen6cFwuWkPPJkXpspsT0upvt3rsPOA4bYs9ZIrS7y4bDN2UOwYBfjvYzTKT3UyylPvM1Zd2MMnFZWkE8vNGDJ0nJ7oYevkCF6Y8OUyMfECvlFw4Q3cF9OUOZidzhr72iUWm5YugO6t+JkW13tn/MXD23KvTpZPkDF1p6Nzlol5zmasfyYUv/LEB43khsajNEot4kazIIhXDYegxwsAdfO0JwVKfNLv/uEWxFxmPrtVyO8G/3vIIlpr9B/T/9a0f4KEDi5XbEHjQLDMTAMUFLuxqiqaGrt4Twu1XJw3dDOgkC5rtuer2x/Q8wtV3Po4jy220klQ7onxAL0GZtsonRbl+2XacSCW5RDlDX7fUf7cPvZ9FHQYhudivu/2ellwMDT3Rr2MW0PsZOSTsYdxIk6GEYup/nljEZY7QIhWDdbmooDFdj4xJUVdA5zW9XeDXpp0UA2aVc4q2+cj1D+DXP3UL/s+uh3U76PatklyWWh389y98Hz/54RtKt3H9HuCQXPhIo9TlkkkuM0py4ZOi0xORoaHT+5tJcsnKBMeh0NvxDvDhQ0t46ye+h6/c+TgOL7Vw2cdvwpXf2wMAmIh9QK9EV9uiEIZ+6WIZR5aV5LJeDN0uwk+g+zSVvdv+6PBWLbn0yNATNinaMVwuiq0GAohY5cp+Ep60y8VatHijgALVZDYUb/FaLswZEgUCx83mFQgHeShzy21M1ULEYYCAp/47CqMR0+y2AANgZoX2xNCz35tvdIz3m6yTq3quqEkHFpul23DwR6VKQ+9kcwh2p0Z/N09EmKqFmeSizs/sVOysm07XmeYHputqu7lGG7/y8ZsAAIHIO4DlVj4XRedFr3HsNXQ3yiSX3IduMnQ7oHz1rr2YW25juh4hDoUablUwiYcOLOLxI4NbvZy3tcp+1WuPvppqi3MGQ+9NQ08ljEnR3Iee6AnBgPlt+3O5uDX0jRLcaeTkGtkZGnoocOFp2/C133oRNtXCgbIzquMCqHvddf15J6va5v79tiG5FDuEapeL+7MWKzWgJmXd2/U7sqySXHhT7IWh6RrR34kowOxkbDD02cnYWtkoc7lMmKaJzRMRGu0Uu/ct4J69auUiXuKBr+C1lH2H7pVh51OMbECn62/3eEZAZ8NdzlzvemwOb/6HXTj5qE14/TknQAiByTjUEzou/NqnbsG7r7prgEdQ9PASjImfXhm6HvL3P6yba3T0pE2vkksnTY1VoXimKMkNAcuf6GfkwDV0fmo2inUx1ZJLcWSXl7FVnZoQAqft2JxNXA5ScmlrwhIGgo0MXJJLYrTNRtuQXNzHUoayz9qJOf9RxtL7DXDGmqKFLHHG0BMzwY1+v9HJO2MK6JqhT7oZOmnoy1mw3zQRKeKS3Zwve+YxuPj0vNhgq5MHdCqFoCfQhxzRRzagl2VT0gUvY+hSSrzv2nsxXY/wfy+/CE89ehqA0iIpWcOF/XMNzDd6t971grLiXHYWZm/7YiyrT5Z+ZLmNY2bqiALRs+SSprlGzhOLmm2V+h+FwmTofchZPH2dM7KVrj4/aNDpJdbVMBh6Pi8Ssh6Nl7gdBCiHAlAWXYoTLYcG3k1Db5dJLsSeK65dWb2hVsfNkAvf7/Oc9OpyaSfmYuW6+JZm6CFmJmMcWWrrz1RAz3eau1zMxMPpiQhNFrR/6eLTDHm3neQ2UAroE5Fn6JXgD87DB5dw/ruvxUMHFk3bItfQUzUEfMs/3YSv3LkXv3zxaUYmKfXWZZhrdAa+eESnVEPvP6C7Zvh7BQ3ft0zFPaf/JylzuRQmRZXDgwf0fhIqEmaF48e/VqWBu4HaV3cx9Ky5pKETQjFMho5qht5FcjFsi8YSdMXPq77L0bbmPxolnUK/5yStIDt2pijvbOziW4bkYjD0og99shZAiDw4b66rgE73o01eOglj6KwDAbyGXgquP9/z+Dz2zTfxgycW9Q0WCoFjZ+qaJbUTiYOLLXzlzr346QtOwuWXPNXYX1VAT1KJhWZn4AyxfE1R9rrHGOaa4e8VR7IyrMRYekEntWyLVi2XMAgQsIC2MslFGudiJZbMYYCaQc4FztDtTFECL6A1CFAOBYByl4uV2FMWPHkmI/9+1WpE+jcqNHRDcilh6P3Oi/CgbX+X/147Mcs42+VxJ6IQs5Mx7tk7j49/5yEAwJapmpIPKWs22zYOA0SB0IFfaei55FILzXu9neTBfqllauje5VICOi+phGaVjXbeM0ZhgKNn6vj6b70Y5560Be0k1Rfz2SfMGg8bAMxO1krZ6UI2U81v7Jt/eAg3PXSw5/ZKWXRsuJwJ6piYFthjROe76De9eC5j6LOTsU5Y6YZUSktDJzaYIMnYKT/FfUkuRkDfgJKLTiwqSi5GpiizbQaiXMNeCY4stbXHPRBCF37ikouupZKd+7LYWZZYpBe46MGHbsNeLLxMQ1+V5FJlW7RGdzlDzxhzHOjzM9do471vPEuXOqa20rMXhQHCQGiGvsmSXOIwQCg4eclNAo22qaH7gF6CPPWfr/OXaB2c5JSTjppCLQrQSfI6yBOOMqKzk3FpejoFOT55+RdX34N3f7F6knS+0cZ19+zDe6+5F8/546vxkvd+A1JKfWOUFb5fyaQoDxatPhn6cjvBVC3EdD3W/uZuSFJ3ca6coQuj0+wrU5TVuElLgs16QksumqEXWXGSOiSXAQ23k1RivtnRNf/pPHN2CRSzJMtdLm4fei8OlDI7Kk/9B8q96P0GuKr5JVtycWvoueRyzslbAQD/8J/Ox384d6cmKI8daeCj1z+gO8c4FIiCQMeP6XqEJM2f4yg073UuuVCS0VoF9F7qoW9I8GzKI5nhv9lOder6FrawQBwGWOh08t45Ki52USW5UEA3bvyO7DqB+MGv7caHv/mA/v98o4OP3/AQ/uBf78S/veNSw57HIaUKljbLqEIiVx74mp0UE3GAmXqEPQeXevu91E4s4i6XFFEojDKh/czu6+JciSwMozcCqBlxoJhbw2Fb7FiToryA1mox32hDyvwe1wFdSothE0tM9eculLlc+skUtaFsi/n/B+Vy4aPcqsSi/fNN3PHIEQAqdZ8vAl0LlfvoZy44CW84d6f2mYdZQP/KnY/jz79yD3754lMBqOvMcyoo0WgxC9a1sGjRtSWXCfasDBMjHNDVX5uh04nkE55RIBRDZ72zjdnJONPJUyPDEchLldr6oqsDaLQTvPuLd2G5nWiGdv3bL8GVNz+C915zL3Y9eAgA8J6r7mLrHtoaurK8tZPqCnn2d/K29XfTNNsJJqIQYlL0LLnwgM4Db7NNPnSBkNsWVyC5JKksJItsBOiJ91Bk2mp+bNx7ze8jbi1cLYhIUECnYJKm7kxPCmYky9j1uA0fuiOgVzH0Ug2dJRYB5Qy93wBX5XKhfT3tmM24d+8C/vM/3wxAFdci2XShmdc/F0LoYA5AP6/U+SwyBs5HWxTQKWkoDgPwkNFKpJYYbcnFV1ssQe5yAQvoqVFWlBCFgaGh1+MiQ6eHwyW75AydPSxS/a6ti//Vtffi4zc8hM/ctAftROKELZM4cduUfri3bVLD5C/cltd8cFWMJDmjVwazGsmlkTH06XrUu+TCinPx36MVi+zEosNLbXzp9sd62jd3/xjunTXO5OU4sKAm3YH8eoVCoBYGzklRkp0Iat3PwbSF5nq2TKp7iX4mLWHoLhcOR6cLQ1+Jhm5LLoPS0Ct96NlPvO3lZ+Anzt2p75fNmW9cSjWq3sIWHeEgHZy+R5JKnGnogKr3QstZLjTVs+JyudB50QzdT4pWg1dbzCdFExxZbqMeB0bQrumAbg5/OKgDcLFuCvKmJUxdNLpghEeyRam3TsUZ21cXmv5SEAfAGLotuYBVMuxxUpRt1o80IaVEq5MqX249RquT9lTulDN0OxmDghm/ya+8+RH86ie+h33zebbt40ca+PA37seND5jLz+YJV+bE2nox9G/cux/P/ZNr8cr3fxOAmesQR4FTcrE1dDHASdHDmcQ4M1ktuejiWO3iexz0nVoUOEsHVJ33KudMb5JLf+fEkFxsIkR1nIS5hvBULUQq1XFQuQ8XyKlCAX2xmTPwKAj0awrOC4yh2y6XMtuil1xKoD2ySWow9MNLLc1cCFEo0Eml1hLLNHTAHdBpaGUmbUBvv2miWKZXJTbk8g39tTsAwF0Pnbbv9QYwNfTebxruyyXXxFyj7RzFGL+X5i4X2kctC26koQes3zzM5jkeOrCI37vydtz56JyWD778GxfjjGNnsn2Ty2D9NXQpJd71he8DYFp01o4wUENxI2DqzkhZNwl8IefVgu7RouQijUlKuq/4Kjxq9Gfuj+6XTbXQmSlaNTJyBfs4FIXUe5sk7N63gJOPmjLu2z/5wvfxymcfi+eevK3091xriqapxF9/bTfu26fS8INAGKSNns9GO1GOrhKGTh1wK1FtXW4nECK7zkTMAqHzD4ih1yyXSzuR+pw12t622BP4kl/knVYBvW1MiAJAFJDLxRz+cMxUMXTHpCgFHVtz5gX/W53cukY3xLIjoNvZdqnMt++VlXLm0o9f2wjo2TmwCy3Z4Mlbgcgf+HoU6NroNkOfz27+JJW46aFD+PbuAzhhyyTe8/pnA1CTWLr9TEM3M0X7D+j2ijT9QEqJ/3fbY9i9bwHHztRzV0/WpFAIxGFgBkwpsX++WWDowQBdLlpDtxl6Kp1rgvIOx9UEOq9TtciaJyKGXhHQHddkdrJmlM8FTIa+f76Jl773G/jDz99pSIof/dYPcM3395X+ljom/lrt/73X3Iu/uvZefOE2JemFQhiEhDRvkmS3dGHolFy13EoQZ50yneM4yhk6PSeKvJg5F/aCIb58bhfw4lxHmORy2DGkUo6RtOukKFAmuWRat+NhsRNxWpqhq2FXTUsu5Qy97ZgU1ZUMe2TbK82opE6uHoc687Db6kK85nwYCB3QaYKp2U71ZwRiM500n8v46M+fh3NO2qI+Z50Id1fY3uJ+8dMfvQF/efU9PW/PV19637X34df++WacsGUSrz37eP0+nxSNQ9Pl8tW79uGiP/0a9s03ELJZ4TAQAysuRgGd7tmASS6diklRwD3io+9M1kI9Ck17OO9qNJZ/tn1zDVEgcNqOTZWJRXS+vnHP/kJ7uCTpgplYpP5+90EzHyQMhGbEQC6/UHyYLQnoWkNnDhXqxKlzjoJAj/AXmm39WVjQ0M1n0GvoXcC1StLQm+0UR5aKPXBsTYq6JBdi9dUMnU/IwLk9MZxUqgcp0pJLpqE72KJLQ+dZmOZn7htixZIL6+SmteTS20MVZCycdwqAGqqGgWlbpOYp6SvbPgr1b/JRAS8rzA9/JZmi++aa2MfY/xXfvB//ktXsduE9V92FX/jYdwEoWWDn1klc9WsXYyIOdTljPikah4HhQ19odtBKUhxaaps+9BLJhaSofnB4uYXpiUjfW3pSNK2uhw64JyFbmqGH+dqoXUZGf/6Vu3HG738Ze+fyc/vsE2Zxxx+/HGccO53VQ6+eFF1uF11ci83q0ZSrLIZ9XwhhPuObbcmlLKBbGvpSq6PPMclntTCXcxaaHW2B5C4XLrkQ6j71vxpa32MsrtFRk6IFySUUhuRSL0ksAoqMG4AuytVOU3zue3tw1e2P6R64GNDzC7bMenhi6MsOBuLS0PniCYRHDi/jzD/8Cs77k2u040J/h08eroChT8Qhk1yqGTqvOR8FQgeESRbQVfZc8budJJ/LqMchpiey32xyhp4nXLlWci/DB792H255+DDedMV38GN//S08engZndScKPzsTY8U3DY8MWrPoWXsySa2Dy+3cOxMHbNTsQ7OqeQdGrKA7g5CfIQihECSdQg/PKC8/o8dWcZz/+TawqRwNxxZamOW3ePEDlNpSi6uNT2l4xR2WEB3LTJtn/fd+xbwN1+/HwCwl01yT0Qh6rGq0U6SSxQUS1NTG5dbSSHAdWPohuRSkvikJBfO0FVAP7DQQipRiA/6e4HJ0NV9bDH0MDdcLDYTLY1y8sIlF4Jm6EPOdh7dgO7o6SixyLYlxWGAdlrN0OMw0AXvbZDkIiXw99/6AT554w+19lfG0AEasmUaelAuuRQ1dKnbyB/QRw8vY7GV4ImFFnbvWzD3wW70fiSXhouhV1SdBEyXR8AklzqrbWJr6Hk7c4Y+EQXYrBl6fh5Xmvr/ga/uxhdvexQ3PHAQtz9yBB+5/gG0O+bD1WArKlFbz3/3tfiVj+/Sv0nBjM/H0MPeSXMCEQVBQXLhMDNFVWf+0vd+Ay/886/jtj2H8cR8C0kq8VhWZ3+h2cFz/ugr+Ma9+yuP87BFWgKmofOiYPbya4CbIdL5URp6LmUCamRpy37f/UEucfDdUdCqRYFeOJsmKLlXnztA7BHnouP54OD3Q9kaAGEgdJ0dANic+c73ZiO1MpcLseycoSf6uY2YW43I1nyjrZ/vskxRgmfoXeA6MVSs3h5S2YlFNYeGDiiW7qrnwic+F5od8Prftt7Mh1o8oNei8klRHmCkVDIDPRyGa8HQR4u6O2HlLpdMQ2fH+7fX7cZff/U+4zvUAVF6f+7vp9omacbMigG9k6ZodBLUImX1CgOBqVpoSC5lGjpf09EFtbBAPv+w2OygnZoPF9d9/+bru/GiP/86Diy2cO1d+7LfTo2APqu93nleAF2vQGQMvcQFYrtcdj10CPfvVyOrAwst3fHSKOnQYgtzjQ5+mGXrXnnzHmOymGA7uThDbycpK9VaDOhO22Kaz4HQ/avr1URhIR+AtGMbFLSUxKnWmQ2FwEQUGgzdvI/NfSw1q8mEIbmU2Cptl8tUJrnsm1MdZ7nkYuZVLLUSxJHJ0OMw19DnG508oFsuF3uUTGRn2Iuej2xAtyfehciHf4WAHir3BQ2h7MJchLL0fyOgNzoZS1P/t7fnN9dSq8OGbDlD3zxhukV5cKb71SW5VHmy+5EmOHg5hKlaiDAQmi032gn+7Mv34C+vuddsL2PokRHQTYYeOu4u6lj5Azddj4xJ0Y6loW/bVMMzjpvBX1x9Dx4uKU1AHWE7SXWAamdFkngAabTzpI+v3rVXa8DPOG5G/6ae8F7mDD0/9iTNOyySF1yImeZEBbQIrSRfTJtGSfnSeynmG2385v+5FZ+5aU9hv4eXLcmFMfR2KgtZia567Rzk6piKQ01UKPBM6IWw8++VuaCIhEzo/IQUgVD/N+qMl9zTQHeGzrenl3bwDCyXC9kW92YBvczlQkGZy04FlwvzoTc7eZ5JsdpiieTiGbob9lDtqE0T2Jc9nLZGRk6TxWbHKbcQygL6fKOjJ57mm53soS/T0E2Gnk+KZhp6uxjQO8ZNql7XHAGdz5y7FsWgNv7vbz+ID3/j/p4W5ODsWgiB6XqE3fsW8NufvhW/+LF/d37HtC3mAX2SBXQ7U5S3s9lJjAduuh5jvskll/yBoro27/vJszHf6OBbu59wtyk7HY12ntDSypKcWhZD54H0pc84Gq941rH6mKjcQDtJsdDsMGsgqyyZ5FmgkWuiIIO9wAVHm+ms1KnStSbyAbgn6Y8smRN7dJpTqVLO7UJQ5opKxXZ2UhV463GIvXNN/Nd/uUV/10UsSgN6RBOHtDRfqoNro4Sh9+9yYa+15OLS0B2SSxYfZks0dBpQ8fslsghZFOY+dACMoef7aVdJLp6hu2H3dMfMTGhrXDGxKB+CuyZECa6Ki1JKzC23sTXT5WklFs7iAOCWhw/j63fvQ7uTGgk3NR3QWS2IuhXQHe4Z6njaJZJLcdk6iaM2T+B3X3kGgkDgf3zpbvzLriK7s8FXcAHUw/qVO/fiX295BN/JJutsiYq7PMJA6H1wZkj6ug1K8OLXYfNE5JRcqDhZIAS2b65l77nZMH2HBwQKihTApZRGWddGJ8FErEYl1IkQQ7eTd+jyUalgGoLXXMOQDLYP3TgPidQjCWLoWu5Ic3nQVVtnqZVgE8uEpM6CRih6/UqdKepm6F+9ay9++qM34K+/thtRGOhj/tz3Hsn90w7przygk+Si2tPoJFr+aDo0dLs9QHeXi2u9XfueCALTmkyTovtKRvCEyJJc1LHYGnpg5LHQsdqVRQsB3dsWq2Gfl2Nm6vp1MbFIneyFHhi6XUFxsZUglcBRm/NOgq+kQw/B6/7m2/jF//3vaCXSWfCH18YuSi5FxuJiRpzJuxh6KAR+5UVPwb9efhGAfKj9kx/+Dn78g9/Cf7ziBlx3j5m4YZcUpprQV771IvzNT52Ls0/cYqyRyH+bNHBiNDxIR6GAi7t2UlVagLOc6boZ0PnCH6lUwTCOig8bB503PulMqds5EzalDZJ++OIT1Flrr7ejRG2SysLIywWuodudWytJdQEnYuh8Mpja6soJsAvIkVRAnY0tufBzxu+bL97+GL69+4De5o3nnag/O5TZKWlfnLUuNNtOYqQZenZtl1uJZv4Nh8vFbg/Qnw89LdHQlQ+9aFsk+6pN+PLvFdunEwO15CKMTpyugxCm5GKPGspsyIPGyAZ0LrnEoTCCuN0D00lfbHWcSUWELVNFyYVkCyqqBZhVAF2SC68jQYGIM/Rpm6EPREPPg452OWQdz40/OIjb9hzBdx44UHBQ2MlWn/ilC/G133oRzjxhFq9+znF4/TknFI6THiRi6C09sWp2ZC65UNkWbckl0qMr2oaOMZVS2QODQL/nAr3PJ52JodM5pGPlCTf1OETEPOKdzD99hMowW8k7NPEYOSQXm63bLhf7POQaN2n+efsaJZIL+eCNxTN4pminOCnaaCdajuPXhIrHEV5w+nb8xf93FoBiQOf36EKzg+2bJ2CDu1wA0tCLDJ2zfZuRL7WSyolD/pFm6FYnHxRsi+oY9s01UQuD0lE6jaIMhh6YDJzqttSsDr2KoavSAf2V8lgpegroQogHhRC3CyFuEULscnwuhBAfEELsFkLcJoQ4d/BNNZGkUmuHs5M1I0DYDJ1O+kIzKXW4qP3EWG4nxgUlC99Rm/IbmFvXjlgWv0JAZzcCoSeG7piMMjV0y+WSSq0BBpn3l9gwAPzeq87A1qnYWEnlnVfejitvfkT9XhaMn37sNE7bsdk4J+o8FG2FUagy5PK1F7kMEDhTxpNMcjEmRSdiy7aYMelEBXSVwENp2V0kl3Z+PYit08PVsJhwo52iHoUqLZ+x4w5j6HQvcSugYuhFycXW07tr6CZD5xq6ZugN+/7K5lh4FipzuXTStMDQm51Uyw7G5HknxeaJCJ/91efjT153JgDoej50/JpYsLVG5xsdHOUK6Lbk0s4kl7hccjnkSKxaLrGBAu5JUTvTOsicNQSaFF1odjAzGTvdV4Bbcilo6Nlf6ry05FJg6GwfgShYSYeFfopzXSKldM9IAa8EcHr27wIAf5f9HRpSCe0w2DIV6xsvDEQhYNJFWWi0sbnu1s8AM/1/x7S6YefKGHp2YeasErqdRBrFuuziXIB6SGgBC8CSUioklyqGnkhp6LRRIIzAMBGphI9OqtwVv/QPu4wJxjLW4iqJQAwqsBh63WLoLrm7nU2KUoABipILHWeSuVzod+zzwUFt4pLLsg7optsjX/ghQT0OsNA0fzORLKAXbItqRXfbnwzQNc5/v0pDV7V+TA2d18cn3Xu+4KJK2W8pGC6XRBYscs1OislaqCy3lhsqCgWee/I2XRCLrjcFWn0fcobe6OC0HZv0/+txYMyL0HdoZFCPQkPK5NfQNem72OoYzxCHa4GLTiJ1G+h88PuZ76ssqQhwT4raGjrZjyeiEPPIM0nLqi0C5gIYo2JbfC2Af5QKNwDYIoQ4bkD7diKVUrPf2clY38Szjh6YetGFZgf1CoaeF+jKWQMxUx7QaUGHiSyBgqd+t5JUuz2AXHLhDzf3stbCwAjUlMlHzK9fDZ1Aqea82pvqACU+8LXdBbdI2dzCzGSUnZP8weO2RbOWixlkXMPLJE2Lk6L1CEutxGDJ6q+SP4RQGmUtDNAueSCoTVxy0fXmC/bAVNesnohChEFgeJp5OYlZe1WgNLMtWhnA9msARi2XYkCXBoMG8tGHydAtSa9Do6P8t3KXizkpyheJpnvSqC+fFBdzoWfg0KIpufD7cK7RMUastO8J5kOn4wpEztA7SYrf/dzt2osPmKUPaPS8VDExaksuJEFxgkCdCG9fPpovD+h26j8/llzONDutmv48308nkcbImi9Rt1E0dAngaiHETUKIyxyfnwCAF8jYk71nQAhxmRBilxBi1/791dlw3ZCmUgfLLZM5Q3d5TOkiLDYTI4PMBmWY8uBFzHE7mxSlHvyoLMiTv5VgMHRyQ7COJGI63kQc4O7H5/HK91+PRjthkgsFdHcQd/nQOUuIA2sd1SjMygin+OSND+GlzzgaL33G0QBUQIhtkTeDllwcPvHctqgeQN6RcV2ao5NI7S4hTGejJvKi6yJYEsZSbnEoukoui82i5NLSk6LkepGGXTMMLIaeShxZaiEQwHR2LY2a46k0NFWCfQ7tWi7mechZHHW62q7JylTMLXcKQRiwJBdL36dg1kml7rhIBuQDnA5LwiJQQD+42M7OT6i3JSw0lQ+ejreuA7qpoSuGrmx+jXaC7z54EP/83R/iAyxR7RBj7sfPKmPDYsXEaGKRGno++L1HnQiBWw2rAnrkGAXmRbnM621LLrzDbrHRl/r9vLzuRlmx6CIp5blQ0srlQogXWp+7okGh5VLKK6SU50kpz9uxY0efTTVBkgugLhKxA5fHlDP0qklRl7yQSy45I6GLtTUL6D+0kl24lpyn/vPJs1zjo4fhrsfmsH++mfvQXQy9wh2QpqaOF4YiY8M5Q6eM2UY7xclHbdJWzIkoKNUVXWWF+aRoFOYOER6kozAoYejKkme4XGhJr2axCFo7yaUkShBzgc4H6a9xKJirJcX/uOoufOzbD2b/5yOXUC3gzEYFAHBwqYXZyVh3kgZDZ+n1cUFyyVHlcjF96JltkUlw2srIisrx9nGGzl0u7USiXsslF11nh96zJBfKhCTkGjox9EC3g77TaKeYnoj0PawZemyy1kY7VRbCjKG7SiTQ72yqhXj+U7YDcJfHIFDnJoS5lu2kZeO05T9qW1VAp/uM33+xlnLNAE7HnhdIM88jnweohYEerW2ISVEp5aPZ330ArgRwvrXJHgAnsv/vBPDoIBpYhoRLLlOxvvFcDN3Wr8vgDOgOyYXY07aSgD5lBTYgvzGoPXZWndpvXnI0zJa9KpNcbB96IvNJYqBcQyd9Lw4D3SFVWTldJXV1cS6rXku9B4beTqXWrgl2xUU+4dvKhu1ANmfSxYfO65Lo3+yk+NIdj+OLWVGuJJU6XV8x9CBPLEryyW4+0rKtgXwFG0IlQ2cf1cJArTtpMfQ264B4yVt+P5Lkwn+Xu1ySVBoMnZw9OUO3NPTAfB421SIEgmvoJkOnUdTmeqTv3botuRBD7yR6grLZTgxpkkDa+v/7Ly/AG567E4A5yrKh5lRoSb+codu+fO5ECVkpgF4kF47Y6rjpvNetzsv+Lpf+yDwAFEsdDBpdA7oQYpMQYppeA3gZgDuszT4P4Ocyt8uFAI5IKXtbQHKFkDKXXAyG7pJcjIBe7UMHYEzgzDUUq6eFZYGcoZcFdHNSNLsR2IMTMQ2dB/QGK1YUCKrj7pZcXC4X21VhFMLKNPQ8oOdWz6o63fU4xEQUuBl6YJZR4MNeXi6Wb5NkjJNfB5JcKKDzjqvVSfVklSoUVe1DJ3CnEU3E0nXrJDlbVBq66QFXv5uUBs1Orxo6nxRlgYGOI/eh54GcfoOzct6ZtvWkaFFysRdwSVOpnT25hp63r5XIQpuDQGC6HuMguVy09Kd+l+ylmyfygE7seMKaL2p3slousap346pjRKn+YSD0M+bajpDIPGktSfN2cYZOHCNvT2DMsZXBFdAjK2BrycWyI9sMnfvpoyBfvWsjMPRjAHxLCHErgO8C+KKU8stCiLcIId6SbXMVgAcA7AbwEQBvHUprGbjksmWSMXTH8lIxu1Cu1YoINNw0NXS1YAa/8Sne6IB+wGLoDsmFP4C8pjJntZyhB1mdkFKG7nC58BsyClSBJFtDp9+Iw0BLLlU2MaCYQcs19NBg6KzTYpOifMTScTB0e2SUpGx5uyTVv0FFn1ywRwP8AW9bsgXPxKzHyldME4iJDuipdT7zIXOS9ia5uFwucRggjrKONfutg4tNvP5vv43b9hxR5yiRhjzBJ0bpfjA6m+xndFliB0OfdNgWO0mqXRscKsGuZeyLfpc63el6rCW2U47ahFoY4LjM0547kqSuTd7qpM5SFK2swwmEwKasjVX1XFIpIYRaKStlC3rwERndL9S+QGDlDN0qn1smudjf5bIRX5N0976FFdX17xVdbYtSygcAnOV4/0PstQRw+WCbVo0kzdnFlqmaoafb4Ay9XsHQozDA9ERkSS4dTNcjZ0bgtiwgPlTJ0IsX3JgU5ZJLO68PHToCel62tShnkL2PoBhyWnC50I2mAro6V92qM9o1bvLFeE2GzjsnpeFnD9tEqOud0yLSfFs7oHcSqR1E7U6qJ9miUHSVXAib2AMupfmAdRhzncgSi1KLoTc7qRmQmY+YSxWVkotD56aHu53mksvDB5fx8MHlfM1SFogBs5wxBTAzoJsMnaf+U0dGnaptW3Td1zOTkSYpuctFfY+C8jSTXM46cRb/8yeezbIqc92dEosAYP9CsXIkXc8gENqBVpUtKpnkkrBzaI8O6TxQETU6jirbYihcAd3W0G2GXnS5qGMwA3oggNeefTyuvPkRnHvSFvzs808pbcdq0Ouk6IaC1BOHuW1Ru1wqJkWBaoYOqElAe1J0ph4XHlb6LSFQqABo2BZZAXyeXZZLLhZDT/NJH+XqKOrmE1GRqdqSS74wdi4txKHQD0scCs3Qu8E+J6T2cDuWfdxRIPTam5w90ZDdxdCJFXbSVF8nFRTUdrUwKGU3dm0fztCBYvo7XzUpFMJYxxRQ18KQsJiPmCcWXXLG0XqbSobOGF4tc+vYx7LYzCUnQ3JhzJYCYOSSXNjCIXQs2oFUK9oWuZ+eY6Yea1cTPVcdS3LhAZ1nQvL20GtqD1/dKJdlcgJDRKiqngvZc4OM1NB146NiOtcTUaDP00oZep5QRBPzprOnxp5vIB8t8U4pDlWn8r6fPBtRIHT9+2FgJAM6kTFK/tm5dbKyB+51UhRQwYsY0fuvvQ/X3/dEpnsWvxeHATZPmGnrgFtyAcwi+dy2SFAaunqtJZeUSy6pCvRRUNDQebVFIGfx3J4XBTlDr0X5pGg32AydtN5AmAW4TMkln2jkgX5RB3Qz9V8IpRWnKXn8sxoiLLBG1pzC1Xc+jgMZ67MZ+lStfCTGpYiJTHIBVLCmY2t2EqftkIII/f9px0zjy79xMd728qcb9X74d9Rr9TcOA+3WsTvlBbaQtiG5GJOiZFssMnS+cAgdZ0NLLrlt8bM37cEHvnofWiWSywxLvrNruZDkspm5XCIrEHLyIxhD50sB1qIAQnCGrtodiG6TolKPDKXMGboR0EUedHPf+Mo0dC25WHMmBYae/SaNDEluUt8N9Lng8t4wMJIBnR7es0/cgn97x6U4/ZhpPaHC7YUEzmaqJkUBpaMTI/qra1Ud8JO2bXKWSQ0C4bxBphySC3/NGXrdZujW8mZm6r/UacTdMkVV6r00qiny5dLiMKgcfnLMTsYGS+STovxhNmq5cMmlxgO6GXTUsQrM1FWnoTNliaF3Us1+uAS13ErwK/90E/5Ptj6oXWWgKqADude5HoX6GBIpSzV07kPnqwIBwBnHzuDyS55amBgr1dAz6cjOel3Qk8JK86eMZ2LLp7zji/qedHU21HnXssCYMoZOkksqJX7r07fivdfcWym5EOxqiyS5bK5H+hqFFss3JoNF3insY/kaSnrKk9ICIbLgH1auuCWlGr2GQugyx0A+RwDkwbWeVdLkx1GdKVouuRQ09JhkQFNSnWLmCSIyxgS2EEPNFh25gP7lOx7HmX/4FQDqAhyfTcSctXML3v+ms3HRU44qfIcPK7sxdD4BuHUqxsWnb8c7X/0MZ5nUqCSgcwuVmRqeP9S6h7dcLinTp+1EmiSzy7kWHC5ILg6GriSXRH9eVnXOxuxkbKy1Ss9bKEzbou0F1pOirINzMXT9G8ttHTj4ZFzoCujZaIbaZbMeLvO4sMikH9PBkgd0ft/YDD1y3A82wwudAT0beXWKAZ0CGTH06XqEeqwcRuT8uOGBg2o/kYuh5x71SHfo5T70jsPlArgZOrWVOhcuc9oM3T5vLoYeZ7ZcCughOz9lFTXp3NAC5EkKNinKJRf1dyIKdNuIbJQtP+c6DiB/fosauim50DXg9x21yS7TMEzr4sgFdGOYxoJJEAi89uwTnA8a7yGfdfxM5f5nWEBvtFOccew0tm2qlT7AxHjPPCHfL5cYXKU2leRCkkI+tOYuF1oNhxe4ImZIDytHKou2xbZlz4tCU3KpKlRmnJN6hPlmhy0CkdfM4KelzIfOXS4kK9gjJR3Q07wDUufHti2qfdJx0cjBlqC6MfSFpplYBKigRfHO1tDpXkuy7EvXw19g6A6dWzF0t+RCIA2dlgWcW27j8LJZxCo2gia1Obu2obLJpZLZFnWyUb6PVpI6R5486OW1XMifr0rnTkQhc3qUd2RccuFLCMahuo/5pCiQr0dahlxyobo6RcnFxdD7SSzisBk6xQG6P23TA3/26ZzzDo7cOcNCP8W5NgR40HA8U07wYHz+qdsqt6Whv8wehrpj2JT/vsAf/Niz8NCBRTzvlG145fuvB6BuSvKQ85s9cjD0ZeZmaHZMH3qUJaAQkjRFmE1EFmq5SLMmM2foQTbByu2bnDVQynXpOZmMIaVarWl2Ms4Zeta55PvM28bro3Dm7poUBfL1XPOVctR3momZWESyRF4vnHRns832pKiNxWY+6eeq4dHspM6ArG2Ljg7ejgf2mqJ0DOreKDJ0Ak1mkkw212gX6vTzDM/c5ZIx9EAxdO6WmYyLDL2dpM6RJw96mqGz+uzE4MsZultyMdofBgUCAmRJVxUMnUpc5JJL8R7LNXSToU/GYaXk6p4UNTV0YuRF26LanncsLsklCNw1jgaFkQvoE46hZjds31zDc0/eiv98yVNLU9wJs5MxFluJXspMB3SHGyAKBF70tB0AduChA3nBIWJh7cRMTuEaOl9/k9Bomz70miW5VGnoaSqNbETucqnHoWb8dltu/L2XdA1+vITu7GTMinOZumMUqGBFPm1i9Dx4V0kujx5Z1sfFE1p06n+Qd3BFhl5uW3RBa+iMxXFnSbNtTopqnT1VyT0uhm7b3twaunKEVAV0Ks9Qj9UE6txypxjQHa4SPT+SaehccqNAw49RSnM/BKeGTouiZ3kZAMo1dEszdsmciqGbgZ/aXrUmbpqqbSkwUrtcLpdjZyaxY3oZAPBjzzmuK3Gpsi3mNV3sSVH1PsUVLi+S/GIvRjLMAl0jF9BNht5bQJ+IQnz2V3+kp23pZqbV1unC0eo8/GLwYMZ7fprwodf6fVZyVS+k2+aSS15x0JlYlK1lSR5zDtIWCVEQYLmd6KE7/W7eRvWar/RUBu4TPxFccgmMTiQQ6hgbUOw2LwVc7XIBVPmGOYeGTr5jQJUuJfucvaJPVaaoC7wdroDeSlKzFouWXFIkiXSyOft+tCcHAcXoaqG6Nnb5BgJn6JsmQhxYbBkVQAGTYNgMnY+Uctuiuq8PWF7wbhq6vRTikeV8PdNSl4vVtl4YOh1DLewS0DPJJcgmF+kcTsZmETUAePsrnq7PyY88dTt+5KnbS/cL5OsI8FspX2LOnPOyC5FRZ8DlRboHozVk6COnoXO253qoVgu6WWn9QV5wqqpWB2chcSj0hTYyCaP8pj1tx2YcMzNhMG3F0MslF6ohEgaiEAzoRieEgcnQVVvyNlathWnDLtDFJ0XpRqcEjnxBAKH12nNO2oLnnbIV2zbVmIZelFyOLLf1w+y6zir71ax/MmdVaCR0G3UsNDo68Lkkl7blZMknRdV1cEpwFdIDlxSiLPW/TCsml8tEHOg5nSrJpcDQw0C7nBpWLZcnCgG9WkOvs5ESoCQuyqjmPnQOU0N3535w0sO/E3eRXBKZ2xYTmc+n0fGZmcthpWbuQmGUZbtctGvG1Me1y6VWDOi1NWToIxfQOdsbQjzX7ISSIHj9dFt2MRi6sXBsYMgrBJ5d+OrnHIcbf++lOOekLfpzpaFn+3ZILlSHm1sCCfakaBRQpmhaGB4CcGrAZbBXLdILXAS5dmjXugiDvM77+aduw6ff8iOYjEOd1u2SXNqJdE6amrbFPPGHt6kfHzqgJkVpFBBqhmsmtBjSQXaciTR96Bz2W1Uul1YiKxm6um6hntM5vGwG9MjB0Cl4q4Bu2hbpWJ9YsJh+F4YeBmaROENy6UFDDwOBnVun8LRjNmMiCvSSd3EoCtIMoBhwq+S8ANmILYC2Zdoulx4H7aWwr6u9wEW38rnctqiDPmfoorge8iAxcgGdM7duevhKMGMxdB54YotVmiVxA2M7dw2XovTx+695Jr706xfjuNm6ydADFF0uKUkuRZeLSiyyGHpWVzu/scxRRK8opObrEgQBY8/mDZ/PL+SlEGLWEbkmRQHgwEKr8Dm3tBUZuprA5gFdWeWqA/pSq6MfSuqYbWZoBia1bapdLsVHh86/vRgCf6+XSdEOSS5xgJnJCHONTmGpNiOxyHK5kBMqyVL/a2zi98BiDwGdaehRQMXEHJJLdl/ZQZCkCzonmyciXP2bL8Ld73oFXvg0JXuQtZKgC2plls4y8MQiKpQG5Hr1akftxYBuXVNrUrRmjVJctkWj8w28D90Af1CHKblohm5Z8Tj48CwydHNR6NHVNsX3JqIQzzhuBvU4NBKLlHxhJhbRZGNpLRfO0PWkKGPoJS6XbihILqzT+cETajL4Vc8+zjjGMBB41+vOxLd+5xLN+Iy6Lw7bIgAcWMzmLvhcibYt5h2ZXuUnm0DkPnR7ws2FxWanoAHbAd2V+k9rjrpti+ov5SG4XBzatpgFdNeEIa8ZP1OPkaQSjx4208UjB7s1EouC3M/OnTxPzHeXXLhMEQQCcWYvlFK6XS4lORqAyZgpcQjIaoQHxG7ZKCxSSVdfufNxfW/Z54byH1Ip9TVzSS4rQXFiOzD+UkfKKzkC+QiO2xbpPHF7sJKKfEDX4MxtKJJLxk5yhl50hhDsDoUvS5Wz8eL3Xfr1RBRkiUXq/5RY1OrYDD3T0F2Toqw52rbGVgcyNPQePeiAClBhILSjhBhGKAR+7KzjcfJRU/jDH3+W8RtRoOYRdm6dMtpEsCWXLRZD54HOkFw6JkMHFEvnrCcOgsJoClDVMTezBYPr2qVRnBSlYyDo8qcViUXUhM1sRJIfA/R7VAmznUhdC56DM3QKrnaJZpfLJbctCnb9lXRDh0Idpms/hMk4z54NhUAcqdWvFlvq/qRnpExy4W0qe0Z4pxtaRKOdpPiVj9+ES/7iusJ+KVOUAjp3RQkxAMmlUGDNZuiq/badeetUDRNRgOedsg2veNax+LWXnI6fyOq7RxYx8C4XBpe2OkjQA5S7XFiSUNQloGcasamh85vVHLbZ3212UsOHXsskFyklbn74sE5oiQJRGK4nqbQshCroN9qpDhquzqUXCCGMei551ccAP33ByfjpC042ftf+Lf2ZUYLB/Jxqoucr5RQTReJQ6No2dr1wLkHFUeDsND/2C8/DvXvn8bbP3IbFZqJrr9guEf27rkxRWZ5YRMxrcz0CjpgjJqPaIpNcNk9EBV2bGPpEFOjRkV0AzvY2A7ljiqr78f3QNr1o6EIIzEzGOLjYQhBA3290/W2Xi7MGSuZ2sp0/nNXnDN2UJTmJ2T/f1DWbgFxyoREouZ6UZTZYveRitbcWmp2WXZyLyNJRmydw2x+9DBNRiBecrmSlPYeW9LESvA/dgmsYPEgQO9mnJRc+qWlJLkHJxeeSS1AMoq6HaCIKVPlcZlukYlSf+94j+K1P3wpA1a8JA4HltnlTSIfLJcnqoe9wJDj0o6ED5EIxHSUOGbmQOcdB568WBgVHCHU6pPFy5kqbGpOiNkO3JBfXOT5x25TOVlxsdnD8lrrRLtt14nJhdBKVK+A6PholvOQZx+BFT9uBU47aVPh+nKWjt7O1Pk86ahMSqeq/0+LJnTTVSW00bOdZluQoys+Pek2jFtLMaYRWj/NV5w8WNHT3fTBTj3BwsaUDZTuRegJaSy5xfr/bIKZrnyYKgMrtExjtp/PD6/O/88rbsdDs4D+cuxNveO5Obc8l2ZHuBxoVrzagF5xK2XGcf9o2/OJFp+hM87NP3IJ3ve5MPP+0vNSIPW+Tj8j5tSrWHRokRk5y4XAFlNWC2IlzUrSb5EIz3wFj6Nbi0Go/xZuuHodouBa46KS489G5fB8lGnoipaXpC12cyzU07oehA+oBtzV0Z9BmtkUbdho2BwXwRw6rRJAdm3NWRg8ZuXvS1Covu9wxzkcUBO5RENOSF1sdfW1thmsfC1DUqV3XkNpw3Gwd73z1M90ul0wO6SRK/92+uYbr334pnnvyVr1tq6PWG1UMvci5CvdhIVNU6ExdcsuUkR+XNAXk8yYh1RRK0jygZ5+df+o2vPbs442Oi0DXv4yhl0kuEywbGACuu2c//u3+A/jqXXsBKFmL5pdarB5OFKp29pqbUoayujQz9Rh/+GPPMkYlP3vhyZXSpWu0GgivoZditRevDLOTsV6NnE/evexZx+BSVv/apQ+q5aZ47fMiK65i6Lbk0s7YGkHVIA+Ka4qm1hA/yFP/td4XFUcLvWJzPcI3792Pyz/5PefScvZ+3Qzd1B85SHLZc0gF9KNnWEAX5nlrp2mBofOAXiuRXHixppRlAVOw64Wh0zZ2diSQd3Suc2tMimYTf500ZZNs+TlZaub1d7iFULfL6kxyl0vW2WTlla/5/l5ce9derS8T+OuyfAT6Xe62OmIx9ONmJ/H+N53jvJ4uOQXIGbopueSfx2GgS/T+99c+C3f88cvx7BNmdQ0iNRJVzxJNUNM5icJg1THB/r6rvHCv2DQRYXoiwrEseS/0LpdyDCugz9SLqc8A8BsvfRp+6QWn6v8XA3pY0M4NycWxyk3+O+RyUf8PAqGHug2+4GwQGAx9uaU6AeVDh7EdFefKXS4rsy0CwGvPPgEAcOMDB/KA7kyVLp8nyPXH4vHXIlXf5pFDxNDZQ8A0dCBztjCG/uufugVfvuPx/HeCXHKxF2EwnDY0KRoSQy/X0HMWX87QUz23UPxMy0ZRgDig1P+81g+fU8jLEgRGks/TjtkMoBiENUMnDT0IDCZ51s4tRme/ecK0JbpAIwM6b62ONCotdgN13na/x+uIaxbPR45RvirVRFZAjpdvpqUWuVOIjnkQkov9fZc9tVfU4xDfesel+PGzjjf2P0yGPnIaOsfQArpRbc5kH1Ua/kRWlAvIGYh9swLuYEAM3cwUVYGbL2cVBiohY6HZwX+84gZ854ED+OQvX6ACuqM4V5ImzgSHfhn6G887ETc9eAhfv2dfJUO3V3jhoO3LlgGcrsc6k5FPhHHbIgC87L3fwGQtxFQt1Ofmuz84aBwbneOpWlha/mAi6p2h02tiwa5jp0GT69zmKxYF2Yr1KgDbiSpA7pZRDF09omftnMWPPvMY/MXV95ZWN2wwOegv33gWDi+18bxTtkIIgcfZKjmbJyLNgsskFwraYaAS3Dopl1y6h41Shq6dYHmGLr9va2ExMWe6HuHxrJa6llyyuj7tJNXPWRyuPljqeZ5ISTouYtIP7M4v8JmiRdCz1GdM6hlGQLfYpKsCH9+2pm/YoFSPK5NcGpYPnbbjKyKRhv7I4WV854EDAJRMkaTSmCgLSUPvpAYrIvQb0AHFGKlmu7KIlUsuLmajK9+VLANIOvpULcwm8tT79DukRT56pIH79y9iuh7hql+7GIBZy4UndlGiR87UuXXSlIeqfOj2up2uDiutmFvQo4wgnzBf4gHd0cmpJdQCfPf3XoLPvfUivY197ej88I72acdM4/xTtxWWRgNMht5NcglFXkyMJJdphwxkg46xGNBzyaWMofPjp7bMM8usllyS1FhshDrL1YAvX0f7HCQCYdaKGTRGMqBT0ByGbRHIb2YhivY6l5WNMBGFLGiLwsNSq7hJ6nGYaejq/yr1X23HVwvitUcItBapnfrfSswFmfM05ZUlZZEsVJZYw3/DGdRIQy9l6CrQzE7GRhIKPaS1sHi+n3n8DDZPRMYkacwkF6rp4iokZdvu7NR/p4ZOE48uH3rFyEUzdKbvq2qHRcmFQNft6Jm6yn6Ny+8fXivG9VzwoOlaxNzGBadtw4ufviOTN5Tbaq7RxvRE1NO945rwBGAcA90PJkMP2LY5Q6cyyTQS5cv45en5q9fQqS2UINQtQa3v/TsMDYPESEoucRig0S56XAeFWVarwn44XA85YSIKNMOIwqLTwl7Gyv4uZ+gkuQDmmpKRIwuy2U60tpi3LdCdAzHRqhFCLyCvvF1mgEMXM3IcIx23axINyAM6t8UttxPDtshRZ7a5TpPbFk3JhfZlt0tPipYx9AqXiyuo0QSd69xwHzr/ql2OlcN+z1WTh+8/gbtomN0mbgktkxQuPeMYXHrGMbrNC82OKszVY7ErCtb2qeCSS+6EyT83Anq27XQ9xnI7QTtJtQ+dnDedNNXHXAsF2slgNPQXP30HpusxTnY4eFaDYbtcRjKg00UfnuRCw/Ty2Xv7NQA875RtujM45agpnGTdDJeccTSWWomTQdXjEEkqjTUWY83QueQSwHZYtJI0qxNd1Hz5ccRMDloJKIAutTqlLK2aoVNAL5FcJtS5m7WKP+W2RTvAuSUIcjwAmeedjbRcFTLLEovMTFFVn6TKtljN0JG1x/SQ06htwtHJ2dJU2fHq/SfukQNgsmBeK76Xe0FNQEocWW47M1tdKLctMpcLjRgNyYVLYrRsXJbd2+io+zwAm1hOmR04QChWZ/Kma3fc7CR+80eftqp9le2/qprkajGSAZ1u6GFJLhRQXIGniqH/J+aAufySp+LyS55qfP68U7bheadsc/5mvoKRGvZzycVOKiky9LTgcnG5OaiWS9lEWDeQVLLUTMoDulX7giPX0LswdCsTMSiRXOzjIvDSC5S1m8srxYChJzwrXC6ACoq0ZGCVbdEVI/UiHQWG3l1yyf+fSws2+AjABcHeppV8VBng7vcCJULNNdo9l6N1TXgCbpeLnfpvb0uaPSWQRYJKEGeSC9PQXQs99wNNHgYstRDCwNdDL4BkjeHZFimgd2HoFb8vhOirw6HfItdGEJRk4JVo6FQnmsAZpL1cVr+WRbuNixUM3a59YX5WLi8A+YNLQaOmGXT2fSuI2iMPApdcoixr1MXQC5OiFS4XQD3smqE7J0Wz7VySCws6rslpOhb+1aLkQlmWFRp9D5JLxM5HL/cCrSI0t9zuWXLR8zXWpc7X4ixxuTgYOnX0840Ok1xUmzqcoUdmZ7kSuCZqBwkhBCqqA68aI83Qh5H6D+QM0TV5x4PKajyqNugBazCG7mJPLg29kU2mmqn/RTeHq9rjStq42EzKMw8rOg1qd1cN3Sr+xCcUOeyFevXvZMFCiDy4T1jBm7eDzluhfK5jDoRYfFXqf6XLxcpmtFfAqUehHqXZkp9d4Y/DTr4q+33ahuoO9VIXPw7UpOhyq+1MdHL+XnYObFLDZSNXRcaaQURylwuQJZBJaIsiFTije+3ip24vlBnuF3SehsbQBYaaWDSSAZ2G00M655WSC58oG2TpgQJDF+VuBnPhBYHlVqJfE1waOpchVtPGKobek4beg8tFtdvUuG1WXLYEGjk94myN04hJLq4CYfRelcsFUA+7ti1WpP67Oju9qn1oZm3SMVGHM1nLA7p9/2mHiGOEE3Zh6MZvhvkSiL1r6CnmGp2eJZfI4WABgC1TMTbVQhy/ZRJ3Pz5vtB2oZuhzyx2dKRpl2avtJNX38y+/8LSe2lYFeqZXm6BUBu9ycWD4tsXySVEz2WTwDJ3WugyDwDkBRZmihNnJGEvtYkA36mMUGPpKJZdsUrRCQ8+X7CrX0MsmRWfq9qSoaVu0AxmVELbrafDjpDU8XezWZuhV1RYBFZRbrLqfjYTlENgo09BtyYWvkFVk6Bm7dU26dmPolk7dj+QShcqBtdDs9JRUxH/Pbup0Pca//7eXYjIO8c179xttt9tP14fuh/lMQ88lFyplPLg4oDNchxRbqOzvsDCSAT1eI4buSoAxkk2GwNApeaMeB7iQVXIjUOElwkw90gyd34Muhr5a2yK1caHZKb3ht0zGmKqFzmtj15K24bItAlxDN3e63E6N9+NQZWDywmhxIHDsbB3HZ0ufuSaLyxa4cFXXbHVS52cAS/13zn1At5EHfLscQp0tnVdg6BV5DPn+3dc2sH5zIgohRG9MtBYKPTHfq+SSyynF/durC3WfFM019CRzc1Gn1mDJWYMALwQ3DPiA7gCximENXaYrJkWHzdAPs6JgYSDwqcsuxNs+cys6icRjRxoILf9uPQ6x3M5YvaGhFwPX6iUX9b1DSy3s3Drp3OZnLjwZl55xtPNB7sbQC5Oi1HGzCUUOvigygGxpMq7jq0zLj7/5fGdJgnohsag8U5T+36xILNIrOVUw9FoYAIZmbDJ0vuJNKUN3SS6iOhAF1m9OxKqmTC+jXH6sfbtcKjoMl7WRL+lG15EyW2m5QZJcACVRrtSG62x31pRhMfRhSy49nwkhRCiEuFkI8QXHZy8WQhwRQtyS/fuDwTbTBF3oslXTV4taFGAyDru6XAY5QpiwGDqx0wtPOwrXv/1SnHyUWvknYi6XKJvcWnJo6PYyd2p7FlRW0sYo1/nL0r83TUQ4/Zhp52e6fG6Jhn72SVvwxvN2amtnbGnotrRiB/QwEHjVmcfhwtPU9y+7+DS89uzjMVWLCms/AmwE0CtDF9WToj/3fLXQx6nbi8koxDKn67FxbWjkQB26GdBLNHTHb4sukosxKggCo+5QN/BOoneXS/dRtMuHbi/xpn4/wKZaiEOLLcw3OjqxCFDF6QbJpnX26pCG/2qBi6HsGkB/DP3XAdwFYKbk8+ullK9ZfZO6gy72MA36x22p46hNtcL7ucY2WA2fjklLLiXsLGQaehyoyS3KJLUXuCAUXC4rLAnKO7heE0w4csnFHXQ2T0T4szecpf8fawaXfd+WXFoU0DM2KATe+5Nn689dk2RuKSoL6El3hr6UVUJ0SS6vP2cnXn/OTuexnXvSVnz6Lc/HmSfM4KEDaiWbX774VJx14hYA6n57xbOOxVOP3oxdDx1yLgJSLbn0Pj8SR0py6TUfgROAmR6ve1lxLqMdeuKUvVciy81Mxvjkd3+IdiLxtpc/XT8nC80OTigZLa4EdKhDmxQVw1MWgB4DuhBiJ4BXA3g3gP86tNb0CF0Xe4iGzn968wVqKTELmh0PeHUNCnJHltuVD3OBobMVXnp1uay07TwQ91KgyUY322Jhe0tysdtNx03b2etBulBlWyy4XBx1x/NaLv098EIIPfI4Zfsm3PnHLzdqqkxEIT70s8/F5299NPt/8Rq5qmYSjput44cHl5wJTzZokrhX6Y2f99mp/jT0Kj+3K+jrzFnr+H/zR5+Gq+98HJeecQxed84J+MSNDwFQAb0sr2ElKEuIGhSCDeJyeR+AtwNwj6UVni+EuBXAowB+W0p5p72BEOIyAJcBwEknndRfSxnygD48hk5DYRv5jTrY36Oge2S57ZyMJUkmDIQxCTgRhZqp8mfHzdBXp6FzqWQlDL2b5GIjLyZGDFod58lHTeHErVP4L5eqTFxdCqKHh1CVXhVZ0TJzeF2stmi5Z4KArQq0uhuAB3PzN7Jz5KoZX3H9fubCk3HjDw7i27uf6PrbUSAyDb23oMVHdP360Kt+wlltsaTezxvPOxFvPO9Etp06B4MP6EOWXITQi9gMA12fSiHEawDsk1LeJIR4cclm3wNwspRyQQjxKgD/F8Dp9kZSyisAXAEA55133oqP6s0vOBVX3f4YfuQpRRfIsEE1PQbN0OkBXmh2jFrg+nPO0JlmPBEHeUDnDN2VKUpe6BVLLpyhr0By6TIpasNOIDt1+2b8xLk7cdkLT8PTj825ReTQYqsQZgHdrrZIAV2IrBKitb9AwFghZxio6vSCTGJzBbBXnHksAODck7Z0/Y04CnDq9k04tNjuui2QX7fTj96Mox33pvM7JeVzOUImlREmShi6DW6M6JUg9AK6rMOTXNa/ONdFAH48C9R1ADNCiH+SUv4MbSClnGOvrxJC/K0QYruUsjtdWAHOOnELdr/nVcPYdU+IAjFwyyRnJK6Ax1dL12sVZpNb2ofuyBQNRH7zVy1S3W8be2VqHHnqf4+Si11LJVALN9joN3M4CgSaKKb+E/uuhYqJuzR0vo9hoFvN+L9841k48/jZwvtxGGDXf3tpT2w1DgK87eVn9Nym1zxHrbjzc88/pafMUqA3Db2qlktZvZ/8u2zSv0eC0AsCR5sGCSW5DGXXav/dNpBS/q6UcqeU8hQAbwLwNR7MAUAIcazIZgiFEOdn+z0whPZuCARC9Hxj9wr+ILoyKTnL5hNgE1GoNblJ5mGONRsO9eRtmHVEKw3oqoa12teaMPRsJNHuskx6VXaqCyE7N0DeEbSyIJ4v7mszdD4CGrDmZrWtrNN7zXOOxykOFw0AbN880dPcRr+T4sdvmcRlL3xKz3MfQG8JOq4Su/bSgWVw+dUHgbIqkYNCGGBj+tCFEG8BACnlhwC8AcCvCiE6AJYBvEkOUyhaZyiGPtgLTinhUronDXktEh7A+M3MLWV5YLB04LB3u5oL9SjAYitZlYbea2AgzdheENtG1cLULtDygPb3OqlELco7rYKGzs7b8Bh6tRNokL8xTLhqnZdt40r973aP8Hu4n46mG3LTw/Aklw0T0KWU1wG4Lnv9Ifb+BwF8cJAN28jgLG5QUCv0qIU7KiWXIM8UJcmFwJM+opLJJb6az0pQz4o6Ue3yftC3yyXobfI76nMiK7I6QltKKXuoyxK3BomyjniQGPSyai64POaFbRyOEm1b7HL80ZAYeiCKncwgIcTGcLl4MNirxw8K9TjMAnox4NUdLpcotBh6nTN099D1zS84FRc4Sgr000YATktnN5x5wizOPWkLjp2p97Q9dUrd7Kl5OeXe2hEFwjjHtjuoLMvRcGMMieXSMQ9yos/GakZovaIX6cLlcqn1qKG7ykMPAsMunxsGwldb3Ghw1SQfBPIZfveCwQB0aVj1OjBufIOhl7Dh//qypw+kjSuRXM48YRafe+tFPW8f64BezdBrfUouYShQZ9NHQgidkt0zQx+yy2WYksuaMHQ2KV++TbEj1pJLPxr6ECZFh7nAxXq7XDwsREMK6BR8nT50VmDLzhQl8Ep4wxq6UweyEpdLv4i1ht6r5NJ7oox9imlNzjDIV45fV5fLEBn6sCyXxm/04BZxLYZCE/fdgrRrKcFBgK79UKstDtHl4gP6CjBshu52ubgyRfOAXo8DIwjo1eQHOGFEvwOsjKH3i7xmT2+SS6/E0zUHotfkDERpZupaBPS10NAHWcyqDBSsq8pjhCVOmMk4NNY9dcFVq2gQCPt0TPWLQGxQl8uTGVEghpIarGtiOzNFc1mBHhI1KWrWjCaUaeirBXU2m0syHQcJ6pS6MfS8Jkjvk6K2FKWCd2qsCGXLKmX15geJ3OUyTIa+sVwudkD/4E+di9OP2Vy5/2HZFstGZwPbv5dcNh5CxuIGCV0T22VbZBUTkzSvJ0KB3pZA+nWU9NPGqVq4JkFBSy5dJpHiHhwVHFEoCp0mfZUzdDtTlB7yWtRb2dmVYG1cLsOXXHpJoS+bfL7kjKO77t/IhB6ght5L2d/VQKX+A1LKodxDPqCvAPaqQYNCJUPX5V8DXTaW2xaLDH04gaEeh2sitwA5k+xWJlkX5+rxATl2ZhJbptznq8rlQvvf0mMJ2ZWgrJbJYH9j7Rh6VdBaTRIPdxkNVHJxWCkHCdp/MuCVlgg+oK8AQ2fojhv06BlVQ+OoTTVdfpUyRYFinephMfQLTt1WCIbDwlk7VYr7a559XOV2/bpc/uanz4GAzb5zbz89y7aGTtd8mMc/Nj70HiSXvOPsf/8823XUJBdALYYyjODrA/oKwHXWQSJn6MUgfMaxM7j+7ZfgxG1TuH//QtaOtWfov3DRqQPdXxVOPmoTHvzTV3fdzuWWqIKL0VFQ2VyP9AiooKETQ58q1skfFKgTGaSMYGMtfehVTHc163e61oYdBIZdy4UOdVgy+vC76jFEOKxJUa2huy/LidvUqkVbs4By6vZN2rdrLzzQbf3OcUK/qf8u0PWcqUes+JkV0LNAuHWIDD13EQ1T1lkLl0svtVz6m/vgMBOLBl/LZViniO6zYWWLeoa+AmyeiIbyUBBz7GY1vPj07fj7nz8PL3raDtz12DyAIkOP1mDovlFg101fCShYz07GOJythlOmoW8dIkPfMlXDP735ApzTQxnclWJtNfSqdqxcrx6abVEH9OEWXxuW08UH9BXgPa9/9pq7XDiEEHjJM44BkLN6W0OnG2fSM/SeoBn6ZJzrqFagCbWGPryADgAvOH37UPe/FolFvbhFVuMoGZbLJSi59oPe/7DS/31AXwFI+hg0tIbeB6smx4m9KEY9DvGu152JFz9tx+AauEERDSCgB1pyiXW2ot1pk7a+VpPCw8KaJBb1VMtFtWMlsdN0uQxQcqFRw5BLOwyrnIsP6BsIxND7ye48bnYSn7rsQpx70tbCZz974ckDa9tGxmqG7gQK1rOTcZZJWgwS8w21QPQwNfS1wLCyXDnCHiSX1VgEgyCvvzNKtkU69V5DfxJgJQwdAC5cRfXEccAgJJeFpgrWM5NRaWmH+YbS1octuQwbw3JwcPRyTfp1JxW+nwX02gAZ+o8+4xgcXmrjmJneltrrF4Fm6D6gjz3yuizjr3sPEhQ8VjOvsdiyGLpjyD2nGfpoB/RhZbly9LIE3WpcLoC67qmUA+2gjp6p4/JLnjqw/dnwLpcnESiQ+4DeH3LP88r3QQ/YTF1NirpkibnM/TLqkstaoL9aLiv7DSW1jdazQp2XD+hPAhw7O4E4FNi2abQZ4FqjxsoirBYzGUN37Ys09Fkf0LuiH4a+Ur06CgMEYrRWuqTzMazEIh/QNxAuefrR+PbvXFpwrHhUI9drV7+vfFK0GGSopsyWSd/hdkPUQ26AzhRdqeQSiJXT+3UC3aPD8qGPf9bJCEEIgaN7XJ7NI0cviyn0CrItuvb1ntc/G0/ZsWmgk3Djim2bFCnZWjHa7IXFVyGOgpGTJwOvoXt4VIMC7CBWmZmuR5iux4VELQD4qQtOwk9dcNKqf+PJgFO3b8K3fucS7NxanrOx2o44Cla34Pl6IPQuFw+PapTVXlkJgkDgbS9/OhYzG6PHylEVzIE8eWfFDN1aU3cU4F0uHh5dEEerty1ybNtU8xPTa4DJOMSp2zfhtB2bVvT9mFUbHRWQZdQzdA+PEvS7BJ0LUSC6row06vjjH38W9s8317sZGnEY4Ou//eIVf3/LVLwmSyEOElpyGdJC0aN1Njw8HIhXmXEIAP/+zpeiPczl2DcAfv5HTlnvJgwU733j2WuS9TpIDNvl4gO6x8hjtRmHQLUbw2NjYhTtvcN2uYyWAOXh4YAQAm9+wam4tIfFhT081hNEPqRn6B4e5fj91zxzvZvg4dEVnqF7eHh4jAl0QPeZoh4eHh6jjWG7XHoO6EKIUAhxsxDiC47PhBDiA0KI3UKI24QQ5w62mR4eHh6jj41Uy+XXAdxV8tkrAZye/bsMwN+tsl0eHh4eY4dgyIlFPQV0IcROAK8G8NGSTV4L4B+lwg0AtgghjhtQGz08PDzGAsNeJLpXhv4+AG8HUKb8nADgYfb/Pdl7BoQQlwkhdgkhdu3fv7+fdnp4eHiMPMIhL3DRNaALIV4DYJ+U8qaqzRzvFVospbxCSnmelPK8HTvGfzV6Dw8PD46NILlcBODHhRAPAvgUgEuFEP9kbbMHwIns/zsBPDqQFnp4eHiMCXKGPpz9dw3oUsrflVLulFKeAuBNAL4mpfwZa7PPA/i5zO1yIYAjUsrHBt9cDw8Pj9EFVafYcNUWhRBvAQAp5YcAXAXgVQB2A1gC8IsDaZ2Hh4fHGCHYSAtcSCmvA3Bd9vpD7H0J4PJBNszDw8Nj3DDsBS58pqiHh4fHGmHdXS4eHh4eHoNBoKstDmn/w9mth4eHh4cNmhTdCKn/Hh4eHh6rgNfQPTw8PMYEw3a5+IDu4eHhsUbwDN3Dw8NjTJAz9CHtfzi79fDw8PCwoTNFPUP38PDwGG1oH7rX0D08PDxGG36RaA8PD48xQb6mqA/oHh4eHiONvB76kPY/nN16eHh4eNjwmaIeHh4eYwIhBALhJRcPDw+PsUAYCM/QPTw8PMYBr3r2cXj6MdND2feKVyzy8PDw8Ogf73/TOUPbt2foHh4eHmMCH9A9PDw8xgQ+oHt4eHiMCXxA9/Dw8BgT+IDu4eHhMSbwAd3Dw8NjTOADuoeHh8eYwAd0Dw8PjzGBkENKQe36w0LsB/DQCr++HcATA2zOesIfy8aEP5aNCX8swMlSyh2uD9YtoK8GQohdUsrz1rsdg4A/lo0JfywbE/5YquElFw8PD48xgQ/oHh4eHmOCUQ3oV6x3AwYIfywbE/5YNib8sVRgJDV0Dw8PD48iRpWhe3h4eHhY8AHdw8PDY0wwcgFdCPEKIcQ9QojdQoh3rHd7+oUQ4kEhxO1CiFuEELuy97YJIa4RQtyX/d263u10QQjxv4QQ+4QQd7D3StsuhPjd7DrdI4R4+fq02o2SY/kjIcQj2bW5RQjxKvbZhjwWIcSJQoivCyHuEkLcKYT49ez9kbsuFccyitelLoT4rhDi1uxY/jh7f7jXRUo5Mv8AhADuB3AagBqAWwE8c73b1ecxPAhgu/XenwF4R/b6HQD+53q3s6TtLwRwLoA7urUdwDOz6zMB4NTsuoXrfQxdjuWPAPy2Y9sNeywAjgNwbvZ6GsC9WXtH7rpUHMsoXhcBYHP2OgZwI4ALh31dRo2hnw9gt5TyASllC8CnALx2nds0CLwWwD9kr/8BwOvWrynlkFJ+E8BB6+2ytr8WwKeklE0p5Q8A7Ia6fhsCJcdShg17LFLKx6SU38tezwO4C8AJGMHrUnEsZdjIxyKllAvZf+Psn8SQr8uoBfQTADzM/r8H1Rd8I0ICuFoIcZMQ4rLsvWOklI8B6qYGcPS6ta5/lLV9VK/VfxZC3JZJMjQcHoljEUKcAuAcKDY40tfFOhZgBK+LECIUQtwCYB+Aa6SUQ78uoxbQheO9UfNdXiSlPBfAKwFcLoR44Xo3aEgYxWv1dwCeAuBsAI8B+Mvs/Q1/LEKIzQA+C+A3pJRzVZs63tvoxzKS10VKmUgpzwawE8D5QogzKzYfyLGMWkDfA+BE9v+dAB5dp7asCFLKR7O/+wBcCTWs2iuEOA4Asr/71q+FfaOs7SN3raSUe7OHMAXwEeRD3g19LEKIGCoAfkJK+bns7ZG8Lq5jGdXrQpBSHgZwHYBXYMjXZdQC+r8DOF0IcaoQogbgTQA+v85t6hlCiE1CiGl6DeBlAO6AOoafzzb7eQD/uj4tXBHK2v55AG8SQkwIIU4FcDqA765D+3oGPWgZXg91bYANfCxCCAHg7wHcJaV8L/to5K5L2bGM6HXZIYTYkr2eBPBSAHdj2NdlvWeDVzB7/Cqo2e/7AbxzvdvTZ9tPg5rJvhXAndR+AEcB+CqA+7K/29a7rSXt/2eoIW8bilG8uartAN6ZXad7ALxyvdvfw7F8HMDtAG7LHrDjNvqxAHgB1ND8NgC3ZP9eNYrXpeJYRvG6PAfAzVmb7wDwB9n7Q70uPvXfw8PDY0wwapKLh4eHh0cJfED38PDwGBP4gO7h4eExJvAB3cPDw2NM4AO6h4eHx5jAB3QPDw+PMYEP6B4eHh5jgv8fcfMcuun5E60AAAAASUVORK5CYII=\n", "text/plain": [ - "
" + "array([[1. , 0.5, 0.5, 0.5, 0. , 0. , 0. , 0. , 0. , 0. ],\n", + " [0. , 0.5, 0. , 0. , 1. , 0.5, 0.5, 0. , 0. , 0. ],\n", + " [0. , 0. , 0.5, 0. , 0. , 0.5, 0. , 1. , 0.5, 0. ],\n", + " [0. , 0. , 0. , 0.5, 0. , 0. , 0.5, 0. , 0.5, 1. ]])" ] }, - "metadata": { - "needs_background": "light" + "execution_count": 216, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "AFxGTxploidy" + ] + }, + { + "cell_type": "code", + "execution_count": 215, + "id": "ae6a3a65-6f77-4d75-9497-168aca2fca1a", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[0. , 0.5, 0.5, 0.5, 1. , 1. , 1. , 1. , 1. , 1. ],\n", + " [1. , 0.5, 1. , 1. , 0. , 0.5, 0.5, 1. , 1. , 1. ],\n", + " [1. , 1. , 0.5, 1. , 1. , 0.5, 1. , 0. , 0.5, 1. ],\n", + " [1. , 1. , 1. , 0.5, 1. , 1. , 0.5, 1. , 0.5, 0. ]])" + ] }, - "output_type": "display_data" + "execution_count": 215, + "metadata": {}, + "output_type": "execute_result" } ], "source": [ - "rng = np.random.default_rng()\n", - "DPh = np.array([5] * 500) # 500 haplotypes each with depth 5\n", - "linked = linked_depth(rng, DPh, 100, 300)\n", - "print(linked.shape)\n", - "plt.plot(np.mean(linked, axis=1), label=\"linked\")\n", - "plt.show()" + "1-AFxGTxploidy" ] }, { "cell_type": "code", - "execution_count": 183, - "id": "b3d3c1bf-dcbb-4844-b86e-0b05c0f733a1", + "execution_count": 213, + "id": "2442add7-9c96-4767-8dd6-aa8875bbf7ff", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "array([4.876, 4.486, 5.898, 4.906, 5.092, 5.288, 4.89 , 5.084, 5.266,\n", - " 6.27 , 5.486, 5.278, 5.488, 4.846, 4.874, 5.284, 5.292, 5.318,\n", - " 5.324, 5.532, 5.716, 5.316, 5.13 , 4.928, 4.716, 4.52 , 4.9 ,\n", - " 4.892, 5.096, 5.318, 4.718, 5.13 , 4.288, 5.256, 5.056, 4.862,\n", - " 4.872, 5.064, 4.64 , 5.212, 5.222, 4.638, 5.24 , 5.24 , 5.246,\n", - " 5.25 , 4.642, 5.238, 5.216, 4.818, 5.61 , 4.802, 4.992, 4.578,\n", - " 4.2 , 4.576, 6.14 , 5.16 , 4.572, 4.55 , 4.74 , 4.328, 5.134,\n", - " 5.132, 4.3 , 4.68 , 4.686, 4.262, 4.464, 5.666, 5.082, 5.078,\n", - " 5.074, 4.272, 4.672, 5.056, 5.052, 5.054, 4.424, 4.998, 5.016,\n", - " 5.028, 4.854, 5.262, 5.462, 5.272, 5.058, 5.072, 5.08 , 5.068,\n", - " 5.106, 4.906, 4.706, 4.71 , 4.332, 4.556, 5.568, 5.772, 4.768,\n", - " 4.786, 5.598, 5.188, 4.558, 5.36 , 6.166, 4.79 , 5.402, 4.82 ,\n", - " 5.204, 4.422, 4.79 , 5.182, 5.182, 5.192, 5.188, 4.958, 4.764,\n", - " 4.54 , 4.74 , 5.532, 4.756, 4.33 , 5.71 , 5.106, 5.09 , 4.488,\n", - " 4.684, 4.5 , 4.896, 4.686, 4.696, 4.09 , 5.13 , 4.538, 5.134,\n", - " 5.126, 4.54 , 5.162, 4.56 , 4.978, 4.764, 5.144, 4.342, 4.722,\n", - " 5.106, 5.106, 5.122, 4.122, 4.326, 5.148, 4.16 , 4.974, 5.16 ,\n", - " 5.164, 5.194, 5.202, 4.178, 4.356, 5.114, 6.112, 5.518, 4.926,\n", - " 4.914, 5.924, 4.536, 5.162, 4.746, 5.568, 5.556, 5.342, 5.726,\n", - " 4.532, 5.116, 5.124, 4.522, 4.536, 5.136, 4.742, 4.94 , 4.526,\n", - " 5.122, 4.3 , 4.686, 5.102, 4.9 , 4.688, 4.706, 4.89 , 4.478,\n", - " 5.08 , 5.266, 5.474, 5.68 , 5.704, 4.512, 4.482, 5.06 , 5.678,\n", - " 4.698, 4.704, 4.704, 5.088, 5.512, 4.128, 4.734, 4.9 , 4.484,\n", - " 5.066, 5.066, 5.042, 5.65 , 5.066, 5.08 , 4.882, 4.666, 5.698,\n", - " 5.074, 4.478, 4.488, 4.694, 4.898, 5.13 , 5.148, 4.732, 5.534,\n", - " 4.946, 5.358, 5.164, 5.48 , 4.974, 4.174, 5.606, 5.198, 4.808,\n", - " 4.806, 5.002, 4.586, 5.176, 4.964, 4.138, 5.36 , 4.986, 5.182,\n", - " 4.902, 4.376, 4.768, 4.732, 4.728, 4.734, 4.516, 4.514, 4.528,\n", - " 5.95 , 4.752, 4.122, 4.536, 5.17 , 5.182, 4.602, 5.826, 5.23 ,\n", - " 5.228, 4.842, 5.646, 5.25 , 4.848, 5.044, 5.234, 5.646, 5.456,\n", - " 5.254, 5.27 , 5.276, 5.662, 5.252, 4.246, 5.244, 4.656, 5.29 ,\n", - " 5.716, 5.538, 4.746, 5.354, 5.56 , 5.146, 4.748, 5.336, 5.326,\n", - " 5.946, 4.766, 5.358, 4.77 , 4.776, 5.358, 4.714, 5.346, 5.962,\n", - " 4.938, 4.93 , 3.732])" + "array([[[[ 16.37769045, 31.24431008, 31.24431008, 31.24431008,\n", + " 105.32481834, 105.32481834, 105.32481834, 105.32481834,\n", + " 105.32481834, 105.32481834],\n", + " [ 0. , 0. , 0. , 0. ,\n", + " 0. , 0. , 0. , 0. ,\n", + " 0. , 0. ],\n", + " [ 0. , 0. , 0. , 0. ,\n", + " 0. , 0. , 0. , 0. ,\n", + " 0. , 0. ],\n", + " [ 0. , 0. , 0. , 0. ,\n", + " 0. , 0. , 0. , 0. ,\n", + " 0. , 0. ]],\n", + "\n", + " [[ 6.69996427, 12.78176322, 12.78176322, 12.78176322,\n", + " 43.08742569, 43.08742569, 43.08742569, 43.08742569,\n", + " 43.08742569, 43.08742569],\n", + " [ 4.78749174, 1.42019591, 4.78749174, 4.78749174,\n", + " 0.74444047, 1.42019591, 1.42019591, 4.78749174,\n", + " 4.78749174, 4.78749174],\n", + " [ 0. , 0. , 0. , 0. ,\n", + " 0. , 0. , 0. , 0. ,\n", + " 0. , 0. ],\n", + " [ 0. , 0. , 0. , 0. ,\n", + " 0. , 0. , 0. , 0. ,\n", + " 0. , 0. ]],\n", + "\n", + " [[ 0. , 0. , 0. , 0. ,\n", + " 0. , 0. , 0. , 0. ,\n", + " 0. , 0. ],\n", + " [114.07564949, 27.99434763, 114.07564949, 114.07564949,\n", + " 14.26699776, 27.99434763, 27.99434763, 114.07564949,\n", + " 114.07564949, 114.07564949],\n", + " [ 0. , 0. , 0. , 0. ,\n", + " 0. , 0. , 0. , 0. ,\n", + " 0. , 0. ],\n", + " [ 0. , 0. , 0. , 0. ,\n", + " 0. , 0. , 0. , 0. ,\n", + " 0. , 0. ]],\n", + "\n", + " [[ 31.64388824, 62.68425072, 62.68425072, 62.68425072,\n", + " 287.86183448, 287.86183448, 287.86183448, 287.86183448,\n", + " 287.86183448, 287.86183448],\n", + " [ 0. , 0. , 0. , 0. ,\n", + " 0. , 0. , 0. , 0. ,\n", + " 0. , 0. ],\n", + " [ 6.39692966, 6.39692966, 1.39298335, 6.39692966,\n", + " 6.39692966, 1.39298335, 6.39692966, 0.70319752,\n", + " 1.39298335, 6.39692966],\n", + " [ 6.39692966, 6.39692966, 6.39692966, 1.39298335,\n", + " 6.39692966, 6.39692966, 1.39298335, 6.39692966,\n", + " 1.39298335, 0.70319752]]],\n", + "\n", + "\n", + " [[[ 0. , 0. , 0. , 0. ,\n", + " 0. , 0. , 0. , 0. ,\n", + " 0. , 0. ],\n", + " [ 0. , 0. , 0. , 0. ,\n", + " 0. , 0. , 0. , 0. ,\n", + " 0. , 0. ],\n", + " [ 4.78749174, 4.78749174, 1.42019591, 4.78749174,\n", + " 4.78749174, 1.42019591, 4.78749174, 0.74444047,\n", + " 1.42019591, 4.78749174],\n", + " [ 62.23739266, 62.23739266, 62.23739266, 18.46254687,\n", + " 62.23739266, 62.23739266, 18.46254687, 62.23739266,\n", + " 18.46254687, 9.67772617]],\n", + "\n", + " [[ 0. , 0. , 0. , 0. ,\n", + " 0. , 0. , 0. , 0. ,\n", + " 0. , 0. ],\n", + " [ 0. , 0. , 0. , 0. ,\n", + " 0. , 0. , 0. , 0. ,\n", + " 0. , 0. ],\n", + " [ 4.78749174, 4.78749174, 1.42019591, 4.78749174,\n", + " 4.78749174, 1.42019591, 4.78749174, 0.74444047,\n", + " 1.42019591, 4.78749174],\n", + " [ 62.23739266, 62.23739266, 62.23739266, 18.46254687,\n", + " 62.23739266, 62.23739266, 18.46254687, 62.23739266,\n", + " 18.46254687, 9.67772617]],\n", + "\n", + " [[ 0. , 0. , 0. , 0. ,\n", + " 0. , 0. , 0. , 0. ,\n", + " 0. , 0. ],\n", + " [148.29834434, 36.39265192, 148.29834434, 148.29834434,\n", + " 18.54709708, 36.39265192, 36.39265192, 148.29834434,\n", + " 148.29834434, 148.29834434],\n", + " [ 11.40756495, 11.40756495, 2.79943476, 11.40756495,\n", + " 11.40756495, 2.79943476, 11.40756495, 1.42669978,\n", + " 2.79943476, 11.40756495],\n", + " [ 0. , 0. , 0. , 0. ,\n", + " 0. , 0. , 0. , 0. ,\n", + " 0. , 0. ]],\n", + "\n", + " [[ 0.70319752, 1.39298335, 1.39298335, 1.39298335,\n", + " 6.39692966, 6.39692966, 6.39692966, 6.39692966,\n", + " 6.39692966, 6.39692966],\n", + " [ 0. , 0. , 0. , 0. ,\n", + " 0. , 0. , 0. , 0. ,\n", + " 0. , 0. ],\n", + " [ 0. , 0. , 0. , 0. ,\n", + " 0. , 0. , 0. , 0. ,\n", + " 0. , 0. ],\n", + " [159.92324138, 159.92324138, 159.92324138, 34.82458373,\n", + " 159.92324138, 159.92324138, 34.82458373, 159.92324138,\n", + " 34.82458373, 17.57993791]]]])" ] }, - "execution_count": 183, + "execution_count": 213, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "linked.mean(axis = 1)" + "np.multiply(-np.log((np.tile(((AFxGTxploidy*(1-ex)+(1-AFxGTxploidy)*(ex/3))/ploidy).reshape(-1), 2).reshape(2, 4, 4, 10))), \n", + " arc.reshape(arc.shape[0], arc.shape[1], arc.shape[2], 1))" ] }, { "cell_type": "code", - "execution_count": 184, - "id": "2fb36f4b-4efc-48f7-91a0-1fcda7f67e0d", + "execution_count": 193, + "id": "16e0b621-c2d2-434a-aba6-e19930cee738", "metadata": {}, "outputs": [ { "data": { - "image/png": "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\n", "text/plain": [ - 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"output_type": "display_data" + "execution_count": 167, + "metadata": {}, + "output_type": "execute_result" } ], "source": [ - "plt.hist(linked.mean(axis = 1))\n", - "plt.show()" + "((1-AFxGTxploidy)*(ex/3)+AFxGTxploidy*(1-ex))" ] }, { "cell_type": "code", "execution_count": null, - "id": "594bdaab-ef8c-4762-84fb-286996d135ac", + "id": "dca5ddb8-285c-4677-887e-a0fb3e8f17d6", "metadata": {}, "outputs": [], "source": [] diff --git a/simGL/simGL.py b/simGL/simGL.py index 16803a3..0451f07 100644 --- a/simGL/simGL.py +++ b/simGL/simGL.py @@ -3,6 +3,11 @@ from itertools import combinations from scipy.stats import binom +def e2q(e): + return -10*np.log(e) + +def q2e(q): + return np.exp(-q/10) def incorporate_monomorphic(gm, pos, start, end): ''' @@ -37,6 +42,12 @@ def incorporate_monomorphic(gm, pos, start, end): gm2[pos.astype(int)] = gm return gm2 +def refalt(ref, alt, n_sit): + if ref is None and alt is None: + ref = np.full(n_sit, "A") + alt = np.full(n_sit, "C") + return ref, alt + def depth_per_haplotype(rng, mean_depth, std_depth, n_hap, ploidy): if isinstance(mean_depth, np.ndarray): return mean_depth @@ -91,6 +102,26 @@ def independent_depth(rng, DPh, size): ''' return rng.poisson(DPh, size=size) +def depth_per_site_per_haplotype(rng, depth_type, DPh, gm_shape, read_length): + if depth_type == "independent": + DP = independent_depth(rng, DPh, gm_shape) + elif depth_type == "linked": + assert check_positive_nonzero_integer(read_length, "read_length") + DP = linked_depth(rng, DPh, read_length, gm_shape[0]) + assert DP.shape == gm_shape + return DP + +def simulate_arc(e, err, rng, DP, gmbp): + if isinstance(e, np.ndarray): + err = err.transpose(2, 0, 1) + return rng.multinomial(DP, err[np.tile(np.arange(gmbp.shape[1]), gmbp.shape[0]), gmbp.reshape(-1)].reshape(gmbp.shape[0], gmbp.shape[1], 4)) + else: + return rng.multinomial(DP, err[gmbp]) + +def ploidy_sum(arr, ploidy): + s = arr.shape + return arr.reshape(-1).reshape(s[0], s[1]//ploidy, ploidy, s[2]).sum(axis = 2) + def sim_allelereadcounts(gm, mean_depth, e, ploidy, seed = None, std_depth = None, ref = None, alt = None, read_length = None, depth_type = "independent"): ''' Simulates allele read counts from a genotype matrix. @@ -104,16 +135,19 @@ def sim_allelereadcounts(gm, mean_depth, e, ploidy, seed = None, std_depth = Non mean_depth : `int` or `float` or `numpy.ndarray` Read depth of the each haplotypic sample in `gm`. If a `int` or `float` value is inputed, the function will sample random values from a normal distribution with mean = `mean_depth` and std = `std_depth`. - If a `numpy.ndarray` is inputed, the array must have size (haplotypic samples, ) and the order must - be the same as the second dimention of `gm`. + If a `numpy.ndarray` is inputed, there must be an error value per haplotype (i.e., the array must have size + (haplotypic samples, )) and the order must be the same as the second dimention of `gm`. std_depth : `int` or `float` The standard deviation parameter of the normal distribution from which read depth values are randomly sampled for each haplotypic sample in `gm`. This value only needs to be provided if the `mean_depth` inputed is an `int` or a `float`. - e : `int` or `float` - Sequencing error probability per base pair per site. The value must be between 0 and 1. + e : `int` or `float` or `numpy.ndarray` + Sequencing error probability per base pair per site. The values must be between 0 and 1. If a `int` or `float` + value is inputed, the function will use the same error probablity value for each haplotype and each site. + If a `numpy.ndarray` is inputed, there must be an error value per haplotype (i.e., the array must have size + (haplotypic samples, )) and the order must be the same as the second dimention of `gm`. ploidy : `int` Number of haplotypic chromosomes per individual. @@ -150,29 +184,22 @@ def sim_allelereadcounts(gm, mean_depth, e, ploidy, seed = None, std_depth = Non ''' #Checks assert check_gm(gm) - if ref is None and alt is None: - ref = np.full(gm.shape[0], "A") - alt = np.full(gm.shape[0], "C") - assert check_mean_depth(gm, mean_depth) and check_std_depth(mean_depth, std_depth) and check_e(e) and check_ploidy(ploidy) and check_gm_ploidy(gm, ploidy) and check_ref_alt(gm, ref, alt) and check_depth_type(depth_type) + ref, alt = refalt(ref, alt, gm.shape[0]) + assert check_mean_depth(gm, mean_depth) and check_std_depth(mean_depth, std_depth) and check_e(gm, e) and check_ploidy(ploidy) and check_gm_ploidy(gm, ploidy) and check_ref_alt(gm, ref, alt) and check_depth_type(depth_type) #Variables err = np.array([[1-e, e/3, e/3, e/3], [e/3, 1-e, e/3, e/3], [e/3, e/3, 1-e, e/3], [e/3, e/3, e/3, 1-e]]) rng = np.random.default_rng(seed) #1. Depths (DP) per haplotype (h) DPh = depth_per_haplotype(rng, mean_depth, std_depth, gm.shape[1], ploidy) #2. Sample depths (DP) per site per haplotype - if depth_type == "independent": - DP = independent_depth(rng, DPh, gm.shape) - elif depth_type == "linked": - assert check_positive_nonzero_integer(read_length, "read_length") - DP = linked_depth(rng, DPh, read_length, gm.shape[0]) - assert DP.shape == gm.shape + DP = depth_per_site_per_haplotype(rng, depth_type, DPh, gm.shape, read_length) #3. Sample correct and error reads per SNP per haplotype (Rh) #3.1. Convert anc = 0/der = 1 encoded gm into "A" = 0, "C" = 1, "G" = 3, "T" = 4 basepair (bp) encoded gm gmbp = refalt_int_encoding(gm, ref, alt) #3.2. Simulate allele read counts (ARC) per haplotype (h) per site (s) - arc = rng.multinomial(DP, err[gmbp]) + arc = simulate_arc(e, err, rng, DP, gmbp) #4. Add n haplotype read allele counts (n = ploidy) to obtain read allele counts per genotype - return arc.reshape(arc.shape[0], arc.shape[1]//ploidy, ploidy, arc.shape[2]).sum(axis = 2) + return ploidy_sum(arc, ploidy) def get_GTxploidy(ploidy): return np.array([list(x) for x in combinations_with_replacement([0, 1, 2, 3], ploidy)]) @@ -184,12 +211,27 @@ def allelereadcounts_to_GL(arc, e, ploidy): Parameters ---------- arc : `numpy.ndarray` - Allele read counts per site per individual. The dimentions of the array are (sites, individuals, alleles). - The third dimention of the array has size = 4, which corresponds to the four possible alleles: 0 = "A", - 1 = "C", 2 = "G" and 3 = "T". + Allele read counts per site per individual or haplotype. The dimentions of the array are + (sites, individuals or haplotypes, alleles). + + The second dimention will depend on the format of the `e` parameter. If the error parameter + is the same for every haplotype (`int` or `float`), the arc inputed can be per individual. + Instead, if the error parameter has a value for every haplotype (`np.array`), the arc must + be per haplotypic sample. This is because to compute GL it is needed to know the number of + reads per haplotype and their error rate. For example, to obtain the arc fir the former case + for diploid organisms one must call: + `sim_allelereadcounts(..., ploidy = 2, ...)` + but the latter, one must use: + `sim_allelereadcounts(..., ploidy = 1, ...)`. + + The third dimention of the array has size = 4, which corresponds to the four possible alleles: + 0 = "A", 1 = "C", 2 = "G" and 3 = "T". - e : `float` - Sequencing error probability per base pair per site. The value must be between 0 and 1. + e : `int` or `float` or `numpy.ndarray` + Sequencing error probability per base pair per site. The values must be between 0 and 1. If a `int` or `float` + value is inputed, the function will use the same error probablity value for each haplotype and each site. + If a `numpy.ndarray` is inputed, there must be an error value per haplotype (i.e., the array must have size + (haplotypic samples, )) and the order must be the same as the second dimention of `arc`. ploidy : `int` Number of haplotypic chromosomes per individual. @@ -208,14 +250,35 @@ def allelereadcounts_to_GL(arc, e, ploidy): 1) McKenna A, Hanna M, Banks E, Sivachenko A, Cibulskis K, Kernytsky A, Garimella K, Altshuler D, Gabriel S, Daly M, DePristo MA (2010). The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data. Genome Res. 20:1297-303. 2) Thorfinn Sand Korneliussen, Anders Albrechtsen, Rasmus Nielsen. ANGSD: Analysis of Next Generation Sequencing Data. BMC Bioinform. 2014 Nov;15,356. ''' - assert check_arc(arc) and check_e(e) and check_ploidy(ploidy) + assert check_arc(arc) and check_e(arc, e) and check_ploidy(ploidy) + #1. Obtain an array which rows are possible genotypes depending (GT) on ploidy (ploidy) and each value is the encoded bp in that genotype (e.g., ["AA", "AC"] = [[0, 0], [0, 1]]) GTxploidy = get_GTxploidy(ploidy) + #2. Obtain an array which rows are the 4 bp, the columns are the GT and each value denotes the frequency of each allele AFxGTxploidy = np.array([(GTxploidy == 0).sum(axis = 1), (GTxploidy == 1).sum(axis = 1), (GTxploidy == 2).sum(axis = 1), (GTxploidy == 3).sum(axis = 1)])/ploidy - GL = np.multiply(-np.log(AFxGTxploidy*(1-e)+(1-AFxGTxploidy)*(e/3)), arc.reshape(arc.shape[0], arc.shape[1], arc.shape[2], 1)).sum(axis = 2) - return GL-GL.min(axis = 2).reshape(GL.shape[0], GL.shape[1], 1) - + #3. We can compute the GL in two different ways: the first, which allows different error values per haplotype, is a generalized form of the second which only allows errors to be the same for all haplotypes and sites + # The reason why I keep both is because the former might be slower than the latter. + if isinstance(e, np.ndarray): + #I reformat the error array such that I can make matrix operations + er = np.repeat(e, AFxGTxploidy.size).reshape(e.shape + AFxGTxploidy.shape) + #Here it is computed the negative log of the multiplication of the error values and the "AFxGTxploidy" which results into an array that determines for every genotype the probabilities of observing a read + #taking into account the error probabilities + ERxAFxGTxploidy = -np.log(((AFxGTxploidy*(1-er)+(1-AFxGTxploidy)*(er/3)))) + #This array is then reformated for later operations + ERxAFxGTxploidy = ERxAFxGTxploidy.reshape((1,) + ERxAFxGTxploidy.shape) + #The number of reads of each base pair are taken into account to compute the likelihood of observing all reads for a given genotype considering the error + RExerxAFxGTxploidy = np.multiply(ERxAFxGTxploidy, arc.reshape(arc.shape + (1,))).sum(axis = 2) + #The likelihoods for haplotypes of the same individual are finally added up together + GL = ploidy_sum(RExerxAFxGTxploidy, ploidy) + #The GL are normalized to the most likely genotype + return GL-GL.min(axis = 2).reshape(GL.shape[0], GL.shape[1], 1) + else: + #All the steps in the prevous if statement are done in a single line since the error is the same and simplifies the calculation + GL = np.multiply(-np.log(AFxGTxploidy*(1-e)+(1-AFxGTxploidy)*(e/3)), arc.reshape(arc.shape[0], arc.shape[1], arc.shape[2], 1)).sum(axis = 2) + #The GL are normalized to the most likely genotype + return GL-GL.min(axis = 2).reshape(GL.shape[0], GL.shape[1], 1) + def get_pGTxMm(ploidy): GTxploidy = np.array([list(x) for x in combinations_with_replacement([0, 1, 2, 3], ploidy)]) Mmxploidy = np.array([list(x) for x in combinations([0, 1, 2, 3], 2)]) @@ -316,9 +379,9 @@ def check_std_depth(mean_depth, std_depth): raise TypeError('Incorrect std_depth format: it has to be an integer or float value > 0 if mean_depth is a integer or float value and not a numpy array') return True -def check_e(e): - if not (isinstance(e, (int, float)) and e >= 0.0 and e <= 1.0) : - raise TypeError('Incorrect e format: it has to be a float value >= 0 and <= 1') +def check_e(arr, e): + if not ((isinstance(e, np.ndarray) and len(e.shape) == 1 and e.shape[0] == arr.shape[1] and ((e >= 0)*(e <= 1)).sum() == e.size) or (isinstance(e, (int, float)) and e >= 0.0 and e <= 1.0)): + raise TypeError('Incorrect e format: it has to be either i) numpy.array with dimentions (haplotypic samples, ) with values 0 <= e <= 1 or ii) integer or float value 0 <= e <= 1') return True def check_ploidy(ploidy): From 86dfe007b745a39840929a45992045719c6865c2 Mon Sep 17 00:00:00 2001 From: MoiColl Date: Tue, 19 Jul 2022 10:35:48 +0200 Subject: [PATCH 4/5] correct quality <-> error functions and adding some description in functions --- notebook/simGL.ipynb | 139 +++++++++++++++++++++++++++++++++++++++---- simGL/simGL.py | 26 +++++--- 2 files changed, 143 insertions(+), 22 deletions(-) diff --git a/notebook/simGL.ipynb b/notebook/simGL.ipynb index f7ebc20..35b4457 100644 --- a/notebook/simGL.ipynb +++ b/notebook/simGL.ipynb @@ -18,19 +18,10 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 1, "id": "a3c58dad-95fa-4fe1-8971-521842ea4182", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The rpy2.ipython extension is already loaded. To reload it, use:\n", - " %reload_ext rpy2.ipython\n" - ] - } - ], + "outputs": [], "source": [ "import time\n", "import numpy as np\n", @@ -48,10 +39,28 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 2, "id": "966418dd-9400-405c-8983-a4714ad51704", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "R[write to console]: ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.1 ──\n", + "\n", + "R[write to console]: ✔ tibble 3.1.7 ✔ dplyr 1.0.9\n", + "✔ tidyr 1.2.0 ✔ stringr 1.4.0\n", + "✔ readr 2.1.2 ✔ forcats 0.5.1\n", + "✔ purrr 0.3.4 \n", + "\n", + "R[write to console]: ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──\n", + "✖ dplyr::filter() masks stats::filter()\n", + "✖ dplyr::lag() masks stats::lag()\n", + "\n" + ] + } + ], "source": [ "%%R\n", "\n", @@ -6819,6 +6828,110 @@ "id": "dca5ddb8-285c-4677-887e-a0fb3e8f17d6", "metadata": {}, "outputs": [], + "source": [ + "-10 * log(0.000001) = 60" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "7846e2d4-dc88-46fd-9f15-dff97b13f9ba", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "60.0" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "-10*np.log10(0.000001)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "be8f646b-ea5d-4dc5-b84e-7b46390979ad", + "metadata": {}, + "outputs": [], + "source": [ + "-10*np.log10(0.000001)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "1d8b345c-771b-4bd8-b3ce-e6f4bc9e180f", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "1e-06" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.power(10, -(60/10))" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "94eb0a1c-fa6c-4970-bf09-59a8cd017d8c", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "60.0" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "-10*np.log10(0.000001)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "e8b5e154-b1b3-4a7c-af06-e74b1f1648b8", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.0024787521766663585" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.exp(-60/10)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a17f675b-2961-4365-aa28-7a77f955e6ee", + "metadata": {}, + "outputs": [], "source": [] } ], diff --git a/simGL/simGL.py b/simGL/simGL.py index 0451f07..a247f53 100644 --- a/simGL/simGL.py +++ b/simGL/simGL.py @@ -4,10 +4,10 @@ from scipy.stats import binom def e2q(e): - return -10*np.log(e) + return -10*np.log10(e) def q2e(q): - return np.exp(-q/10) + return np.power(10, -(q/10)) def incorporate_monomorphic(gm, pos, start, end): ''' @@ -46,7 +46,7 @@ def refalt(ref, alt, n_sit): if ref is None and alt is None: ref = np.full(n_sit, "A") alt = np.full(n_sit, "C") - return ref, alt + return ref, alt def depth_per_haplotype(rng, mean_depth, std_depth, n_hap, ploidy): if isinstance(mean_depth, np.ndarray): @@ -66,7 +66,7 @@ def refalt_int_encoding(gm, ref, alt): refalt_int[refalt_str == "T"] = 3 return refalt_int[gm.reshape(-1), np.repeat(np.arange(gm.shape[0]), gm.shape[1])].reshape(gm.shape) -def linked_depth(rng, DPh, read_length, sites_n): +def linked_depth(rng, DPh, read_length, n_sit): ''' Simulates reads in a contiguous genomic region to compute the depth per position. @@ -78,7 +78,7 @@ def linked_depth(rng, DPh, read_length, sites_n): Numpy array with the depth per haplotype read_length : `int` Read length in base pair units - sites_n : `int` + n_sit : `int` number of sites that depth has to be simulated for Returns @@ -87,10 +87,10 @@ def linked_depth(rng, DPh, read_length, sites_n): Depth per site per haplotype ''' DP = [] - read_n = ((DPh*sites_n)/read_length).astype("int") + read_n = ((DPh*n_sit)/read_length).astype("int") for r in read_n: - dp = np.zeros((sites_n,), dtype=int) - for p in rng.integers(low=0, high=sites_n-read_length+1, size=r): + dp = np.zeros((n_sit,), dtype=int) + for p in rng.integers(low=0, high=n_sit-read_length+1, size=r): dp[p:p+read_length] += 1 DP.append(dp.tolist()) return np.array(DP).T @@ -150,7 +150,7 @@ def sim_allelereadcounts(gm, mean_depth, e, ploidy, seed = None, std_depth = Non (haplotypic samples, )) and the order must be the same as the second dimention of `gm`. ploidy : `int` - Number of haplotypic chromosomes per individual. + Number of haplotypic chromosomes per individual. It is recomended to read Notes about ploidy. ref : `numpy.ndarray`, optional Reference alleles list per site. The size of the array must be (sites, ) and the order has to @@ -181,6 +181,14 @@ def sim_allelereadcounts(gm, mean_depth, e, ploidy, seed = None, std_depth = Non must be 15. - If monomorphic sites are included, the `alt` values corresponding to those sites are not taken into account, but they must be still indicated. + - Regarding ploidy, if the error parameter is specified as a constant for all individuals, the user can specify + the desired ploidy of the organisms simulated. + If different error rate per haplotype is inputed and the user wants to compute Genotype Likelihoods (GL) for + organisms with ploidy > 1, ploidy should be equal to 1 for this function, and when the later function + `allelereadcounts_to_GL()` is used, then, the desired ploidy can be specified. This is because the error values + must be inputed again to compute GL and if ploidy > 1 is specified for this function, the dimentions of `arc` + will be smaller than the dimentions of `e`. Nonetheless, if the user desires to obtain the output `arc` in + a certain ploidy, one can use `ploidy_sum(arc, ploidy)` fucntion. ''' #Checks assert check_gm(gm) From 5b91fb9064b2bfee90f9dfc20fc899369ab232a7 Mon Sep 17 00:00:00 2001 From: MoiColl Date: Wed, 7 Dec 2022 10:24:48 +0100 Subject: [PATCH 5/5] make depth_per_haplotype callable for the user --- notebook/simGL.ipynb | 416 ++++++++++++++++++++++++++++++++++++++++++- 1 file changed, 407 insertions(+), 9 deletions(-) diff --git a/notebook/simGL.ipynb b/notebook/simGL.ipynb index 35b4457..867a93b 100644 --- a/notebook/simGL.ipynb +++ b/notebook/simGL.ipynb @@ -47,6 +47,9 @@ "name": "stderr", "output_type": "stream", "text": [ + "R[write to console]: RStudio Community is a great place to get help:\n", + "https://community.rstudio.com/c/tidyverse\n", + "\n", "R[write to console]: ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.1 ──\n", "\n", "R[write to console]: ✔ tibble 3.1.7 ✔ dplyr 1.0.9\n", @@ -5153,12 +5156,12 @@ "source": [] }, { - "cell_type": "code", - "execution_count": null, - "id": "8bd53327-4ff2-44d0-b60c-f2419b54a0ff", + "cell_type": "markdown", + "id": "eadfc241-68eb-4582-b613-3ddf911e3428", "metadata": {}, - "outputs": [], - "source": [] + "source": [ + "## 11. Error rate flexibility\n" + ] }, { "cell_type": "code", @@ -5586,7 +5589,7 @@ }, { "cell_type": "code", - "execution_count": 263, + "execution_count": 3, "id": "01ede551-2772-45a1-92ae-9f12f04eebe9", "metadata": {}, "outputs": [], @@ -5997,7 +6000,7 @@ }, { "cell_type": "code", - "execution_count": 252, + "execution_count": 4, "id": "1d4227e9-3158-4286-8394-fcb522312217", "metadata": {}, "outputs": [ @@ -6026,7 +6029,7 @@ " [ 2, 0, 0, 24]]])" ] }, - "execution_count": 252, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" } @@ -6926,11 +6929,406 @@ "np.exp(-60/10)" ] }, + { + "cell_type": "markdown", + "id": "36ab6188-0865-4ed2-b0a2-1730debf83ab", + "metadata": {}, + "source": [ + "## 12. Check that GP can be obtained from the normalized GL" + ] + }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "id": "a17f675b-2961-4365-aa28-7a77f955e6ee", "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "DPh\n", + "[15 12 24 32]\n", + "DP\n", + "[[22 10 20 47]\n", + " [14 14 28 26]]\n" + ] + }, + { + "data": { + "text/plain": [ + "array([[[22, 0, 0, 0],\n", + " [ 9, 1, 0, 0],\n", + " [ 0, 20, 0, 0],\n", + " [41, 2, 2, 2]],\n", + "\n", + " [[ 0, 0, 1, 13],\n", + " [ 0, 0, 1, 13],\n", + " [ 0, 24, 3, 1],\n", + " [ 2, 0, 0, 24]]])" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "seed = 1234\n", + "gm = np.array([[0, 0, 1, 0], \n", + " [1, 1, 0, 1]])\n", + "ref = np.array([\"A\", \"C\"])\n", + "alt = np.array([\"C\", \"T\"])\n", + "e = np.array([0.05, 0.05, 0.05, 0.05])\n", + "mean_depth = np.array([15, 12, 24, 32])\n", + "ploidy = 2\n", + "arc = sim_allelereadcounts(gm, mean_depth, e, ploidy = 1, seed = seed, std_depth = None, ref = ref, alt = alt, read_length = None, depth_type = \"independent\")\n", + "arc" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "f6118734-7382-41a4-beb5-517704397233", + "metadata": {}, + "outputs": [], + "source": [ + "def ploidy_sum(arr, ploidy):\n", + " s = arr.shape\n", + " return arr.reshape(-1).reshape(s[0], s[1]//ploidy, ploidy, s[2]).sum(axis = 2)\n", + "\n", + "def allelereadcounts_to_GL(arc, e, ploidy):\n", + " '''\n", + " Computes genotype likelihoods from allele read counts per site per individual. \n", + " \n", + " Parameters\n", + " ----------\n", + " arc : `numpy.ndarray`\n", + " Allele read counts per site per individual or haplotype. The dimentions of the array are \n", + " (sites, individuals or haplotypes, alleles). \n", + " \n", + " The second dimention will depend on the format of the `e` parameter. If the error parameter \n", + " is the same for every haplotype (`int` or `float`), the arc inputed can be per individual. \n", + " Instead, if the error parameter has a value for every haplotype (`np.array`), the arc must \n", + " be per haplotypic sample. This is because to compute GL it is needed to know the number of \n", + " reads per haplotype and their error rate. For example, to obtain the arc fir the former case \n", + " for diploid organisms one must call:\n", + " `sim_allelereadcounts(..., ploidy = 2, ...)` \n", + " but the latter, one must use:\n", + " `sim_allelereadcounts(..., ploidy = 1, ...)`. \n", + " \n", + " The third dimention of the array has size = 4, which corresponds to the four possible alleles: \n", + " 0 = \"A\", 1 = \"C\", 2 = \"G\" and 3 = \"T\".\n", + " \n", + " e : `int` or `float` or `numpy.ndarray`\n", + " Sequencing error probability per base pair per site. The values must be between 0 and 1. If a `int` or `float` \n", + " value is inputed, the function will use the same error probablity value for each haplotype and each site. \n", + " If a `numpy.ndarray` is inputed, there must be an error value per haplotype (i.e., the array must have size \n", + " (haplotypic samples, )) and the order must be the same as the second dimention of `arc`.\n", + "\n", + " ploidy : `int` \n", + " Number of haplotypic chromosomes per individual. \n", + "\n", + " Returns \n", + " -------\n", + "\n", + " GL : `numpy.ndarray`\n", + " Normalized genotype likelihoods per site per individual. The dimentions of the array are (sites, individuals, genotypes). \n", + " The third dimention of the array corresponds to the combinations with replacement of all 4 possible alleles \n", + " {\"A\", \"C\", \"G\", \"T\"} (i.e., for a diploid, there are 10 possible genotypes and the combination order is \"AA\", \"AC\",\n", + " \"AG\", \"AT\", \"CC\", \"CG\", ..., \"TT\"). \n", + "\n", + " References\n", + " ----------\n", + " 1) McKenna A, Hanna M, Banks E, Sivachenko A, Cibulskis K, Kernytsky A, Garimella K, Altshuler D, Gabriel S, Daly M, DePristo MA (2010). The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data. Genome Res. 20:1297-303.\n", + " 2) Thorfinn Sand Korneliussen, Anders Albrechtsen, Rasmus Nielsen. ANGSD: Analysis of Next Generation Sequencing Data. BMC Bioinform. 2014 Nov;15,356.\n", + " '''\n", + " assert check_arc(arc) and check_e(arc, e) and check_ploidy(ploidy)\n", + " \n", + " #1. Obtain an array which rows are possible genotypes depending (GT) on ploidy (ploidy) and each value is the encoded bp in that genotype (e.g., [\"AA\", \"AC\"] = [[0, 0], [0, 1]])\n", + " GTxploidy = get_GTxploidy(ploidy)\n", + " #2. Obtain an array which rows are the 4 bp, the columns are the GT and each value denotes the frequency of each allele\n", + " AFxGTxploidy = np.array([(GTxploidy == 0).sum(axis = 1), (GTxploidy == 1).sum(axis = 1), (GTxploidy == 2).sum(axis = 1), (GTxploidy == 3).sum(axis = 1)])/ploidy\n", + " \n", + " #3. We can compute the GL in two different ways: the first, which allows different error values per haplotype, is a generalized form of the second which only allows errors to be the same for all haplotypes and sites\n", + " # The reason why I keep both is because the former might be slower than the latter.\n", + " if isinstance(e, np.ndarray):\n", + " #I reformat the error array such that I can make matrix operations\n", + " er = np.repeat(e, AFxGTxploidy.size).reshape(e.shape + AFxGTxploidy.shape)\n", + " #Here it is computed the negative log of the multiplication of the error values and the \"AFxGTxploidy\" which results into an array that determines for every genotype the probabilities of observing a read\n", + " #taking into account the error probabilities\n", + " ERxAFxGTxploidy = -np.log(((AFxGTxploidy*(1-er)+(1-AFxGTxploidy)*(er/3))))\n", + " #This array is then reformated for later operations\n", + " ERxAFxGTxploidy = ERxAFxGTxploidy.reshape((1,) + ERxAFxGTxploidy.shape)\n", + " #The number of reads of each base pair are taken into account to compute the likelihood of observing all reads for a given genotype considering the error\n", + " RExerxAFxGTxploidy = np.multiply(ERxAFxGTxploidy, arc.reshape(arc.shape + (1,))).sum(axis = 2)\n", + " #The likelihoods for haplotypes of the same individual are finally added up together\n", + " GL = ploidy_sum(RExerxAFxGTxploidy, ploidy)\n", + " #The GL are normalized to the most likely genotype\n", + " return GL-GL.min(axis = 2).reshape(GL.shape[0], GL.shape[1], 1)\n", + " else:\n", + " #All the steps in the prevous if statement are done in a single line since the error is the same and simplifies the calculation\n", + " GL = np.multiply(-np.log(AFxGTxploidy*(1-e)+(1-AFxGTxploidy)*(e/3)), arc.reshape(arc.shape[0], arc.shape[1], arc.shape[2], 1)).sum(axis = 2)\n", + " #The GL are normalized to the most likely genotype\n", + " return GL-GL.min(axis = 2).reshape(GL.shape[0], GL.shape[1], 1)" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "84cf0c5a-39c4-413c-8cd5-42f91ad48c03", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[[ 0. , 17.58112274, 20.94841857, 20.94841857,\n", + " 121.29153804, 121.96729347, 121.96729347, 125.3345893 ,\n", + " 125.3345893 , 125.3345893 ],\n", + " [ 46.37453531, 0. , 67.3459166 , 67.3459166 ,\n", + " 123.1925094 , 131.32453737, 131.32453737, 204.05353475,\n", + " 198.67045397, 204.05353475]],\n", + "\n", + " [[105.11933296, 105.11933296, 98.3847413 , 17.56964138,\n", + " 105.11933296, 98.3847413 , 17.56964138, 97.03323043,\n", + " 10.83504972, 0. ],\n", + " [156.91139313, 77.44780409, 148.16101652, 74.08050826,\n", + " 67.96426524, 74.08050826, 0. , 152.86834187,\n", + " 70.71321243, 63.92121397]]])" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "GL_norm" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "fa5676d3-aece-4e18-beb6-68407b943267", + "metadata": {}, + "outputs": [], + "source": [ + ".transpose((1, 0, 2)).reshape(-1).reshape(GL.shape[1], GL.shape[0]*3)" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "id": "2a010180-f518-4cad-a14c-677b0bb2523b", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[[ 0. , 17.58112274, 121.29153804],\n", + " [ 46.37453531, 0. , 123.1925094 ]],\n", + "\n", + " [[105.11933296, 105.11933296, 105.11933296],\n", + " [156.91139313, 77.44780409, 67.96426524]]])" + ] + }, + "execution_count": 39, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "GL_norm[:, :, [0, 1, 4]]" + ] + }, + { + "cell_type": "code", + "execution_count": 67, + "id": "be55b850-e327-4ca8-a981-7db2449b8762", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[[9.99999954e-01, 4.63068653e-08, 2.10743559e-53],\n", + " [3.62047222e-21, 1.00000000e+00, 1.57450108e-54]],\n", + "\n", + " [[2.50000000e-01, 5.00000000e-01, 2.50000000e-01],\n", + " [2.34794049e-39, 1.52165191e-04, 9.99847835e-01]]])" + ] + }, + "execution_count": 67, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#GL_norm = allelereadcounts_to_GL(arc, e, ploidy = 2)\n", + "np.exp(-GL_norm[:, :, [0, 1, 4]])*np.array([1/4, 1/2, 1/4])/np.sum(np.exp(-GL_norm[:, :, [0, 1, 4]])*np.array([1/4, 1/2, 1/4]), axis = 2).reshape(2, 2, 1)" + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "id": "466b7ce6-416f-41f9-91d2-bb81fe72190a", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[[9.99999954e-01, 4.63068653e-08, 2.10743559e-53],\n", + " [3.62047222e-21, 1.00000000e+00, 1.57450108e-54]],\n", + "\n", + " [[2.50000000e-01, 5.00000000e-01, 2.50000000e-01],\n", + " [2.34794049e-39, 1.52165191e-04, 9.99847835e-01]]])" + ] + }, + "execution_count": 62, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#GL_norm = allelereadcounts_to_GL(arc, e, ploidy = 2)\n", + "np.exp(-GL_norm[:, :, [0, 1, 4]])*np.array([1/4, 1/2, 1/4])/np.repeat(np.sum(np.exp(-GL_norm[:, :, [0, 1, 4]])*np.array([1/4, 1/2, 1/4]), axis = 2), 3).reshape(2, 2, 3)" + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "id": "330951c9-0de9-412d-acb6-814f37822b0d", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[[2.50000012e-01, 2.50000012e-01, 2.50000012e-01],\n", + " [5.00000000e-01, 5.00000000e-01, 5.00000000e-01]],\n", + "\n", + " [[2.22460932e-46, 2.22460932e-46, 2.22460932e-46],\n", + " [7.61203431e-31, 7.61203431e-31, 7.61203431e-31]]])" + ] + }, + "execution_count": 61, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.repeat(np.sum(np.exp(-GL_norm[:, :, [0, 1, 4]])*np.array([1/4, 1/2, 1/4]), axis = 2), 3).reshape(2, 2, 3)" + ] + }, + { + "cell_type": "code", + "execution_count": 73, + "id": "b960168b-7a4f-411d-abff-9634897ef403", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(2, 2, 1)" + ] + }, + "execution_count": 73, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "GL_norm.shape[:2] + (1,)" + ] + }, + { + "cell_type": "code", + "execution_count": 70, + "id": "54e415a5-da7c-4a46-97a6-eefbf20abe24", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([9.99999954e-01, 4.63068653e-08, 2.10743559e-53])" + ] + }, + "execution_count": 70, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.exp(-GL_norm[[0, 0, 0], [0, 0, 0], [0, 1, 4]])*np.array([1/4, 1/2, 1/4])/np.sum(np.exp(-GL_norm[[0, 0, 0], [0, 0, 0], [0, 1, 4]])*np.array([1/4, 1/2, 1/4]))" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "id": "9d858005-6c52-432b-b047-6448b4a8e3d8", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([9.99999953e-01, 4.63068652e-08, 7.98394228e-10])" + ] + }, + "execution_count": 33, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#GL_notnorm = allelereadcounts_to_GL(arc, e, ploidy = 2)\n", + "np.exp(-GL_notnorm[[0, 0, 0], [0, 0, 0], [0, 1, 2]])*np.array([1/4, 1/2, 1/4])/np.sum(np.exp(-GL_notnorm[[0, 0, 0], [0, 0, 0], [0, 1, 2]])*np.array([1/4, 1/2, 1/4]))" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "e448a917-2a8f-4751-b516-ee29b8cc8b37", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.049787068367863944" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.exp(-3)" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "6629c645-b39a-4c6a-8cb5-a8e44dcbfe17", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "3.0" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "-np.log(0.049787068367863944)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "33ad2a60-492c-4117-82e7-388bf2439818", + "metadata": {}, "outputs": [], "source": [] }