import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np

pd_data = {"locus": [], "node": [], "evalue": [], "bitscore": []}
with open("testblast_tops.out", "r") as f:
    for line in f.read().splitlines():
        l = line.split("\t")
        pd_data["locus"].append(l[0])
        pd_data["node"].append(l[1])
        pd_data["evalue"].append(float(l[-2]))
        pd_data["bitscore"].append(float(l[-1]))


our_loci = [1, 2, 3, 4, 5, 6, 7, 9, 10, 11, 12, 13, 15, 16, 17, 18, 19, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 60, 61, 62, 62, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 131, 133, 133, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 168, 168, 169, 171, 172, 173, 176, 177, 179, 180, 181, 182, 183, 185, 185, 186, 187, 188, 189, 191, 192, 193, 194, 195, 196, 197, 198, 199, 201, 202, 203, 205, 206, 207, 208, 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 220, 221, 222, 223, 224, 225, 226, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247, 248, 249, 250, 251, 251, 252, 253, 254, 255, 256, 257, 259, 260, 260, 260, 261, 261, 261, 261, 261, 263, 264, 265, 266, 267, 268, 269, 270, 271, 272, 274, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286, 287, 288, 289, 290, 292, 293, 293, 294, 295, 296, 297, 298, 299, 300, 302, 303, 304, 305, 306, 307, 308, 309, 310, 311, 312, 314, 315, 316, 317, 318, 319, 320, 321, 323, 324, 326, 327, 328, 329, 330, 331, 331, 332, 333, 334, 335, 336, 337, 338, 339, 340, 341, 342, 343, 344, 345, 346, 348, 349, 350, 351, 352, 353, 353, 354, 355, 356, 357, 358, 359, 360, 361, 362, 363, 364, 365, 366, 367, 368, 369, 370, 371, 372, 373, 374, 375, 376, 377, 378, 379, 380, 381, 382, 383, 385, 386, 387, 388, 389, 390, 391, 392, 393, 393, 396, 397, 398, 399, 400, 401, 402, 404, 405, 406, 407, 408, 409, 410, 411, 412, 412, 413, 414, 415, 417, 418, 419, 421, 422, 423, 425, 426, 426, 427, 427, 429, 430, 431, 432, 432, 433, 434, 435, 436, 437, 438, 439, 440, 441, 442, 444, 445, 446, 447, 448, 449, 450, 453, 454, 455, 457, 458, 459, 460, 460, 461, 462, 463, 464, 465, 466, 467, 468, 469, 470, 471, 473, 474, 475, 476, 477, 478, 479, 480, 481, 482, 483, 484, 485, 486, 487, 488, 489, 490, 491, 492, 493, 494, 495, 496, 497, 498, 499, 500, 501, 502, 503, 504, 505, 506, 507, 508, 509, 510, 511, 512, 513, 514, 5401, 2, 3, 4, 5, 6, 7, 9, 10, 11, 12, 13, 15, 16, 17, 18, 19, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 60, 61, 62, 62, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 131, 133, 133, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 168, 168, 169, 171, 172, 173, 176, 177, 179, 180, 181, 182, 183, 185, 185, 186, 187, 188, 189, 191, 192, 193, 194, 195, 196, 197, 198, 199, 201, 202, 203, 205, 206, 207, 208, 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 220, 221, 222, 223, 224, 225, 226, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247, 248, 249, 250, 251, 251, 252, 253, 254, 255, 256, 257, 259, 260, 260, 260, 261, 261, 261, 261, 261, 263, 264, 265, 266, 267, 268, 269, 270, 271, 272, 274, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286, 287, 288, 289, 290, 292, 293, 293, 294, 295, 296, 297, 298, 299, 300, 302, 303, 304, 305, 306, 307, 308, 309, 310, 311, 312, 314, 315, 316, 317, 318, 319, 320, 321, 323, 324, 326, 327, 328, 329, 330, 331, 331, 332, 333, 334, 335, 336, 337, 338, 339, 340, 341, 342, 343, 344, 345, 346, 348, 349, 350, 351, 352, 353, 353, 354, 355, 356, 357, 358, 359, 360, 361, 362, 363, 364, 365, 366, 367, 368, 369, 370, 371, 372, 373, 374, 375, 376, 377, 378, 379, 380, 381, 382, 383, 385, 386, 387, 388, 389, 390, 391, 392, 393, 393, 396, 397, 398, 399, 400, 401, 402, 404, 405, 406, 407, 408, 409, 410, 411, 412, 412, 413, 414, 415, 417, 418, 419, 421, 422, 423, 425, 426, 426, 427, 427, 429, 430, 431, 432, 432, 433, 434, 435, 436, 437, 438, 439, 440, 441, 442, 444, 445, 446, 447, 448, 449, 450, 453, 454, 455, 457, 458, 459, 460, 460, 461, 462, 463, 464, 465, 466, 467, 468, 469, 470, 471, 473, 474, 475, 476, 477, 478, 479, 480, 481, 482, 483, 484, 485, 486, 487, 488, 489, 490, 491, 492, 493, 494, 495, 496, 497, 498, 499, 500, 501, 502, 503, 504, 505, 506, 507, 508, 509, 510, 511, 512, 513, 514, 540]

df = pd.DataFrame(pd_data)

df["targeted"] = [int(y) in our_loci for y in [x[12:-12] for x in df["locus"]]]

# df["evalue"].plot.kde()
# plt.xlim(0.1,0)
# plt.savefig("./tblastx_evals.png")
# plt.clf()

#evalue distribution
sns.stripplot(data=df, x="targeted", y="evalue", alpha = 0.5)
plt.yscale('log', nonpositive='clip')
plt.tight_layout()
plt.savefig("./tblastx_evals.png")
plt.clf()

#bitscore distribution
sns.stripplot(data=df, x="targeted", y="bitscore", alpha = 0.5)
plt.tight_layout()
plt.savefig("./tblastx_bitscores.png")
plt.clf()

a0a659382cb7bab01fe55f502e578e6a150d3720
<mask token>

<mask token>
with open('testblast_tops.out', 'r') as f:
    for line in f.read().splitlines():
        l = line.split('\t')
        pd_data['locus'].append(l[0])
        pd_data['node'].append(l[1])
        pd_data['evalue'].append(float(l[-2]))
        pd_data['bitscore'].append(float(l[-1]))
<mask token>
sns.stripplot(data=df, x='targeted', y='evalue', alpha=0.5)
plt.yscale('log', nonpositive='clip')
plt.tight_layout()
plt.savefig('./tblastx_evals.png')
plt.clf()
sns.stripplot(data=df, x='targeted', y='bitscore', alpha=0.5)
plt.tight_layout()
plt.savefig('./tblastx_bitscores.png')
plt.clf()

<mask token>
pd_data = {'locus': [], 'node': [], 'evalue': [], 'bitscore': []}
with open('testblast_tops.out', 'r') as f:
    for line in f.read().splitlines():
        l = line.split('\t')
        pd_data['locus'].append(l[0])
        pd_data['node'].append(l[1])
        pd_data['evalue'].append(float(l[-2]))
        pd_data['bitscore'].append(float(l[-1]))
our_loci = [1, 2, 3, 4, 5, 6, 7, 9, 10, 11, 12, 13, 15, 16, 17, 18, 19, 21,
    22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 32, 33, 34, 35, 36, 37, 38,
    39, 40, 41, 42, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57,
    58, 59, 60, 60, 61, 62, 62, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72,
    73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 88, 89,
    90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 
    106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 
    120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 131, 133, 133, 133, 
    134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 146, 147, 148, 
    149, 150, 151, 152, 153, 154, 155, 156, 158, 159, 160, 161, 162, 163, 
    164, 165, 166, 167, 168, 168, 168, 169, 171, 172, 173, 176, 177, 179, 
    180, 181, 182, 183, 185, 185, 186, 187, 188, 189, 191, 192, 193, 194, 
    195, 196, 197, 198, 199, 201, 202, 203, 205, 206, 207, 208, 209, 210, 
    211, 212, 213, 214, 215, 216, 217, 218, 220, 221, 222, 223, 224, 225, 
    226, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 
    241, 242, 243, 244, 245, 246, 247, 248, 249, 250, 251, 251, 252, 253, 
    254, 255, 256, 257, 259, 260, 260, 260, 261, 261, 261, 261, 261, 263, 
    264, 265, 266, 267, 268, 269, 270, 271, 272, 274, 277, 278, 279, 280, 
    281, 282, 283, 284, 285, 286, 287, 288, 289, 290, 292, 293, 293, 294, 
    295, 296, 297, 298, 299, 300, 302, 303, 304, 305, 306, 307, 308, 309, 
    310, 311, 312, 314, 315, 316, 317, 318, 319, 320, 321, 323, 324, 326, 
    327, 328, 329, 330, 331, 331, 332, 333, 334, 335, 336, 337, 338, 339, 
    340, 341, 342, 343, 344, 345, 346, 348, 349, 350, 351, 352, 353, 353, 
    354, 355, 356, 357, 358, 359, 360, 361, 362, 363, 364, 365, 366, 367, 
    368, 369, 370, 371, 372, 373, 374, 375, 376, 377, 378, 379, 380, 381, 
    382, 383, 385, 386, 387, 388, 389, 390, 391, 392, 393, 393, 396, 397, 
    398, 399, 400, 401, 402, 404, 405, 406, 407, 408, 409, 410, 411, 412, 
    412, 413, 414, 415, 417, 418, 419, 421, 422, 423, 425, 426, 426, 427, 
    427, 429, 430, 431, 432, 432, 433, 434, 435, 436, 437, 438, 439, 440, 
    441, 442, 444, 445, 446, 447, 448, 449, 450, 453, 454, 455, 457, 458, 
    459, 460, 460, 461, 462, 463, 464, 465, 466, 467, 468, 469, 470, 471, 
    473, 474, 475, 476, 477, 478, 479, 480, 481, 482, 483, 484, 485, 486, 
    487, 488, 489, 490, 491, 492, 493, 494, 495, 496, 497, 498, 499, 500, 
    501, 502, 503, 504, 505, 506, 507, 508, 509, 510, 511, 512, 513, 514, 
    5401, 2, 3, 4, 5, 6, 7, 9, 10, 11, 12, 13, 15, 16, 17, 18, 19, 21, 22, 
    23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 32, 33, 34, 35, 36, 37, 38, 39,
    40, 41, 42, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58,
    59, 60, 60, 61, 62, 62, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73,
    74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 88, 89, 90,
    91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 
    107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 
    121, 122, 123, 124, 125, 126, 127, 128, 129, 131, 133, 133, 133, 134, 
    135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 146, 147, 148, 149, 
    150, 151, 152, 153, 154, 155, 156, 158, 159, 160, 161, 162, 163, 164, 
    165, 166, 167, 168, 168, 168, 169, 171, 172, 173, 176, 177, 179, 180, 
    181, 182, 183, 185, 185, 186, 187, 188, 189, 191, 192, 193, 194, 195, 
    196, 197, 198, 199, 201, 202, 203, 205, 206, 207, 208, 209, 210, 211, 
    212, 213, 214, 215, 216, 217, 218, 220, 221, 222, 223, 224, 225, 226, 
    228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 
    242, 243, 244, 245, 246, 247, 248, 249, 250, 251, 251, 252, 253, 254, 
    255, 256, 257, 259, 260, 260, 260, 261, 261, 261, 261, 261, 263, 264, 
    265, 266, 267, 268, 269, 270, 271, 272, 274, 277, 278, 279, 280, 281, 
    282, 283, 284, 285, 286, 287, 288, 289, 290, 292, 293, 293, 294, 295, 
    296, 297, 298, 299, 300, 302, 303, 304, 305, 306, 307, 308, 309, 310, 
    311, 312, 314, 315, 316, 317, 318, 319, 320, 321, 323, 324, 326, 327, 
    328, 329, 330, 331, 331, 332, 333, 334, 335, 336, 337, 338, 339, 340, 
    341, 342, 343, 344, 345, 346, 348, 349, 350, 351, 352, 353, 353, 354, 
    355, 356, 357, 358, 359, 360, 361, 362, 363, 364, 365, 366, 367, 368, 
    369, 370, 371, 372, 373, 374, 375, 376, 377, 378, 379, 380, 381, 382, 
    383, 385, 386, 387, 388, 389, 390, 391, 392, 393, 393, 396, 397, 398, 
    399, 400, 401, 402, 404, 405, 406, 407, 408, 409, 410, 411, 412, 412, 
    413, 414, 415, 417, 418, 419, 421, 422, 423, 425, 426, 426, 427, 427, 
    429, 430, 431, 432, 432, 433, 434, 435, 436, 437, 438, 439, 440, 441, 
    442, 444, 445, 446, 447, 448, 449, 450, 453, 454, 455, 457, 458, 459, 
    460, 460, 461, 462, 463, 464, 465, 466, 467, 468, 469, 470, 471, 473, 
    474, 475, 476, 477, 478, 479, 480, 481, 482, 483, 484, 485, 486, 487, 
    488, 489, 490, 491, 492, 493, 494, 495, 496, 497, 498, 499, 500, 501, 
    502, 503, 504, 505, 506, 507, 508, 509, 510, 511, 512, 513, 514, 540]
df = pd.DataFrame(pd_data)
df['targeted'] = [(int(y) in our_loci) for y in [x[12:-12] for x in df[
    'locus']]]
sns.stripplot(data=df, x='targeted', y='evalue', alpha=0.5)
plt.yscale('log', nonpositive='clip')
plt.tight_layout()
plt.savefig('./tblastx_evals.png')
plt.clf()
sns.stripplot(data=df, x='targeted', y='bitscore', alpha=0.5)
plt.tight_layout()
plt.savefig('./tblastx_bitscores.png')
plt.clf()

import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np
pd_data = {'locus': [], 'node': [], 'evalue': [], 'bitscore': []}
with open('testblast_tops.out', 'r') as f:
    for line in f.read().splitlines():
        l = line.split('\t')
        pd_data['locus'].append(l[0])
        pd_data['node'].append(l[1])
        pd_data['evalue'].append(float(l[-2]))
        pd_data['bitscore'].append(float(l[-1]))
our_loci = [1, 2, 3, 4, 5, 6, 7, 9, 10, 11, 12, 13, 15, 16, 17, 18, 19, 21,
    22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 32, 33, 34, 35, 36, 37, 38,
    39, 40, 41, 42, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57,
    58, 59, 60, 60, 61, 62, 62, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72,
    73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 88, 89,
    90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 
    106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 
    120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 131, 133, 133, 133, 
    134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 146, 147, 148, 
    149, 150, 151, 152, 153, 154, 155, 156, 158, 159, 160, 161, 162, 163, 
    164, 165, 166, 167, 168, 168, 168, 169, 171, 172, 173, 176, 177, 179, 
    180, 181, 182, 183, 185, 185, 186, 187, 188, 189, 191, 192, 193, 194, 
    195, 196, 197, 198, 199, 201, 202, 203, 205, 206, 207, 208, 209, 210, 
    211, 212, 213, 214, 215, 216, 217, 218, 220, 221, 222, 223, 224, 225, 
    226, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 
    241, 242, 243, 244, 245, 246, 247, 248, 249, 250, 251, 251, 252, 253, 
    254, 255, 256, 257, 259, 260, 260, 260, 261, 261, 261, 261, 261, 263, 
    264, 265, 266, 267, 268, 269, 270, 271, 272, 274, 277, 278, 279, 280, 
    281, 282, 283, 284, 285, 286, 287, 288, 289, 290, 292, 293, 293, 294, 
    295, 296, 297, 298, 299, 300, 302, 303, 304, 305, 306, 307, 308, 309, 
    310, 311, 312, 314, 315, 316, 317, 318, 319, 320, 321, 323, 324, 326, 
    327, 328, 329, 330, 331, 331, 332, 333, 334, 335, 336, 337, 338, 339, 
    340, 341, 342, 343, 344, 345, 346, 348, 349, 350, 351, 352, 353, 353, 
    354, 355, 356, 357, 358, 359, 360, 361, 362, 363, 364, 365, 366, 367, 
    368, 369, 370, 371, 372, 373, 374, 375, 376, 377, 378, 379, 380, 381, 
    382, 383, 385, 386, 387, 388, 389, 390, 391, 392, 393, 393, 396, 397, 
    398, 399, 400, 401, 402, 404, 405, 406, 407, 408, 409, 410, 411, 412, 
    412, 413, 414, 415, 417, 418, 419, 421, 422, 423, 425, 426, 426, 427, 
    427, 429, 430, 431, 432, 432, 433, 434, 435, 436, 437, 438, 439, 440, 
    441, 442, 444, 445, 446, 447, 448, 449, 450, 453, 454, 455, 457, 458, 
    459, 460, 460, 461, 462, 463, 464, 465, 466, 467, 468, 469, 470, 471, 
    473, 474, 475, 476, 477, 478, 479, 480, 481, 482, 483, 484, 485, 486, 
    487, 488, 489, 490, 491, 492, 493, 494, 495, 496, 497, 498, 499, 500, 
    501, 502, 503, 504, 505, 506, 507, 508, 509, 510, 511, 512, 513, 514, 
    5401, 2, 3, 4, 5, 6, 7, 9, 10, 11, 12, 13, 15, 16, 17, 18, 19, 21, 22, 
    23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 32, 33, 34, 35, 36, 37, 38, 39,
    40, 41, 42, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58,
    59, 60, 60, 61, 62, 62, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73,
    74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 88, 89, 90,
    91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 
    107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 
    121, 122, 123, 124, 125, 126, 127, 128, 129, 131, 133, 133, 133, 134, 
    135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 146, 147, 148, 149, 
    150, 151, 152, 153, 154, 155, 156, 158, 159, 160, 161, 162, 163, 164, 
    165, 166, 167, 168, 168, 168, 169, 171, 172, 173, 176, 177, 179, 180, 
    181, 182, 183, 185, 185, 186, 187, 188, 189, 191, 192, 193, 194, 195, 
    196, 197, 198, 199, 201, 202, 203, 205, 206, 207, 208, 209, 210, 211, 
    212, 213, 214, 215, 216, 217, 218, 220, 221, 222, 223, 224, 225, 226, 
    228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 
    242, 243, 244, 245, 246, 247, 248, 249, 250, 251, 251, 252, 253, 254, 
    255, 256, 257, 259, 260, 260, 260, 261, 261, 261, 261, 261, 263, 264, 
    265, 266, 267, 268, 269, 270, 271, 272, 274, 277, 278, 279, 280, 281, 
    282, 283, 284, 285, 286, 287, 288, 289, 290, 292, 293, 293, 294, 295, 
    296, 297, 298, 299, 300, 302, 303, 304, 305, 306, 307, 308, 309, 310, 
    311, 312, 314, 315, 316, 317, 318, 319, 320, 321, 323, 324, 326, 327, 
    328, 329, 330, 331, 331, 332, 333, 334, 335, 336, 337, 338, 339, 340, 
    341, 342, 343, 344, 345, 346, 348, 349, 350, 351, 352, 353, 353, 354, 
    355, 356, 357, 358, 359, 360, 361, 362, 363, 364, 365, 366, 367, 368, 
    369, 370, 371, 372, 373, 374, 375, 376, 377, 378, 379, 380, 381, 382, 
    383, 385, 386, 387, 388, 389, 390, 391, 392, 393, 393, 396, 397, 398, 
    399, 400, 401, 402, 404, 405, 406, 407, 408, 409, 410, 411, 412, 412, 
    413, 414, 415, 417, 418, 419, 421, 422, 423, 425, 426, 426, 427, 427, 
    429, 430, 431, 432, 432, 433, 434, 435, 436, 437, 438, 439, 440, 441, 
    442, 444, 445, 446, 447, 448, 449, 450, 453, 454, 455, 457, 458, 459, 
    460, 460, 461, 462, 463, 464, 465, 466, 467, 468, 469, 470, 471, 473, 
    474, 475, 476, 477, 478, 479, 480, 481, 482, 483, 484, 485, 486, 487, 
    488, 489, 490, 491, 492, 493, 494, 495, 496, 497, 498, 499, 500, 501, 
    502, 503, 504, 505, 506, 507, 508, 509, 510, 511, 512, 513, 514, 540]
df = pd.DataFrame(pd_data)
df['targeted'] = [(int(y) in our_loci) for y in [x[12:-12] for x in df[
    'locus']]]
sns.stripplot(data=df, x='targeted', y='evalue', alpha=0.5)
plt.yscale('log', nonpositive='clip')
plt.tight_layout()
plt.savefig('./tblastx_evals.png')
plt.clf()
sns.stripplot(data=df, x='targeted', y='bitscore', alpha=0.5)
plt.tight_layout()
plt.savefig('./tblastx_bitscores.png')
plt.clf()

import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np

pd_data = {"locus": [], "node": [], "evalue": [], "bitscore": []}
with open("testblast_tops.out", "r") as f:
    for line in f.read().splitlines():
        l = line.split("\t")
        pd_data["locus"].append(l[0])
        pd_data["node"].append(l[1])
        pd_data["evalue"].append(float(l[-2]))
        pd_data["bitscore"].append(float(l[-1]))


our_loci = [1, 2, 3, 4, 5, 6, 7, 9, 10, 11, 12, 13, 15, 16, 17, 18, 19, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 60, 61, 62, 62, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 131, 133, 133, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 168, 168, 169, 171, 172, 173, 176, 177, 179, 180, 181, 182, 183, 185, 185, 186, 187, 188, 189, 191, 192, 193, 194, 195, 196, 197, 198, 199, 201, 202, 203, 205, 206, 207, 208, 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 220, 221, 222, 223, 224, 225, 226, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247, 248, 249, 250, 251, 251, 252, 253, 254, 255, 256, 257, 259, 260, 260, 260, 261, 261, 261, 261, 261, 263, 264, 265, 266, 267, 268, 269, 270, 271, 272, 274, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286, 287, 288, 289, 290, 292, 293, 293, 294, 295, 296, 297, 298, 299, 300, 302, 303, 304, 305, 306, 307, 308, 309, 310, 311, 312, 314, 315, 316, 317, 318, 319, 320, 321, 323, 324, 326, 327, 328, 329, 330, 331, 331, 332, 333, 334, 335, 336, 337, 338, 339, 340, 341, 342, 343, 344, 345, 346, 348, 349, 350, 351, 352, 353, 353, 354, 355, 356, 357, 358, 359, 360, 361, 362, 363, 364, 365, 366, 367, 368, 369, 370, 371, 372, 373, 374, 375, 376, 377, 378, 379, 380, 381, 382, 383, 385, 386, 387, 388, 389, 390, 391, 392, 393, 393, 396, 397, 398, 399, 400, 401, 402, 404, 405, 406, 407, 408, 409, 410, 411, 412, 412, 413, 414, 415, 417, 418, 419, 421, 422, 423, 425, 426, 426, 427, 427, 429, 430, 431, 432, 432, 433, 434, 435, 436, 437, 438, 439, 440, 441, 442, 444, 445, 446, 447, 448, 449, 450, 453, 454, 455, 457, 458, 459, 460, 460, 461, 462, 463, 464, 465, 466, 467, 468, 469, 470, 471, 473, 474, 475, 476, 477, 478, 479, 480, 481, 482, 483, 484, 485, 486, 487, 488, 489, 490, 491, 492, 493, 494, 495, 496, 497, 498, 499, 500, 501, 502, 503, 504, 505, 506, 507, 508, 509, 510, 511, 512, 513, 514, 5401, 2, 3, 4, 5, 6, 7, 9, 10, 11, 12, 13, 15, 16, 17, 18, 19, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 60, 61, 62, 62, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 131, 133, 133, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 168, 168, 169, 171, 172, 173, 176, 177, 179, 180, 181, 182, 183, 185, 185, 186, 187, 188, 189, 191, 192, 193, 194, 195, 196, 197, 198, 199, 201, 202, 203, 205, 206, 207, 208, 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 220, 221, 222, 223, 224, 225, 226, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247, 248, 249, 250, 251, 251, 252, 253, 254, 255, 256, 257, 259, 260, 260, 260, 261, 261, 261, 261, 261, 263, 264, 265, 266, 267, 268, 269, 270, 271, 272, 274, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286, 287, 288, 289, 290, 292, 293, 293, 294, 295, 296, 297, 298, 299, 300, 302, 303, 304, 305, 306, 307, 308, 309, 310, 311, 312, 314, 315, 316, 317, 318, 319, 320, 321, 323, 324, 326, 327, 328, 329, 330, 331, 331, 332, 333, 334, 335, 336, 337, 338, 339, 340, 341, 342, 343, 344, 345, 346, 348, 349, 350, 351, 352, 353, 353, 354, 355, 356, 357, 358, 359, 360, 361, 362, 363, 364, 365, 366, 367, 368, 369, 370, 371, 372, 373, 374, 375, 376, 377, 378, 379, 380, 381, 382, 383, 385, 386, 387, 388, 389, 390, 391, 392, 393, 393, 396, 397, 398, 399, 400, 401, 402, 404, 405, 406, 407, 408, 409, 410, 411, 412, 412, 413, 414, 415, 417, 418, 419, 421, 422, 423, 425, 426, 426, 427, 427, 429, 430, 431, 432, 432, 433, 434, 435, 436, 437, 438, 439, 440, 441, 442, 444, 445, 446, 447, 448, 449, 450, 453, 454, 455, 457, 458, 459, 460, 460, 461, 462, 463, 464, 465, 466, 467, 468, 469, 470, 471, 473, 474, 475, 476, 477, 478, 479, 480, 481, 482, 483, 484, 485, 486, 487, 488, 489, 490, 491, 492, 493, 494, 495, 496, 497, 498, 499, 500, 501, 502, 503, 504, 505, 506, 507, 508, 509, 510, 511, 512, 513, 514, 540]

df = pd.DataFrame(pd_data)

df["targeted"] = [int(y) in our_loci for y in [x[12:-12] for x in df["locus"]]]

# df["evalue"].plot.kde()
# plt.xlim(0.1,0)
# plt.savefig("./tblastx_evals.png")
# plt.clf()

#evalue distribution
sns.stripplot(data=df, x="targeted", y="evalue", alpha = 0.5)
plt.yscale('log', nonpositive='clip')
plt.tight_layout()
plt.savefig("./tblastx_evals.png")
plt.clf()

#bitscore distribution
sns.stripplot(data=df, x="targeted", y="bitscore", alpha = 0.5)
plt.tight_layout()
plt.savefig("./tblastx_bitscores.png")
plt.clf()
