"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"Comparison_threshold= 5\n",
"Outlier_threshold= 1e-4\n",
"color_ref= ['red','yellow','black','orange','purple','green','silver','red3','deepskyeblue','navy','chartreuse','darkorchid3','goldenrod2']\n",
"\n",
"Blocks3 = Merge_class_mix(Windows_class,focus_indexes,Out,Comparison_threshold,Outlier_threshold)\n",
"\n",
"if not Mix_sizes:\n",
"\n",
" Ideo_KDE = []\n",
" chromosome_list= []\n",
" chromosomes= Blocks3.keys()\n",
"\n",
" for here in range(len(label_vector)):\n",
" Subject = 'sample' + str(here)\n",
"\n",
" chromosome_list.extend(['Region_chr'+str(Chr)+ '_' + Subject for Chr in chromosomes])\n",
"\n",
" Stock = [[['Region_chr'+str(Chr)+ '_' + Subject,bl,Out[Chr][bl],color_ref[Blocks3[Chr][bl][here] - 1]] for bl in sorted(Blocks3[Chr].keys())] for Chr in chromosomes]\n",
" Stock = [y for y in it.chain(*[z for z in it.chain(*[Stock])])]\n",
"\n",
" Ideo_KDE.extend(Stock)\n",
"\n",
" #### begin by compressing assignments by individuals. Lightens the load of the following plot.\n",
" import re\n",
" ideo_kde = pd.DataFrame(Ideo_KDE,columns = ['chrom', 'start', 'end', 'gieStain'])\n",
"\n",
" # Filter out chromosomes not in our list\n",
" ideo_kde = ideo_kde[ideo_kde.chrom.apply(lambda x: x in chromosome_list)]\n",
"\n",
" ideo_kde = compress_ideo(ideo_kde,chromosome_list,Out)\n",
"\n",
" fig= return_ideogram(ideo_kde,chromosome_list,'kde',height=9,width= 15)\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Data analysis.\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
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"layout": {
"title": "Misassignment by overlap. Unimodal Cp = 1",
"xaxis": {
"title": "Internal volume shared, P (Other | Pop)"
},
"yaxis": {
"range": [
0,
1
],
"title": "Error rate"
}
}
},
"text/html": [
""
],
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""
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"source": [
"references= [0,1]\n",
"labels= [0,1]\n",
"Error_rate= {x:[] for x in target}\n",
"\n",
"Global_error_rate= []\n",
"\n",
"where= []\n",
"\n",
"\n",
"for bl in sorted(Blocks[1].keys()):\n",
" label_indicies= label_indicies_bl[bl]\n",
" ##everyone:\n",
" non_outlier= [x for x in range(sum(Sizes)) if Blocks2[1][bl][x] != len(references) + 1]\n",
" misshaps= [x for x in range(sum(Sizes)) if Blocks[1][bl][x] != Blocks2[1][bl][x] and Blocks2[1][bl][x] -1 in labels]\n",
" rate= len(misshaps) / float(len(non_outlier))\n",
" Global_error_rate.append(rate)\n",
"\n",
" \n",
" for pop in label_indicies.keys():\n",
" \n",
" non_outlier= [x for x in label_indicies[pop] if Blocks2[1][bl][x] != len(labels) + 1]\n",
" misshaps= [x for x in label_indicies[pop] if Blocks[1][bl][x] != Blocks2[1][bl][x] and Blocks2[1][bl][x] -1 in labels]\n",
" rate= len(misshaps) / float(len(non_outlier))\n",
" Error_rate[pop].append(rate)\n",
" where.append(bl)\n",
"\n",
"\n",
"tup= '01'\n",
"Xs= []\n",
"pop1= []\n",
"labels= []\n",
"\n",
"for window in Windows_union.keys():\n",
" Xs.extend([window] * 4)\n",
" pop1.append(Windows_union[window][tuple((int(tup[0]),int(tup[1])))][int(tup[0])])\n",
" pop1.append(Windows_union[window][tuple((int(tup[0]),int(tup[1])))][int(tup[1])])\n",
" pop1.append(Windows_union[window][tuple((int(tup[0]),int(tup[1])))]['empty'])\n",
" pop1.append(Windows_union[window][tuple((int(tup[0]),int(tup[1])))]['PU'])\n",
" labels.extend([0,1,'leftover space','PU'])\n",
"\n",
"\n",
"coords= {i:[x for x in range(len(labels)) if labels[x] == i] for i in list(set(labels))}\n",
"\n",
"datum= np.array([Xs,pop1]).T\n",
"\n",
"\n",
"fig_data= [go.Scatter(\n",
"#x= [Fst_crawl[x] for x in range(len(Fst_labels)) if len([y for y in Fst_labels[x] if y in target]) == 2],\n",
"#x= [min([datum[coords[z][x],1] for z in [0,1]]) / max([datum[coords[z][x],1] for z in [0,1]]) for x in range(len(Windows_union.keys()))],\n",
"x= [datum[coords[1-i][x],1] for x in range(len(Windows_union.keys()))],\n",
"#x= [datum[coords[i][x],1] - datum[coords[1-i][x],1] for x in range(len(Windows_union.keys()))],\n",
"#x= datum[coords[i],1],\n",
"#x= [Fst_crawl[x] for x in fst_indexes[fst_target]],\n",
"y= Error_rate[i],\n",
"name= \"Error pop {}, P(Other | Pop {})\".format(i,i),\n",
"mode= 'markers',\n",
") for i in references\n",
"]\n",
"\n",
"\n",
"fig_data.append(go.Scatter(\n",
"#x= [Fst_crawl[x] for x in range(len(Fst_labels)) if len([y for y in Fst_labels[x] if y in target]) == 2],\n",
"#x= [sum([datum[coords[z][x],1] for z in [0,1]]) for x in range(len(Windows_union.keys()))],\n",
"#x= [datum[coords[i][x],1] for x in range(len(Windows_union.keys()))],\n",
"x= datum[coords['PU'],1],\n",
"y= Global_error_rate,\n",
"name= \"Global\",\n",
"mode= 'markers',\n",
"))\n",
"\n",
"\n",
"layout = go.Layout(\n",
" title= 'Misassignment by overlap. Unimodal Cp = {}'.format(Comparison_threshold),\n",
" yaxis=dict(\n",
" range= [0,1],\n",
" title='Error rate'),\n",
" #title='Internal volume shared, P (Other | Pop)'),\n",
" xaxis=dict(\n",
" #title= 'Fst')\n",
" title='Internal volume shared, P (Other | Pop)')\n",
")\n",
"\n",
"fig= go.Figure(data=fig_data, layout=layout)\n",
"iplot(fig)"
]
},
{
"cell_type": "code",
"execution_count": 19,
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"title": "Intermediates by Fst. Unimodal. Cp = 6",
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"labels= [0,1]\n",
"Error_rate= {x:[] for x in target}\n",
"Initial_error= {x:[] for x in target}\n",
"where= []\n",
"Intermediate_rate= {x:[] for x in target}\n",
"Info_loss= {x:[] for x in target}\n",
"Correction_rate= {x:[] for x in target}\n",
"\n",
"Global_initial_error= []\n",
"Globl_initial_error= []\n",
"Global_error= []\n",
"Global_info_loss= []\n",
"Global_intermediate_rate= []\n",
"Global_correction_rate= []\n",
"\n",
"for bl in Blocks3[1].keys():\n",
" label_indicies= label_indicies_bl[bl]\n",
" Swiss_range= range(len(Blocks3[1][bl]))\n",
" non_outlier= [x for x in Swiss_range if Blocks3[1][bl][x] != len(labels) + 1]\n",
" \n",
" misshaps= [x for x in Swiss_range if Blocks[1][bl][x] != Blocks3[1][bl][x] and Blocks3[1][bl][x] -1 in labels]\n",
" intermediates= [x for x in Swiss_range if Blocks3[1][bl][x] > len(labels)]\n",
"\n",
" Ori_miss= [x for x in Swiss_range if Blocks[1][bl][x] != Blocks2[1][bl][x] and Blocks2[1][bl][x] -1 in labels]\n",
" loss= [x for x in intermediates if x not in Ori_miss]\n",
" correction= [x for x in Ori_miss if x in intermediates]\n",
"\n",
"\n",
" FirstSin= len(Ori_miss) / float(len(non_outlier))\n",
" intimicy= len(intermediates) / float(len(non_outlier))\n",
" rate= len(misshaps) / float(len(non_outlier))\n",
" loss= len(loss) / float(len(non_outlier))\n",
"\n",
" if float(len(Ori_miss)) == 0:\n",
" correction= 1\n",
" else:\n",
" correction= len(correction) / float(len(Ori_miss))\n",
"\n",
" Global_info_loss.append(loss)\n",
" Globl_initial_error.append(FirstSin)\n",
" Global_correction_rate.append(correction)\n",
" Global_intermediate_rate.append(intimicy)\n",
" Global_error.append(rate)\n",
" where.append(bl)\n",
"\n",
" \n",
" for pop in label_indicies.keys():\n",
" \n",
" non_outlier= [x for x in label_indicies[pop] if Blocks3[1][bl][x] != len(labels) + 1]\n",
" misshaps= [x for x in label_indicies[pop] if Blocks[1][bl][x] != Blocks3[1][bl][x] and Blocks3[1][bl][x] -1 in labels]\n",
" intermediates= [x for x in label_indicies[pop] if Blocks3[1][bl][x] > len(labels)]\n",
" \n",
" Ori_miss= [x for x in label_indicies[pop] if Blocks[1][bl][x] != Blocks2[1][bl][x] and Blocks2[1][bl][x] -1 in labels]\n",
" loss= [x for x in intermediates if x not in Ori_miss]\n",
" correction= [x for x in Ori_miss if x in intermediates]\n",
" \n",
" \n",
" FirstSin= len(Ori_miss) / float(len(non_outlier))\n",
" intimicy= len(intermediates) / float(len(non_outlier))\n",
" rate= len(misshaps) / float(len(non_outlier))\n",
" loss= len(loss) / float(len(non_outlier))\n",
" \n",
" if float(len(Ori_miss)) == 0:\n",
" correction= 1\n",
" else:\n",
" correction= len(correction) / float(len(Ori_miss))\n",
" \n",
" Info_loss[pop].append(loss)\n",
" Initial_error[pop].append(FirstSin)\n",
" Correction_rate[pop].append(correction)\n",
" Intermediate_rate[pop].append(intimicy)\n",
" Error_rate[pop].append(rate)\n",
" \n",
"\n",
"tup= '01'\n",
"Xs= []\n",
"pop1= []\n",
"labels= []\n",
"\n",
"for window in Windows_union.keys():\n",
" Xs.extend([window] * 4)\n",
" pop1.append(Windows_union[window][tuple((int(tup[0]),int(tup[1])))][int(tup[0])])\n",
" pop1.append(Windows_union[window][tuple((int(tup[0]),int(tup[1])))][int(tup[1])])\n",
" pop1.append(Windows_union[window][tuple((int(tup[0]),int(tup[1])))]['empty'])\n",
" pop1.append(Windows_union[window][tuple((int(tup[0]),int(tup[1])))]['PU'])\n",
" labels.extend([0,1,'leftover space','PU'])\n",
"\n",
"coords= {i:[x for x in range(len(labels)) if labels[x] == i] for i in list(set(labels))}\n",
"\n",
"datum= np.array([Xs,pop1]).T\n",
"\n",
"fig_data= [go.Scatter(\n",
"x= [abs(datum[coords[i][x],1] - datum[coords[1-i][x],1]) for x in range(len(Windows_union.keys()))],\n",
"#x= [datum[coords[i][x],1] for x in range(len(Windows_union.keys()))],\n",
"#x= [Fst_crawl[x] for x in fst_indexes[fst_target]],\n",
"y= [Intermediate_rate[i][x] for x in range(len(Windows_union.keys()))],\n",
"#x= datum[coords[i],1],\n",
"mode= 'markers',\n",
"name= 'Pop {} error., P(Other | {})'.format(i,i)\n",
") for i in [0,1]\n",
"]\n",
"\n",
"\n",
"fig_data.append(go.Scatter(\n",
"#x= [sum([datum[coords[z][x],1] for z in [0,1]]) for x in range(len(Windows_union.keys()))],\n",
"#x= [datum[coords[0][x],1] / datum[coords[1][x],1] for x in range(len(Windows_union.keys()))],\n",
"y= Global_intermediate_rate,\n",
"x= datum[coords['PU'],1],\n",
"name= 'Global',\n",
"mode= 'markers',\n",
"))\n",
"\n",
"'''\n",
"\n",
"'''\n",
"#### LOESS\n",
"\n",
"\n",
"####\n",
"\n",
"layout = go.Layout(\n",
" title= 'Intermediates by Fst. Unimodal. Cp = {}'.format(Comparison_threshold),\n",
" yaxis=dict(\n",
" range= [0,1.1],\n",
" title='Intermediates'),\n",
" xaxis=dict(\n",
" #range= [0,1],\n",
" title= 'Fst')\n",
")\n",
"\n",
"fig= go.Figure(data=fig_data, layout=layout)\n",
"iplot(fig)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## B. Frequency. *Changing proportion at a given pop without moving it about.*\n",
"\n",
"### or, *Dominance over a particular branch.*\n",
"\n",
"In this section we will perform supervised classification of structured reference populations overlapping at a given positition.\n",
"\n",
"Overlap will be controlled by varying the proportion of samples from a single frequency vector labelled to each population.\n",
"\n",
"Three frequency vectors are used. For two vectors sample labell will be left uniform, corresponding to the two reference populations. From the third vector the proportion of samples assigned to each reference will vary. Total number of samples from each vector will not vary."
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [],
"source": [
"Mix_sizes= False\n",
"\n",
"labels= [0,1,2]\n",
"target= [0,1]\n",
"references= [0,1]\n",
"\n",
"Sizes= [100,100,500]\n",
"\n",
"Origins= {\n",
" x:{\n",
" y:[int(z == x) for z in range(len(Sizes))] for y in range(Sizes[x])\n",
" } for x in range(len(Sizes))\n",
"}\n",
"\n",
"import random\n",
"\n",
"Admixed_proportions= [random.randrange(0,100) / float(100) for x in range(Sizes[-1])]\n",
"\"\"\"\n",
"Origins[len(Sizes) - 1]= {\n",
" y: [Admixed_proportions[y], 1 - Admixed_proportions[y]] for y in range(Sizes[-1])\n",
"}\n",
"\"\"\"\n",
"\n",
"#### we can store these in a way that will facilitate later requests:\n",
"Whose= []\n",
"ind_to_group= {}\n",
"\n",
"d= 0\n",
"\n",
"for gp in Origins.keys():\n",
" for acc in range(len(Origins[gp])):\n",
" Whose.append(d)\n",
" ind_to_group[d]= [gp,acc]\n",
" d += 1\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"Proportion_range= np.arange(0,10,.1)\n",
"\n",
"label_vector= np.repeat(labels,Sizes)\n",
"label_indicies= {x:[y for y in range(len(label_vector)) if label_vector[y] == x] for x in labels}\n",
"target_indx= {z:[x for x in range(len(label_vector)) if label_vector[x] == z] for z in labels}\n",
"\n",
"Pops= np.random.choice(vector_lib.shape[0],len(Sizes),replace= False)\n",
"\n",
"label_vector_bl= recursively_default_dict()\n",
"label_indicies_bl= recursively_default_dict()\n",
"target_indx_bl= recursively_default_dict()\n",
"\n",
"\n",
"Chr= 1\n",
"color_ref= ['red','yellow','blue','black','orange','purple','green','silver','red3','deepskyeblue','navy','chartreuse','darkorchid3','goldenrod2']\n",
"\n",
"COp= 1\n",
"\n",
"\n",
"Windows= recursively_default_dict()\n",
"Windows_class= recursively_default_dict()\n",
"\n",
"Windows_union= recursively_default_dict()\n",
"\n",
"#haplotypes= {x:[] for x in Whose}\n",
"Coordinates= recursively_default_dict()\n",
"Out= {1:{}}\n",
"Ideo= []\n",
"Blocks= {1:{}}\n",
"Fst_windows= []\n",
"Fst_crawl= []\n",
"Fst_labels= []\n",
"\n",
"threshold= .005\n",
"P= 30\n",
"\n",
"current= recursively_default_dict()\n",
"\n",
"\n",
"for angle in Proportion_range:\n",
" print(angle)\n",
" if Mix_sizes:\n",
" Sizes= np.random.choice(range(50,200),len(Sizes))\n",
"\n",
" Origins= {\n",
" x:{\n",
" y:[int(z == x) for z in range(len(Sizes))] for y in range(Sizes[x])\n",
" } for x in range(len(Sizes))\n",
" }\n",
"\n",
" import random\n",
"\n",
" Admixed_proportions= [random.randrange(0,100) / float(100) for x in range(Sizes[-1])]\n",
" \n",
" #### we can store these in a way that will facilitate later requests:\n",
" \n",
" Whose= []\n",
" ind_to_group= {}\n",
"\n",
" d= 0\n",
" for gp in Origins.keys():\n",
" for acc in range(len(Origins[gp])):\n",
" Whose.append(d)\n",
" ind_to_group[d]= [gp,acc]\n",
" d += 1\n",
" \n",
" label_vector= np.repeat(labels,Sizes)\n",
" label_indicies= {x:[y for y in range(len(label_vector)) if label_vector[y] == x] for x in labels}\n",
" target_indx= {z:[x for x in range(len(label_vector)) if label_vector[x] == z] for z in labels}\n",
" \n",
" coords= features[Pops,:]\n",
"\n",
" new_freqs= pca.inverse_transform(coords)\n",
" \n",
" #new_freqs[target[0]]= pca.inverse_transform(coords[target[0]])\n",
" new_freqs[new_freqs > 1] = 1\n",
" new_freqs[new_freqs < 0] = 0\n",
"\n",
" N_pops= len(Pops)\n",
" bl= int(angle*10000)\n",
" end= bl+ 999\n",
" Out[1][bl]= end\n",
" \n",
" scramble= [x for x in range(new_freqs.shape[1])]\n",
" shuffle(scramble)\n",
" \n",
" new_freqs= new_freqs[:,scramble]\n",
"\n",
" data= []\n",
" local_labels= []\n",
" \n",
" \n",
" for acc in range(len(Whose)):\n",
" Subject = 'sample' + str(acc)\n",
" \n",
" transition_p= Origins[ind_to_group[acc][0]][ind_to_group[acc][1]]\n",
" \n",
" if current[acc]:\n",
" cross_over= np.random.choice([0,1], p=[1-COp,COp])\n",
" if cross_over == 1:\n",
" k= np.random.choice(range(new_freqs.shape[0]), p=transition_p)\n",
" current[acc]= k\n",
" else:\n",
" k= current[acc]\n",
" else:\n",
" k= np.random.choice(range(new_freqs.shape[0]), p=transition_p)\n",
" current[acc]= k\n",
" \n",
" probs= new_freqs[k,:]\n",
" \n",
" probs[(probs > 1)]= 1\n",
" probs[(probs < 0)]= 0\n",
" \n",
" Haps= [np.random.choice([1,0],p= [1-probs[x],probs[x]]) for x in range(L)]\n",
" \n",
" \n",
" P_label= np.sin(angle) / 2 + .5\n",
" \n",
" if ind_to_group[acc][0] == 2:\n",
" k= np.random.choice(references,p= [P_label,1-P_label])\n",
" \n",
" \n",
" Stock = ['Region_'+str(Chr)+ '_' + Subject,bl,end,color_ref[k]]\n",
" Ideo.append(Stock)\n",
" data.append(Haps)\n",
" local_labels.append(labels[k] + 1)\n",
" \n",
" data= np.array(data)\n",
" \n",
" print(data.shape)\n",
" \n",
" '''\n",
" if not Mix_sizes:\n",
" for hap in range(data.shape[0]):\n",
" haplotypes[hap].extend(data[hap,:])\n",
" '''\n",
" \n",
" pca2 = PCA(n_components=3, whiten=False,svd_solver='randomized')\n",
" \n",
" data= pca2.fit_transform(data)\n",
" \n",
" ref_indicies= {z:[x for x in range(len(local_labels)) if local_labels[x] == z] for z in list(set(local_labels))}\n",
" \n",
" profiles_union= extract_profiles_union(data,ref_indicies,threshold,P)\n",
" print([profiles_union[x]['PU'] for x in profiles_union.keys()])\n",
" profiles_class= extract_profiles_class(data,ref_indicies)\n",
" \n",
" ##### FSTs\n",
" Pairwise= return_fsts2(new_freqs)\n",
" Fst_labels.extend(Pairwise.pops)\n",
" \n",
" Fst_crawl.extend(Pairwise.fst)\n",
" \n",
" Fst_windows.extend([angle] * Pairwise.shape[0])\n",
"\n",
" ### store stuff.\n",
" \n",
" Coordinates[bl]= data[:,:3]\n",
" \n",
" label_vector_bl[bl]= label_vector\n",
" label_indicies_bl[bl]= label_indicies\n",
" target_indx_bl[bl]= target_indx\n",
" \n",
" Windows_class[bl]= profiles_class\n",
" Blocks[1][bl]= local_labels\n",
" Windows_union[bl]= profiles_union\n",
"\n",
"\n",
"\n",
"\n",
"Windows_class= {1:Windows_class}\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
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\n",
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"source": [
"# Plot overlaps\n",
"#Windows_union= recursively_default_dict()\n",
"fst_indexes= {z:[x for x in range(len(Fst_labels)) if Fst_labels[x] == z] for z in list(set(Fst_labels))}\n",
"fst_target= [z for z in fst_indexes.keys() if len([x for x in target if x in z]) == len(target)][0]\n",
"\n",
"def plot_proximities(tup):\n",
" Xs= []\n",
" pop1= []\n",
" labels= []\n",
" \n",
" for window in Windows_union.keys():\n",
" Xs.extend([window] * 3)\n",
" pop1.append(Windows_union[window][tuple((int(tup[0]),int(tup[1])))][int(tup[0])])\n",
" pop1.append(Windows_union[window][tuple((int(tup[0]),int(tup[1])))][int(tup[1])])\n",
" pop1.append(Windows_union[window][tuple((int(tup[0]),int(tup[1])))]['PU'])\n",
" labels.extend(['P2|1','P1|2','Union'])\n",
" \n",
" coords= {i:[x for x in range(len(labels)) if labels[x] == i] for i in list(set(labels))}\n",
" \n",
" datum= np.array([Xs,pop1]).T\n",
" \n",
" fig_data= [go.Scatter(\n",
" x= Proportion_range,\n",
" y= datum[coords[i],1],\n",
" mode= 'lines',\n",
" name= i\n",
" ) for i in coords.keys()\n",
" ]\n",
" layout = go.Layout(\n",
" title= 'Variation between',\n",
" yaxis=dict(\n",
" title= 'volume',\n",
" range=[0, 1]\n",
" ),\n",
" xaxis= dict(\n",
" title= 'Fst'\n",
" )\n",
" )\n",
" fig = go.Figure(data=fig_data, layout=layout)\n",
" iplot(fig)\n",
"\n",
"\n",
"\n",
"interact(plot_proximities, tup=['12'])"
]
},
{
"cell_type": "code",
"execution_count": 23,
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{
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Failed to display Jupyter Widget of type interactive.
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"\n",
"### \n",
"# Plot overlaps\n",
"#Windows_union= recursively_default_dict()\n",
"fst_indexes= {z:[x for x in range(len(Fst_labels)) if Fst_labels[x] == z] for z in list(set(Fst_labels))}\n",
"fst_target= [z for z in fst_indexes.keys() if len([x for x in target if x in z]) == len(target)][0]\n",
"\n",
"def plot_proximities(bl):\n",
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" chains= {z:[x for x in range(len(Blocks[1][bl])) if Blocks2[1][bl][x] == z] for z in list(set(Blocks[1][bl]))}\n",
" \n",
" fig_data= [go.Scatter3d(\n",
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" y= Coordinates[bl][chains[i],1],\n",
" z= Coordinates[bl][chains[i],2],\n",
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" 'line': {'width': 0},\n",
" 'size': 2,\n",
" 'symbol': 'circle',\n",
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" yaxis=dict(\n",
" title= 'volume',\n",
" range=[0, 1]\n",
" ),\n",
" xaxis= dict(\n",
" title= 'Fst'\n",
" )\n",
" )\n",
" fig = go.Figure(data=fig_data)\n",
" iplot(fig)\n",
"\n",
"\n",
"\n",
"interact(plot_proximities, bl=[x for x in Blocks2[1].keys()])\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Fig. PCA projection of haplotypes generated for requested data set.**\n",
"\n",
"Let us also have a look at the relative frequencies of material from each reference at the shared branch along simulations."
]
},
{
"cell_type": "code",
"execution_count": 37,
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"layout": {
"title": "Info_loss by overlap. Unimodal. Cp = 4",
"xaxis": {
"title": "Internal volume shared, P (Other | Pop)"
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0,
1.1
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"title": "Info_loss"
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"labels= [0,1]\n",
"target= [2]\n",
"Error_rate= {x:[] for x in target}\n",
"Initial_error= {x:[] for x in target}\n",
"where= []\n",
"Intermediate_rate= {x:[] for x in target}\n",
"Info_loss= {x:[] for x in target}\n",
"\n",
"Correction_rate= {x:[] for x in target}\n",
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"\n",
"for bl in Blocks3[1].keys():\n",
" label_indicies= label_indicies_bl[bl]\n",
" \n",
" for pop in target:\n",
" \n",
" non_outlier= [x for x in label_indicies[pop] if Blocks3[1][bl][x] != len(labels) + 1]\n",
" misshaps= [x for x in label_indicies[pop] if Blocks[1][bl][x] != Blocks3[1][bl][x] and Blocks3[1][bl][x] -1 in labels]\n",
" intermediates= [x for x in label_indicies[pop] if Blocks3[1][bl][x] > len(labels)]\n",
" \n",
" Ori_miss= [x for x in label_indicies[pop] if Blocks[1][bl][x] != Blocks2[1][bl][x] and Blocks2[1][bl][x] -1 in labels]\n",
" loss= [x for x in intermediates if x not in Ori_miss]\n",
" correction= [x for x in Ori_miss if x in intermediates]\n",
" \n",
" \n",
" FirstSin= len(Ori_miss) / float(len(non_outlier))\n",
" intimicy= len(intermediates) / float(len(non_outlier))\n",
" rate= len(misshaps) / float(len(non_outlier))\n",
" loss= len(loss) / float(len(non_outlier))\n",
" \n",
" if float(len(Ori_miss)) == 0:\n",
" correction= 1\n",
" else:\n",
" correction= len(correction) / float(len(Ori_miss))\n",
" \n",
" Info_loss[pop].append(loss)\n",
" Initial_error[pop].append(FirstSin)\n",
" Correction_rate[pop].append(correction)\n",
" Intermediate_rate[pop].append(intimicy)\n",
" Error_rate[pop].append(rate)\n",
" where.append(bl)\n",
"\n",
"tup= '12'\n",
"Xs= []\n",
"pop1= []\n",
"labels= []\n",
"\n",
"for window in Windows_union.keys():\n",
" Xs.extend([window] * 4)\n",
" pop1.append(Windows_union[window][tuple((int(tup[0]),int(tup[1])))][int(tup[0])])\n",
" pop1.append(Windows_union[window][tuple((int(tup[0]),int(tup[1])))][int(tup[1])])\n",
" pop1.append(Windows_union[window][tuple((int(tup[0]),int(tup[1])))]['empty'])\n",
" pop1.append(Windows_union[window][tuple((int(tup[0]),int(tup[1])))]['PU'])\n",
" labels.extend([0,1,'leftover space','PU'])\n",
"\n",
"coords= {i:[x for x in range(len(labels)) if labels[x] == i] for i in list(set(labels))}\n",
"\n",
"datum= np.array([Xs,pop1]).T\n",
"\n",
"freq1= [(Sizes[2] * (.5 + np.sin(angle) / 2)) / float(Sizes[2] * (.5 + np.sin(angle) / 2) + Sizes[0]) for angle in Proportion_range]\n",
"freq2= [(Sizes[2] - Sizes[2] * (.5 + np.sin(angle) / 2)) / float((Sizes[2] - Sizes[2] * (.5 + np.sin(angle) / 2)) + Sizes[1]) for angle in Proportion_range]\n",
"\n",
"Combo= [freq1,freq2]\n",
"\n",
"fig_data= [go.Scatter(\n",
"x= Proportion_range,\n",
"y= Combo[i],\n",
"name= \"Pop {}\".format(i),\n",
"mode= 'markers',\n",
") for i in [0,1]\n",
"]\n",
"\n",
"\n",
"#### LOESS\n",
"\n",
"\n",
"####\n",
"\n",
"layout = go.Layout(\n",
" title= 'Label frequency',\n",
" yaxis=dict(\n",
" range= [0,1.1],\n",
" title='Proportion'),\n",
" xaxis=dict(\n",
" title='Overlap')\n",
")\n",
"\n",
"fig= go.Figure(data=fig_data, layout=layout)\n",
"iplot(fig)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Proportion of samples from mixed population labelled to each reference as a function of relative overlap.**\n",
"\n",
"And finally study the relation between the variables we are interested in obtaining.\n",
"\n",
"Here, the game is to understand the relation between relative overlap and classification output. We want to know how overlap impacts the number of errors and intermediate classificaiton. On the flip side, what proportion of haplotypes are classed into reference classes as a measure of relative overlap?\n",
"\n",
"I have found the best descriptor of the probability of classification into a given class (pure or intermediate), to be the relative frequency of observations at the shared frequency. That is, the proportion of elements of one classs that are the shared source, divided by the proportion of elements from the other class that are found there as well. I believe this is because of some issues with the calculation of of overlap; but relative frequency is already an informative descriptor.\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {
"scrolled": false
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"title": "Intermediate assignment and max local overlap. Cp threshold= 4",
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"\n",
"labels= [0,1,2]\n",
"Error_rate= {x:[] for x in labels if x not in references}\n",
"Global_error_rate= []\n",
"Global_intermediates= []\n",
"where= []\n",
"\n",
"Pure_errors= {x:[] for x in labels if x in references}\n",
"Pure_assignment= {x:[] for x in labels if x in references}\n",
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"\n",
"for bl in Blocks[1].keys():\n",
" label_indicies= label_indicies_bl[bl]\n",
" \n",
" ##everyone:\n",
" non_outlier= [x for x in range(sum(Sizes)) if Blocks3[1][bl][x] != len(references) + 1]\n",
" misshaps= [x for x in range(sum(Sizes)) if Blocks[1][bl][x] != Blocks3[1][bl][x] and Blocks3[1][bl][x] -1 in labels]\n",
" intermediates= [x for x in range(sum(Sizes)) if Blocks3[1][bl][x] > len(labels)]\n",
" \n",
" intimicy= len(intermediates) / float(len(non_outlier))\n",
" rate= len(misshaps) / float(len(non_outlier))\n",
" \n",
" Global_intermediates.append(intimicy)\n",
" Global_error_rate.append(rate)\n",
"\n",
" \n",
" \n",
" origins_place= {z:[x for x in ind_to_group.keys() if ind_to_group[x][0] == z] for z in labels}\n",
" for pop in references:\n",
" non_outlier= [x for x in origins_place[2] if Blocks3[1][bl][x] != len(references) + 1]\n",
" original= [x for x in non_outlier if Blocks2[1][bl][x] == pop + 1]\n",
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" error_by_pop= [x for x in original if Blocks3[1][bl][x] != pop + 1]\n",
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" full_assignment= [x for x in non_outlier if Blocks3[1][bl][x] == pop + 1]\n",
" full_assignment= len(full_assignment) / float(len(non_outlier))\n",
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" Pure_errors[pop].append(error_by_pop)\n",
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" \n",
" for pop in [x for x in label_indicies.keys() if x not in references]:\n",
" \n",
" non_outlier= [x for x in label_indicies[pop] if Blocks3[1][bl][x] != len(references) + 1]\n",
" misshaps= [x for x in label_indicies[pop] if Blocks[1][bl][x] != Blocks3[1][bl][x] and Blocks3[1][bl][x] -1 in labels]\n",
" intermediates= [x for x in label_indicies[pop] if Blocks3[1][bl][x] > len(labels)]\n",
" intimicy= len(intermediates) / float(len(non_outlier))\n",
" rate= len(misshaps) / float(len(non_outlier))\n",
" \n",
" Intermediate_rate[pop].append(intimicy)\n",
" Error_rate[pop].append(rate)\n",
" where.append(bl)\n",
"\n",
"\n",
"tup= '12'\n",
"Xs= []\n",
"pop1= []\n",
"labels= []\n",
"\n",
"for window in sorted(Windows_union.keys()):\n",
" Xs.extend([window] * 4)\n",
" pop1.append(Windows_union[window][tuple((int(tup[0]),int(tup[1])))][int(tup[0])])\n",
" pop1.append(Windows_union[window][tuple((int(tup[0]),int(tup[1])))][int(tup[1])])\n",
" pop1.append(Windows_union[window][tuple((int(tup[0]),int(tup[1])))]['empty'])\n",
" pop1.append(Windows_union[window][tuple((int(tup[0]),int(tup[1])))]['PU'])\n",
" labels.extend([0,1,'leftover space','PU'])\n",
"\n",
"coords= {i:[x for x in range(len(labels)) if labels[x] == i] for i in list(set(labels))}\n",
"\n",
"datum= np.array([Xs,pop1]).T\n",
"\n",
"\n",
"\n",
"freqs= {u:[[angle / len(Proportion_range) * 10,1-(angle / len(Proportion_range) * 10)][u] * Sizes[2] / \\\n",
" (sum([Sizes[u],[angle / len(Proportion_range) * 10,1-(angle / len(Proportion_range) * 10)][u] * Sizes[2]])) for angle in Proportion_range] for u in [0,1]}\n",
"\n",
"freq1= [(Sizes[2] * (.5 + np.sin(angle) / 2)) / float(Sizes[2] * (.5 + np.sin(angle) / 2) + Sizes[0]) for angle in Proportion_range]\n",
"freq2= [(Sizes[2] - Sizes[2] * (.5 + np.sin(angle) / 2)) / float((Sizes[2] - Sizes[2] * (.5 + np.sin(angle) / 2)) + Sizes[1]) for angle in Proportion_range]\n",
"\n",
"Combo= [freq1,freq2]\n",
"Frequency_differences= [min([Combo[u][x] for u in range(len(Combo))]) / max([Combo[u][x] for u in range(len(Combo))]) for x in range(len(Combo[0]))]\n",
"Freq_0= [Combo[0][x] / Combo[1][x] for x in range(len(Combo[0]))]\n",
"\n",
"\n",
"PU_threshold= .4\n",
"PU_high= [int(datum[coords['PU'],1][x] > PU_threshold) for x in range(len(datum[coords['PU'],1]))]\n",
"PU_indexes= {z:[x for x in range(len(PU_high)) if PU_high[x] == z] for z in [0,1]}\n",
"\n",
"\n",
"Error_threshold= .3\n",
"Error_high= [int(x > Error_threshold) for x in Global_error_rate]\n",
"Error_indexes= {z:[x for x in range(len(Error_high)) if Error_high[x] == z] for z in [0,1]}\n",
"\n",
"\n",
"\n",
"fig_data= [go.Scatter(\n",
"#x= [(.5 + angle * .5 / len(Proportion_range) * 10) / (.5 - angle * .5 / len(Proportion_range) * 10) for angle in Proportion_range],\n",
"#x= [Frequency_differences[x] for x in Error_indexes[i]],\n",
"#x= [np.log(Freq_0[x]) for x in Error_indexes[i]],\n",
"#x= Freq_0,\n",
"#x= [sum([Combo[z][x] for z in range(len(Combo))]) for x in Error_indexes[i]],\n",
"#x= [np.sin(angle) / 2 + .5 for angle in Proportion_range],\n",
"#x= [angle * .5 / len(Proportion_range) * 10 for angle in Proportion_range],\n",
"#x= [max([datum[coords[z][x],1] for z in [0,1]]) for x in range(len(Windows_union.keys()))],\n",
"#x= [datum[coords[i][x],1] for x in range(len(Windows_union.keys()))],\n",
"#x= [datum[coords['PU'],1][x] for x in Error_indexes[i]],\n",
"#x= datum[coords['PU'],1],\n",
"x= [max([datum[coords[i][x],1],datum[coords[1-i][x],1]]) for x in range(len(Windows_union.keys()))],\n",
"#y= [Pure_assignment[0][x] for x in Error_indexes[i]],\n",
"#y= Pure_assignment[i],\n",
"y= [Intermediate_rate[2][x] for x in Error_indexes[i]],\n",
"mode= 'markers',\n",
"name= \"Error {} {}\".format(['<','>'][i],Error_threshold),\n",
"marker= dict(\n",
" color= ['grey','red'][i]\n",
")\n",
") for i in [0]\n",
"]\n",
"\n",
"\n",
"layout = go.Layout(\n",
" title= 'Intermediate assignment and max local overlap. Cp threshold= {}'.format([Comparison_threshold,'None'][int(Comparison_threshold <= 1)]),\n",
" yaxis=dict(\n",
" range= [0,1.1],\n",
" title='Intermediate Assignment (%)'),\n",
" xaxis=dict(\n",
" #range= [0,.5],\n",
" title='maximum overlap')\n",
")\n",
"\n",
"fig= go.Figure(data=fig_data, layout=layout)\n",
"iplot(fig)"
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {},
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"layout": {
"title": "Intermediate assignment and relative frequency. Cp threshold= 4",
"xaxis": {
"title": "relative frequency."
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"yaxis": {
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0,
1.1
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"title": "Intermediate Assignment (%)"
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"\n",
"labels= [0,1,2]\n",
"Error_rate= {x:[] for x in labels if x not in references}\n",
"Global_error_rate= []\n",
"Global_intermediates= []\n",
"where= []\n",
"\n",
"Pure_errors= {x:[] for x in labels if x in references}\n",
"Pure_assignment= {x:[] for x in labels if x in references}\n",
"Intermediate_rate= {x:[] for x in labels if x not in references}\n",
"\n",
"for bl in Blocks[1].keys():\n",
" label_indicies= label_indicies_bl[bl]\n",
" \n",
" ##everyone:\n",
" non_outlier= [x for x in range(sum(Sizes)) if Blocks3[1][bl][x] != len(references) + 1]\n",
" misshaps= [x for x in range(sum(Sizes)) if Blocks[1][bl][x] != Blocks3[1][bl][x] and Blocks3[1][bl][x] -1 in labels]\n",
" intermediates= [x for x in range(sum(Sizes)) if Blocks3[1][bl][x] > len(labels)]\n",
" \n",
" intimicy= len(intermediates) / float(len(non_outlier))\n",
" rate= len(misshaps) / float(len(non_outlier))\n",
" \n",
" Global_intermediates.append(intimicy)\n",
" Global_error_rate.append(rate)\n",
"\n",
" \n",
" \n",
" origins_place= {z:[x for x in ind_to_group.keys() if ind_to_group[x][0] == z] for z in labels}\n",
" for pop in references:\n",
" non_outlier= [x for x in origins_place[2] if Blocks3[1][bl][x] != len(references) + 1]\n",
" original= [x for x in non_outlier if Blocks2[1][bl][x] == pop + 1]\n",
" \n",
" error_by_pop= [x for x in original if Blocks3[1][bl][x] != pop + 1]\n",
" \n",
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" error_by_pop= 1\n",
" else:\n",
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" \n",
" full_assignment= [x for x in non_outlier if Blocks3[1][bl][x] == pop + 1]\n",
" full_assignment= len(full_assignment) / float(len(non_outlier))\n",
" \n",
" Pure_errors[pop].append(error_by_pop)\n",
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" \n",
" for pop in [x for x in label_indicies.keys() if x not in references]:\n",
" \n",
" non_outlier= [x for x in label_indicies[pop] if Blocks3[1][bl][x] != len(references) + 1]\n",
" misshaps= [x for x in label_indicies[pop] if Blocks[1][bl][x] != Blocks3[1][bl][x] and Blocks3[1][bl][x] -1 in labels]\n",
" intermediates= [x for x in label_indicies[pop] if Blocks3[1][bl][x] > len(labels)]\n",
" intimicy= len(intermediates) / float(len(non_outlier))\n",
" rate= len(misshaps) / float(len(non_outlier))\n",
" \n",
" Intermediate_rate[pop].append(intimicy)\n",
" Error_rate[pop].append(rate)\n",
" where.append(bl)\n",
"\n",
"\n",
"tup= '12'\n",
"Xs= []\n",
"pop1= []\n",
"labels= []\n",
"\n",
"for window in sorted(Windows_union.keys()):\n",
" Xs.extend([window] * 4)\n",
" pop1.append(Windows_union[window][tuple((int(tup[0]),int(tup[1])))][int(tup[0])])\n",
" pop1.append(Windows_union[window][tuple((int(tup[0]),int(tup[1])))][int(tup[1])])\n",
" pop1.append(Windows_union[window][tuple((int(tup[0]),int(tup[1])))]['empty'])\n",
" pop1.append(Windows_union[window][tuple((int(tup[0]),int(tup[1])))]['PU'])\n",
" labels.extend([0,1,'leftover space','PU'])\n",
"\n",
"coords= {i:[x for x in range(len(labels)) if labels[x] == i] for i in list(set(labels))}\n",
"\n",
"datum= np.array([Xs,pop1]).T\n",
"\n",
"\n",
"\n",
"freqs= {u:[[angle / len(Proportion_range) * 10,1-(angle / len(Proportion_range) * 10)][u] * Sizes[2] / \\\n",
" (sum([Sizes[u],[angle / len(Proportion_range) * 10,1-(angle / len(Proportion_range) * 10)][u] * Sizes[2]])) for angle in Proportion_range] for u in [0,1]}\n",
"\n",
"freq1= [(Sizes[2] * (.5 + np.sin(angle) / 2)) / float(Sizes[2] * (.5 + np.sin(angle) / 2) + Sizes[0]) for angle in Proportion_range]\n",
"freq2= [(Sizes[2] - Sizes[2] * (.5 + np.sin(angle) / 2)) / float((Sizes[2] - Sizes[2] * (.5 + np.sin(angle) / 2)) + Sizes[1]) for angle in Proportion_range]\n",
"\n",
"Combo= [freq1,freq2]\n",
"Frequency_differences= [min([Combo[u][x] for u in range(len(Combo))]) / max([Combo[u][x] for u in range(len(Combo))]) for x in range(len(Combo[0]))]\n",
"Freq_0= [Combo[0][x] / Combo[1][x] for x in range(len(Combo[0]))]\n",
"\n",
"\n",
"PU_threshold= .4\n",
"PU_high= [int(datum[coords['PU'],1][x] > PU_threshold) for x in range(len(datum[coords['PU'],1]))]\n",
"PU_indexes= {z:[x for x in range(len(PU_high)) if PU_high[x] == z] for z in [0,1]}\n",
"\n",
"\n",
"Error_threshold= .3\n",
"Error_high= [int(x > Error_threshold) for x in Global_error_rate]\n",
"Error_indexes= {z:[x for x in range(len(Error_high)) if Error_high[x] == z] for z in [0,1]}\n",
"\n",
"\n",
"\n",
"fig_data= [go.Scatter(\n",
"#x= [(.5 + angle * .5 / len(Proportion_range) * 10) / (.5 - angle * .5 / len(Proportion_range) * 10) for angle in Proportion_range],\n",
"x= [Frequency_differences[x] for x in Error_indexes[i]],\n",
"#x= [np.log(Freq_0[x]) for x in Error_indexes[i]],\n",
"#x= Freq_0,\n",
"#x= [sum([Combo[z][x] for z in range(len(Combo))]) for x in Error_indexes[i]],\n",
"#x= [np.sin(angle) / 2 + .5 for angle in Proportion_range],\n",
"#x= [angle * .5 / len(Proportion_range) * 10 for angle in Proportion_range],\n",
"#x= [max([datum[coords[z][x],1] for z in [0,1]]) for x in range(len(Windows_union.keys()))],\n",
"#x= [datum[coords[i][x],1] for x in range(len(Windows_union.keys()))],\n",
"#x= [datum[coords['PU'],1][x] for x in Error_indexes[i]],\n",
"#x= datum[coords['PU'],1],\n",
"#x= [datum[coords[i][x],1] / datum[coords[1-i][x],1] for x in range(len(Windows_union.keys()))],\n",
"#y= [Pure_assignment[0][x] for x in Error_indexes[i]],\n",
"#y= Pure_assignment[i],\n",
"y= [Intermediate_rate[2][x] for x in Error_indexes[i]],\n",
"mode= 'markers',\n",
"name= \"Error {} {}\".format(['<','>'][i],Error_threshold),\n",
"marker= dict(\n",
" color= ['grey','red'][i]\n",
")\n",
") for i in [0]\n",
"]\n",
"\n",
"\n",
"layout = go.Layout(\n",
" title= 'Intermediate assignment and relative frequency. Cp threshold= {}'.format([Comparison_threshold,'None'][int(Comparison_threshold <= 1)]),\n",
" yaxis=dict(\n",
" range= [0,1.1],\n",
" title='Intermediate Assignment (%)'),\n",
" xaxis=dict(\n",
" #range= [0,.5],\n",
" title= \"relative frequency.\")\n",
")\n",
"\n",
"fig= go.Figure(data=fig_data, layout=layout)\n",
"iplot(fig)"
]
},
{
"cell_type": "code",
"execution_count": 43,
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"title": "Pure assignment and log relative frequency. Cp threshold= 4",
"xaxis": {
"title": "log relative frequency."
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"yaxis": {
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"title": "Error rate (%)"
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"\n",
"labels= [0,1,2]\n",
"Error_rate= {x:[] for x in labels if x not in references}\n",
"Global_error_rate= []\n",
"Global_intermediates= []\n",
"where= []\n",
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"Pure_errors= {x:[] for x in labels if x in references}\n",
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"for bl in Blocks[1].keys():\n",
" label_indicies= label_indicies_bl[bl]\n",
" \n",
" ##everyone:\n",
" non_outlier= [x for x in range(sum(Sizes)) if Blocks3[1][bl][x] != len(references) + 1]\n",
" misshaps= [x for x in range(sum(Sizes)) if Blocks[1][bl][x] != Blocks3[1][bl][x] and Blocks3[1][bl][x] -1 in labels]\n",
" intermediates= [x for x in range(sum(Sizes)) if Blocks3[1][bl][x] > len(labels)]\n",
" \n",
" intimicy= len(intermediates) / float(len(non_outlier))\n",
" rate= len(misshaps) / float(len(non_outlier))\n",
" \n",
" Global_intermediates.append(intimicy)\n",
" Global_error_rate.append(rate)\n",
"\n",
" \n",
" \n",
" origins_place= {z:[x for x in ind_to_group.keys() if ind_to_group[x][0] == z] for z in labels}\n",
" for pop in references:\n",
" non_outlier= [x for x in origins_place[2] if Blocks3[1][bl][x] != len(references) + 1]\n",
" original= [x for x in non_outlier if Blocks2[1][bl][x] == pop + 1]\n",
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" full_assignment= len(full_assignment) / float(len(non_outlier))\n",
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" Pure_errors[pop].append(error_by_pop)\n",
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" non_outlier= [x for x in label_indicies[pop] if Blocks3[1][bl][x] != len(references) + 1]\n",
" misshaps= [x for x in label_indicies[pop] if Blocks[1][bl][x] != Blocks3[1][bl][x] and Blocks3[1][bl][x] -1 in labels]\n",
" intermediates= [x for x in label_indicies[pop] if Blocks3[1][bl][x] > len(labels)]\n",
" intimicy= len(intermediates) / float(len(non_outlier))\n",
" rate= len(misshaps) / float(len(non_outlier))\n",
" \n",
" Intermediate_rate[pop].append(intimicy)\n",
" Error_rate[pop].append(rate)\n",
" where.append(bl)\n",
"\n",
"\n",
"tup= '12'\n",
"Xs= []\n",
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"\n",
"for window in sorted(Windows_union.keys()):\n",
" Xs.extend([window] * 4)\n",
" pop1.append(Windows_union[window][tuple((int(tup[0]),int(tup[1])))][int(tup[0])])\n",
" pop1.append(Windows_union[window][tuple((int(tup[0]),int(tup[1])))][int(tup[1])])\n",
" pop1.append(Windows_union[window][tuple((int(tup[0]),int(tup[1])))]['empty'])\n",
" pop1.append(Windows_union[window][tuple((int(tup[0]),int(tup[1])))]['PU'])\n",
" labels.extend([0,1,'leftover space','PU'])\n",
"\n",
"coords= {i:[x for x in range(len(labels)) if labels[x] == i] for i in list(set(labels))}\n",
"\n",
"datum= np.array([Xs,pop1]).T\n",
"\n",
"\n",
"\n",
"freqs= {u:[[angle / len(Proportion_range) * 10,1-(angle / len(Proportion_range) * 10)][u] * Sizes[2] / \\\n",
" (sum([Sizes[u],[angle / len(Proportion_range) * 10,1-(angle / len(Proportion_range) * 10)][u] * Sizes[2]])) for angle in Proportion_range] for u in [0,1]}\n",
"\n",
"freq1= [(Sizes[2] * (.5 + np.sin(angle) / 2)) / float(Sizes[2] * (.5 + np.sin(angle) / 2) + Sizes[0]) for angle in Proportion_range]\n",
"freq2= [(Sizes[2] - Sizes[2] * (.5 + np.sin(angle) / 2)) / float((Sizes[2] - Sizes[2] * (.5 + np.sin(angle) / 2)) + Sizes[1]) for angle in Proportion_range]\n",
"\n",
"Combo= [freq1,freq2]\n",
"Frequency_differences= [min([Combo[u][x] for u in range(len(Combo))]) / max([Combo[u][x] for u in range(len(Combo))]) for x in range(len(Combo[0]))]\n",
"Freq_0= [Combo[0][x] / Combo[1][x] for x in range(len(Combo[0]))]\n",
"\n",
"\n",
"PU_threshold= .4\n",
"PU_high= [int(datum[coords['PU'],1][x] > PU_threshold) for x in range(len(datum[coords['PU'],1]))]\n",
"PU_indexes= {z:[x for x in range(len(PU_high)) if PU_high[x] == z] for z in [0,1]}\n",
"\n",
"\n",
"Error_threshold= .3\n",
"Error_high= [int(x > Error_threshold) for x in Global_error_rate]\n",
"Error_indexes= {z:[x for x in range(len(Error_high)) if Error_high[x] == z] for z in [0,1]}\n",
"\n",
"\n",
"\n",
"fig_data= [go.Scatter(\n",
"#x= [(.5 + angle * .5 / len(Proportion_range) * 10) / (.5 - angle * .5 / len(Proportion_range) * 10) for angle in Proportion_range],\n",
"#x= [Frequency_differences[x] for x in Error_indexes[i]],\n",
"x= [np.log(Freq_0[x]) for x in Error_indexes[i]],\n",
"#x= Freq_0,\n",
"#x= [sum([Combo[z][x] for z in range(len(Combo))]) for x in Error_indexes[i]],\n",
"#x= [np.sin(angle) / 2 + .5 for angle in Proportion_range],\n",
"#x= [angle * .5 / len(Proportion_range) * 10 for angle in Proportion_range],\n",
"#x= [max([datum[coords[z][x],1] for z in [0,1]]) for x in range(len(Windows_union.keys()))],\n",
"#x= [datum[coords[i][x],1] for x in range(len(Windows_union.keys()))],\n",
"#x= [datum[coords['PU'],1][x] for x in Error_indexes[i]],\n",
"#x= datum[coords['PU'],1],\n",
"#x= [max([datum[coords[i][x],1],datum[coords[1-i][x],1]]) for x in range(len(Windows_union.keys()))],\n",
"y= [Pure_assignment[0][x] for x in Error_indexes[i]],\n",
"#y= Pure_assignment[i],\n",
"#y= [Intermediate_rate[2][x] for x in Error_indexes[i]],\n",
"mode= 'markers',\n",
"name= \"Error {} {}\".format(['<','>'][i],Error_threshold),\n",
"marker= dict(\n",
" color= ['grey','red'][i]\n",
")\n",
") for i in [0]\n",
"]\n",
"\n",
"\n",
"layout = go.Layout(\n",
" title= 'Pure assignment and log relative frequency. Cp threshold= {}'.format([Comparison_threshold,'None'][int(Comparison_threshold <= 1)]),\n",
" yaxis=dict(\n",
" range= [0,1.1],\n",
" #title='Intermediate Assignment (%)'),\n",
" title= 'Pure assignment (%)'),\n",
" xaxis=dict(\n",
" #range= [0,.5],\n",
" #title= 'Total Overlap'),\n",
" title= \"log relative frequency.\")\n",
" #title='maximum frequency')\n",
")\n",
"\n",
"fig= go.Figure(data=fig_data, layout=layout)\n",
"iplot(fig)"
]
},
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