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"""pysat URL Configuration for User.Info
"""
from django.urls import path
from utils.views import view_maker
from utils.params import ParamType
from utils.permission import ActionType
from user.views import info
urlpatterns = [
path('get', view_maker(info.get_info, 'GET', [
ParamType.UsernameWithDefault
], action=ActionType.GetUserInfo)),
path('modify', view_maker(info.modify_info, 'POST', [
ParamType.UsernameWithDefault,
ParamType.RealnameForModify,
ParamType.MottoForModify,
ParamType.PermissionPrivateForModify,
ParamType.PermissionPublicForModify
], [
ParamType.RealnameForModify,
ParamType.MottoForModify,
], action=ActionType.ModifyMyInfo)),
path('setphone', view_maker(info.set_phone, 'POST', [
ParamType.Phone,
ParamType.CAPTCHA
], [
ParamType.Phone
], action=ActionType.ModifyMyInfo))
]
|
nilq/baby-python
|
python
|
"""Helpers for Roku Client."""
from ipaddress import ip_address
from socket import gaierror as SocketGIAError
from .exceptions import RokuConnectionError
from .resolver import ThreadedResolver
def is_ip_address(host: str) -> bool:
"""Determine if host is an IP Address."""
try:
ip_address(host)
except ValueError:
return False
return True
async def resolve_hostname(host: str) -> str:
"""Resolve hostname to IP Address (asynchronously)."""
try:
resolver = ThreadedResolver()
results = await resolver.resolve(host)
ips = [ip_address(x["host"]) for x in results]
return str(ips[0])
except (OSError, SocketGIAError, ValueError) as exception:
raise RokuConnectionError(
f"Error occurred while resolving hostname: {host}"
) from exception
|
nilq/baby-python
|
python
|
import asyncio_redis
from vibora import Vibora
from vibora.hooks import Events
from vibora.sessions import AsyncRedis
from vibora.responses import Response
app = Vibora(
sessions=AsyncRedis()
)
@app.route('/', cache=False)
async def home(request):
print(request.session.dump())
await request.load_session()
if request.session.get('count') is None:
request.session['count'] = 0
request.session['count'] += 1
return Response(str(request.session['count']).encode())
@app.handle(Events.BEFORE_SERVER_START)
async def open_connections(loop):
pool = await asyncio_redis.Pool.create(host='localhost', port=6379, poolsize=10)
app.session_engine.connection = pool
if __name__ == '__main__':
app.run(debug=True, port=8000, host='0.0.0.0', workers=6)
|
nilq/baby-python
|
python
|
# -*- coding: utf-8 -*-
from django.urls import reverse_lazy
from django.views.generic import View
from django.http import HttpResponse
from django.core import serializers
from djangoSIGE.apps.base.custom_views import CustomCreateView, CustomListView, CustomUpdateView
from djangoSIGE.apps.vendas.forms import CondicaoPagamentoForm
from djangoSIGE.apps.vendas.models import CondicaoPagamento
class AdicionarCondicaoPagamentoView(CustomCreateView):
form_class = CondicaoPagamentoForm
template_name = "vendas/pagamento/condicao_pagamento_add.html"
success_url = reverse_lazy('vendas:listacondicaopagamentoview')
success_message = "Condição de pagamento <b>%(descricao)s </b>adicionada com sucesso."
permission_codename = 'add_condicaopagamento'
def get_success_message(self, cleaned_data):
return self.success_message % dict(cleaned_data, descricao=self.object.descricao)
def get_context_data(self, **kwargs):
context = super(AdicionarCondicaoPagamentoView,
self).get_context_data(**kwargs)
context['title_complete'] = 'ADICIONAR CONDIÇÃO DE PAGAMENTO'
context['return_url'] = reverse_lazy(
'vendas:listacondicaopagamentoview')
return context
class CondicaoPagamentoListView(CustomListView):
template_name = 'vendas/pagamento/condicao_pagamento_list.html'
model = CondicaoPagamento
context_object_name = 'all_cond_pagamento'
success_url = reverse_lazy('vendas:listacondicaopagamentoview')
permission_codename = 'view_condicaopagamento'
def get_context_data(self, **kwargs):
context = super(CondicaoPagamentoListView,
self).get_context_data(**kwargs)
context['title_complete'] = 'CONDIÇÕES DE PAGAMENTO CADASTRADAS'
context['add_url'] = reverse_lazy('vendas:addcondicaopagamentoview')
return context
class EditarCondicaoPagamentoView(CustomUpdateView):
form_class = CondicaoPagamentoForm
model = CondicaoPagamento
template_name = "vendas/pagamento/condicao_pagamento_edit.html"
success_url = reverse_lazy('vendas:listacondicaopagamentoview')
success_message = "Condição de pagamento <b>%(descricao)s </b>editada com sucesso."
permission_codename = 'change_condicaopagamento'
def get_success_message(self, cleaned_data):
return self.success_message % dict(cleaned_data, descricao=self.object.descricao)
def get_context_data(self, **kwargs):
context = super(EditarCondicaoPagamentoView,
self).get_context_data(**kwargs)
context['return_url'] = reverse_lazy(
'vendas:listacondicaopagamentoview')
return context
class InfoCondicaoPagamento(View):
def post(self, request, *args, **kwargs):
pag = CondicaoPagamento.objects.get(pk=request.POST['pagamentoId'])
data = serializers.serialize('json', [pag, ], fields=(
'n_parcelas', 'parcela_inicial', 'dias_recorrencia'))
return HttpResponse(data, content_type='application/json')
|
nilq/baby-python
|
python
|
import numpy as np
import pandas as pd
from scipy.stats import poisson
from cooltools.api.dotfinder import (
histogram_scored_pixels,
determine_thresholds,
annotate_pixels_with_qvalues,
extract_scored_pixels,
)
# helper functions for BH-FDR copied from www.statsmodels.org
def _fdrcorrection(pvals, alpha=0.05):
"""
pvalue correction for false discovery rate.
This covers Benjamini/Hochberg for independent or positively correlated tests.
Parameters
----------
pvals : np.ndarray
Sorted set of p-values of the individual tests.
alpha : float, optional
Family-wise error rate. Defaults to ``0.05``.
Returns
-------
rejected : ndarray, bool
True if a hypothesis is rejected, False if not
pvalue-corrected : ndarray
pvalues adjusted for multiple hypothesis testing to limit FDR
"""
ntests = len(pvals)
# empirical Cumulative Distribution Function for pvals:
ecdffactor = np.arange(1, ntests + 1) / float(ntests)
reject = pvals <= ecdffactor * alpha
if reject.any():
rejectmax = max(np.nonzero(reject)[0])
reject[:rejectmax] = True
pvals_corrected_raw = pvals / ecdffactor
pvals_corrected = np.minimum.accumulate(pvals_corrected_raw[::-1])[::-1]
del pvals_corrected_raw
pvals_corrected[pvals_corrected > 1] = 1
return reject, pvals_corrected
def multipletests(pvals, alpha=0.1, is_sorted=False):
"""
Test results and p-value correction for multiple tests
Using FDR Benjamini-Hochberg method (non-negative)
Parameters
----------
pvals : array_like, 1-d
uncorrected p-values. Must be 1-dimensional.
alpha : float
FWER, family-wise error rate, e.g. 0.1
is_sorted : bool
If False (default), the p_values will be sorted, but the corrected
pvalues are in the original order. If True, then it assumed that the
pvalues are already sorted in ascending order.
Returns
-------
reject : ndarray, boolean
true for hypothesis that can be rejected for given alpha
pvals_corrected : ndarray
p-values corrected for multiple tests
Notes
-----
the p-value correction is independent of the
alpha specified as argument. In these cases the corrected p-values
can also be compared with a different alpha
All procedures that are included, control FWER or FDR in the independent
case, and most are robust in the positively correlated case.
"""
pvals = np.asarray(pvals)
if not is_sorted:
sortind = np.argsort(pvals)
pvals = np.take(pvals, sortind)
reject, pvals_corrected = _fdrcorrection(pvals, alpha=alpha)
if is_sorted:
return reject, pvals_corrected
else:
pvals_corrected_ = np.empty_like(pvals_corrected)
pvals_corrected_[sortind] = pvals_corrected
del pvals_corrected
reject_ = np.empty_like(reject)
reject_[sortind] = reject
return reject_, pvals_corrected_
# mock input data to perform some p-value calculation and correction on
num_pixels = 2500
max_value = 99
# fake kernels just for the sake of their names 'd' and 'v':
fake_kernels = {
"d": np.random.randint(2, size=9).reshape(3, 3),
"v": np.random.randint(2, size=9).reshape(3, 3),
}
# table with the "scored" pixel (as if they are returned by dotfinder-scoring function)
pixel_dict = {}
# enrich fake counts to all for more significant calls
pixel_dict["count"] = np.random.randint(max_value, size=num_pixels) + 9
for k in fake_kernels:
pixel_dict[f"la_exp.{k}.value"] = max_value * np.random.random(num_pixels)
scored_df = pd.DataFrame(pixel_dict)
# design lambda-bins as in dot-calling:
num_lchunks = 6
ledges = np.r_[[-np.inf], np.linspace(0, max_value, num_lchunks), [np.inf]]
# set FDR parameter
FDR = 0.1
# helper functions working on a chunk of counts or pvals
# associated with a given lambda-bin:
def get_pvals_chunk(counts_series_lchunk):
"""
Parameters:
-----------
counts_series_lchunk : pd.Series(int)
Series of raw pixel counts where the name of the Series
is pd.Interval of the lambda-bin where the pixel belong.
I.e. counts_series_lchunk.name.right - is the upper limit of the chunk
and is used as "expected" in Poisson distribution to estimate p-value.
Returns:
--------
pvals: ndarray[float]
array of p-values for each pixel
Notes:
------
poisson.sf = 1.0 - poisson.cdf
"""
return poisson.sf(counts_series_lchunk.values, counts_series_lchunk.name.right)
def get_qvals_chunk(pvals_series_lchunk):
"""
Parameters:
-----------
pvals_series_lchunk : pd.Series(float)
Series of p-values calculated for each pixel, where the name
of the Series is pd.Interval of the lambda-bin where the pixel belong.
Returns:
--------
qvals: ndarray[float]
array of q-values, i.e. p-values corrected with the multiple hypothesis
testing procedure BH-FDR, for each pixel
Notes:
------
level of False Discore Rate (FDR) is fixed for testing
"""
_, qvals = multipletests(pvals_series_lchunk.values, alpha=FDR, is_sorted=False)
return qvals
def get_reject_chunk(pvals_series_lchunk):
"""
Parameters:
-----------
pvals_series_lchunk : pd.Series(float)
Series of p-values calculated for each pixel, where the name
of the Series is pd.Interval of the lambda-bin where the pixel belong.
Returns:
--------
rej: ndarray[bool]
array of rejection statuses, i.e. for every p-values return if corresponding
null hypothesis can be rejected or not, using multiple hypothesis testing
procedure BH-FDR.
Notes:
------
- pixels with rejected status (not null) are considered as significantly enriched
- level of False Discore Rate (FDR) is fixed for testing
"""
rej, _ = multipletests(pvals_series_lchunk.values, alpha=FDR, is_sorted=False)
return rej
# for the fake scored-pixel table calculate p-vals, q-vals, l-chunk where they belong
# and rejection status using introduced statsmodels-based helper functions:
for k in fake_kernels:
lbin = pd.cut(scored_df[f"la_exp.{k}.value"], ledges)
scored_df[f"{k}.pval"] = scored_df.groupby(lbin)["count"].transform(get_pvals_chunk)
scored_df[f"{k}.qval"] = scored_df.groupby(lbin)[f"{k}.pval"].transform(
get_qvals_chunk
)
scored_df[f"{k}.rej"] = scored_df.groupby(lbin)[f"{k}.pval"].transform(
get_reject_chunk
)
# test functions in dotfinder using this reference from statsmodels
def test_histogramming_summary():
gw_hists = histogram_scored_pixels(
scored_df, kernels=fake_kernels, ledges=ledges, obs_raw_name="count"
)
# make sure total sum of the histogram yields total number of pixels:
for k, _hist in gw_hists.items():
assert _hist.sum().sum() == num_pixels
assert _hist.index.is_monotonic # is index sorted
# test threshold and rejection tables and only then try q-values
def test_thresholding():
# rebuild hists
gw_hists = histogram_scored_pixels(
scored_df, kernels=fake_kernels, ledges=ledges, obs_raw_name="count"
)
# # we have to make sure there is nothing in the last lambda-bin
# # this is a temporary implementation detail, until we implement dynamic lambda-bins
for k in fake_kernels:
last_lambda_bin = gw_hists[k].iloc[:, -1]
assert last_lambda_bin.sum() == 0 # should be True by construction:
# drop that last column/bin (last_edge, +inf]:
gw_hists[k] = gw_hists[k].drop(columns=last_lambda_bin.name)
# calculate q-values and rejection threshold using dotfinder built-in methods
# that are the reimplementation of HiCCUPS statistical procedures:
threshold_df, qvalues = determine_thresholds(gw_hists, FDR)
enriched_pixels_df = extract_scored_pixels(
scored_df, threshold_df, obs_raw_name="count"
)
# all enriched pixels have their Null hypothesis rejected
assert enriched_pixels_df["d.rej"].all()
assert enriched_pixels_df["v.rej"].all()
# number of enriched pixels should match that number of
# pixels with both null-hypothesis rejected:
assert (scored_df["d.rej"] & scored_df["v.rej"]).sum() == len(enriched_pixels_df)
def test_qvals():
# rebuild hists
gw_hists = histogram_scored_pixels(
scored_df, kernels=fake_kernels, ledges=ledges, obs_raw_name="count"
)
# # we have to make sure there is nothing in the last lambda-bin
# # this is a temporary implementation detail, until we implement dynamic lambda-bins
for k in fake_kernels:
last_lambda_bin = gw_hists[k].iloc[:, -1]
assert last_lambda_bin.sum() == 0 # should be True by construction:
# drop that last column/bin (last_edge, +inf]:
gw_hists[k] = gw_hists[k].drop(columns=last_lambda_bin.name)
# calculate q-values and rejection threshold using dotfinder built-in methods
# that are the reimplementation of HiCCUPS statistical procedures:
threshold_df, qvalues = determine_thresholds(gw_hists, FDR)
# annotate scored pixels with q-values:
scored_df_qvals = annotate_pixels_with_qvalues(
scored_df, qvalues, obs_raw_name="count"
)
# our procedure in dotfiner should match these q-values exactly, including >1.0
assert np.allclose(scored_df_qvals["v.qval"], scored_df_qvals["la_exp.v.qval"])
assert np.allclose(scored_df_qvals["d.qval"], scored_df_qvals["la_exp.d.qval"])
|
nilq/baby-python
|
python
|
import unittest
from flatten import flattener
class FlattenTests(unittest.TestCase):
def test_single_small_nested_list(self):
calculated = flattener([1, [2, 3]])
expected = [1, 2, 3]
self.assertEqual(calculated, expected)
if __name__ == "__main__":
unittest.main()
|
nilq/baby-python
|
python
|
# Generated by Django 3.2.6 on 2021-08-30 14:52
from django.db import migrations, models
import django.db.models.deletion
import taggit.managers
class Migration(migrations.Migration):
dependencies = [
('taggit', '0003_taggeditem_add_unique_index'),
('todo', '0002_alter_profile_unique_together'),
]
operations = [
migrations.CreateModel(
name='Task',
fields=[
('id', models.BigAutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('title', models.CharField(blank=True, max_length=100, null=True)),
('description', models.TextField(blank=True, max_length=1000, null=True)),
('done', models.BooleanField(default=False)),
('favorite', models.BooleanField(default=False)),
('created_at', models.DateField(auto_now_add=True)),
('parent', models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.CASCADE, to='todo.task')),
('profile', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='todo.profile')),
('tags', taggit.managers.TaggableManager(help_text='A comma-separated list of tags.', through='taggit.TaggedItem', to='taggit.Tag', verbose_name='Tags')),
],
),
]
|
nilq/baby-python
|
python
|
categories = [
{"name": "Action"},
{"name": "Drama"},
{"name": "Comedy"},
{"name": "Fantasy"},
{"name": "Sci-fi"},
]
|
nilq/baby-python
|
python
|
# Copyright (c) 2021 China Unicom Cloud Data Co.,Ltd.
# All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License"); you may
# not use this file except in compliance with the License. You may obtain
# a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
# License for the specific language governing permissions and limitations
# under the License.
import time
from unittest import mock
from neutron_lib import constants
from os_ken.lib.packet import dhcp6
from os_ken.lib.packet import ether_types
from os_ken.lib.packet import ethernet
from os_ken.lib.packet import in_proto as inet
from os_ken.lib.packet import ipv6
from os_ken.lib.packet import packet
from os_ken.lib.packet import udp
from neutron.agent.l2.extensions.dhcp import ipv6 as dhcp_ipv6
from neutron.tests.unit.agent.l2.extensions.dhcp \
import test_base as dhcp_test_base
ONE_SEC_AFTER_2000 = dhcp_ipv6.TIME_FIRST_DAY_2000 + 1
class DHCPIPv6ResponderTestCase(dhcp_test_base.DHCPResponderBaseTestCase):
def setUp(self):
super(DHCPIPv6ResponderTestCase, self).setUp()
self.dhcp6_responer = dhcp_ipv6.DHCPIPv6Responder(self.agent_api,
self.ext_api)
self.dhcp6_responer.int_br = self.int_br
def _compare_option_values(self, expect_options, test_options):
# os_ken dhcp.option class does not have __eq__ method so
# compare one by one
expected = [(option.code, option.length, option.data)
for option in expect_options]
test = [(option.code, option.length, option.data)
for option in test_options]
for i in test:
self.assertIn(i, expected)
def _create_test_dhcp6_packet(self, zero_time=False):
ret_pkt = packet.Packet()
ret_pkt.add_protocol(
ethernet.ethernet(
ethertype=ether_types.ETH_TYPE_IPV6,
dst='33:33:00:01:00:02',
src=self.port_info['mac_address']))
ret_pkt.add_protocol(
ipv6.ipv6(
src='fe80::f816:3eff:fe60:714b',
dst='ff02::1:2',
nxt=inet.IPPROTO_UDP))
ret_pkt.add_protocol(
udp.udp(
src_port=constants.DHCPV6_RESPONSE_PORT,
dst_port=constants.DHCPV6_CLIENT_PORT))
options = [dhcp6.option(
code=1,
data=b"\x00\x01\x00\x01",
length=4)]
if zero_time:
options.append(dhcp6.option(
code=3,
data=b"\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00",
length=12))
else:
options.append(dhcp6.option(
code=3,
data=b"\x01\x02\x03\x04\x05\x06\x07\x08\x0a\x0b\x0c\x0d",
length=12))
ret_pkt.add_protocol(dhcp6.dhcp6(
dhcp6.DHCPV6_REQUEST, dhcp6.options(option_list=options)))
return ret_pkt
def test_get_dhcpv6_client_ident(self):
packet_in = self._create_test_dhcp6_packet()
header_dhcp = packet_in.get_protocol(dhcp6.dhcp6)
client_ident = self.dhcp6_responer.get_dhcpv6_client_ident(
self.port_info['mac_address'], header_dhcp.options.option_list)
self.assertEqual(header_dhcp.options.option_list[0].data,
client_ident)
expect_ident = (
b'\x00\x01\x00\x01\x00\x00\x00\x01\x00\x01\x02\x03\x04\x05')
time.time = mock.Mock(return_value=ONE_SEC_AFTER_2000)
client_ident = client_ident = (
self.dhcp6_responer.get_dhcpv6_client_ident(
self.port_info['mac_address'], []))
self.assertEqual(expect_ident, client_ident)
def test_get_dhcpv6_server_ident(self):
self.dhcp6_responer.get_dhcpv6_server_ident()
def test_get_dhcpv6_status_code(self):
expect_status_code = b'\x00\x00success'
status_code = self.dhcp6_responer.get_dhcpv6_status_code(
"success", code=0)
self.assertEqual(expect_status_code, status_code)
def test_get_dhcp_options(self):
self._test_get_dhcp_options()
def test_get_dhcp_options_zero_time(self):
self._test_get_dhcp_options(zero_time=True)
def _test_get_dhcp_options(self, zero_time=False):
ip_info = self.dhcp6_responer.get_port_ip(self.port_info, ip_version=6)
mac = self.port_info['mac_address']
option_list = [
dhcp6.option(
code=1,
data=b"\x00\x01\x00\x01",
length=4),
dhcp6.option(
code=2,
data=b'\x00\x01\x00\x01\x00\x00\x00\x01\xfa\x16>\x00\x00\x00',
length=14),
dhcp6.option(code=13,
data=b'\x00\x00success',
length=9),
dhcp6.option(
code=23,
data=(b'\xfd\xa7\xa5\xcc4`\x00\x01\x00'
b'\x00\x00\x00\x00\x00\x00\x01'),
length=16),
dhcp6.option(
code=24,
data=b'\x0eopenstacklocal\x00',
length=16),
dhcp6.option(
code=39,
data=b'\x03(host-fda7-a5cc-3460-1--bf.openstacklocal',
length=42)]
if zero_time:
option_list.append(dhcp6.option(code=3,
data=(b'\x00\x00\x00\x01\x00\x01Q\x80\x00\x01Q'
b'\x80\x00\x05\x00\x18\xfd\xa7\xa5\xcc4`'
b'\x00\x01\x00\x00\x00\x00\x00\x00\x00'
b'\xbf\x00\x01Q\x80\x00\x01Q\x80'),
length=40))
else:
option_list.append(dhcp6.option(code=3,
data=(b'\x01\x02\x03\x04\x05\x06\x07\x08\n\x0b\x0c\r'
b'\x00\x05\x00\x18\xfd\xa7\xa5\xcc4`'
b'\x00\x01\x00\x00\x00\x00\x00\x00\x00'
b'\xbf\x05\x06\x07\x08\n\x0b\x0c\r'),
length=40))
test_options = dhcp6.options(
option_list=option_list,
options_len=0)
time.time = mock.Mock(return_value=ONE_SEC_AFTER_2000)
packet_in = self._create_test_dhcp6_packet(zero_time=zero_time)
pkt_dhcp = packet_in.get_protocol(dhcp6.dhcp6)
dhcp_req_state = dhcp_ipv6.DHCPV6_TYPE_MAP.get(pkt_dhcp.msg_type)
dhcp_options = self.dhcp6_responer.get_dhcp_options(
mac, ip_info, pkt_dhcp.options.option_list, dhcp_req_state)
self._compare_option_values(test_options.option_list,
dhcp_options.option_list)
def test_get_ret_packet(self):
packet_in = self._create_test_dhcp6_packet()
pkt_dhcp = packet_in.get_protocol(dhcp6.dhcp6)
dhcp_req_state = dhcp_ipv6.DHCPV6_TYPE_MAP.get(pkt_dhcp.msg_type)
ret_packet = self.dhcp6_responer.get_ret_packet(
packet_in, self.port_info, dhcp_req_state)
header_eth = ret_packet.get_protocol(ethernet.ethernet)
header_ipv6 = ret_packet.get_protocol(ipv6.ipv6)
header_dhcp = ret_packet.get_protocol(dhcp6.dhcp6)
self.assertIsNotNone(header_eth)
self.assertIsNotNone(header_ipv6)
self.assertIsNotNone(header_dhcp)
def test_get_reply_dhcp_options(self):
mac = '00:01:02:03:04:05'
packet_in = self._create_test_dhcp6_packet()
header_dhcp = packet_in.get_protocol(dhcp6.dhcp6)
time.time = mock.Mock(return_value=ONE_SEC_AFTER_2000)
dhcp_options = self.dhcp6_responer.get_reply_dhcp_options(
mac, message="all addresses still on link",
req_options=header_dhcp.options.option_list)
test_options = dhcp6.options(option_list=[
dhcp6.option(code=1, data=b'\x00\x01\x00\x01', length=4),
dhcp6.option(
code=2,
data=b'\x00\x01\x00\x01\x00\x00\x00\x01\xfa\x16>\x00\x00\x00',
length=14),
dhcp6.option(code=13, data=b'\x00\x00all addresses still on link',
length=29)],
options_len=0)
self._compare_option_values(test_options.option_list,
dhcp_options.option_list)
def test_handle_dhcp(self):
self.dhcp6_responer.packet_out = mock.Mock()
datapath = mock.Mock()
ofport = 1
packet_in = self._create_test_dhcp6_packet()
self.dhcp6_responer.handle_dhcp(
datapath, ofport, packet_in, self.port_info)
self.dhcp6_responer.packet_out.assert_called_once_with(
datapath, ofport, mock.ANY)
|
nilq/baby-python
|
python
|
import os
from operator import itemgetter
import fpdf
def gen_report(target, rep):
print("[=] Creating PDF...")
class PPDF(fpdf.FPDF):
def footer(self):
self.set_y(-15)
self.set_font("Arial", style="I", size=8)
self.cell(0, 10, "soc_recon report", align="C")
pdf = PPDF()
pdf.alias_nb_pages()
pdf.add_page()
pdf.add_font("DejaVu", "", "/usr/share/fonts/TTF/DejaVuSansCondensed.ttf", uni=True)
pdf.set_font("DejaVu", size=12)
for k in rep.keys():
mc = sorted(rep[k], key=itemgetter(1), reverse=True)[0]
pdf.cell(0, 10,
"User %s is connected to %s with ID %s. Probability is %s" % (target, k, mc[0], mc[1]),
border=0, ln=1)
if "reports" not in os.listdir("."):
os.mkdir("reports")
os.chdir("reports")
pdf.output(str(target) + "_report.pdf")
os.chdir("..")
print("[+] Done")
|
nilq/baby-python
|
python
|
# -*- coding: utf-8 -*-
"""Physics Guided Neural Network python library."""
import os
from .model_interfaces import PhygnnModel, RandomForestModel, TfModel
from .phygnn import PhysicsGuidedNeuralNetwork, p_fun_dummy
from .utilities import Layers, HiddenLayers, PreProcess, tf_isin, tf_log10
from phygnn.version import __version__
PHYGNNDIR = os.path.dirname(os.path.realpath(__file__))
TESTDATADIR = os.path.join(os.path.dirname(PHYGNNDIR), 'tests', 'data')
|
nilq/baby-python
|
python
|
import numpy as np
class BodyModel:
def __init__(self, mapping, pairs) -> None:
self.mapping = mapping
self.pairs = pairs
BODY18 = BodyModel(
mapping=[
"nose",
"left_eye",
"right_eye",
"left_ear",
"right_ear",
"left_shoulder",
"right_shoulder",
"left_elbow",
"right_elbow",
"left_wrist",
"right_wrist",
"left_hip",
"right_hip",
"left_knee",
"right_knee",
"left_ankle",
"right_ankle",
"neck",
],
pairs=[
[15, 13],
[13, 11],
[16, 14],
[14, 12],
[11, 12],
[5, 7],
[6, 8],
[7, 9],
[8, 10],
[1, 2],
[0, 1],
[0, 2],
[1, 3],
[2, 4],
[3, 5],
[4, 6],
[17, 0],
[17, 5],
[17, 6],
[17, 11],
[17, 12],
],
)
BODY18_FLAT = BodyModel(
mapping=[
"nose_x",
"nose_y",
"left_eye_x",
"left_eye_y",
"right_eye_x",
"right_eye_y",
"left_ear_x",
"left_ear_y",
"right_ear_x",
"right_ear_y",
"left_shoulder_x",
"left_shoulder_y",
"right_shoulder_x",
"right_shoulder_y",
"left_elbow_x",
"left_elbow_y",
"right_elbow_x",
"right_elbow_y",
"left_wrist_x",
"left_wrist_y",
"right_wrist_x",
"right_wrist_y",
"left_hip_x",
"left_hip_y",
"right_hip_x",
"right_hip_y",
"left_knee_x",
"left_knee_y",
"right_knee_x",
"right_knee_y",
"left_ankle_x",
"left_ankle_y",
"right_ankle_x",
"right_ankle_y",
"neck_x",
"neck_y",
],
pairs=[
[(30, 31), (26, 27)],
[(26, 27), (22, 23)],
[(32, 33), (28, 29)],
[(28, 29), (24, 25)],
[(22, 23), (24, 25)],
[(10, 11), (14, 15)],
[(12, 13), (16, 17)],
[(14, 15), (18, 19)],
[(16, 17), (20, 21)],
[(2, 3), (4, 5)],
[(0, 1), (2, 3)],
[(0, 1), (4, 5)],
[(2, 3), (6, 7)],
[(4, 5), (8, 9)],
[(6, 7), (10, 11)],
[(8, 9), (12, 13)],
[(34, 35), (0, 1)],
[(34, 35), (10, 11)],
[(34, 35), (12, 13)],
[(34, 35), (22, 23)],
[(34, 35), (24, 25)],
],
)
BODY25 = BodyModel(
mapping=[
"nose",
"neck",
"right_shoulder",
"right_elbow",
"right_wrist",
"left_shoulder",
"left_elbow",
"left_wrist",
"mid_hip",
"right_hip",
"right_knee",
"right_ankle",
"left_hip",
"left_knee",
"left_ankle",
"right_eye",
"left_eye",
"right_ear",
"left_ear",
"left_bigtoe",
"left_smalltoe",
"left_heel",
"right_bigtoe",
"right_smalltoe",
"right_heel",
],
pairs=[
[1, 8],
[1, 2],
[1, 5],
[2, 3],
[3, 4],
[5, 6],
[6, 7],
[8, 9],
[9, 10],
[10, 11],
[8, 12],
[12, 13],
[13, 14],
[1, 0],
[0, 15],
[15, 17],
[0, 16],
[16, 18],
[2, 17],
[5, 18],
[14, 19],
[19, 20],
[14, 21],
[11, 22],
[22, 23],
[11, 24],
],
)
""" #BODY25 annotated
pairs_annotated={
"Torso":[1, 8],
"Shoulder (right)":[1, 2],
"Shoulder (left)":[1, 5],
"Arm (right)":[2, 3],
"Forearm (right)":[3, 4],
"Arm (left)":[5, 6],
"Forearm (left)":[6, 7],
"Hip (right)":[8, 9],
"Thigh (right)":[9, 10],
"Leg (right)":[10, 11],
"Hip (left)":[8, 12],
"Thigh (left)":[12, 13],
"Leg (left)":[13, 14],
"Neck":[1, 0],
"Eye (right)":[0, 15],
"Ear (right)":[15, 17],
"Eye (left)":[0, 16],
"Ear (left)":[16, 18],
"Foot (left)":[14, 19],
"Toe (left)":[19, 20],
"Heel (left)":[14, 21],
"Foot (right)":[11, 22],
"Toe (right)":[22, 23],
"Heel (right)":[11, 24],
}
"""
BODY25_FLAT = BodyModel(
mapping=[
"nose_x",
"nose_y",
"neck_x",
"neck_y",
"right_shoulder_x",
"right_shoulder_y",
"right_elbow_x",
"right_elbow_y",
"right_wrist_x",
"right_wrist_y",
"left_shoulder_x",
"left_shoulder_y",
"left_elbow_x",
"left_elbow_y",
"left_wrist_x",
"left_wrist_y",
"mid_hip_x",
"mid_hip_y",
"right_hip_x",
"right_hip_y",
"right_knee_x",
"right_knee_y",
"right_ankle_x",
"right_ankle_y",
"left_hip_x",
"left_hip_y",
"left_knee_x",
"left_knee_y",
"left_ankle_x",
"left_ankle_y",
"right_eye_x",
"right_eye_y",
"left_eye_x",
"left_eye_y",
"right_ear_x",
"right_ear_y",
"left_ear_x",
"left_ear_y",
"left_bigtoe_x",
"left_bigtoe_y",
"left_smalltoe_x",
"left_smalltoe_y",
"left_heel_x",
"left_heel_y",
"right_bigtoe_x",
"right_bigtoe_y",
"right_smalltoe_x",
"right_smalltoe_y",
"right_heel_x",
"right_heel_y",
],
pairs=[
[(2, 3), (16, 17)],
[(2, 3), (4, 5)],
[(2, 3), (10, 11)],
[(4, 5), (6, 7)],
[(6, 7), (8, 9)],
[(10, 11), (12, 13)],
[(12, 13), (14, 15)],
[(16, 17), (18, 19)],
[(18, 19), (20, 21)],
[(20, 21), (22, 23)],
[(16, 17), (24, 25)],
[(24, 25), (26, 27)],
[(26, 27), (28, 29)],
[(2, 3), (0, 1)],
[(0, 1), (30, 31)],
[(30, 31), (34, 35)],
[(0, 1), (32, 33)],
[(32, 33), (36, 37)],
[(4, 5), (34, 35)],
[(10, 11), (36, 37)],
[(28, 29), (38, 39)],
[(38, 39), (40, 41)],
[(28, 29), (42, 43)],
[(22, 23), (44, 45)],
[(44, 45), (46, 47)],
[(22, 23), (48, 49)],
],
)
# fmt: off
BODY25_to_BODY18_indices = [0, 16, 15, 18, 17, 5, 2, 6, 3, 7, 4, 12, 9, 13, 10, 14, 11, 1]
BODY25flat_to_BODY18flat_indices = [0, 1, 32, 33, 30, 31, 36, 37, 34, 35, 10, 11, 4, 5, 12, 13, 6, 7, 14, 15, 8, 9, 24, 25, 18, 19, 26, 27, 20, 21, 28, 29, 22, 23, 2, 3]
# fmt: on
def BODY25_to_BODY18(body25_keypoints: np.ndarray):
assert body25_keypoints.shape == 25
return body25_keypoints[BODY25_to_BODY18_indices]
|
nilq/baby-python
|
python
|
from datetime import datetime as dt
from . import __version__
import typing as t
def client_imports_f() -> str:
"""
Creates the string to import the client dependencies and
a default module doc string. Usually the first part
of the client module.
Returns:
str: import statements string ready to be appended to client module
"""
time_stamp = dt.now().strftime('%Y-%m-%d %H:%M:%S')
py_code = f'''\"\"\"
auto-generated {time_stamp}
... using [swagccg-py2py](https://erkandem.github.io/swagccg-py2py)' version {__version__}
your module level doc-string goes here
\"\"\"
# #######################################################################
# DO NOT MODIFY THIS FILE!
# Your changes will be lost if you rerun ``make_client.py``!
# Edit the template!
# #######################################################################
from datetime import datetime as dt
import json
import typing as t
import urllib
import urllib3
from urllib3.response import HTTPResponse
import certifi
JSONEncodable = t.Union[t.List[t.Any], t.Dict[str, t.Any]]
'''
return py_code
def client_class_def_template_f(args: t.Dict[str, t.Any]) -> str:
"""
Args:
args (dict): desired name of client class name default:MyApiClient
Returns:
str: class definition string ready to be appended to client-module
"""
py_code = f'''
class {args['class_name']}(object):
\"\"\"your client class level doc-string goes here\"\"\"
def __init__(self, deployment: str = 'remote', base_path: str = None):
if deployment == 'remote':
self.API_PORT = '{args["api_port_remote"]}'
self.API_URL_BASE = '{args["api_url_base_remote"]}'
self.API_PROTOCOL = '{args["api_protocol_remote"]}'
elif deployment == 'local':
self.API_PORT = '{args["api_port_local"]}'
self.API_URL_BASE = '{args["api_url_base_local"]}'
self.API_PROTOCOL = '{args["api_protocol_local"]}'
self.BASE_PATH = '{args["basePath"]}'
if base_path:
self.BASE_PATH = base_path
self.LOGIN_TIMESTAMP = None
self.API_TOKEN = None
self.AUTH_HEADER_NAME = 'Authorization'
self.AUTH_PREFIX = 'Bearer ' # mind the whitespace
self.AUTH_TOKEN_KEY = 'access_token'
if self.API_PORT == '80':
self.API_URL = f'{{self.API_PROTOCOL}}://{{self.API_URL_BASE}}'
else:
self.API_URL = f'{{self.API_PROTOCOL}}://{{self.API_URL_BASE}}:{{self.API_PORT}}'
if self.API_PROTOCOL == 'https':
self.http = urllib3.PoolManager(
cert_reqs='CERT_REQUIRED',
ca_certs=certifi.where()
)
else:
self.http = urllib3.PoolManager()
self.API_LOGIN_URL = f'{{self.API_URL}}{{self.BASE_PATH}}/login'
self.API_BASE_URL = f'{{self.API_URL}}{{self.BASE_PATH}}'
# def __dir__(self):
def login_with_api(
self,
*,
body,
headers: t.Dict[str, t.Any] = None,
**kwargs: t.Dict[str, t.Any],
):
\"\"\"
login with the target API and save the JWT token within the class
Args:
data: login data externally supplied
body: data to be sent in body (typically credentials)
headers: option to supply custom headers if needed
\"\"\"
if headers is None:
headers = {{'Content-Type': 'application/json'}}
else:
if 'content-type' not in [h.lower() for h in headers]:
headers['Content-Type'] = 'application/json'
r = self._do_call(
method='POST',
url=self.API_LOGIN_URL,
headers=headers,
body=body,
**kwargs
)
if r.status == 200:
res = json.loads(r.data.decode('utf-8'))
self.API_TOKEN = res[self.AUTH_TOKEN_KEY]
self.LOGIN_TIMESTAMP = dt.now()
else:
print(f'login failed \\nstatus:{{r.status}} \\n \\nurl: {{self.API_LOGIN_URL}}'
'\\nIs the username and password correct?')
'''
return py_code
def dir_template_f(method_names: []) -> str:
"""
generate `__dir__` method code to deliver
a list of all methods which are mapped
to an API routes
"""
method_names_ = "'" + "',\n '".join(method_names) + "'"
return f'''
def __dir__(self) -> t.List[str]:
method_names = [
{method_names_}
]
return method_names
'''
def client_encoding_decoding_point_f() -> str:
"""
provides method code to encode and decode response data
"""
py_code = '''
def _encode(self, data, format: str = None) -> bytes:
\"\"\"
Abstracted encoding point. Mount your custom function.
Main focus here is on building a JSON or URL/"percent" encoded bytes.
Args:
data(): python object
format(str): `json` or `url`
Returns:
data_encoded: :func:`json.dumps` and encode from utf-8 to binary
\"\"\"
if isinstance(data, bytes):
return data
if format == 'url':
return (urllib.parse.urlencode(data)).encode('utf-8')
if format is None:
return (json.dumps(data)).encode('utf-8')
elif format == 'json':
return (json.dumps(data)).encode('utf-8')
else:
msg = f"received format = {format}.\\nUse 'json' or 'url'.\\n 'json' is default."
raise NotImplementedError(msg)
def _decode(self, data: bytes):
\"\"\"
abstracted decoding point
Mount your custom function. Focus here is on JSON.
Args:
data: python object (dict, list, ...)
Returns:
data_decoded: first decode from binary to utf-8 and parse with
built-in :func:`json.loads`
\"\"\"
return json.loads(data.decode('utf-8'))
'''
return py_code
def client_point_of_execution_f() -> str:
"""
The idea is to separate details of the endpoint and transmitting the request.
``status_code`` handling could be placed or called here
"""
py_code = f'''
def _add_auth_header(
self,
headers: t.Union[None, t.Dict[str, t.Any]] = None,
) -> t.Dict[str, t.Any]:
\"\"\" adds the preconfigured authorization header \"\"\"
if headers is None:
headers = {{}}
headers[self.AUTH_HEADER_NAME] = f'{{self.AUTH_PREFIX}}{{self.API_TOKEN}}'
return headers
def _do_call(
self,
method: str = None,
url: str = None,
headers: t.Dict[str, str] = None,
fields: t.Dict[str, t.Any] = None,
body: JSONEncodable = None,
**kwargs: t.Dict[str, t.Any],
) -> HTTPResponse:
\"\"\"
A way to separate each resource from the actual request dispatching point
Response is assumed to be json by default.
Good point to add hooks.
Args:
method (str): HTTP-Method
url (str): endpoint
headers (dict): each key:value pair represents one header field:value. Don't nest!
fields (dict): each key:value pair will be urlencoded and passed as query string. Don't nest!
body (dict): will be encoded to JSON and bytes afterwards
You can get a urlencoding by setting
'Content-Type': 'application/x-www-form-urlencoded'
\"\"\"
r = HTTPResponse()
headers = self._add_auth_header(headers)
if body is not None and method in ['POST', 'PUT', 'PATCH']:
if 'Content-Type' not in headers:
headers['Content-Type'] = 'application/json'
r = self.http.request(
method=method,
url=url,
body=self._encode(body),
headers=headers
)
else:
if headers['Content-Type'] == 'application/x-www-form-urlencoded':
r = self.http.urlopen(
method,
url,
body=self._encode(body, 'url'),
headers=headers
)
elif headers['Content-Type'] == 'application/json':
r = self.http.request(
method=method,
url=url,
body=self._encode(body),
headers=headers
)
else:
msg = f\'\'\' The Content-Type header was set to {{headers['Content-Type']}}\\n
However, anything else than 'application/json' or 'application/x-www-form-urlencoded'\\n
is not accounted for in the client.\\n If you would like to add it, look for:\\n\\n
"_do_call" to hook the logic\\n
client_encoding_decoding_point_f for handling encoding\\n\\n
\'\'\'
raise NotImplementedError(msg)
else:
r = self.http.request_encode_url(
method=method,
url=url,
headers=headers,
fields=fields
)
return r
'''
return py_code
def client_method_template_f(
method_name: str = '',
http_verb: str = '',
api_path: str = '',
doc_string: str = '',
path_params_list: t.List[str] = None,
) -> str:
"""
one size fits *most* method template
Args:
http_verb (str): `GET`, `POST`, `PUT`, `DELETE` and `PATCH`
method_name (str): a valid python function name as a string
api_path (str): a valid URL part which is joined with the `BASE_PATH`.
Can contain path parameters. Will be evaluated to a string.
doc_string (str): some description of the method and or endpoint
path_params_list (str): e.g. pagination is frequently used in the path
Returns:
str: a method code string ready to be appended to python-client-module
"""
if path_params_list is not None and len(path_params_list) > 0:
path_params = '\n '.join(f'{p},' for p in path_params_list)
else:
path_params = ''
py_code = f'''
def {method_name}(
self,
{path_params}
headers: t.Dict[str, str] = None,
body: JSONEncodable = None,
fields_data: t.Dict[str, str] = None,
**kwargs
):
\"\"\" {doc_string} \"\"\"
r = self._do_call(
method='{http_verb.upper()}',
url=f'{{self.API_BASE_URL}}{api_path}',
headers=headers,
body=body,
fields=fields_data,
**kwargs
)
return r
'''
return py_code
|
nilq/baby-python
|
python
|
from django.utils import timezone
from rest_framework import serializers
from .models import Client
class ClientSerializer(serializers.ModelSerializer):
class Meta:
model = Client
fields = (
'id',
'business_owner',
'first_name',
'last_name',
'birthdate',
'email',
'phone_number',
'address',
'gender',
'profile_picture'
)
def validate(self, data):
if data['birthdate'] >= timezone.now():
raise serializers.ValidationError({'birthdate': 'Cannot be in the future'})
return data
|
nilq/baby-python
|
python
|
from django.apps import AppConfig
class PhoneCodeConfig(AppConfig):
name = 'phonecode'
|
nilq/baby-python
|
python
|
from collections import namedtuple
import math
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd.function import InplaceFunction, Function
QParams = namedtuple('QParams', ['range', 'zero_point', 'num_bits'])
_DEFAULT_FLATTEN = (1, -1)
_DEFAULT_FLATTEN_GRAD = (0, -1)
def _deflatten_as(x, x_full):
shape = list(x.shape) + [1] * (x_full.dim() - x.dim())
return x.view(*shape)
def calculate_qparams(x, num_bits, flatten_dims=_DEFAULT_FLATTEN, reduce_dim=0, reduce_type='mean', keepdim=False, true_zero=False):
with torch.no_grad():
x_flat = x.flatten(*flatten_dims)
if x_flat.dim() == 1:
min_values = _deflatten_as(x_flat.min(), x)
max_values = _deflatten_as(x_flat.max(), x)
else:
min_values = _deflatten_as(x_flat.min(-1)[0], x)
max_values = _deflatten_as(x_flat.max(-1)[0], x)
if reduce_dim is not None:
if reduce_type == 'mean':
min_values = min_values.mean(reduce_dim, keepdim=keepdim)
max_values = max_values.mean(reduce_dim, keepdim=keepdim)
else:
min_values = min_values.min(reduce_dim, keepdim=keepdim)[0]
max_values = max_values.max(reduce_dim, keepdim=keepdim)[0]
# TODO: re-add true zero computation
range_values = max_values - min_values
return QParams(range=range_values, zero_point=min_values,
num_bits=num_bits)
class UniformQuantize(InplaceFunction):
@staticmethod
def forward(ctx, input, num_bits=None, qparams=None, flatten_dims=_DEFAULT_FLATTEN,
reduce_dim=0, dequantize=True, signed=False, stochastic=False, inplace=False):
ctx.inplace = inplace
if ctx.inplace:
ctx.mark_dirty(input)
output = input
else:
output = input.clone()
if qparams is None:
assert num_bits is not None, "either provide qparams of num_bits to quantize"
qparams = calculate_qparams(
input, num_bits=num_bits, flatten_dims=flatten_dims, reduce_dim=reduce_dim)
zero_point = qparams.zero_point
num_bits = qparams.num_bits
qmin = -(2.**(num_bits - 1)) if signed else 0.
qmax = qmin + 2.**num_bits - 1.
scale = qparams.range / (qmax - qmin)
with torch.no_grad():
output.add_(qmin * scale - zero_point).div_(scale)
if stochastic:
noise = output.new(output.shape).uniform_(-0.5, 0.5)
output.add_(noise)
# quantize
output.clamp_(qmin, qmax).round_()
if dequantize:
output.mul_(scale).add_(
zero_point - qmin * scale) # dequantize
return output
@staticmethod
def backward(ctx, grad_output):
# straight-through estimator
grad_input = grad_output
return grad_input, None, None, None, None, None, None, None, None
class UniformQuantizeGrad(InplaceFunction):
@staticmethod
def forward(ctx, input, num_bits=None, qparams=None, flatten_dims=_DEFAULT_FLATTEN_GRAD,
reduce_dim=0, dequantize=True, signed=False, stochastic=True):
ctx.num_bits = num_bits
ctx.qparams = qparams
ctx.flatten_dims = flatten_dims
ctx.stochastic = stochastic
ctx.signed = signed
ctx.dequantize = dequantize
ctx.reduce_dim = reduce_dim
ctx.inplace = False
return input
@staticmethod
def backward(ctx, grad_output):
qparams = ctx.qparams
with torch.no_grad():
if qparams is None:
assert ctx.num_bits is not None, "either provide qparams of num_bits to quantize"
qparams = calculate_qparams(
grad_output, num_bits=ctx.num_bits, flatten_dims=ctx.flatten_dims, reduce_dim=ctx.reduce_dim, reduce_type='extreme')
grad_input = quantize(grad_output, num_bits=None,
qparams=qparams, flatten_dims=ctx.flatten_dims, reduce_dim=ctx.reduce_dim,
dequantize=True, signed=ctx.signed, stochastic=ctx.stochastic, inplace=False)
return grad_input, None, None, None, None, None, None, None
def conv2d_biprec(input, weight, bias=None, stride=1, padding=0, dilation=1, groups=1, num_bits_grad=None):
out1 = F.conv2d(input.detach(), weight, bias,
stride, padding, dilation, groups)
out2 = F.conv2d(input, weight.detach(), bias.detach() if bias is not None else None,
stride, padding, dilation, groups)
out2 = quantize_grad(out2, num_bits=num_bits_grad, flatten_dims=(1, -1))
return out1 + out2 - out1.detach()
def linear_biprec(input, weight, bias=None, num_bits_grad=None):
out1 = F.linear(input.detach(), weight, bias)
out2 = F.linear(input, weight.detach(), bias.detach()
if bias is not None else None)
out2 = quantize_grad(out2, num_bits=num_bits_grad)
return out1 + out2 - out1.detach()
def quantize(x, num_bits=None, qparams=None, flatten_dims=_DEFAULT_FLATTEN, reduce_dim=0, dequantize=True, signed=False, stochastic=False, inplace=False):
return UniformQuantize().apply(x, num_bits, qparams, flatten_dims, reduce_dim, dequantize, signed, stochastic, inplace)
def quantize_grad(x, num_bits=None, qparams=None, flatten_dims=_DEFAULT_FLATTEN_GRAD, reduce_dim=0, dequantize=True, signed=False, stochastic=True):
return UniformQuantizeGrad().apply(x, num_bits, qparams, flatten_dims, reduce_dim, dequantize, signed, stochastic)
class QuantNode():
def __init__(self):
self.enable_quant = True
self.measure = False
def set_measure_mode(self,measure,momentum=None):
self.enable_quant = not measure
self.measure = measure
if momentum and isinstance(self,QuantMeasure):
self.momentum = momentum
else:
if isinstance(self,nn.Module):
for m in self.children():
if isinstance(m,QuantNode):
m.set_measure_mode(measure,momentum=momentum)
class QuantMeasure(nn.Module,QuantNode):
"""docstring for QuantMeasure."""
def __init__(self, num_bits=8, shape_measure=(1,), flatten_dims=_DEFAULT_FLATTEN,
inplace=False, dequantize=True, stochastic=False, momentum=0.1, measure=False):
super(QuantMeasure, self).__init__()
QuantNode.__init__(self)
self.register_buffer('running_zero_point', torch.zeros(*shape_measure))
self.register_buffer('running_range', torch.zeros(*shape_measure))
self.register_buffer('num_measured', torch.zeros(1))
self.measure = measure
self.flatten_dims = flatten_dims
self.momentum = momentum
self.dequantize = dequantize
self.stochastic = stochastic
self.inplace = inplace
self.num_bits = num_bits
def forward(self, input, qparams=None):
if self.training or self.measure:
if qparams is None:
qparams = calculate_qparams(
input, num_bits=self.num_bits, flatten_dims=self.flatten_dims, reduce_dim=0)
with torch.no_grad():
if self.measure:
momentum = self.num_measured / (self.num_measured + 1)
self.num_measured += 1
else:
momentum = self.momentum
self.running_zero_point.mul_(momentum).add_(
qparams.zero_point * (1 - momentum))
self.running_range.mul_(momentum).add_(
qparams.range * (1 - momentum))
else:
qparams = QParams(range=self.running_range,
zero_point=self.running_zero_point, num_bits=self.num_bits)
if self.measure:
return input
else:
q_input = quantize(input, qparams=qparams, dequantize=self.dequantize,
stochastic=self.stochastic, inplace=self.inplace)
return q_input
class QConv2d(nn.Conv2d):
"""docstring for QConv2d."""
def __init__(self, in_channels, out_channels, kernel_size,
stride=1, padding=0, dilation=1, groups=1, bias=True, num_bits=8, num_bits_weight=8, num_bits_grad=8, biprecision=True):
super(QConv2d, self).__init__(in_channels, out_channels, kernel_size,
stride, padding, dilation, groups, bias)
self.num_bits = num_bits
self.num_bits_weight = num_bits_weight or num_bits
self.num_bits_grad = num_bits_grad
self.quantize_input = QuantMeasure(
self.num_bits, shape_measure=(1, 1, 1, 1), flatten_dims=(1, -1))
self.biprecision = biprecision
def forward(self, input):
qinput = self.quantize_input(input)
weight_qparams = calculate_qparams(
self.weight, num_bits=self.num_bits_weight, flatten_dims=(1, -1), reduce_dim=None)
qweight = quantize(self.weight, qparams=weight_qparams)
if self.bias is not None:
qbias = quantize(
self.bias, num_bits=self.num_bits_weight + self.num_bits,
flatten_dims=(0, -1))
else:
qbias = None
if not self.biprecision or self.num_bits_grad is None:
output = F.conv2d(qinput, qweight, qbias, self.stride,
self.padding, self.dilation, self.groups)
if self.num_bits_grad is not None:
output = quantize_grad(
output, num_bits=self.num_bits_grad, flatten_dims=(1, -1))
else:
output = conv2d_biprec(qinput, qweight, qbias, self.stride,
self.padding, self.dilation, self.groups, num_bits_grad=self.num_bits_grad)
return output
class QLinear(nn.Linear):
"""docstring for QConv2d."""
def __init__(self, in_features, out_features, bias=True, num_bits=8, num_bits_weight=8, num_bits_grad=8, biprecision=True):
super(QLinear, self).__init__(in_features, out_features, bias)
self.num_bits = num_bits
self.num_bits_weight = num_bits_weight or num_bits
self.num_bits_grad = num_bits_grad
self.biprecision = biprecision
self.quantize_input = QuantMeasure(self.num_bits)
def forward(self, input):
qinput = self.quantize_input(input)
weight_qparams = calculate_qparams(
self.weight, num_bits=self.num_bits_weight, flatten_dims=(1, -1), reduce_dim=None)
qweight = quantize(self.weight, qparams=weight_qparams)
if self.bias is not None:
qbias = quantize(
self.bias, num_bits=self.num_bits_weight + self.num_bits,
flatten_dims=(0, -1))
else:
qbias = None
if not self.biprecision or self.num_bits_grad is None:
output = F.linear(qinput, qweight, qbias)
if self.num_bits_grad is not None:
output = quantize_grad(
output, num_bits=self.num_bits_grad)
else:
output = linear_biprec(qinput, qweight, qbias, self.num_bits_grad)
return output
class RangeBN(nn.Module):
# this is normalized RangeBN
def __init__(self, num_features, dim=1, momentum=0.1, affine=True, num_chunks=16, eps=1e-5, num_bits=8, num_bits_grad=8):
super(RangeBN, self).__init__()
self.register_buffer('running_mean', torch.zeros(num_features))
self.register_buffer('running_var', torch.zeros(num_features))
self.momentum = momentum
self.dim = dim
if affine:
self.bias = nn.Parameter(torch.Tensor(num_features))
self.weight = nn.Parameter(torch.Tensor(num_features))
self.num_bits = num_bits
self.num_bits_grad = num_bits_grad
self.quantize_input = QuantMeasure(
self.num_bits, inplace=True, shape_measure=(1, 1, 1, 1), flatten_dims=(1, -1))
self.eps = eps
self.num_chunks = num_chunks
self.reset_params()
def reset_params(self):
if self.weight is not None:
self.weight.data.uniform_()
if self.bias is not None:
self.bias.data.zero_()
def forward(self, x):
x = self.quantize_input(x)
if x.dim() == 2: # 1d
x = x.unsqueeze(-1,).unsqueeze(-1)
if self.training:
B, C, H, W = x.shape
y = x.transpose(0, 1).contiguous() # C x B x H x W
y = y.view(C, self.num_chunks, (B * H * W) // self.num_chunks)
mean_max = y.max(-1)[0].mean(-1) # C
mean_min = y.min(-1)[0].mean(-1) # C
mean = y.view(C, -1).mean(-1) # C
scale_fix = (0.5 * 0.35) * (1 + (math.pi * math.log(4)) **
0.5) / ((2 * math.log(y.size(-1))) ** 0.5)
scale = (mean_max - mean_min) * scale_fix
with torch.no_grad():
self.running_mean.mul_(self.momentum).add_(
mean * (1 - self.momentum))
self.running_var.mul_(self.momentum).add_(
scale * (1 - self.momentum))
else:
mean = self.running_mean
scale = self.running_var
# scale = quantize(scale, num_bits=self.num_bits, min_value=float(
# scale.min()), max_value=float(scale.max()))
out = (x - mean.view(1, -1, 1, 1)) / \
(scale.view(1, -1, 1, 1) + self.eps)
if self.weight is not None:
qweight = self.weight
# qweight = quantize(self.weight, num_bits=self.num_bits,
# min_value=float(self.weight.min()),
# max_value=float(self.weight.max()))
out = out * qweight.view(1, -1, 1, 1)
if self.bias is not None:
qbias = self.bias
# qbias = quantize(self.bias, num_bits=self.num_bits)
out = out + qbias.view(1, -1, 1, 1)
if self.num_bits_grad is not None:
out = quantize_grad(
out, num_bits=self.num_bits_grad, flatten_dims=(1, -1))
if out.size(3) == 1 and out.size(2) == 1:
out = out.squeeze(-1).squeeze(-1)
return out
class RangeBN1d(RangeBN):
# this is normalized RangeBN
def __init__(self, num_features, dim=1, momentum=0.1, affine=True, num_chunks=16, eps=1e-5, num_bits=8, num_bits_grad=8):
super(RangeBN1d, self).__init__(num_features, dim, momentum,
affine, num_chunks, eps, num_bits, num_bits_grad)
self.quantize_input = QuantMeasure(
self.num_bits, inplace=True, shape_measure=(1, 1), flatten_dims=(1, -1))
if __name__ == '__main__':
x = torch.rand(2, 3)
x_q = quantize(x, flatten_dims=(-1), num_bits=8, dequantize=True)
print(x)
print(x_q)
|
nilq/baby-python
|
python
|
from invoke import Collection
from tasks import build
ns = Collection()
ns.add_collection(build)
|
nilq/baby-python
|
python
|
from flask import Flask
from flask_bcrypt import Bcrypt
from flask_login import LoginManager
from flask_sqlalchemy import SQLAlchemy
from flask_mail import Mail
from app.config import Config
# Init
db = SQLAlchemy()
bcrypt = Bcrypt()
login_manager = LoginManager()
login_manager.login_view = 'users.login'
login_manager.login_message_category = 'info'
mail = Mail()
def create_app(config_class=Config):
# init
app = Flask(__name__)
app.config.from_object(Config)
# dependancies
db.init_app(app)
bcrypt.init_app(app)
login_manager.init_app(app)
mail.init_app(app)
# routes
from app.users.routes import users
from app.main.routes import main
from app.departments.routes import departments
from app.requesttypes.routes import requesttypes
from app.serverapprovals.routes import serverapprovals
from app.serverrequestgroups.routes import serverrequestgroups
from app.serverrights.routes import serverrights
from app.servers.routes import servers
from app.vpnapprovals.routes import vpnapprovals
from app.vpns.routes import vpns
from app.approvekinds.routes import approvekinds
app.register_blueprint(users)
app.register_blueprint(main)
app.register_blueprint(departments)
app.register_blueprint(requesttypes)
app.register_blueprint(serverapprovals)
app.register_blueprint(serverrequestgroups)
app.register_blueprint(serverrights)
app.register_blueprint(servers)
app.register_blueprint(vpnapprovals)
app.register_blueprint(vpns)
app.register_blueprint(approvekinds)
return app
|
nilq/baby-python
|
python
|
search_customizations = {
## The name of the variable to contain the list of results (default 'search_results')
# 'context_object_name': '',
## The list of models to exclude from search results (default empty list)
# 'exclude': (),
## The page to redirect to if the user enters an empty search term (default None)
# 'empty_query_redirect': '',
## A dictionary of values to add to the template context. By default, this is an empty dictionary. If a value in the dictionary is callable, the view will call it just before rendering the template.
# 'extra_context': {},
## The list of models to search from (default all)
# 'models': ('Person'),
## An integer specifying how many objects should be displayed per page (default None)
# 'paginate_by': '',
## An integer specifying the page to display, or 'last'. If blank, then the GET parameter 'page' will be used (default None)
# 'page': ,
## The GET parameter to use for the search term (default 'q').
# 'query_param': '',
## The name of the template used to render the search results (default 'watson/search_results.html')
'template_name': 'results.html',
}
|
nilq/baby-python
|
python
|
#!/usr/bin/python3
# -*- coding: utf-8 -*-
# @Created on : 2019-03-06 17:36
# @Author : zpy
# @Software: PyCharm
import logging
from logging.handlers import RotatingFileHandler
import functools
import inspect
from datetime import datetime
from collections import defaultdict
import os
from config import LOGPATH as log_path
class NamedRotatingFileHandler(RotatingFileHandler):
def __init__(self, filename):
super(NamedRotatingFileHandler, self).__init__(
filename=os.path.join(log_path, "{0}.log".format(filename)),
maxBytes=100 * 1024 * 1024,
backupCount=2
)
def get_logger(logger_name):
"""
初始化 logger get 可以获取到，为单例模式
"""
if not os.path.exists(log_path):
os.makedirs(log_path)
os.mkdir(log_path)
# getLogger 为单例模式
service_platform_logger = logging.getLogger(logger_name)
service_platform_logger.setLevel(logging.DEBUG)
datefmt = "%Y-%m-%d %H:%M:%S"
file_log_format = "%(asctime)-15s %(threadName)s %(filename)s:%(lineno)d %(levelname)s: %(message)s"
formtter = logging.Formatter(file_log_format, datefmt)
# handler 存在的判定
if not service_platform_logger.handlers:
# rotating file logger
file_handle = NamedRotatingFileHandler(logger_name)
file_handle.setFormatter(formtter)
service_platform_logger.addHandler(file_handle)
steam_handler = logging.StreamHandler()
service_platform_logger.addHandler(steam_handler)
return service_platform_logger
func_count_dict = defaultdict(int)
time_logger = get_logger('func_time_logger')
def func_time_logger(fun):
@functools.wraps(fun)
def logging(*args, **kw):
try:
func_file = inspect.getfile(fun)
except Exception:
func_file = ''
func_name = fun.__name__
func_key = (func_file, func_name)
func_count_dict[func_key] += 1
begin = datetime.now()
result = fun(*args, **kw)
end = datetime.now()
time_logger.debug('file={} func={} costtime={} times={} args={} kwargs={}'.format(
func_file, func_name, end - begin, func_count_dict[func_key], args, kw
))
func_count_dict[func_key] -= 1
return result
return logging
|
nilq/baby-python
|
python
|
from copy import deepcopy
import dendropy
from iterpop import iterpop as ip
import itertools as it
import numpy as np
from sortedcontainers import SortedSet
def dendropy_tree_to_scipy_linkage_matrix(tree: dendropy.Tree) -> np.array:
# scipy linkage format
# http://docs.scipy.org/doc/scipy/reference/generated/scipy.cluster.hierarchy.linkage.html#scipy.cluster.hierarchy.linkage
# simplify tree
tree = deepcopy(tree)
tree.resolve_polytomies()
tree.suppress_unifurcations()
assert all(len(node.child_nodes()) in [0, 2] for node in tree)
for node in tree.postorder_node_iter():
node.num_leaf_descendants = max(
sum(chld.num_leaf_descendants for chld in node.child_node_iter()),
1,
)
cluster_id_generator = it.count()
for leaf in tree.leaf_node_iter():
if not hasattr(leaf, 'cluster_id'):
leaf.cluster_id = next(cluster_id_generator)
one_leaf_parents = {
leaf.parent_node
for leaf in tree.leaf_node_iter()
if leaf.parent_node is not None
and not ip.popsingleton(leaf.sibling_nodes()).is_leaf()
}
two_leaf_parents = SortedSet(
(
leaf.parent_node
for leaf in tree.leaf_node_iter()
if leaf.parent_node is not None
and ip.popsingleton(leaf.sibling_nodes()).is_leaf()
),
key=lambda node: sum(n.edge_length for n in node.child_node_iter()),
)
res = []
while len(two_leaf_parents):
two_leaf_parent = two_leaf_parents.pop(0)
if not hasattr(two_leaf_parent, 'cluster_id'):
two_leaf_parent.cluster_id = next(cluster_id_generator)
if two_leaf_parent.parent_node is not None:
if two_leaf_parent.parent_node in one_leaf_parents:
one_leaf_parents.remove(two_leaf_parent.parent_node)
two_leaf_parents.add(two_leaf_parent.parent_node)
else:
one_leaf_parents.add(two_leaf_parent.parent_node)
child1, child2 = two_leaf_parent.child_node_iter()
assert child1 not in two_leaf_parents
assert child2 not in two_leaf_parents
# see https://stackoverflow.com/a/40983611/17332200
# for explainer on scipy linkage format
joined_cluster1 = child1.cluster_id
joined_cluster2 = child2.cluster_id
assert None not in (joined_cluster1, joined_cluster2)
cluster_distance = child1.edge_length + child2.edge_length
cluster_size = two_leaf_parent.num_leaf_descendants
res.append([
joined_cluster1,
joined_cluster2,
float(cluster_distance),
cluster_size,
])
return np.array(res)
|
nilq/baby-python
|
python
|
from __future__ import absolute_import
from .ABuGridSearch import ParameterGrid, GridSearch
from .ABuCrossVal import AbuCrossVal
from .ABuMetricsBase import AbuMetricsBase, MetricsDemo
from .ABuMetricsFutures import AbuMetricsFutures
from .ABuMetricsTC import AbuMetricsTC
from .ABuMetricsScore import AbuBaseScorer, WrsmScorer, AbuScoreTuple, make_scorer
from . import ABuGridHelper
from . import ABuMetrics as metrics
__all__ = [
'ParameterGrid',
'GridSearch',
'AbuCrossVal',
'AbuMetricsBase',
'AbuMetricsFutures',
'AbuMetricsTC',
'MetricsDemo',
'AbuBaseScorer',
'WrsmScorer',
'make_scorer',
'ABuGridHelper',
'metrics']
|
nilq/baby-python
|
python
|
#!/usr/bin/env python3
'''
Helper script to clean up some Redis keys
This should probably become part of the regular scheduler code!
'''
import click
import random
import redis
import collections
@click.command()
@click.argument('redis-host')
@click.argument('redis-port')
def main(redis_host, redis_port):
r = redis.StrictRedis(redis_host, redis_port)
keys = r.keys('zmon:checks:*')
sizes = collections.Counter()
for key in random.sample(keys, 10000):
print('.', end='')
try:
res = r.debug_object(key)
except:
pass
else:
sizes[key] = res['serializedlength']
print(sizes.most_common(20))
if __name__ == '__main__':
main()
|
nilq/baby-python
|
python
|
import math
print(math.pi)
|
nilq/baby-python
|
python
|
# -*- coding: utf-8 -*-
###########################################################################
# Copyright (c), The AiiDA team. All rights reserved. #
# This file is part of the AiiDA code. #
# #
# The code is hosted on GitHub at https://github.com/aiidateam/aiida-core #
# For further information on the license, see the LICENSE.txt file #
# For further information please visit http://www.aiida.net #
###########################################################################
"""The main `verdi` click group."""
from __future__ import division
from __future__ import print_function
from __future__ import absolute_import
import click
from aiida.cmdline.params import options, types
@click.group(context_settings={'help_option_names': ['-h', '--help']})
@options.PROFILE(type=types.ProfileParamType(load_profile=True))
@click.version_option(None, '-v', '--version', message='AiiDA version %(version)s')
@click.pass_context
def verdi(ctx, profile):
"""The command line interface of AiiDA."""
from aiida.common import extendeddicts
from aiida.manage.configuration import get_config
if ctx.obj is None:
ctx.obj = extendeddicts.AttributeDict()
ctx.obj.config = get_config()
ctx.obj.profile = profile
|
nilq/baby-python
|
python
|
"""
arguments.py
Handles the arguments of training and scoring Hasse diagrams.
"""
from tempfile import TemporaryDirectory
from tap import Tap
from typing import List
from typing_extensions import Literal
import json
class CommonArgs(Tap):
"""
CommonArgs contains arguments that are used in both TrainArgs and PredictArgs.
Attributes
----------
verbose: bool, default False
Whether to print additional information.
quiet: bool, default False
Whether to silence all output.
compute_aam: bool, default False
Whether to compute atom-mappings for reactions.
"""
verbose: bool = False
quiet: bool = False
compute_aam: bool = False
def process_args(self):
if self.verbose is True and self.quiet is True:
raise ValueError("Cannot use verbose and quiet output at the same time")
if self.verbose is True:
print("Outputting more information to screen")
class TrainArgs(CommonArgs):
"""
TrainArgs includes CommonArgs along with additional arguments used for generating a
template tree from a list of reaction smiles.
Attributes
----------
data_path: str
Path to data CSV file.
save_path: str, default None
File to which diagram is saved.
save_plot: str, default None
File to which save image of diagram.
train_mode: Literal["single_reactant", "transition_state"], default "transition_state"
Train mode, either transition states extracted from reaction smiles or single reactants extracted from smiles.
seed: List[str], default []
List of SMILES seeds for the reactant algorithm, usually a single seed is given.
no_props: bool, default False
Do not compute any properties, just output the diagram.
plot_only_branches: bool, default False
Plot only substructures that branch off.
"""
data_path: str
save_path: str = None
save_plot: str = None
train_mode: Literal["single_reactant", "transition_state"] = "transition_state"
seed: List[str] = []
no_props: bool = False
plot_only_branches: bool = False
def process_args(self):
super(TrainArgs, self).process_args()
global temp_dir # Prevents the temporary directory from being deleted upon function return
if self.save_plot: # Create temporary directory for plotting routine
temp_dir = TemporaryDirectory()
self.temp_dir_img = temp_dir.name
else:
self.temp_dir_img = None
if self.seed != [] and self.train_mode != "single_reactant":
raise ValueError(
"A seed can only be specified when working in reactant mode "
"Please specify --train_mode single_reactant or omit the --seed."
)
if self.compute_aam and self.train_mode != "transition_state":
raise ValueError(
"Computing atom maps is only possible for reactions, please specify --train_mode transition_state."
)
class PredictArgs(CommonArgs):
"""
PredictArgs includes CommonArgs along with additional arguments used for predicting reaction scores
from a template tree and a list of reaction smiles.
Attributes
----------
load_path: str
File from which diagram is loaded.
test_path: str
Path to CSV file containing testing data for which predictions will be made.
preds_path: str, default None
Path to CSV file where predictions will be saved.
predict_mode: Literal["single_reactant", "multi_reactant", "transition_state"], default "transition_state"
Predict mode, either transition states (input reaction smiles, with train_mode='transition_state')
or multiple reactants (input list of smiles, with train_mode='transition_state') or single reactants
(input smiles, compatible with both train_modes).
hyper_params: str, default None
File from which hyperparameters are loaded.
stereochemistry: bool, default False
Whether to use stereochemistry for scoring.
"""
load_path: str
test_path: str
preds_path: str = None
predict_mode: Literal[
"single_reactant", "multi_reactant", "transition_state"
] = "transition_state"
hyper_params: str = None
stereochemistry: bool = False
def process_args(self):
super(PredictArgs, self).process_args()
if self.hyper_params:
with open(self.hyper_params) as json_file:
self.params = json.load(json_file)
else:
self.params = None
if self.compute_aam and self.predict_mode != "transition_state":
raise ValueError(
"Computing atom maps is only possible for reactions, please specify --predict_mode transition_state."
)
|
nilq/baby-python
|
python
|
# (c) Copyright IBM Corp. 2021
# (c) Copyright Instana Inc. 2020
"""
Host Collector: Manages the periodic collection of metrics & snapshot data
"""
from time import time
from ..log import logger
from .base import BaseCollector
from ..util import DictionaryOfStan
from .helpers.runtime import RuntimeHelper
class HostCollector(BaseCollector):
""" Collector for AWS Fargate """
def __init__(self, agent):
super(HostCollector, self).__init__(agent)
logger.debug("Loading Host Collector")
# Indicates if this Collector has all requirements to run successfully
self.ready_to_start = True
# Populate the collection helpers
self.helpers.append(RuntimeHelper(self))
def start(self):
if self.ready_to_start is False:
logger.warning("Host Collector is missing requirements and cannot monitor this environment.")
return
super(HostCollector, self).start()
def prepare_and_report_data(self):
"""
We override this method from the base class so that we can handle the wait4init
state machine case.
"""
try:
if self.agent.machine.fsm.current == "wait4init":
# Test the host agent if we're ready to send data
if self.agent.is_agent_ready():
if self.agent.machine.fsm.current != "good2go":
logger.debug("Agent is ready. Getting to work.")
self.agent.machine.fsm.ready()
else:
return
if self.agent.machine.fsm.current == "good2go" and self.agent.is_timed_out():
logger.info("The Instana host agent has gone offline or is no longer reachable for > 1 min. Will retry periodically.")
self.agent.reset()
except Exception:
logger.debug('Harmless state machine thread disagreement. Will self-correct on next timer cycle.')
super(HostCollector, self).prepare_and_report_data()
def should_send_snapshot_data(self):
delta = int(time()) - self.snapshot_data_last_sent
if delta > self.snapshot_data_interval:
return True
return False
def prepare_payload(self):
payload = DictionaryOfStan()
payload["spans"] = []
payload["profiles"] = []
payload["metrics"]["plugins"] = []
try:
if not self.span_queue.empty():
payload["spans"] = self.queued_spans()
if not self.profile_queue.empty():
payload["profiles"] = self.queued_profiles()
with_snapshot = self.should_send_snapshot_data()
plugins = []
for helper in self.helpers:
plugins.extend(helper.collect_metrics(with_snapshot))
payload["metrics"]["plugins"] = plugins
if with_snapshot is True:
self.snapshot_data_last_sent = int(time())
except Exception:
logger.debug("non-fatal prepare_payload:", exc_info=True)
return payload
|
nilq/baby-python
|
python
|
# Tests invocation of the interpreter with various command line arguments
# All tests are executed with environment variables ignored
# See test_cmd_line_script.py for testing of script execution
import test.test_support
import sys
import unittest
from test.script_helper import (
assert_python_ok, assert_python_failure, spawn_python, kill_python,
python_exit_code
)
from test.test_support import check_impl_detail
class CmdLineTest(unittest.TestCase):
def start_python(self, *args):
p = spawn_python(*args)
return kill_python(p)
def exit_code(self, *args):
return python_exit_code(*args)
def test_directories(self):
self.assertNotEqual(self.exit_code('.'), 0)
self.assertNotEqual(self.exit_code('< .'), 0)
def verify_valid_flag(self, cmd_line):
data = self.start_python(cmd_line)
self.assertTrue(data == '' or data.endswith('\n'))
self.assertNotIn('Traceback', data)
def test_optimize(self):
self.verify_valid_flag('-O')
self.verify_valid_flag('-OO')
def test_q(self):
self.verify_valid_flag('-Qold')
self.verify_valid_flag('-Qnew')
self.verify_valid_flag('-Qwarn')
self.verify_valid_flag('-Qwarnall')
def test_site_flag(self):
self.verify_valid_flag('-S')
def test_usage(self):
self.assertIn('usage', self.start_python('-h'))
def test_version(self):
version = 'Python %d.%d' % sys.version_info[:2]
self.assertTrue(self.start_python('-V').startswith(version))
def test_run_module(self):
# Test expected operation of the '-m' switch
# Switch needs an argument
self.assertNotEqual(self.exit_code('-m'), 0)
# Check we get an error for a nonexistent module
self.assertNotEqual(
self.exit_code('-m', 'fnord43520xyz'),
0)
# Check the runpy module also gives an error for
# a nonexistent module
self.assertNotEqual(
self.exit_code('-m', 'runpy', 'fnord43520xyz'),
0)
# All good if module is located and run successfully
self.assertEqual(
self.exit_code('-m', 'timeit', '-n', '1'),
0)
def test_run_module_bug1764407(self):
# -m and -i need to play well together
# Runs the timeit module and checks the __main__
# namespace has been populated appropriately
p = spawn_python('-i', '-m', 'timeit', '-n', '1')
p.stdin.write('Timer\n')
p.stdin.write('exit()\n')
data = kill_python(p)
self.assertTrue(data.startswith('1 loop'))
self.assertIn('__main__.Timer', data)
def test_run_code(self):
# Test expected operation of the '-c' switch
# Switch needs an argument
self.assertNotEqual(self.exit_code('-c'), 0)
# Check we get an error for an uncaught exception
self.assertNotEqual(
self.exit_code('-c', 'raise Exception'),
0)
# All good if execution is successful
self.assertEqual(
self.exit_code('-c', 'pass'),
0)
def test_hash_randomization(self):
# Verify that -R enables hash randomization:
self.verify_valid_flag('-R')
hashes = []
for i in range(2):
code = 'print(hash("spam"))'
data = self.start_python('-R', '-c', code)
hashes.append(data)
if check_impl_detail(pypy=False): # PyPy does not really implement it!
self.assertNotEqual(hashes[0], hashes[1])
# Verify that sys.flags contains hash_randomization
code = 'import sys; print sys.flags'
data = self.start_python('-R', '-c', code)
self.assertTrue('hash_randomization=1' in data)
def test_del___main__(self):
# Issue #15001: PyRun_SimpleFileExFlags() did crash because it kept a
# borrowed reference to the dict of __main__ module and later modify
# the dict whereas the module was destroyed
filename = test.test_support.TESTFN
self.addCleanup(test.test_support.unlink, filename)
with open(filename, "w") as script:
print >>script, "import sys"
print >>script, "del sys.modules['__main__']"
assert_python_ok(filename)
def test_unknown_options(self):
rc, out, err = assert_python_failure('-E', '-z')
self.assertIn(b'Unknown option: -z', err)
self.assertEqual(err.splitlines().count(b'Unknown option: -z'), 1)
self.assertEqual(b'', out)
# Add "without='-E'" to prevent _assert_python to append -E
# to env_vars and change the output of stderr
rc, out, err = assert_python_failure('-z', without='-E')
self.assertIn(b'Unknown option: -z', err)
self.assertEqual(err.splitlines().count(b'Unknown option: -z'), 1)
self.assertEqual(b'', out)
rc, out, err = assert_python_failure('-a', '-z', without='-E')
self.assertIn(b'Unknown option: -a', err)
# only the first unknown option is reported
self.assertNotIn(b'Unknown option: -z', err)
self.assertEqual(err.splitlines().count(b'Unknown option: -a'), 1)
self.assertEqual(b'', out)
def test_main():
test.test_support.run_unittest(CmdLineTest)
test.test_support.reap_children()
if __name__ == "__main__":
test_main()
|
nilq/baby-python
|
python
|
from AutoMxL.__main__ import AML
import unittest
import pandas as pd
from AutoMxL.Preprocessing.Categorical import CategoricalEncoder
from AutoMxL.Preprocessing.Date import DateEncoder
from AutoMxL.Preprocessing.Missing_Values import NAEncoder
from AutoMxL.Select_Features.Select_Features import FeatSelector
import numpy as np
# import numpy as np
# Defaults HP grid for RF
default_RF_grid_param = {
'n_estimators': np.random.uniform(low=20, high=100, size=20).astype(int),
'max_features': ['auto', 'log2'],
'max_depth': np.random.uniform(low=2, high=10, size=20).astype(int),
'min_samples_split': [5, 10, 15]}
df_test = pd.read_csv('tests/df_test.csv')
# init
auto_df_init = AML(df_test, target='y_yes')
# explore
auto_df_explore = auto_df_init.duplicate()
auto_df_explore.explore()
# preprocess
auto_df_preprocess = auto_df_explore.duplicate()
auto_df_preprocess.preprocess(date_ref=None, process_outliers=False, cat_method='one_hot')
# select_features
auto_df_select = auto_df_preprocess.duplicate()
auto_df_select.select_features(method='pca')
# model_train_test
auto_df_model = auto_df_select.duplicate()
d_res_model, l_valid, best_model_idx, df_res = auto_df_model.model_train_test(grid_param=default_RF_grid_param,
n_comb=5, comb_seed=2, verbose=True)
# apply
df_prep = auto_df_select.preprocess_apply(df_test)
df_sel = auto_df_select.select_features_apply(df_prep)
class TestInit(unittest.TestCase):
""" init method"""
# Test autoML object instantiation from DataFrame
def test_df_is_not_none(self):
self.assertIsNotNone(auto_df_init)
# Test target Attribute
def test_target_is_not_none(self):
self.assertIsNotNone(auto_df_init.target)
"""
-------------------------------------------------------------------------------------------------------------------------
"""
# Instantiate autoML object from df_test and target
auto_df = AML(df_test.copy(), target='y_yes')
# Explore
auto_df.explore(verbose=False)
class TestExplore(unittest.TestCase):
""" explore method """
def test_d_features(self):
# numerical features
self.assertEqual(auto_df_explore.d_features['numerical'], ['age', 'euribor3m'])
# boolean features
self.assertEqual(auto_df_explore.d_features['boolean'], [])
# categorical features
self.assertEqual(auto_df_explore.d_features['categorical'], ['job', 'education'])
# date features
self.assertEqual(auto_df_explore.d_features['date'], ['date_1', 'date_2'])
# features containing NA values
self.assertEqual(auto_df_explore.d_features['NA'], ['job', 'age', 'date_1'])
# null variance features
self.assertEqual(auto_df_explore.d_features['low_variance'], ['null_var'])
# unchange dataset
self.assertEqual(df_test.columns.tolist(), auto_df_explore.columns.tolist())
self.assertTrue(auto_df_explore.isnull().sum().max() > 0)
"""
-------------------------------------------------------------------------------------------------------------------------
"""
class TestPreprocess(unittest.TestCase):
""" preprocess method """
def test_encoders(self):
self.assertEqual(auto_df_preprocess.d_preprocess['remove'], ['null_var'])
self.assertTrue(isinstance(auto_df_preprocess.d_preprocess['date'], DateEncoder))
self.assertTrue(isinstance(auto_df_preprocess.d_preprocess['NA'], NAEncoder))
self.assertTrue(isinstance(auto_df_preprocess.d_preprocess['categorical'], CategoricalEncoder))
self.assertNotIn('outlier', auto_df_preprocess.d_preprocess.keys())
def test_preprocessing(self):
self.assertTrue(auto_df_preprocess.isnull().sum().max() == 0)
self.assertEqual(auto_df_preprocess.columns.tolist(), auto_df_preprocess._get_numeric_data().columns.tolist())
self.assertTrue(auto_df_preprocess.is_fitted_preprocessing)
"""
-------------------------------------------------------------------------------------------------------------------------
"""
class TestPreprocessApply(unittest.TestCase):
""" preproces method """
def test_preprocessing_apply(self):
self.assertTrue(auto_df_preprocess.isnull().sum().max() == 0)
self.assertEqual(auto_df_preprocess.columns.tolist(), auto_df_preprocess._get_numeric_data().columns.tolist())
self.assertEqual((df_prep == auto_df_preprocess).sum().min(), 41188)
"""
-------------------------------------------------------------------------------------------------------------------------
"""
class TestSelectFeatures(unittest.TestCase):
""" select_features method """
def test_select_features(self):
#
self.assertLess(auto_df_select.shape[1], auto_df_preprocess.shape[1])
self.assertFalse(auto_df_preprocess.is_fitted_selector)
self.assertTrue(auto_df_select.is_fitted_selector)
self.assertTrue(isinstance(auto_df_select.features_selector, FeatSelector))
"""
-------------------------------------------------------------------------------------------------------------------------
"""
class TestSelectFeaturesApply(unittest.TestCase):
""" select_features method """
def test_select_features_apply(self):
#
self.assertLess(df_sel.shape[1], df_prep.shape[1])
self.assertEqual((df_sel == auto_df_select).sum().min(), 41188)
"""
-------------------------------------------------------------------------------------------------------------------------
"""
class TestModelTrainTest(unittest.TestCase):
""" method """
def test_comb_samples(self):
# tests HP comb values
self.assertEqual(auto_df_model.hyperopt.d_train_model[0]['HP']['min_samples_split'], 15)
def test_best_model(self):
if best_model_idx is not None:
# test best model is valid (delta_auc)
self.assertTrue(d_res_model[best_model_idx]['metrics']['delta_auc'] < 0.03)
# best model have the max F1 score among valid models
for i in range(5):
if d_res_model[i]['metrics']['delta_auc'] < 0.03:
self.assertTrue(
d_res_model[best_model_idx]['metrics']['F1'] >= d_res_model[i]['metrics']['F1'])
|
nilq/baby-python
|
python
|
from django import forms
from .models import GonderiModel
class GonderiForm(forms.ModelForm):
class Meta:
model = GonderiModel
fields = ("tur","baslik","yazi","olusturzaman")
|
nilq/baby-python
|
python
|
import unittest
from utils import VCRTestBase
import utils
from pyVim import connect
import sys
sys.path.insert(0, '../')
import inventory
class InventoryTests(VCRTestBase):
@VCRTestBase.my_vcr.use_cassette('test_inventory_run.yaml',
cassette_library_dir=utils.fixtures_path,
record_mode='none')
def test_inventory_run(self):
si = connect.SmartConnectNoSSL(host='192.168.1.60',
user='administrator@vsphere.local',
pwd='Abcd123$')
mor_sync_interval = 300
vc_name = 'TestVcenter'
instance_id = 'VCenterInstance'
inventory_mgr = inventory.InventoryManager(si, mor_sync_interval, vc_name, instance_id)
inventory_mgr.start()
inventory_mgr.block_until_inventory(timeout=5)
current_inventory = inventory_mgr.current_inventory()
self.assertIsNotNone(current_inventory)
self.assertEqual(2, len(current_inventory['datacenter']))
self.assertEqual(1, len(current_inventory['cluster']))
self.assertEqual(2, len(current_inventory['host']))
self.assertEqual(2, len(current_inventory['vm']))
inventory_mgr.stop()
@VCRTestBase.my_vcr.use_cassette('test_inventory_sync.yaml',
cassette_library_dir=utils.fixtures_path,
record_mode='none')
def test_inventory_sync(self):
si = connect.SmartConnectNoSSL(host='192.168.1.60',
user='administrator@vsphere.local',
pwd='Abcd123$')
mor_sync_interval = 300
vc_name = 'TestVcenter'
instance_id = 'VCenterInstance'
inventory_mgr = inventory.InventoryManager(si, mor_sync_interval, vc_name, instance_id)
inventory_mgr.sync_inventory()
current_inventory = inventory_mgr.current_inventory()
self.assertIsNotNone(current_inventory)
self.assertEqual(2, len(current_inventory['datacenter']))
self.assertEqual(1, len(current_inventory['cluster']))
self.assertEqual(2, len(current_inventory['host']))
self.assertEqual(2, len(current_inventory['vm']))
|
nilq/baby-python
|
python
|
import numpy as np
import matplotlib.pyplot as plt
import random
# Pulling data from Data sets
datafile_1 = np.genfromtxt('data_1.csv', delimiter=',')
# print(datafile_1)
datafile_2 = np.genfromtxt('data_2.csv', delimiter=',')
# print(datafile_2)
datafile_1 = datafile_1[1:]
datafile_2 = datafile_2[1:]
# Least square method for Data Set 1
# Fitting a parabola for y=Ax+B
def fit_parabola(points):
n_points = len(points)
# calculating matrix A and B for least squares
A = []
B = []
for point in points:
x = point[0]
y = point[1]
A.append([x**2,x,1])
B.append([y])
# Array A, B
A = np.array(A)
B = np.array(B)
# print(A)
# print(B)
# Matrix multiplication for getting solution (sol) = (ATA)^-1.AT.B
sol = np.matmul(np.transpose(A),A)
# print(sol)
sol = np.linalg.inv(sol)
# print(sol)
sol = np.matmul(sol,np.transpose(A))
# print(sol)
sol = np.matmul(sol,B)
return sol
plt.scatter(datafile_1[:,0],datafile_1[:,1])
# print(datafile_1[:,0])
datafile_1_list = datafile_1.tolist()
least_square_sol = fit_parabola(datafile_1_list)
# print(least_square_sol)
a = least_square_sol[0]
b = least_square_sol[1]
c = least_square_sol[2]
x = np.linspace(0,500,1000)
# print(x)
y = a*(x**2)+b*x+c
# print(y)
#plotting the parabola
plt.plot(x,y)
plt.title('Least Square Method')
plt.show()
#RANSAC Method for Data Set 2
datafile_2_list = datafile_2.tolist()
dist_thresh = 70 # Distance threshold for line fundamental matrix, we considered this by looking at the data set
thresh_prob = 0.8 # desired probability that we get a good sample
plt.scatter(datafile_2[:,0],datafile_2[:,1])
sol_found = False
for n_interations in range(150):
#choosing random 3 points
random_points = []
random_indices = random.sample(range(len(datafile_2_list)), 3)
# printing (random_indices)
for random_index in random_indices:
random_points.append(datafile_2_list[random_index])
# print(random_points)
random_points_sol = fit_parabola(random_points)
a = random_points_sol[0]
b = random_points_sol[1]
c = random_points_sol[2]
# fit a parabola using these fit_parabola(points)
inliers = []
# finding distance of all the points from parbola and append to inliers
for point in datafile_2_list:
x_curr_point = point[0]
y_curr_point = point[1]
# find distsance of point from parabola
dist = abs(y_curr_point- (a*(x_curr_point**2)+b*x_curr_point+c))
# print(dist)
# if distance is less than the threshold that will be an inlier
if dist<dist_thresh:
inliers.append([x_curr_point,y_curr_point])
# If the probability to find the best sample is more than our considered threshold probability then we have the best solution
if((float(len(inliers))/len(datafile_2_list))>thresh_prob):
sol_found = True
break
# plotting parabola
if(sol_found==True):
inliers_arr = np.array(inliers)
plt.scatter(inliers_arr[:,0],inliers_arr[:,1],color="r")
least_square_sol = fit_parabola(inliers)
# print(least_square_sol)
a = least_square_sol[0]
b = least_square_sol[1]
c = least_square_sol[2]
x = np.linspace(0,500,1000)
y = a*(x**2)+b*x+c
plt.title('RANSAC Method')
plt.plot(x,y)
plt.show()
else:
print('I counld not find a solution')
|
nilq/baby-python
|
python
|
#!/usr/bin/env python3
import inspect
import sys
from xml.etree import ElementTree
def match(text, *queries):
doc = ElementTree.parse(text)
return [doc.findtext(query) for query in queries]
def _main(argv=None):
"""
Usage: python3 -m pup.xpath QUERY [QUERY...]
Where
- Each QUERY is a valid XPath query.
- An XML document is provided via stdin.
"""
if argv is None:
argv = sys.argv
args = argv[1:] # Drop the process name.
if len(args) < 1 or "-h" in args or "--help" in args:
print(inspect.getdoc(_main), file=sys.stderr)
return 1
print("\n".join(match(sys.stdin, *args)))
return 0
if __name__ == "__main__":
exit(_main())
|
nilq/baby-python
|
python
|
import pytest
from pybuildkite.agents import Agents
def test_get_agent(fake_client):
"""
Test the get_agent method
"""
agents = Agents(fake_client, "base")
agents.get_agent("org_slug", "agent_id")
fake_client.get.assert_called_with(agents.path.format("org_slug") + "agent_id")
def test_stop_agent(fake_client):
agents = Agents(fake_client, "base")
agents.stop_agent("org_slug", "agent_id")
fake_client.put.assert_called_with(
agents.path.format("org_slug") + "agent_id/stop", body={"force": True}
)
def test_list_all_agents(fake_client):
agents = Agents(fake_client, "base")
agents.list_all("org_slug")
fake_client.get.assert_called_with(
agents.path.format("org_slug"),
{"name": None, "hostname": None, "version": None, "page": 0},
with_pagination=False,
)
|
nilq/baby-python
|
python
|
# Copyright (c) 2015 Microsoft Corporation
"""
>>> from z3 import *
>>> v = BitVecVal(0xbadc0de, 32)
>>> v.sexpr()
'#x0badc0de'
>>> v
195936478
>>> v.as_long() == 195936478
True
"""
if __name__ == "__main__":
import doctest
if doctest.testmod().failed:
exit(1)
|
nilq/baby-python
|
python
|
def main():
print("Not implemented yet.")
|
nilq/baby-python
|
python
|
import tensorflow_datasets as tfds
import config
def load_dataset():
imdb, info = tfds.load('imdb_reviews',
data_dir=config.DATA_PATH,
with_info=True,
as_supervised=True)
return imdb, info
if __name__ == '__main__':
load_dataset()
|
nilq/baby-python
|
python
|
'''
作者：邱少一
日期：2018/03/06
1、准备：
python版本：python3 --version
选择Web异步的框架aiohttp：pip3 install aiohttp(比较底层，需要再次封装)
前端模板引擎jinja2：pip3 install jinja2
MySQL的Python异步驱动程序aiomysql：pip3 install aiomysql
监控目录文件变化：pip3 install watchdog
2、流程：
1、编写web 骨架
2、编写ORM 和 Model
3、编写 web框架（基于aiohttp）
4、编写配置文件
5、编写MVC
6、构建前端
7、编写API（返回的是机器可解析的数据，而不是HTML 的URL这样的就是API）：如果一个URL返回的不是HTML，而是机器能直接解析的数据，这个URL就可以看成是一个Web API
8、用户注册登陆：（ 用户口令是客户端传递的经过SHA1计算后的40位Hash字符串，所以服务器端并不知道用户的原始口令；
服务器要跟踪web用户的登陆状态，只能通过客户端cookie实现，web端保存在Session中。。。
Session的优点可直接读取，缺点是服务器需要在内存中维护：1个映射表，来存储用户登录信息，
问题是，多台服务器时，需要Session做集群，另1个服务器为Redis：存储各个服务器中的Session，然后对通过Redis和服务端进行交互 ）
（实际开发：不这么操作）🙋解决方案：采用直接读取cookie的方式来验证用户登录，每次用户访问任意URL，都会对cookie进行验证。保证服务器处理任意的URL：都是无状态的，可以扩展到多台服务器。
9、编写日志：Vue这个MVVM框架：来实现创建Blog的页面 和 页面分页，维护成本变得更低
10、提升开发效率：django 可以在debug模式下自动重新加载，保证开发过程中同步性；
我们没有django处理上，我们解决方案：检测www目录下的代码改动，一旦有改动，就自动重启服务器。
编写一个辅助程序pymonitor.py
功能：1、检测www目录下的代码改动 2、把当前wsgiapp.py进程杀掉 3、重启服务
最终实现了： Debug模式的自动重新加载
操作：
先执行:python3 pymonitor.py wsgiapp.py（未部署到远程服务器中，可先不用wsgiapp.py）
再执行:./pymonitor.py app.py
11、完成web app：高效开发先执行：python3 pymonitor.py 再执行./pymonitor.py app.py
12、部署web开发服务器：截至到现在，已经在本地服务器。
但不够，我们需要将app部署到远程服务器上：项目+数据库都部署上去！
1、确保ssh启动
2、服务端确定项目目录结构：新增版本控制的机制，linux的软连接，让项目目录指向不同版本就可以。
Nginx和python代码的配置文件只需要指向www目录
3、总结下用到的服务：
Nginx：高性能Web服务器+负责反向代理；
Supervisor：监控服务进程的工具；
MySQL：数据库服务。
13、编写移动app：编写app，再调用API:/api/blogs 接口即可
0、
Web框架：基于asyncio的aiohttp
1、HTTP请求的流程3步走：
#
# 步骤1：浏览器首先向服务器发送HTTP请求，请求包括：
#
# 请求方式：GET还是POST，GET仅请求资源，POST会附带用户数据；
# 域名：由Host头指定：Host: www.sina.com.cn
# 路径：/full/url/path；
# Header：其他相关的Header；
# 如果是POST，那么请求还包括一个可选的Body，包含用户数据。
#
# 步骤2：服务器向浏览器返回HTTP响应，响应内容包括：
#
# 响应代码：200表示成功，3xx表示重定向，4xx表示客户端发送的请求有错误，5xx表示服务器端处理时发生了错误；
# 响应类型：由Content-Type指定；
# Header：以及其他相关的Header；
# 通常服务器的HTTP响应会携带内容，也就是有一个Body：包含响应的内容，网页的HTML源码就在Body中。
#
# 步骤3：如果浏览器还需要继续向服务器请求其他资源，比如图片，就再次发出HTTP请求，重复步骤1、2。
2、
async：使用场景 -> 异步网络操作、并发、协程！！！等效于 @asyncio.coroutine
await： 用于挂起阻塞的异步调用接口 ！！！ 等效于 yield from
'''
import logging;logging.basicConfig(level=logging.INFO)
import asyncio, os, json, time
from datetime import datetime
from aiohttp import web,web_runner
from jinja2 import Environment, FileSystemLoader
from config import configs
import orm
from coroweb import add_routes, add_static
from handlers import cookie2user, COOKIE_NAME
'''
========================== 0:初始化前端模板 ==========================
app的模板 绑定为env
'''
def init_jinja2(app, **kw):
logging.info('init jinja2...')
options = dict(
autoescape = kw.get('autoescape', True),
block_start_string = kw.get('block_start_string', '{%'),
block_end_string = kw.get('block_end_string', '%}'),
variable_start_string = kw.get('variable_start_string', '{{'),
variable_end_string = kw.get('variable_end_string', '}}'),
auto_reload = kw.get('auto_reload', True)
)
path = kw.get('path', None)
if path is None:
BASE_DIR = os.path.dirname(os.path.abspath(__file__))
path = os.path.join(BASE_DIR, 'templates')
logging.info('set jinja2 template path: %s' % path)
env = Environment(loader=FileSystemLoader(path), **options)
filters = kw.get('filters', None)
if filters is not None:
for name, f in filters.items():
env.filters[name] = f
app['__templating__'] = env
@asyncio.coroutine
def logger_factory(app, handler):
@asyncio.coroutine
def logger(request):
logging.info('logger_factory -> Request: %s %s' % (request.method, request.path))
# yield from asyncio.sleep(0.3)
return (yield from handler(request))
return logger
# 对于每个URL处理函数，如果我们都去写解析cookie的代码，那会导致代码重复很多次。
# 利用middle在处理URL之前，把cookie解析出来，并将登录用户绑定到request对象上
@asyncio.coroutine
def auth_factory(app, handler):
@asyncio.coroutine
def auth(request):
logging.info('check user: %s %s' % (request.method, request.path))
request.__user__ = None
cookie_str = request.cookies.get(COOKIE_NAME)
if cookie_str:
user = yield from cookie2user(cookie_str)
if user:
logging.info('set current user: %s' % user.email)
request.__user__ = user
if request.path.startswith('/manage/') and (request.__user__ is None or not request.__user__.admin):
return web.HTTPFound('/signin')
return (yield from handler(request))
return auth
@asyncio.coroutine
def data_factory(app, handler):
@asyncio.coroutine
def parse_data(request):
if request.method == 'POST':
if request.content_type.startswith('application/json'):
request.__data__ = yield from request.json()
logging.info('request json: %s' % str(request.__data__))
elif request.content_type.startswith('application/x-www-form-urlencoded'):
request.__data__ = yield from request.post()
logging.info('request form: %s' % str(request.__data__))
return (yield from handler(request))
return parse_data
# 获取请求体
@asyncio.coroutine
def response_factory(app, handler):
@asyncio.coroutine
def response(request):
logging.info('Response handler...')
r = yield from handler(request)
if isinstance(r, web.StreamResponse):
return r
if isinstance(r, bytes):
resp = web.Response(body=r)
resp.content_type = 'application/octet-stream'
return resp
if isinstance(r, str):
if r.startswith('redirect:'):
return web.HTTPFound(r[9:])
resp = web.Response(body=r.encode('utf-8'))
resp.content_type = 'text/html'
resp.charset = 'utf-8'
return resp
if isinstance(r, dict): # 模板的请求返回内容
template = r.get('__template__')
if template is None:
resp = web.Response(body=json.dumps(r, ensure_ascii=False, default=lambda o: o.__dict__).encode('utf-8'))
resp.content_type = 'application/json;charset=utf-8'
return resp
else:
r['__user__'] = request.__user__
resp = web.Response(body=app['__templating__'].get_template(template).render(**r).encode('utf-8'))
resp.content_type = 'text/html;charset=utf-8'
return resp
if isinstance(r, int) and r >= 100 and r < 600:
return web.Response(r)
if isinstance(r, tuple) and len(r) == 2:
t, m = r
if isinstance(t, int) and t >= 100 and t < 600:
return web.Response(t, str(m))
# default:
resp = web.Response(body=str(r).encode('utf-8'))
resp.content_type = 'text/plain;charset=utf-8'
return resp
return response
def datetime_filter(t):
delta = int(time.time() - t)
if delta < 60:
return u'1分钟前'
if delta < 3600:
return u'%s分钟前' % (delta // 60)
if delta < 86400:
return u'%s小时前' % (delta // 3600)
if delta < 604800:
return u'%s天前' % (delta // 86400)
dt = datetime.fromtimestamp(t)
return u'%s年%s月%s日' % (dt.year, dt.month, dt.day)
'''
========================== 暂时放这儿调试用 -> 1: 路由 ==========================
'''
# body：为显示html的body，要不然一片空白
# 内容类型为text/html，要不然会被当做file执行下载；
# body中字符的编码charset：utf-8，要不然中文会乱码
def index(request):
logging.info('server response...')
r = web.Response()
r.body = '<h1> Awesome </h1>'
r.content_type = 'text/html'
r.charset = 'utf-8'
return r
def hello(request):
logging.info('我要访问接口了...')
hello_text = '<h1> 我就是帅,哈哈哈 </h1>'
r = web.Response()
r.body = hello_text
r.content_type = 'text/html'
r.charset = 'utf-8'
return r
'''
========================== 2: 初始化loop ==========================
'''
@asyncio.coroutine
def init(loop):
# 创建数据库池：数据库的账户和密码，以及db需要首先创建好！！！
yield from orm.create_pool(loop=loop,**configs.db)
# middleware是一种拦截器,URL在被某个函数处理前,可以经过一系列的 middleware 的处理
app = web.Application(loop=loop,middlewares=[
logger_factory,auth_factory,response_factory
])
# 适配python3.0
app = web_runner.AppRunner(app=app).app
# 手动添加路由:
# app.router.add_route('GET','/testIndex',index)
# app.router.add_route('GET','/testHello',hello)
# 初始化模板引擎：绑定了block 和
init_jinja2(app,filters=dict(datetime=datetime_filter))
# 给web app添加路由，统一放在handlers模块中处理
add_routes(app,'handlers')
# 注册静态文件
add_static(app)
logging.info('server before...')
# 创建1个服务器对象
# 'localhost' == '127.0.0.1' 该地址已经被Apache服务器使用
# 故使用10.9.3.240
host ='10.9.3.240'
port = 9000
server = yield from loop.create_server(app.make_handler(),host,port)
logging.info('server start at http://%s:%s' % (host,port))
return server
loop = asyncio.get_event_loop()
loop.run_until_complete(init(loop))
loop.run_forever() # server 才会执行listen
|
nilq/baby-python
|
python
|
# -*- coding: utf-8 -*-
from __future__ import print_function
import os
import sys
import codecs
import unittest
from StringIO import StringIO
# We include <pkg_root>/src, <pkg_root>/lib/python
extend_path = lambda root_path, folder: sys.path.insert(
0, os.path.join(root_path, folder))
ROOT = os.path.dirname(os.path.dirname(__file__))
extend_path(ROOT, '')
# import tree_output
from tree_output.houtput import HierarchicalOutput
EXPECTED_ANSI = os.path.join(os.path.dirname(__file__),
'mock', 'expected_ansi')
class TestHierarchicalOutput(unittest.TestCase):
def bake_tree_mock(self, tree_output):
tree_output.emit('foo')
tree_output.add_level()
tree_output.emit('foO')
tree_output.add_level()
tree_output.emit('bar')
tree_output.add_level()
for num in range(10):
tree_output.emit(num)
tree_output.emit(10, closed=True)
tree_output.remove_level()
tree_output.emit('baz', closed=True)
tree_output.emit('foo2')
def test_json(self):
# Emit output
tree_output = HierarchicalOutput.factory('json')
self.bake_tree_mock(tree_output)
# Assertion:
expected = '["foo", ["foO", ["bar", [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10]], "baz"], "foo2"]'
obtained = str(tree_output)
#print(obtained)
assert expected == obtained
def test_ascii(self):
# Capture STDOUT.
output = StringIO()
sys_output = sys.stdout
sys.stdout = output
# Do output
tree_output = HierarchicalOutput.factory('ascii')
self.bake_tree_mock(tree_output)
# Assertion:
expected = '''+-- foo
| +-- foO
| | +-- bar
| | | +-- 0
| | | +-- 1
| | | +-- 2
| | | +-- 3
| | | +-- 4
| | | +-- 5
| | | +-- 6
| | | +-- 7
| | | +-- 8
| | | +-- 9
| | | `-- 10
| `-- baz
+-- foo2
'''
sys.stdout = sys_output
obtained = output.getvalue()
#print(obtained)
assert expected == obtained
def test_ansi(self):
# Call colorama.init() before STDOUT capture.
tree_output = HierarchicalOutput.factory('ansi')
import colorama
# Comment this out in order to see output in colors.
# colorama.deinit()
# Capture STDOUT.
output = StringIO()
sys_output = sys.stdout
sys.stdout = output
# Output
self.bake_tree_mock(tree_output)
# Assertion:
expected = codecs.open(EXPECTED_ANSI, 'r', 'utf-8').read()
sys.stdout = sys_output
obtained = output.getvalue()
# Write to mock
# with codecs.open(EXPECTED_ANSI, 'w+', 'utf-8') as mock:
# mock.write(obtained)
# print('\n' + obtained)
assert expected == obtained
|
nilq/baby-python
|
python
|
# """
# Modul containing import methods from different packages / repositories.
# """
#
# import copy
# import json
# import os.path
# import tempfile
# from multiprocessing import Process
#
# import arff
# import networkx as nx
# import numpy as np
# import openml as oml
# import pandas as pd
# import pypadre.pod.backend.http.protobuffer.proto_organizer as proto
# import requests
# from requests.exceptions import ConnectionError
#
# import pypadre
# from pypadre.core.model.dataset.attribute import Attribute
# from pypadre.core.model.dataset.dataset import Dataset
#
# # def _split_DESCR(s):
# # s = s.strip()
# # k = s.find("\n")
# # return s[0:k], s[k + 1:]
# #
# #
# # def _create_dataset_data(bunch):
# # n_feat = bunch.data.shape[1]
# # if len(bunch.target.shape) == 1:
# # data = np.concatenate([bunch.data[:, :], bunch.target[:, None]], axis=1)
# # else:
# # data = np.concatenate([bunch.data[:, :], bunch.target[:, :]], axis=1)
# # fn = bunch.get("feature_names")
# # atts = []
# # for ix in range(data.shape[1]):
# # if fn is not None and len(fn) > ix:
# # atts.append(Attribute(fn[ix], "Ratio", None, None, n_feat <= ix))
# # else:
# # atts.append(Attribute(str(ix), "Ratio", None, None, n_feat <= ix))
# #
# # return data, atts
# #
# # def _create_dataset(bunch, type,source):
# # meta = dict()
# # meta["id"] = str(uuid.uuid4())
# # meta["name"], meta["description"] = _split_DESCR(bunch["DESCR"])
# # meta["type"] = type
# # meta["originalSource"]=source
# # meta["creator"] = ""
# # meta["version"] = ""
# # meta["context"] = {}
# #
# # dataset = Dataset(meta["id"], **meta)
# # dataset.set_data(lambda: _create_dataset_data(bunch))
# # return dataset
# #
# #
# # @deprecated(reason ="use updated load_csv function")
# # def load_csv_file(path_dataset,path_target=None,target_features=[],originalSource="imported by csv",
# # description="imported form csv",type="multivariate"):
# # """Takes the path of a csv file and a list of the target columns and creates a padre-Dataset.
# #
# # Args:
# # path_dataset (str): The path of the csv-file
# # path_target (list): The column names of the target features of the csv-file.
# #
# # Returns:
# # pypadre.Dataset() A dataset containing the data of the .csv file
# #
# # """
# # assert_condition(condition=os.path.exists(os.path.abspath(path_dataset)), source='ds_import.load_csv',
# # message='Dataset path does not exist')
# #
# # trigger_event('EVENT_WARN', condition=len(target_features)>0, source='ds_import.load_csv',
# # message='No targets defined. Program will crash when used for supervised learning')
# #
# # dataset_path_list = path_dataset.split('/')
# # nameOfDataset = dataset_path_list[-1].split('.csv')[0]
# # data =pd.read_csv(path_dataset)
# #
# # meta =dict()
# # meta["name"]=nameOfDataset
# #
# # meta["description"]=description
# # meta["originalSource"]=originalSource
# # meta["creator"]=""
# # meta["version"]=""
# # meta["type"]=type
# # meta["context"]={}
# #
# # dataset=Dataset(None, **meta)
# # trigger_event('EVENT_WARN', condition=data.applymap(np.isreal).all(1).all() == True, source='ds_import.load_csv',
# # message='Non-numeric data values found. Program may crash if not handled by estimators')
# #
# # targets=None
# # if path_target != None:
# # target = pd.read_csv(path_dataset)
# # data=data.join(target,lsuffix="data",rsuffix="target")
# # targets=list(target.columns.values)
# #
# # else:
# # targets=target_features
# #
# # atts = []
# #
# # for feature in data.columns.values:
# # atts.append(Attribute(feature,None, None, None,feature in targets,None,None))
# #
# # dataset.set_data(data,atts)
# # return dataset
# #
# #
# # def load_csv(csv_path, targets=None, name=None, description="imported form csv", source="csvloaded",
# # type="Multivariat"):
# # """Takes the path of a csv file and a list of the target columns and creates a padre-Dataset.
# #
# # Args:
# # csv_path (str): The path of the csv-file
# # targets (list): The column names of the target features of the csv-file.
# # name(str): Optional name of dataset
# # description(str): Optional description of the dataset
# # source(str): original source - should be url
# # type(str): type of dataset
# #
# # Returns:
# # <class 'pypadre.datasets.Dataset'> A dataset containing the data of the .csv file
# #
# # """
# # assert_condition(condition=os.path.exists(os.path.abspath(csv_path)), source='ds_import.load_csv',
# # message='Dataset path does not exist')
# #
# # if targets is None:
# # targets = []
# # trigger_event('EVENT_WARN', condition=len(targets)>0, source='ds_import.load_csv',
# # message='No targets defined. Program will crash when used for supervised learning')
# #
# # dataset_path_list = csv_path.split('/')
# # if name is None:
# # name = dataset_path_list[-1].split('.csv')[0]
# #
# # data = pd.read_csv(csv_path)
# # meta = dict()
# # meta["id"] = str(uuid.uuid4())
# # meta["name"] = name
# # meta["description"] = description
# # meta["originalSource"]="http://" + source
# # meta["version"] = 1
# # meta["type"] = type
# # meta["published"] = True
# #
# # dataset = Dataset(None, **meta)
# # trigger_event('EVENT_WARN', condition=data.applymap(np.isreal).all(1).all() == True,
# # source='ds_import.load_csv',
# # message='Non-numeric data values found. Program may crash if not handled by estimators')
# #
# # for col_name in targets:
# # data[col_name] = data[col_name].astype('category')
# # data[col_name] = data[col_name].cat.codes
# # atts = []
# # for feature in data.columns.values:
# # atts.append(Attribute(name=feature,
# # measurementLevel="Ratio" if feature in targets else None,
# # defaultTargetAttribute=feature in targets))
# # dataset.set_data(data,atts)
# # return dataset
# #
# #
# # def load_pandas_df(pandas_df,target_features=[]):
# # """
# # Takes a pandas dataframe and a list of the names of target columns and creates a padre-Dataset.
# #
# # Args:
# # pandas_df (str): The pandas dataset.
# # path_target (list): The column names of the target features of the csv-file.
# #
# # Returns:
# # pypadre.Dataset() A dataset containing the data of the .csv file
# #
# # """
# # meta = dict()
# #
# # meta["name"] = "pandas_imported_df"
# # meta["description"]="imported by pandas_df"
# # meta["originalSource"]="https://imported/from/pandas/Dataframe.html"
# # meta["creator"]=""
# # meta["version"]=""
# # meta["context"]={}
# # meta["type"]="multivariate"
# # dataset = Dataset(None, **meta)
# #
# # atts = []
# #
# # if len(target_features) == 0:
# # targets = [0] * len(pandas_df)
# #
# # for feature in pandas_df.columns.values:
# # atts.append(Attribute(name=feature, measurementLevel=None, unit=None, description=None,
# # defaultTargetAttribute=feature in target_features, context=None))
# # dataset.set_data(pandas_df, atts)
# # return dataset
# #
# #
# # def load_numpy_array_multidimensional(features, targets, columns=None, target_features=None):
# # """
# # Takes a multidimensional numpy array and creates a dataset out of it
# # :param features: The input n dimensional numpy array
# # :param targets: The targets corresponding to every feature
# # :param columns: Array of data column names
# # :param target_features: Target features column names
# # :return: A dataset object
# # """
# # meta = dict()
# #
# # meta["name"] = "numpy_imported"
# # meta["description"] = "imported by numpy multidimensional"
# # meta["originalSource"] = ""
# # meta["creator"] = ""
# # meta["version"] = ""
# # meta["context"] = {}
# # meta["type"] = "multivariate"
# # dataset = Dataset(None, **meta)
# # atts = []
# #
# # if len(target_features) == 0:
# # targets = [0] * len(features)
# #
# # for feature in columns:
# # atts.append(Attribute(name=feature, measurementLevel=None, unit=None, description=None,
# # defaultTargetAttribute=feature in target_features, context=None))
# # dataset.set_data_multidimensional(features, targets, atts)
# # return dataset
#
#
# # def load_sklearn_toys():
# # #returns an iterator loading different sklearn datasets
# # loaders = [(ds.load_boston, ("regression", "Multivariat"),"https://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_boston.html#sklearn.datasets.load_boston"),
# # (ds.load_breast_cancer, ("classification", "Multivariat"),"https://archive.ics.uci.edu/ml/datasets/Breast+Cancer+Wisconsin+(Diagnostic)"),
# # (ds.load_diabetes, ("regression", "Multivariat"),"https://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_diabetes.html#sklearn.datasets.load_diabetes"),
# # (ds.load_digits, ("classification", "Multivariat"),"http://archive.ics.uci.edu/ml/datasets/Optical+Recognition+of+Handwritten+Digits"),
# # (ds.load_iris, ("classification", "Multivariat"),"https://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_iris.html#sklearn.datasets.load_iris"),
# # (ds.load_linnerud, ("mregression", "Multivariat"),"https://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_linnerud.html#sklearn.datasets.load_linnerud")]
# #
# # for loader in loaders:
# # yield _create_dataset(loader[0](), loader[1][1],loader[2])
#
# #possible Datatypes of imported open-ml dataset columns
# LEGAL_DATA_TYPES = ['NOMINAL','INTEGER', 'NUMERIC', 'REAL', 'STRING']
#
# DATATYPE_MAP={'INTEGER': np.int64,'NUMERIC':np.float64, 'REAL':np.float64, 'STRING': str}
#
#
#
# def storeDataset(dataset,file_backend):
# if dataset.uid not in file_backend.datasets.list_datasets():
# file_backend._dataset_repository.put_dataset(dataset)
#
# def __check_ds_meta(dataset_meta,check_key_value):
# for key in check_key_value.keys():
# try:
# if dataset_meta[key] != check_key_value[key]:
# return False
# except KeyError:
# return False
# return True
#
#
# def get_did_from_meta(check_dict,auth_token,max_hits=10,url="http://localhost:8080"):
# """Takes a dictionary of dataset attributes to search for. Searches the Server for a Dataset, that matches the
# requirements. Returns the dids of the matches.
#
# Args:
# check_dict (dict): A dictionary, that specifies the required metadata of a dataset.
# auth_token (str): The auth_token for authentication at the server.
# max_hits (int): Amount of maximum results.
# url (str): The url of the server.
#
# Returns:
# pypadre.Dataset() A list of several did, that fulfill the required check_dict.
#
# """
# hed = {'Authorization': auth_token}
#
# morePages=True
# hit_list = []
# page=0
#
# while len(hit_list)<max_hits and morePages:
# url = url+"/api/datasets?page=" + str(page) + "&size=9"
# response = requests.get(url, headers=hed)
# try:
# content = json.loads(response.content,encoding="utf-8")
#
# for dataset_meta in content["_embedded"]["datasets"]:
# if(__check_ds_meta(dataset_meta,check_dict)):
#
# hit_list.append(dataset_meta["uid"])
# except KeyError as err:
# print("invalid name!"+str(err))
#
# page+=1
# if content["page"]["totalPages"]<=page:
# morePages=False
# if len(hit_list)>max_hits:
# hit_list=hit_list[:max_hits]
# return hit_list
#
#
# def getDataset_load_or_cached(did,file_backend,force_download=False,auth_token=None):
# """Fetches the requested Dataset from the Server or (if available) from the local cache. A downloaded dataset gets
# cached. Returns the Dataset as pypadre.Dataset().
#
# Args:
# did (str): id of the requested dataset
# path (str): path of the pypadre directory
# force_download (bool): If set to True, downloads the dataset from the server unconditionally.
# auth_token (str): The Token for identification.
#
# Returns:
# pypadre.Dataset() A dataset containing with the requested data.
#
# """
#
#
# dataset=None
#
# if(force_download or did not in file_backend.datasets.list_datasets(search_metadata="")):
# dataset=requestServerDataset(did,auth_token)
# file_backend.datasets.put_dataset(dataset)
# return dataset
# return file_backend.datasets.get_dataset(did)
#
#
# def sendTop100Datasets_single(auth_token,server_url="http://localhost:8080"):
# """Takes a list of the Top-100 Datasets and downloads all of them from open-ml and uploads them to the Server.
# Those Datasets are not cached locally. The list of Datasets is available under
# /datasets/config/top100datasetIDs.txt and can be customized.
#
# Args:
# auth_token (str): The Token for identification.
#
# Returns:
# pypadre.Dataset() A dataset containing with the given source
#
# """
# for i in pypadre.ds_import.load_sklearn_toys():
# pypadre.ds_import.createServerDataset(i, auth_token, server_url)
#
# data = "18,12,22,23,28,60,46,32,36,14,1112,1114,1120,1489,1494,1497,1501,1067,1068,300,1049,1050,1053,182," \
# "4135,4134,1487,1466,1471,1475,6,4534,4538,38,3,1504,23512,24,1493,44,554,11,1038,29,151,15,40981," \
# "40499,42,1590,307,16,37,6332,1476,1479,458,1480,334,335,333,1515,188,1461,1046,1063,1467,1459,1464," \
# "50,1478,377,54,375,451,40496,1462,1485,1510,40668,1468,40536,1486,23380,23381,470,469,20,312,1492,1491,31"
#
# id list = data.split(",")
#
# i=0
# amount=str(len(id_list))
# for id in id list:
# print("Progress: ("+str(i)+"/"+amount+") id of next dataset:" + str(id))
# ds = load_openML_dataset("/" + id, destpath=None)
# createServerDataset(ds, auth_token, server_url)
# i = i+1
#
#
# def _scratchSnap(url_list,auth_token,server_url="http://localhost:8080"):
# for url in url_list:
# ds = pypadre.graph_import.create_from_snap(url,0)
# createServerDataset(ds,auth_token,server_url)
#
#
# def _scratchKonect(url_list,auth_token,server_url="http://localhost:8080"):
# for url in url_list:
# ds = pypadre.graph_import.create_from_konect(url)
# createServerDataset(ds,auth_token,server_url)
#
#
# def send_top_graphs(auth_token,server_url="http://localhost:8080",multithread=False):
# """Downloads a set of Datasets from snap and konect and uploads them to the server.
#
# Args:
# path (str): path of the pypadre directory
# auth_token (str): The Token for identification.
# multithread (bool): Requires at least 12GB of ram
# Returns:
# pypadre.Dataset() A dataset containing with the given source
#
# """
# #"https://snap.stanford.edu/data/gemsec-Deezer.html","https://snap.stanford.edu/data/gemsec-Facebook.html",
# snap_normal=[
# "https://snap.stanford.edu/data/soc-sign-bitcoin-alpha.html",
# "https://snap.stanford.edu/data/com-Youtube.html",#failed
# "https://snap.stanford.edu/data/ca-CondMat.html",
# "https://snap.stanford.edu/data/Oregon-2.html",
# "https://snap.stanford.edu/data/web-Google.html",#failed
# "https://snap.stanford.edu/data/web-Stanford.html",#failed
# "https://snap.stanford.edu/data/roadNet-TX.html",#failed
# "https://snap.stanford.edu/data/CollegeMsg.html",
# "https://snap.stanford.edu/data/soc-sign-bitcoin-otc.html"]#failed
#
# snap_biosnap=["https://snap.stanford.edu/biodata/datasets/10001/10001-ChCh-Miner.html",
# "https://snap.stanford.edu/biodata/datasets/10017/10017-ChChSe-Decagon.html",
# "https://snap.stanford.edu/biodata/datasets/10024/10024-GF-Miner.html",
# "https://snap.stanford.edu/biodata/datasets/10029/10029-SS-Butterfly.html",
# "https://snap.stanford.edu/biodata/datasets/10021/10021-D-DoMiner.html",
# "https://snap.stanford.edu/biodata/datasets/10025/10025-D-OmimMiner.html",
# "https://snap.stanford.edu/biodata/datasets/10011/10011-G-MtfPathways.html"]
#
# konect=["http://konect.cc/networks/edit-rnwiktionary/",
# "http://konect.cc/networks/league-be1-2016/",
# "http://konect.cc/networks/moreno_train/",
# "http://konect.cc/networks/moreno_highschool/",
# "http://konect.cc/networks/maayan-faa/",
# "http://konect.cc/networks/petster-hamster-household/",
# "http://konect.cc/networks/flickrEdges/",#failed
# "http://konect.cc/networks/stackexchange-stackoverflow/",#failed
# "http://konect.cc/networks/lasagne-yahoo/",#failed
# "http://konect.cc/networks/youtube-u-growth/",
# "http://konect.cc/networks/dimacs9-E/"]
#
# if multithread:
# snap_normal_p = Process(target=_scratchSnap, args=(snap_normal,auth_token, server_url))
# snap_biosnap_p = Process(target=_scratchSnap, args=(snap_biosnap,auth_token, server_url))
# konect_p = Process(target=_scratchKonect, args=(konect,auth_token, server_url))
# snap_normal_p.start()
# snap_biosnap_p.start()
# konect_p.start()
# snap_normal_p.join()
# snap_biosnap_p.join()
# konect_p.join()
# else:
# _scratchSnap(snap_normal,auth_token, server_url)
# _scratchSnap(snap_biosnap, auth_token, server_url)
# _scratchKonect(konect, auth_token, server_url)
#
# def sendTop100Datasets_multi(auth_token,server_url="http://localhost:8080",worker=1):
# """Takes a list of the Top-100 Datasets and downloads all of them from open-ml and uploads them to the Server.
# Those Datasets are not cached locally. The list of Datasets is available under
# /datasets/config/top100datasetIDs.txt and can be customized.
#
# Args:
# path (str): path of the pypadre directory. If None, a predefined list of datasets is taken.
# auth_token (str): The Token for identification.
# server_url (str): The url of the server
# worker (int): The amount of Threads that should be used. Ensure at least 2GB of free Ram for each Thread!
#
# """
#
# print("Upload of toy scikit- toydatasets")
# for i in pypadre.ds_import.load_sklearn_toys():
# pypadre.ds_import.createServerDataset(i, auth_token, server_url)
#
# data = "11,12,14,15,16,18,20,3,6,32,36,38,22,23,24,28,29,42,44,46,54,60,182,188,151,300,312,307,375,377,333,334,335," \
# "451,458,469,470,554,1046,1049,1050,1038,1114,1120,1063,1067,1068,1053,1459,1471,1479,1480,1466,1467,1486," \
# "1489,1504,4135,23380,1461,1476,1475,1492,1491,1485,1468,1501,1462,1487,1494,1493,1478,1590,1112,1515,1510," \
# "1497,23381,4538,23512,4134,6332,4534,1464,37,31,50,40536,40496,40668,40499,40981"
#
#
#
#
# id list = data.split(",")
#
# #datasets = []
# i=0
# import math
# amount=len(id_list)
# workerDatasets=[[] for x in range(worker)]
# for i in range(worker):
# for j in range(0,int(math.floor(amount/worker))):
# workerDatasets[i].append(id_list.pop())
# if(i<amount%worker):
# workerDatasets[i].append(id_list.pop())
#
# plist=[]
# for i in range(worker):
# p=Process(target=_sendDatasetWorker,args=(auth_token,workerDatasets[i],i,server_url))
# p.start()
# plist.append(p)
# print("thread started: "+str(i))
# for i in plist:
# i.join()
#
#
# def _sendDatasetWorker(auth_token,id_list,worker,server_url):
# """This function gets called by each Thread of sendTop100DatasetsToServer. For each entry in id list, it
# downloads the corresponding dataset from openml and uploades it to server_url.
#
# Args:
# path (str): path of the pypadre directory. If None, the local data is stored in a temporary directory.
# auth_token (str): The Token for identification.
# id list (list): A list containing all the id's of the ompenml-datasets.
# worker (int): The amount of Threads that should be used. Ensure at least 2GB of free Ram for each Thread!
# server_url (str): The url of the server
# """
# i=0
# for id in id list:
# amount = str(len(id_list))
# print("Worker: "+str(worker)+" Progress: (" + str(i) + "/" + amount + ") id of next dataset:" + str(id))
# ds = load_openML_dataset("/" + id, destpath=None)
# did=createServerDataset(ds,auth_token,server_url)
# i = i + 1
#
#
# def requestServerDataset(did,auth_token,url="http://localhost:8080"):
# """Downloads a dataset and the matching meta-data from the server and converts it to a padre.Dataset
#
# Args:
# did (str): The id of the Dataset of interest.
# auth_token (str): The Token for identification.
# url (str): The url of the server.
#
# Returns:
# (pypadre.Dataset) A Dataset that contains the requested Data.
#
# """
# hed = {'Authorization': auth_token}
# response = requests.get(url+"/api/datasets/"+str(did), headers=hed)
#
# k = response.content.decode("utf-8")
# response_meta=json.loads(k)
# response.close()
# requests.session().close()
#
# attribute_name_list = []
# atts = []
# for attr in response_meta["attributes"]:
# if attr["name"] != "INVALID_COLUMN":
# atts.append(Attribute(**attr))
# attribute_name_list.append(attr["name"])
#
# del response_meta["attributes"]
# df_data = proto.get_Server_Dataframe(did, auth_token, url=url)
# dataset = Dataset(None, **response_meta)
# df_data.columns = attribute_name_list
#
# if "INVALID_COLUMN" in list(df_data.columns.values):
# df_data=df_data.drop(["INVALID_COLUMN"], axis=1)
#
# if dataset.isgraph:
# node_attr = []
# edge_attr = []
#
# for attr in atts:
# graph_role = attr.context["graph_role"]
# if graph_role == "source":
# source = attr.name
# elif graph_role == "target":
# target = attr.name
# elif graph_role == "nodeattribute":
# node_attr.append(attr.name)
# elif graph_role == "edgeattribute":
# edge_attr.append(attr.name)
# network=nx.Graph()if response_meta["type"]=="graph" else nx.DiGraph()
# pypadre.graph_import.pandas_to_networkx(df_data, source, target, network, node_attr, edge_attr)
# dataset.set_data(network, atts)
# else:
# dataset.set_data(df_data, atts)
# print("Load dataset "+did+" from server:")
#
# return dataset
#
#
#
# def createServerDataset(dataset,auth_token,url="http://localhost:8080"):
# """Creates a dataset on the server and transferees its' content. It returns a
# String, that stands for the id the Dataset on the server side.
#
# Args:
# dataset (pypadre.Dataset): Dataset that metadata should be used to create a new dataset at the server
# auth_token (str): Token for identification for communication with the server.
# url (str): The url of the server.
#
# Returns:
# str: did of the created dataset at the server. The datset can be downloaded using this did
#
# """
#
# binary = tempfile.TemporaryFile(mode='w+b')
#
# proto_enlarged = pypadre.pod.backend.http.http.protobuffer.proto_organizer.createProtobuffer(dataset, binary)
#
# hed = {'Authorization': auth_token}
#
# attributes=copy.deepcopy(dataset.attributes)
# if proto_enlarged:
# attributes.append({"name":"INVALID_COLUMN","context":"{}"})
#
# data=dataset.metadata
# data["attributes"]=attributes
# response=requests.post(url+"/api/datasets",json=data,headers=hed)
#
# for i in range(3):
# if "Location" in response.headers:
# did = str((response.headers["Location"]).split("/")[-1])
# break;
# else:
# print("retry sending for dataset" + str(data["name"]))
# print("statuscode: "+ str(response.status_code))
# print("header: " +str(response.headers))
# print("content: "+ str(response.content))
# response = requests.post(url + "/api/datasets", json=data, headers=hed)
#
# if "Location" not in response.headers:
# raise ValueError('Server does not accept metadata of dataset. Dataset could not be uploaded!, response header. '
# + str(response.headers)+" response content: "+str(response.content))
#
# binary.seek(0)
# print("Send dataset " + str(dataset.name) + " to server")
# proto.send_Dataset(dataset,did,auth_token,binary,url=url)
# binary.close()
#
#
# response.close()
# requests.session().close()
# del data["attributes"]
# return did
#
# def search_oml_datasets(name, root_dir, key):
# path = root_dir + '/temp/openml'
#
# oml.config.apikey = key
# oml.config.cache_directory = path
# meta = {"data_name": name}
# return oml.datasets.list_datasets(**meta)
#
#
# def load_openML_dataset(url,destpath=os.path.expanduser('~/.pypadre'),apikey="1f8766e1615225a727bdea12ad4c72fa"):
# """Downloads a dataset from the given open-ml url and Converts it to a padre.Dataset. The metadata is also added to
# the pypadre.Dataset
#
# Args:
# url (str): url of the open-ml dataset
# destpath (str): path of padre directory. If None, the directory of openml will be in a temporary directory. As
# a result openml will not cache the dataset arff files.
# apikey (str): apikey of openml. The default value is linked to the openml account of markush.473@gmail.com
#
# Returns:
# pypadre.Dataset() A dataset filled with the values of the given source.
#
# """
# # apikey is from useraccount markush.473@gmail.com
#
# if destpath is None:
# path = tempfile.mkdtemp()
#
# else:
# path = destpath+'/datasets/temp/openml'
# dataset_id = url.split("/")[-1]
# dataset_id = dataset_id.strip(" ")
# oml.config.apikey = apikey
# oml.config.cache_directory = path
# try:
# load = oml.datasets.get_dataset(dataset_id)
# except oml.exceptions.OpenMLServerException as err:
# print("Dataset not found! \nErrormessage: " + str(err))
# return None
# except ConnectionError as err:
# print("openML unreachable! \nErrormessage: " + str(err))
# return None
# except OSError as err:
# print("Invalid datapath! \nErrormessage: " + str(err))
# return None
#
# meta = dict()
# meta["name"] = load.name
# meta["version"] = load.version
# meta["description"] = load.description
# meta["originalSource"] = load.url
# meta["type"] = "multivariate"
# meta["published"] = False
#
# dataset = Dataset(None, **meta)
# raw_data = arff.load(open(path+'/org/openml/www/datasets/'+dataset_id+'/dataset.arff', encoding='utf-8'))
# df_attributes = raw_data['attributes']
# attribute_list = []
#
# for att in df_attributes:
# attribute_list.append(att[0])
#
# df_data = pd.DataFrame(data=raw_data['data'])
# raw_data = None
# atts = []
#
# for col in df_data.keys():
# data_class=None
# current_attribute=df_attributes[col]
# if load.features[col].name!=current_attribute[0]:
# print("Name failure. Inconsistency encountered!")
#
# if isinstance(current_attribute[1],list):
# data_class='NOMINAL'
# df_data[col] = df_data[col].astype('category')
# elif current_attribute[1] in DATATYPE_MAP.keys() and isinstance(current_attribute[1],str):
# data_class = current_attribute[1]
# else:
# print("failed to recognize format")
# raise ValueError('Invalid data format from openml!')
#
# atts.append(Attribute(name=current_attribute[0], measurementLevel="nominal" if isinstance(current_attribute[1],list) else None,
# unit=None, description=None, defaultTargetAttribute=(current_attribute[0] == load.default_target_attribute)))
#
# df_data.columns = attribute_list
# dataset.set_data(df_data, atts)
#
# return dataset
|
nilq/baby-python
|
python
|
# This code is part of the epytope distribution and governed by its
# license. Please see the LICENSE file that should have been included
# as part of this package.
"""
.. module:: EpitopePrediction.ANN
:synopsis: This module contains all classes for ANN-based epitope prediction methods.
.. moduleauthor:: schubert, walzer
"""
import abc
import itertools
import warnings
import logging
import pandas
import subprocess
import csv
import os
import math
import re
from collections import defaultdict
from enum import IntEnum
from epytope.Core.Allele import Allele, CombinedAllele, MouseAllele
from epytope.Core.Peptide import Peptide
from epytope.Core.Result import EpitopePredictionResult
from epytope.Core.Base import AEpitopePrediction, AExternal
from tempfile import NamedTemporaryFile, mkstemp
class AExternalEpitopePrediction(AEpitopePrediction, AExternal):
"""
Abstract class representing an external prediction function. Implementations shall wrap external binaries by
following the given abstraction.
"""
@abc.abstractmethod
def prepare_input(self, input, file):
"""
Prepares input for external tools
and writes them to _file in the specific format
NO return value!
:param: list(str) _input: The :class:`~epytope.Core.Peptide.Peptide` sequences to write into file
:param File file: File-handler to input file for external tool
"""
return NotImplementedError
def predict(self, peptides, alleles=None, command=None, options=None, **kwargs):
"""
Overwrites AEpitopePrediction.predict
:param peptides: A list of or a single :class:`~epytope.Core.Peptide.Peptide` object
:type peptides: list(:class:`~epytope.Core.Peptide.Peptide`) or :class:`~epytope.Core.Peptide.Peptide`
:param alleles: A list of or a single :class:`~epytope.Core.Allele.Allele` object. If no
:class:`~epytope.Core.Allele.Allele` are provided, predictions are made for all
:class:`~epytope.Core.Allele.Allele` supported by the prediction method
:type alleles: list(:class:`~epytope.Core.Allele.Allele`)/:class:`~epytope.Core.Allele.Allele`
:param str command: The path to a alternative binary (can be used if binary is not globally executable)
:param str options: A string of additional options directly past to the external tool.
:keyword chunksize: denotes the chunksize in which the number of peptides are bulk processed
:return: A :class:`~epytope.Core.Result.EpitopePredictionResult` object
:rtype: :class:`~epytope.Core.Result.EpitopePredictionResult`
"""
if not self.is_in_path() and command is None:
raise RuntimeError("{name} {version} could not be found in PATH".format(name=self.name,
version=self.version))
external_version = self.get_external_version(path=command)
if self.version != external_version and external_version is not None:
raise RuntimeError("Internal version {internal_version} does "
"not match external version {external_version}".format(internal_version=self.version,
external_version=external_version))
if isinstance(peptides, Peptide):
pep_seqs = {str(peptides): peptides}
else:
pep_seqs = {}
for p in peptides:
if not isinstance(p, Peptide):
raise ValueError("Input is not of type Protein or Peptide")
pep_seqs[str(p)] = p
chunksize = len(pep_seqs)
if 'chunks' in kwargs:
chunksize = kwargs['chunks']
if alleles is None:
alleles = [Allele(a) for a in self.supportedAlleles]
else:
if isinstance(alleles, Allele):
alleles = [alleles]
if any(not isinstance(p, Allele) for p in alleles):
raise ValueError("Input is not of type Allele")
# Create dictionary containing the predictors string representation and the Allele Obj representation of the allele
alleles_string = {conv_a: a for conv_a, a in zip(self.convert_alleles(alleles), alleles)}
# Create empty result dictionary to fill downstream
result = {}
# group alleles in blocks of 80 alleles (NetMHC can't deal with more)
_MAX_ALLELES = 50
# allow custom executable specification
if command is not None:
exe = self.command.split()[0]
_command = self.command.replace(exe, command)
else:
_command = self.command
allele_groups = []
c_a = 0
allele_group = []
for a in alleles_string.keys():
if c_a >= _MAX_ALLELES:
c_a = 0
allele_groups.append(allele_group)
if str(alleles_string[a]) not in self.supportedAlleles:
logging.warning("Allele %s is not supported by %s" % (str(alleles_string[a]), self.name))
allele_group = []
continue
allele_group = [a]
else:
if str(alleles_string[a]) not in self.supportedAlleles:
logging.warning("Allele %s is not supported by %s" % (str(alleles_string[a]), self.name))
continue
allele_group.append(a)
c_a += 1
if len(allele_group) > 0:
allele_groups.append(allele_group)
# export peptides to peptide list
pep_groups = list(pep_seqs.keys())
pep_groups.sort(key=len)
for length, peps in itertools.groupby(pep_groups, key=len):
if length not in self.supportedLength:
logging.warning("Peptide length must be at least %i or at most %i for %s but is %i" % (min(self.supportedLength), max(self.supportedLength),
self.name, length))
continue
peps = list(peps)
for i in range(0, len(peps), chunksize):
# Create a temporary file for subprocess to write to. The
# handle is not needed on the python end, as only the path will
# be passed to the subprocess.
_, tmp_out_path = mkstemp()
# Create a temporary file to be used for the peptide input
tmp_file = NamedTemporaryFile(mode="r+", delete=False)
self.prepare_input(peps[i:i+chunksize], tmp_file)
tmp_file.close()
# generate cmd command
for allele_group in allele_groups:
try:
stdo = None
stde = None
cmd = _command.format(peptides=tmp_file.name, alleles=",".join(allele_group),
options="" if options is None else options, out=tmp_out_path, length=str(length))
p = subprocess.Popen(cmd, shell=True, stdin=subprocess.PIPE, stdout=subprocess.PIPE,
stderr=subprocess.STDOUT)
stdo, stde = p.communicate()
stdr = p.returncode
if stdr > 0:
raise RuntimeError("Unsuccessful execution of " + cmd + " (EXIT!=0) with output:\n" + stdo.decode())
if os.path.getsize(tmp_out_path) == 0:
raise RuntimeError("Unsuccessful execution of " + cmd + " (empty output file) with output:\n" + stdo.decode())
except Exception as e:
raise RuntimeError(e)
# Obtain parsed output dataframe containing the peptide scores and/or ranks
res_tmp = self.parse_external_result(tmp_out_path)
for allele_string, scores in res_tmp.items():
allele = alleles_string[allele_string]
if allele not in result.keys():
result[allele] = {}
for scoretype, pep_scores in scores.items():
if scoretype not in result[allele].keys():
result[allele][scoretype] = {}
for pep, score in pep_scores.items():
result[allele][scoretype][pep_seqs[pep]] = score
os.remove(tmp_file.name)
os.remove(tmp_out_path)
if not result:
raise ValueError("No predictions could be made with " + self.name +
" for given input. Check your epitope length and HLA allele combination.")
df_result = EpitopePredictionResult.from_dict(result, list(pep_seqs.values()), self.name)
return df_result
class NetMHC_3_4(AExternalEpitopePrediction):
"""
Implements the NetMHC binding (in current form for netMHC3.4).
.. note::
NetMHC-3.0: accurate web accessible predictions of human, mouse and monkey MHC class I affinities for peptides
of length 8-11. Lundegaard C, Lamberth K, Harndahl M, Buus S, Lund O, Nielsen M.
Nucleic Acids Res. 1;36(Web Server issue):W509-12. 2008
Accurate approximation method for prediction of class I MHC affinities for peptides of length 8, 10 and 11 using
prediction tools trained on 9mers. Lundegaard C, Lund O, Nielsen M. Bioinformatics, 24(11):1397-98, 2008.
"""
__alleles = frozenset(['HLA-A*01:01', 'HLA-A*02:01', 'HLA-A*02:02', 'HLA-A*02:03', 'HLA-A*02:06', 'HLA-A*02:11', 'HLA-A*02:12', 'HLA-A*02:16',
'HLA-A*02:17', 'HLA-A*02:19', 'HLA-A*02:50', 'HLA-A*03:01', 'HLA-A*11:01', 'HLA-A*23:01', 'HLA-A*24:02', 'HLA-A*24:03',
'HLA-A*25:01', 'HLA-A*26:01', 'HLA-A*26:02', 'HLA-A*26:03', 'HLA-A*29:02', 'HLA-A*30:01', 'HLA-A*30:02', 'HLA-A*31:01',
'HLA-A*32:01', 'HLA-A*32:07', 'HLA-A*32:15', 'HLA-A*33:01', 'HLA-A*66:01', 'HLA-A*68:01', 'HLA-A*68:02', 'HLA-A*68:23',
'HLA-A*69:01', 'HLA-A*80:01', 'HLA-B*07:02', 'HLA-B*08:01', 'HLA-B*08:02', 'HLA-B*08:03', 'HLA-B*14:02', 'HLA-B*15:01',
'HLA-B*15:02', 'HLA-B*15:03', 'HLA-B*15:09', 'HLA-B*15:17', 'HLA-B*18:01', 'HLA-B*27:05', 'HLA-B*27:20', 'HLA-B*35:01',
'HLA-B*35:03', 'HLA-B*38:01', 'HLA-B*39:01', 'HLA-B*40:01', 'HLA-B*40:02', 'HLA-B*40:13', 'HLA-B*42:01', 'HLA-B*44:02',
'HLA-B*44:03', 'HLA-B*45:01', 'HLA-B*46:01', 'HLA-B*48:01', 'HLA-B*51:01', 'HLA-B*53:01', 'HLA-B*54:01', 'HLA-B*57:01',
'HLA-B*58:01', 'HLA-B*73:01', 'HLA-B*83:01', 'HLA-C*03:03', 'HLA-C*04:01', 'HLA-C*05:01', 'HLA-C*06:02', 'HLA-C*07:01',
'HLA-C*07:02', 'HLA-C*08:02', 'HLA-C*12:03', 'HLA-C*14:02', 'HLA-C*15:02', 'HLA-E*01:01',
'H-2-Db', 'H-2-Dd', 'H-2-Kb', 'H-2-Kd', 'H-2-Kk', 'H-2-Ld'])
__supported_length = frozenset([8, 9, 10, 11])
__name = "netmhc"
__command = "netMHC -p {peptides} -a {alleles} -x {out} {options}"
__version = "3.4"
@property
def version(self):
"""The version of the predictor"""
return self.__version
def _represent(self, allele):
"""
Internal function transforming an allele object into its representative string
:param allele: The :class:`~epytope.Core.Allele.Allele` for which the internal predictor representation is
needed
:type alleles: :class:`~epytope.Core.Allele.Allele`
:return: str
"""
if isinstance(allele, MouseAllele):
return "H-2-%s%s%s" % (allele.locus, allele.supertype, allele.subtype)
else:
return "HLA-%s%s:%s" % (allele.locus, allele.supertype, allele.subtype)
def convert_alleles(self, alleles):
"""
Converts :class:`~epytope.Core.Allele.Allele` into the internal :class:`~epytope.Core.Allele.Allele` representation
of the predictor and returns a string representation
:param alleles: The :class:`~epytope.Core.Allele.Allele` for which the internal predictor representation is
needed
:type alleles: :class:`~epytope.Core.Allele.Allele`
:return: Returns a string representation of the input :class:`~epytope.Core.Allele.Allele`
:rtype: list(str)
"""
return [self._represent(a) for a in alleles]
@property
def supportedAlleles(self):
"""
A list of valid allele models
"""
return self.__alleles
@property
def name(self):
"""The name of the predictor"""
return self.__name
@property
def command(self):
"""
Defines the commandline call for external tool
"""
return self.__command
@property
def supportedLength(self):
"""
A list of supported :class:`~epytope.Core.Peptide.Peptide` lengths
"""
return self.__supported_length
def parse_external_result(self, file):
"""
Parses external results and returns the result containing the predictors string representation
of alleles and peptides.
:param str file: The file path or the external prediction results
:return: A dictionary containing the prediction results
:rtype: dict
"""
result = defaultdict(defaultdict)
f = csv.reader(open(file, "r"), delimiter='\t')
next(f)
next(f)
alleles = [x.split()[0] for x in f.next()[3:]]
for l in f:
if not l:
continue
pep_seq = l[PeptideIndex.NETMHC_3_4]
for ic_50, a in zip(l[ScoreIndex.NETMHC_3_0:], alleles):
sc = 1.0 - math.log(float(ic_50), 50000)
result[a][pep_seq] = sc if sc > 0.0 else 0.0
return dict(result)
def get_external_version(self, path=None):
"""
Returns the external version of the tool by executing
>{command} --version
might be dependent on the method and has to be overwritten
therefore it is declared abstract to enforce the user to
overwrite the method. The function in the base class can be called
with super()
:param str path: Optional specification of executable path if deviant from :attr:`self.__command`
:return: The external version of the tool or None if tool does not support versioning
:rtype: dict
"""
return super(NetMHC_3_4, self).get_external_version()
def prepare_input(self, input, file):
"""
Prepares input for external tools
and writes them to file in the specific format
NO return value!
:param: list(str) input: The : sequences to write into _file
:param File file: File-handler to input file for external tool
"""
file.write("\n".join(input))
class NetMHC_3_0(NetMHC_3_4):
"""
Implements the NetMHC binding (for netMHC3.0)::
.. note::
NetMHC-3.0: accurate web accessible predictions of human, mouse and monkey MHC class I affinities for peptides
of length 8-11. Lundegaard C, Lamberth K, Harndahl M, Buus S, Lund O, Nielsen M.
Nucleic Acids Res. 1;36(Web Server issue):W509-12. 2008
Accurate approximation method for prediction of class I MHC affinities for peptides of length 8, 10 and 11
using prediction tools trained on 9mers. Lundegaard C, Lund O, Nielsen M. Bioinformatics, 24(11):1397-98, 2008.
"""
__alleles = frozenset(['HLA-A*01:01', 'HLA-A*02:01', 'HLA-A*02:02', 'HLA-A*02:03', 'HLA-A*02:04', 'HLA-A*02:06', 'HLA-A*02:11', 'HLA-A*02:12',
'HLA-A*02:16', 'HLA-A*02:19', 'HLA-A*03:01', 'HLA-A*11:01', 'HLA-A*23:01', 'HLA-A*24:02', 'HLA-A*24:03', 'HLA-A*26:01',
'HLA-A*26:02', 'HLA-A*29:02', 'HLA-A*30:01', 'HLA-A*30:02', 'HLA-A*31:01', 'HLA-A*33:01', 'HLA-A*68:01', 'HLA-A*68:02',
'HLA-A*69:01', 'HLA-B*07:02', 'HLA-B*08:01', 'HLA-B*08:02', 'HLA-B*15:01', 'HLA-B*18:01', 'HLA-B*27:05', 'HLA-B*35:01',
'HLA-B*39:01', 'HLA-B*40:01', 'HLA-B*40:02', 'HLA-B*44:02', 'HLA-B*44:03', 'HLA-B*45:01', 'HLA-B*51:01', 'HLA-B*53:01',
'HLA-B*54:01', 'HLA-B*57:01', 'HLA-B*58:01',
'H-2-Db', 'H-2-Dd', 'H-2-Kb', 'H-2-Kd', 'H-2-Kk', 'H-2-Ld']) # no PSSM predictors
__supported_length = frozenset([8, 9, 10, 11])
__name = "netmhc"
__version = "3.0a"
__command = "netMHC-3.0 -p {peptides} -a {alleles} -x {out} -l {length} {options}"
@property
def version(self):
"""The version of the predictor"""
return self.__version
@property
def name(self):
"""The name of the predictor"""
return self.__name
@property
def command(self):
"""
Defines the commandline call for external tool
"""
return self.__command
@property
def supportedAlleles(self):
"""
A list of valid :class:`~epytope.Core.Allele.Allele` models
"""
return self.__alleles
@property
def supportedLength(self):
"""
A list of supported :class:`~epytope.Core.Peptide.Peptide` lengths
"""
return self.__supported_length
def _represent(self, allele):
"""
Internal function transforming an allele object into its representative string
:param allele: The :class:`~epytope.Core.Allele.Allele` for which the internal predictor representation is
needed
:type alleles: :class:`~epytope.Core.Allele.Allele`
:return: str
"""
if isinstance(allele, MouseAllele):
return "H-2-%s%s%s" % (allele.locus, allele.supertype, allele.subtype)
else:
return "HLA-%s%s%s" % (allele.locus, allele.supertype, allele.subtype)
def parse_external_result(self, file):
"""
Parses external results and returns the result containing the predictors string representation
of alleles and peptides.
:param str file: The file path or the external prediction results
:return: A dictionary containing the prediction results
:rtype: dict
"""
result = defaultdict(dict)
with open(file, 'r') as f:
next(f, None) # skip first line with logging stuff
next(f, None) # skip first line with nothing
csvr = csv.reader(f, delimiter='\t')
alleles = [x.split()[0] for x in csvr.next()[3:]]
for l in csvr:
if not l:
continue
pep_seq = l[PeptideIndex.NETMHC_3_0]
for ic_50, a in zip(l[ScoreIndex.NETMHC_3_0:], alleles):
sc = 1.0 - math.log(float(ic_50), 50000)
result[a][pep_seq] = sc if sc > 0.0 else 0.0
if 'Average' in result:
result.pop('Average')
return dict(result)
class NetMHC_4_0(NetMHC_3_4):
"""
Implements the NetMHC 4.0 binding
.. note::
Andreatta M, Nielsen M. Gapped sequence alignment using artificial neural networks:
application to the MHC class I system. Bioinformatics (2016) Feb 15;32(4):511-7
"""
__command = "netMHC -p {peptides} -a {alleles} -xls -xlsfile {out} {options}"
__version = "4.0"
@property
def version(self):
"""The version of the predictor"""
return self.__version
@property
def command(self):
"""
Defines the commandline call for external tool
"""
return self.__command
def _represent(self, allele):
"""
Internal function transforming an allele object into its representative string
:param allele: The :class:`~epytope.Core.Allele.Allele` for which the internal predictor representation is
needed
:type alleles: :class:`~epytope.Core.Allele.Allele`
:return: str
"""
if isinstance(allele, MouseAllele):
return "H-2-%s%s%s" % (allele.locus, allele.supertype, allele.subtype)
else:
return "HLA-%s%s%s" % (allele.locus, allele.supertype, allele.subtype)
def parse_external_result(self, file):
"""
Parses external results and returns the result containing the predictors string representation
of alleles and peptides.
:param str file: The file path or the external prediction results
:return: A dictionary containing the prediction results
:rtype: dict
"""
scores = defaultdict(defaultdict)
ranks = defaultdict(defaultdict)
f = csv.reader(open(file, "r"), delimiter='\t')
alleles = [x.split()[0] for x in [x for x in next(f) if x.strip() != ""]]
next(f)
for l in f:
if not l:
continue
pep_seq = l[PeptideIndex.NETMHC_4_0]
for i, a in enumerate(alleles):
ic_50 = l[(i+1) * Offset.NETMHC_4_0]
sc = 1.0 - math.log(float(ic_50), 50000)
rank = l[(i+1)* Offset.NETMHC_4_0 + 1]
scores[a][pep_seq] = sc if sc > 0.0 else 0.0
ranks[a][pep_seq] = float(rank)
result = {allele: {metric:(list(scores.values())[j] if metric == "Score" else list(ranks.values())[j]) for metric in ["Score", "Rank"]} for j, allele in enumerate(alleles)}
return result
def get_external_version(self, path=None):
"""
Returns the external version of the tool by executing
>{command} --version
might be dependent on the method and has to be overwritten
therefore it is declared abstract to enforce the user to
overwrite the method. The function in the base class can be called
with super()
:param str path: Optional specification of executable path if deviant from :attr:`self.__command`
:return: The external version of the tool or None if tool does not support versioning
:rtype: str
"""
# can not be determined netmhcpan does not support --version or similar
return None
class NetMHCpan_2_4(AExternalEpitopePrediction):
"""
Implements the NetMHC binding (in current form for netMHCpan 2.4).
Supported MHC alleles currently only restricted to HLA alleles.
.. note::
Nielsen, Morten, et al. "NetMHCpan, a method for quantitative predictions of peptide binding to any HLA-A and-B
locus protein of known sequence." PloS one 2.8 (2007): e796.
"""
__supported_length = frozenset([8, 9, 10, 11])
__name = "netmhcpan"
__command = "netMHCpan-2.4 -p {peptides} -a {alleles} {options} -ic50 -xls -xlsfile {out}"
__alleles = frozenset(
['HLA-A*01:01', 'HLA-A*01:02', 'HLA-A*01:03', 'HLA-A*01:06', 'HLA-A*01:07', 'HLA-A*01:08', 'HLA-A*01:09', 'HLA-A*01:10', 'HLA-A*01:12',
'HLA-A*01:13', 'HLA-A*01:14', 'HLA-A*01:17', 'HLA-A*01:19', 'HLA-A*01:20', 'HLA-A*01:21', 'HLA-A*01:23', 'HLA-A*01:24', 'HLA-A*01:25',
'HLA-A*01:26', 'HLA-A*01:28', 'HLA-A*01:29', 'HLA-A*01:30', 'HLA-A*01:32', 'HLA-A*01:33', 'HLA-A*01:35', 'HLA-A*01:36', 'HLA-A*01:37',
'HLA-A*01:38', 'HLA-A*01:39', 'HLA-A*01:40', 'HLA-A*01:41', 'HLA-A*01:42', 'HLA-A*01:43', 'HLA-A*01:44', 'HLA-A*01:45', 'HLA-A*01:46',
'HLA-A*01:47', 'HLA-A*01:48', 'HLA-A*01:49', 'HLA-A*01:50', 'HLA-A*01:51', 'HLA-A*01:54', 'HLA-A*01:55', 'HLA-A*01:58', 'HLA-A*01:59',
'HLA-A*01:60', 'HLA-A*01:61', 'HLA-A*01:62', 'HLA-A*01:63', 'HLA-A*01:64', 'HLA-A*01:65', 'HLA-A*01:66', 'HLA-A*02:01', 'HLA-A*02:02',
'HLA-A*02:03', 'HLA-A*02:04', 'HLA-A*02:05', 'HLA-A*02:06', 'HLA-A*02:07', 'HLA-A*02:08', 'HLA-A*02:09', 'HLA-A*02:10', 'HLA-A*02:101',
'HLA-A*02:102', 'HLA-A*02:103', 'HLA-A*02:104', 'HLA-A*02:105', 'HLA-A*02:106', 'HLA-A*02:107', 'HLA-A*02:108', 'HLA-A*02:109',
'HLA-A*02:11', 'HLA-A*02:110', 'HLA-A*02:111', 'HLA-A*02:112', 'HLA-A*02:114', 'HLA-A*02:115', 'HLA-A*02:116', 'HLA-A*02:117',
'HLA-A*02:118', 'HLA-A*02:119', 'HLA-A*02:12', 'HLA-A*02:120', 'HLA-A*02:121', 'HLA-A*02:122', 'HLA-A*02:123', 'HLA-A*02:124',
'HLA-A*02:126', 'HLA-A*02:127', 'HLA-A*02:128', 'HLA-A*02:129', 'HLA-A*02:13', 'HLA-A*02:130', 'HLA-A*02:131', 'HLA-A*02:132',
'HLA-A*02:133', 'HLA-A*02:134', 'HLA-A*02:135', 'HLA-A*02:136', 'HLA-A*02:137', 'HLA-A*02:138', 'HLA-A*02:139', 'HLA-A*02:14',
'HLA-A*02:140', 'HLA-A*02:141', 'HLA-A*02:142', 'HLA-A*02:143', 'HLA-A*02:144', 'HLA-A*02:145', 'HLA-A*02:146', 'HLA-A*02:147',
'HLA-A*02:148', 'HLA-A*02:149', 'HLA-A*02:150', 'HLA-A*02:151', 'HLA-A*02:152', 'HLA-A*02:153', 'HLA-A*02:154', 'HLA-A*02:155',
'HLA-A*02:156', 'HLA-A*02:157', 'HLA-A*02:158', 'HLA-A*02:159', 'HLA-A*02:16', 'HLA-A*02:160', 'HLA-A*02:161', 'HLA-A*02:162',
'HLA-A*02:163', 'HLA-A*02:164', 'HLA-A*02:165', 'HLA-A*02:166', 'HLA-A*02:167', 'HLA-A*02:168', 'HLA-A*02:169', 'HLA-A*02:17',
'HLA-A*02:170', 'HLA-A*02:171', 'HLA-A*02:172', 'HLA-A*02:173', 'HLA-A*02:174', 'HLA-A*02:175', 'HLA-A*02:176', 'HLA-A*02:177',
'HLA-A*02:178', 'HLA-A*02:179', 'HLA-A*02:18', 'HLA-A*02:180', 'HLA-A*02:181', 'HLA-A*02:182', 'HLA-A*02:183', 'HLA-A*02:184',
'HLA-A*02:185', 'HLA-A*02:186', 'HLA-A*02:187', 'HLA-A*02:188', 'HLA-A*02:189', 'HLA-A*02:19', 'HLA-A*02:190', 'HLA-A*02:191',
'HLA-A*02:192', 'HLA-A*02:193', 'HLA-A*02:194', 'HLA-A*02:195', 'HLA-A*02:196', 'HLA-A*02:197', 'HLA-A*02:198', 'HLA-A*02:199',
'HLA-A*02:20', 'HLA-A*02:200', 'HLA-A*02:201', 'HLA-A*02:202', 'HLA-A*02:203', 'HLA-A*02:204', 'HLA-A*02:205', 'HLA-A*02:206',
'HLA-A*02:207', 'HLA-A*02:208', 'HLA-A*02:209', 'HLA-A*02:21', 'HLA-A*02:210', 'HLA-A*02:211', 'HLA-A*02:212', 'HLA-A*02:213',
'HLA-A*02:214', 'HLA-A*02:215', 'HLA-A*02:216', 'HLA-A*02:217', 'HLA-A*02:218', 'HLA-A*02:219', 'HLA-A*02:22', 'HLA-A*02:220',
'HLA-A*02:221', 'HLA-A*02:224', 'HLA-A*02:228', 'HLA-A*02:229', 'HLA-A*02:230', 'HLA-A*02:231', 'HLA-A*02:232', 'HLA-A*02:233',
'HLA-A*02:234', 'HLA-A*02:235', 'HLA-A*02:236', 'HLA-A*02:237', 'HLA-A*02:238', 'HLA-A*02:239', 'HLA-A*02:24', 'HLA-A*02:240',
'HLA-A*02:241', 'HLA-A*02:242', 'HLA-A*02:243', 'HLA-A*02:244', 'HLA-A*02:245', 'HLA-A*02:246', 'HLA-A*02:247', 'HLA-A*02:248',
'HLA-A*02:249', 'HLA-A*02:25', 'HLA-A*02:251', 'HLA-A*02:252', 'HLA-A*02:253', 'HLA-A*02:254', 'HLA-A*02:255', 'HLA-A*02:256',
'HLA-A*02:257', 'HLA-A*02:258', 'HLA-A*02:259', 'HLA-A*02:26', 'HLA-A*02:260', 'HLA-A*02:261', 'HLA-A*02:262', 'HLA-A*02:263',
'HLA-A*02:264', 'HLA-A*02:265', 'HLA-A*02:266', 'HLA-A*02:27', 'HLA-A*02:28', 'HLA-A*02:29', 'HLA-A*02:30', 'HLA-A*02:31', 'HLA-A*02:33',
'HLA-A*02:34', 'HLA-A*02:35', 'HLA-A*02:36', 'HLA-A*02:37', 'HLA-A*02:38', 'HLA-A*02:39', 'HLA-A*02:40', 'HLA-A*02:41', 'HLA-A*02:42',
'HLA-A*02:44', 'HLA-A*02:45', 'HLA-A*02:46', 'HLA-A*02:47', 'HLA-A*02:48', 'HLA-A*02:49', 'HLA-A*02:50', 'HLA-A*02:51', 'HLA-A*02:52',
'HLA-A*02:54', 'HLA-A*02:55', 'HLA-A*02:56', 'HLA-A*02:57', 'HLA-A*02:58', 'HLA-A*02:59', 'HLA-A*02:60', 'HLA-A*02:61', 'HLA-A*02:62',
'HLA-A*02:63', 'HLA-A*02:64', 'HLA-A*02:65', 'HLA-A*02:66', 'HLA-A*02:67', 'HLA-A*02:68', 'HLA-A*02:69', 'HLA-A*02:70', 'HLA-A*02:71',
'HLA-A*02:72', 'HLA-A*02:73', 'HLA-A*02:74', 'HLA-A*02:75', 'HLA-A*02:76', 'HLA-A*02:77', 'HLA-A*02:78', 'HLA-A*02:79', 'HLA-A*02:80',
'HLA-A*02:81', 'HLA-A*02:84', 'HLA-A*02:85', 'HLA-A*02:86', 'HLA-A*02:87', 'HLA-A*02:89', 'HLA-A*02:90', 'HLA-A*02:91', 'HLA-A*02:92',
'HLA-A*02:93', 'HLA-A*02:95', 'HLA-A*02:96', 'HLA-A*02:97', 'HLA-A*02:99', 'HLA-A*03:01', 'HLA-A*03:02', 'HLA-A*03:04', 'HLA-A*03:05',
'HLA-A*03:06', 'HLA-A*03:07', 'HLA-A*03:08', 'HLA-A*03:09', 'HLA-A*03:10', 'HLA-A*03:12', 'HLA-A*03:13', 'HLA-A*03:14', 'HLA-A*03:15',
'HLA-A*03:16', 'HLA-A*03:17', 'HLA-A*03:18', 'HLA-A*03:19', 'HLA-A*03:20', 'HLA-A*03:22', 'HLA-A*03:23', 'HLA-A*03:24', 'HLA-A*03:25',
'HLA-A*03:26', 'HLA-A*03:27', 'HLA-A*03:28', 'HLA-A*03:29', 'HLA-A*03:30', 'HLA-A*03:31', 'HLA-A*03:32', 'HLA-A*03:33', 'HLA-A*03:34',
'HLA-A*03:35', 'HLA-A*03:37', 'HLA-A*03:38', 'HLA-A*03:39', 'HLA-A*03:40', 'HLA-A*03:41', 'HLA-A*03:42', 'HLA-A*03:43', 'HLA-A*03:44',
'HLA-A*03:45', 'HLA-A*03:46', 'HLA-A*03:47', 'HLA-A*03:48', 'HLA-A*03:49', 'HLA-A*03:50', 'HLA-A*03:51', 'HLA-A*03:52', 'HLA-A*03:53',
'HLA-A*03:54', 'HLA-A*03:55', 'HLA-A*03:56', 'HLA-A*03:57', 'HLA-A*03:58', 'HLA-A*03:59', 'HLA-A*03:60', 'HLA-A*03:61', 'HLA-A*03:62',
'HLA-A*03:63', 'HLA-A*03:64', 'HLA-A*03:65', 'HLA-A*03:66', 'HLA-A*03:67', 'HLA-A*03:70', 'HLA-A*03:71', 'HLA-A*03:72', 'HLA-A*03:73',
'HLA-A*03:74', 'HLA-A*03:75', 'HLA-A*03:76', 'HLA-A*03:77', 'HLA-A*03:78', 'HLA-A*03:79', 'HLA-A*03:80', 'HLA-A*03:81', 'HLA-A*03:82',
'HLA-A*11:01', 'HLA-A*11:02', 'HLA-A*11:03', 'HLA-A*11:04', 'HLA-A*11:05', 'HLA-A*11:06', 'HLA-A*11:07', 'HLA-A*11:08', 'HLA-A*11:09',
'HLA-A*11:10', 'HLA-A*11:11', 'HLA-A*11:12', 'HLA-A*11:13', 'HLA-A*11:14', 'HLA-A*11:15', 'HLA-A*11:16', 'HLA-A*11:17', 'HLA-A*11:18',
'HLA-A*11:19', 'HLA-A*11:20', 'HLA-A*11:22', 'HLA-A*11:23', 'HLA-A*11:24', 'HLA-A*11:25', 'HLA-A*11:26', 'HLA-A*11:27', 'HLA-A*11:29',
'HLA-A*11:30', 'HLA-A*11:31', 'HLA-A*11:32', 'HLA-A*11:33', 'HLA-A*11:34', 'HLA-A*11:35', 'HLA-A*11:36', 'HLA-A*11:37', 'HLA-A*11:38',
'HLA-A*11:39', 'HLA-A*11:40', 'HLA-A*11:41', 'HLA-A*11:42', 'HLA-A*11:43', 'HLA-A*11:44', 'HLA-A*11:45', 'HLA-A*11:46', 'HLA-A*11:47',
'HLA-A*11:48', 'HLA-A*11:49', 'HLA-A*11:51', 'HLA-A*11:53', 'HLA-A*11:54', 'HLA-A*11:55', 'HLA-A*11:56', 'HLA-A*11:57', 'HLA-A*11:58',
'HLA-A*11:59', 'HLA-A*11:60', 'HLA-A*11:61', 'HLA-A*11:62', 'HLA-A*11:63', 'HLA-A*11:64', 'HLA-A*23:01', 'HLA-A*23:02', 'HLA-A*23:03',
'HLA-A*23:04', 'HLA-A*23:05', 'HLA-A*23:06', 'HLA-A*23:09', 'HLA-A*23:10', 'HLA-A*23:12', 'HLA-A*23:13', 'HLA-A*23:14', 'HLA-A*23:15',
'HLA-A*23:16', 'HLA-A*23:17', 'HLA-A*23:18', 'HLA-A*23:20', 'HLA-A*23:21', 'HLA-A*23:22', 'HLA-A*23:23', 'HLA-A*23:24', 'HLA-A*23:25',
'HLA-A*23:26', 'HLA-A*24:02', 'HLA-A*24:03', 'HLA-A*24:04', 'HLA-A*24:05', 'HLA-A*24:06', 'HLA-A*24:07', 'HLA-A*24:08', 'HLA-A*24:10',
'HLA-A*24:100', 'HLA-A*24:101', 'HLA-A*24:102', 'HLA-A*24:103', 'HLA-A*24:104', 'HLA-A*24:105', 'HLA-A*24:106', 'HLA-A*24:107',
'HLA-A*24:108', 'HLA-A*24:109', 'HLA-A*24:110', 'HLA-A*24:111', 'HLA-A*24:112', 'HLA-A*24:113', 'HLA-A*24:114', 'HLA-A*24:115',
'HLA-A*24:116', 'HLA-A*24:117', 'HLA-A*24:118', 'HLA-A*24:119', 'HLA-A*24:120', 'HLA-A*24:121', 'HLA-A*24:122', 'HLA-A*24:123',
'HLA-A*24:124', 'HLA-A*24:125', 'HLA-A*24:126', 'HLA-A*24:127', 'HLA-A*24:128', 'HLA-A*24:129', 'HLA-A*24:13', 'HLA-A*24:130',
'HLA-A*24:131', 'HLA-A*24:133', 'HLA-A*24:134', 'HLA-A*24:135', 'HLA-A*24:136', 'HLA-A*24:137', 'HLA-A*24:138', 'HLA-A*24:139',
'HLA-A*24:14', 'HLA-A*24:140', 'HLA-A*24:141', 'HLA-A*24:142', 'HLA-A*24:143', 'HLA-A*24:144', 'HLA-A*24:15', 'HLA-A*24:17', 'HLA-A*24:18',
'HLA-A*24:19', 'HLA-A*24:20', 'HLA-A*24:21', 'HLA-A*24:22', 'HLA-A*24:23', 'HLA-A*24:24', 'HLA-A*24:25', 'HLA-A*24:26', 'HLA-A*24:27',
'HLA-A*24:28', 'HLA-A*24:29', 'HLA-A*24:30', 'HLA-A*24:31', 'HLA-A*24:32', 'HLA-A*24:33', 'HLA-A*24:34', 'HLA-A*24:35', 'HLA-A*24:37',
'HLA-A*24:38', 'HLA-A*24:39', 'HLA-A*24:41', 'HLA-A*24:42', 'HLA-A*24:43', 'HLA-A*24:44', 'HLA-A*24:46', 'HLA-A*24:47', 'HLA-A*24:49',
'HLA-A*24:50', 'HLA-A*24:51', 'HLA-A*24:52', 'HLA-A*24:53', 'HLA-A*24:54', 'HLA-A*24:55', 'HLA-A*24:56', 'HLA-A*24:57', 'HLA-A*24:58',
'HLA-A*24:59', 'HLA-A*24:61', 'HLA-A*24:62', 'HLA-A*24:63', 'HLA-A*24:64', 'HLA-A*24:66', 'HLA-A*24:67', 'HLA-A*24:68', 'HLA-A*24:69',
'HLA-A*24:70', 'HLA-A*24:71', 'HLA-A*24:72', 'HLA-A*24:73', 'HLA-A*24:74', 'HLA-A*24:75', 'HLA-A*24:76', 'HLA-A*24:77', 'HLA-A*24:78',
'HLA-A*24:79', 'HLA-A*24:80', 'HLA-A*24:81', 'HLA-A*24:82', 'HLA-A*24:85', 'HLA-A*24:87', 'HLA-A*24:88', 'HLA-A*24:89', 'HLA-A*24:91',
'HLA-A*24:92', 'HLA-A*24:93', 'HLA-A*24:94', 'HLA-A*24:95', 'HLA-A*24:96', 'HLA-A*24:97', 'HLA-A*24:98', 'HLA-A*24:99', 'HLA-A*25:01',
'HLA-A*25:02', 'HLA-A*25:03', 'HLA-A*25:04', 'HLA-A*25:05', 'HLA-A*25:06', 'HLA-A*25:07', 'HLA-A*25:08', 'HLA-A*25:09', 'HLA-A*25:10',
'HLA-A*25:11', 'HLA-A*25:13', 'HLA-A*26:01', 'HLA-A*26:02', 'HLA-A*26:03', 'HLA-A*26:04', 'HLA-A*26:05', 'HLA-A*26:06', 'HLA-A*26:07',
'HLA-A*26:08', 'HLA-A*26:09', 'HLA-A*26:10', 'HLA-A*26:12', 'HLA-A*26:13', 'HLA-A*26:14', 'HLA-A*26:15', 'HLA-A*26:16', 'HLA-A*26:17',
'HLA-A*26:18', 'HLA-A*26:19', 'HLA-A*26:20', 'HLA-A*26:21', 'HLA-A*26:22', 'HLA-A*26:23', 'HLA-A*26:24', 'HLA-A*26:26', 'HLA-A*26:27',
'HLA-A*26:28', 'HLA-A*26:29', 'HLA-A*26:30', 'HLA-A*26:31', 'HLA-A*26:32', 'HLA-A*26:33', 'HLA-A*26:34', 'HLA-A*26:35', 'HLA-A*26:36',
'HLA-A*26:37', 'HLA-A*26:38', 'HLA-A*26:39', 'HLA-A*26:40', 'HLA-A*26:41', 'HLA-A*26:42', 'HLA-A*26:43', 'HLA-A*26:45', 'HLA-A*26:46',
'HLA-A*26:47', 'HLA-A*26:48', 'HLA-A*26:49', 'HLA-A*26:50', 'HLA-A*29:01', 'HLA-A*29:02', 'HLA-A*29:03', 'HLA-A*29:04', 'HLA-A*29:05',
'HLA-A*29:06', 'HLA-A*29:07', 'HLA-A*29:09', 'HLA-A*29:10', 'HLA-A*29:11', 'HLA-A*29:12', 'HLA-A*29:13', 'HLA-A*29:14', 'HLA-A*29:15',
'HLA-A*29:16', 'HLA-A*29:17', 'HLA-A*29:18', 'HLA-A*29:19', 'HLA-A*29:20', 'HLA-A*29:21', 'HLA-A*29:22', 'HLA-A*30:01', 'HLA-A*30:02',
'HLA-A*30:03', 'HLA-A*30:04', 'HLA-A*30:06', 'HLA-A*30:07', 'HLA-A*30:08', 'HLA-A*30:09', 'HLA-A*30:10', 'HLA-A*30:11', 'HLA-A*30:12',
'HLA-A*30:13', 'HLA-A*30:15', 'HLA-A*30:16', 'HLA-A*30:17', 'HLA-A*30:18', 'HLA-A*30:19', 'HLA-A*30:20', 'HLA-A*30:22', 'HLA-A*30:23',
'HLA-A*30:24', 'HLA-A*30:25', 'HLA-A*30:26', 'HLA-A*30:28', 'HLA-A*30:29', 'HLA-A*30:30', 'HLA-A*30:31', 'HLA-A*30:32', 'HLA-A*30:33',
'HLA-A*30:34', 'HLA-A*30:35', 'HLA-A*30:36', 'HLA-A*30:37', 'HLA-A*30:38', 'HLA-A*30:39', 'HLA-A*30:40', 'HLA-A*30:41', 'HLA-A*31:01',
'HLA-A*31:02', 'HLA-A*31:03', 'HLA-A*31:04', 'HLA-A*31:05', 'HLA-A*31:06', 'HLA-A*31:07', 'HLA-A*31:08', 'HLA-A*31:09', 'HLA-A*31:10',
'HLA-A*31:11', 'HLA-A*31:12', 'HLA-A*31:13', 'HLA-A*31:15', 'HLA-A*31:16', 'HLA-A*31:17', 'HLA-A*31:18', 'HLA-A*31:19', 'HLA-A*31:20',
'HLA-A*31:21', 'HLA-A*31:22', 'HLA-A*31:23', 'HLA-A*31:24', 'HLA-A*31:25', 'HLA-A*31:26', 'HLA-A*31:27', 'HLA-A*31:28', 'HLA-A*31:29',
'HLA-A*31:30', 'HLA-A*31:31', 'HLA-A*31:32', 'HLA-A*31:33', 'HLA-A*31:34', 'HLA-A*31:35', 'HLA-A*31:36', 'HLA-A*31:37', 'HLA-A*32:01',
'HLA-A*32:02', 'HLA-A*32:03', 'HLA-A*32:04', 'HLA-A*32:05', 'HLA-A*32:06', 'HLA-A*32:07', 'HLA-A*32:08', 'HLA-A*32:09', 'HLA-A*32:10',
'HLA-A*32:12', 'HLA-A*32:13', 'HLA-A*32:14', 'HLA-A*32:15', 'HLA-A*32:16', 'HLA-A*32:17', 'HLA-A*32:18', 'HLA-A*32:20', 'HLA-A*32:21',
'HLA-A*32:22', 'HLA-A*32:23', 'HLA-A*32:24', 'HLA-A*32:25', 'HLA-A*33:01', 'HLA-A*33:03', 'HLA-A*33:04', 'HLA-A*33:05', 'HLA-A*33:06',
'HLA-A*33:07', 'HLA-A*33:08', 'HLA-A*33:09', 'HLA-A*33:10', 'HLA-A*33:11', 'HLA-A*33:12', 'HLA-A*33:13', 'HLA-A*33:14', 'HLA-A*33:15',
'HLA-A*33:16', 'HLA-A*33:17', 'HLA-A*33:18', 'HLA-A*33:19', 'HLA-A*33:20', 'HLA-A*33:21', 'HLA-A*33:22', 'HLA-A*33:23', 'HLA-A*33:24',
'HLA-A*33:25', 'HLA-A*33:26', 'HLA-A*33:27', 'HLA-A*33:28', 'HLA-A*33:29', 'HLA-A*33:30', 'HLA-A*33:31', 'HLA-A*34:01', 'HLA-A*34:02',
'HLA-A*34:03', 'HLA-A*34:04', 'HLA-A*34:05', 'HLA-A*34:06', 'HLA-A*34:07', 'HLA-A*34:08', 'HLA-A*36:01', 'HLA-A*36:02', 'HLA-A*36:03',
'HLA-A*36:04', 'HLA-A*36:05', 'HLA-A*43:01', 'HLA-A*66:01', 'HLA-A*66:02', 'HLA-A*66:03', 'HLA-A*66:04', 'HLA-A*66:05', 'HLA-A*66:06',
'HLA-A*66:07', 'HLA-A*66:08', 'HLA-A*66:09', 'HLA-A*66:10', 'HLA-A*66:11', 'HLA-A*66:12', 'HLA-A*66:13', 'HLA-A*66:14', 'HLA-A*66:15',
'HLA-A*68:01', 'HLA-A*68:02', 'HLA-A*68:03', 'HLA-A*68:04', 'HLA-A*68:05', 'HLA-A*68:06', 'HLA-A*68:07', 'HLA-A*68:08', 'HLA-A*68:09',
'HLA-A*68:10', 'HLA-A*68:12', 'HLA-A*68:13', 'HLA-A*68:14', 'HLA-A*68:15', 'HLA-A*68:16', 'HLA-A*68:17', 'HLA-A*68:19', 'HLA-A*68:20',
'HLA-A*68:21', 'HLA-A*68:22', 'HLA-A*68:23', 'HLA-A*68:24', 'HLA-A*68:25', 'HLA-A*68:26', 'HLA-A*68:27', 'HLA-A*68:28', 'HLA-A*68:29',
'HLA-A*68:30', 'HLA-A*68:31', 'HLA-A*68:32', 'HLA-A*68:33', 'HLA-A*68:34', 'HLA-A*68:35', 'HLA-A*68:36', 'HLA-A*68:37', 'HLA-A*68:38',
'HLA-A*68:39', 'HLA-A*68:40', 'HLA-A*68:41', 'HLA-A*68:42', 'HLA-A*68:43', 'HLA-A*68:44', 'HLA-A*68:45', 'HLA-A*68:46', 'HLA-A*68:47',
'HLA-A*68:48', 'HLA-A*68:50', 'HLA-A*68:51', 'HLA-A*68:52', 'HLA-A*68:53', 'HLA-A*68:54', 'HLA-A*69:01', 'HLA-A*74:01', 'HLA-A*74:02',
'HLA-A*74:03', 'HLA-A*74:04', 'HLA-A*74:05', 'HLA-A*74:06', 'HLA-A*74:07', 'HLA-A*74:08', 'HLA-A*74:09', 'HLA-A*74:10', 'HLA-A*74:11',
'HLA-A*74:13', 'HLA-A*80:01', 'HLA-A*80:02', 'HLA-B*07:02', 'HLA-B*07:03', 'HLA-B*07:04', 'HLA-B*07:05', 'HLA-B*07:06', 'HLA-B*07:07',
'HLA-B*07:08', 'HLA-B*07:09', 'HLA-B*07:10', 'HLA-B*07:100', 'HLA-B*07:101', 'HLA-B*07:102', 'HLA-B*07:103', 'HLA-B*07:104',
'HLA-B*07:105', 'HLA-B*07:106', 'HLA-B*07:107', 'HLA-B*07:108', 'HLA-B*07:109', 'HLA-B*07:11', 'HLA-B*07:110', 'HLA-B*07:112',
'HLA-B*07:113', 'HLA-B*07:114', 'HLA-B*07:115', 'HLA-B*07:12', 'HLA-B*07:13', 'HLA-B*07:14', 'HLA-B*07:15', 'HLA-B*07:16', 'HLA-B*07:17',
'HLA-B*07:18', 'HLA-B*07:19', 'HLA-B*07:20', 'HLA-B*07:21', 'HLA-B*07:22', 'HLA-B*07:23', 'HLA-B*07:24', 'HLA-B*07:25', 'HLA-B*07:26',
'HLA-B*07:27', 'HLA-B*07:28', 'HLA-B*07:29', 'HLA-B*07:30', 'HLA-B*07:31', 'HLA-B*07:32', 'HLA-B*07:33', 'HLA-B*07:34', 'HLA-B*07:35',
'HLA-B*07:36', 'HLA-B*07:37', 'HLA-B*07:38', 'HLA-B*07:39', 'HLA-B*07:40', 'HLA-B*07:41', 'HLA-B*07:42', 'HLA-B*07:43', 'HLA-B*07:44',
'HLA-B*07:45', 'HLA-B*07:46', 'HLA-B*07:47', 'HLA-B*07:48', 'HLA-B*07:50', 'HLA-B*07:51', 'HLA-B*07:52', 'HLA-B*07:53', 'HLA-B*07:54',
'HLA-B*07:55', 'HLA-B*07:56', 'HLA-B*07:57', 'HLA-B*07:58', 'HLA-B*07:59', 'HLA-B*07:60', 'HLA-B*07:61', 'HLA-B*07:62', 'HLA-B*07:63',
'HLA-B*07:64', 'HLA-B*07:65', 'HLA-B*07:66', 'HLA-B*07:68', 'HLA-B*07:69', 'HLA-B*07:70', 'HLA-B*07:71', 'HLA-B*07:72', 'HLA-B*07:73',
'HLA-B*07:74', 'HLA-B*07:75', 'HLA-B*07:76', 'HLA-B*07:77', 'HLA-B*07:78', 'HLA-B*07:79', 'HLA-B*07:80', 'HLA-B*07:81', 'HLA-B*07:82',
'HLA-B*07:83', 'HLA-B*07:84', 'HLA-B*07:85', 'HLA-B*07:86', 'HLA-B*07:87', 'HLA-B*07:88', 'HLA-B*07:89', 'HLA-B*07:90', 'HLA-B*07:91',
'HLA-B*07:92', 'HLA-B*07:93', 'HLA-B*07:94', 'HLA-B*07:95', 'HLA-B*07:96', 'HLA-B*07:97', 'HLA-B*07:98', 'HLA-B*07:99', 'HLA-B*08:01',
'HLA-B*08:02', 'HLA-B*08:03', 'HLA-B*08:04', 'HLA-B*08:05', 'HLA-B*08:07', 'HLA-B*08:09', 'HLA-B*08:10', 'HLA-B*08:11', 'HLA-B*08:12',
'HLA-B*08:13', 'HLA-B*08:14', 'HLA-B*08:15', 'HLA-B*08:16', 'HLA-B*08:17', 'HLA-B*08:18', 'HLA-B*08:20', 'HLA-B*08:21', 'HLA-B*08:22',
'HLA-B*08:23', 'HLA-B*08:24', 'HLA-B*08:25', 'HLA-B*08:26', 'HLA-B*08:27', 'HLA-B*08:28', 'HLA-B*08:29', 'HLA-B*08:31', 'HLA-B*08:32',
'HLA-B*08:33', 'HLA-B*08:34', 'HLA-B*08:35', 'HLA-B*08:36', 'HLA-B*08:37', 'HLA-B*08:38', 'HLA-B*08:39', 'HLA-B*08:40', 'HLA-B*08:41',
'HLA-B*08:42', 'HLA-B*08:43', 'HLA-B*08:44', 'HLA-B*08:45', 'HLA-B*08:46', 'HLA-B*08:47', 'HLA-B*08:48', 'HLA-B*08:49', 'HLA-B*08:50',
'HLA-B*08:51', 'HLA-B*08:52', 'HLA-B*08:53', 'HLA-B*08:54', 'HLA-B*08:55', 'HLA-B*08:56', 'HLA-B*08:57', 'HLA-B*08:58', 'HLA-B*08:59',
'HLA-B*08:60', 'HLA-B*08:61', 'HLA-B*08:62', 'HLA-B*13:01', 'HLA-B*13:02', 'HLA-B*13:03', 'HLA-B*13:04', 'HLA-B*13:06', 'HLA-B*13:09',
'HLA-B*13:10', 'HLA-B*13:11', 'HLA-B*13:12', 'HLA-B*13:13', 'HLA-B*13:14', 'HLA-B*13:15', 'HLA-B*13:16', 'HLA-B*13:17', 'HLA-B*13:18',
'HLA-B*13:19', 'HLA-B*13:20', 'HLA-B*13:21', 'HLA-B*13:22', 'HLA-B*13:23', 'HLA-B*13:25', 'HLA-B*13:26', 'HLA-B*13:27', 'HLA-B*13:28',
'HLA-B*13:29', 'HLA-B*13:30', 'HLA-B*13:31', 'HLA-B*13:32', 'HLA-B*13:33', 'HLA-B*13:34', 'HLA-B*13:35', 'HLA-B*13:36', 'HLA-B*13:37',
'HLA-B*13:38', 'HLA-B*13:39', 'HLA-B*14:01', 'HLA-B*14:02', 'HLA-B*14:03', 'HLA-B*14:04', 'HLA-B*14:05', 'HLA-B*14:06', 'HLA-B*14:08',
'HLA-B*14:09', 'HLA-B*14:10', 'HLA-B*14:11', 'HLA-B*14:12', 'HLA-B*14:13', 'HLA-B*14:14', 'HLA-B*14:15', 'HLA-B*14:16', 'HLA-B*14:17',
'HLA-B*14:18', 'HLA-B*15:01', 'HLA-B*15:02', 'HLA-B*15:03', 'HLA-B*15:04', 'HLA-B*15:05', 'HLA-B*15:06', 'HLA-B*15:07', 'HLA-B*15:08',
'HLA-B*15:09', 'HLA-B*15:10', 'HLA-B*15:101', 'HLA-B*15:102', 'HLA-B*15:103', 'HLA-B*15:104', 'HLA-B*15:105', 'HLA-B*15:106',
'HLA-B*15:107', 'HLA-B*15:108', 'HLA-B*15:109', 'HLA-B*15:11', 'HLA-B*15:110', 'HLA-B*15:112', 'HLA-B*15:113', 'HLA-B*15:114',
'HLA-B*15:115', 'HLA-B*15:116', 'HLA-B*15:117', 'HLA-B*15:118', 'HLA-B*15:119', 'HLA-B*15:12', 'HLA-B*15:120', 'HLA-B*15:121',
'HLA-B*15:122', 'HLA-B*15:123', 'HLA-B*15:124', 'HLA-B*15:125', 'HLA-B*15:126', 'HLA-B*15:127', 'HLA-B*15:128', 'HLA-B*15:129',
'HLA-B*15:13', 'HLA-B*15:131', 'HLA-B*15:132', 'HLA-B*15:133', 'HLA-B*15:134', 'HLA-B*15:135', 'HLA-B*15:136', 'HLA-B*15:137',
'HLA-B*15:138', 'HLA-B*15:139', 'HLA-B*15:14', 'HLA-B*15:140', 'HLA-B*15:141', 'HLA-B*15:142', 'HLA-B*15:143', 'HLA-B*15:144',
'HLA-B*15:145', 'HLA-B*15:146', 'HLA-B*15:147', 'HLA-B*15:148', 'HLA-B*15:15', 'HLA-B*15:150', 'HLA-B*15:151', 'HLA-B*15:152',
'HLA-B*15:153', 'HLA-B*15:154', 'HLA-B*15:155', 'HLA-B*15:156', 'HLA-B*15:157', 'HLA-B*15:158', 'HLA-B*15:159', 'HLA-B*15:16',
'HLA-B*15:160', 'HLA-B*15:161', 'HLA-B*15:162', 'HLA-B*15:163', 'HLA-B*15:164', 'HLA-B*15:165', 'HLA-B*15:166', 'HLA-B*15:167',
'HLA-B*15:168', 'HLA-B*15:169', 'HLA-B*15:17', 'HLA-B*15:170', 'HLA-B*15:171', 'HLA-B*15:172', 'HLA-B*15:173', 'HLA-B*15:174',
'HLA-B*15:175', 'HLA-B*15:176', 'HLA-B*15:177', 'HLA-B*15:178', 'HLA-B*15:179', 'HLA-B*15:18', 'HLA-B*15:180', 'HLA-B*15:183',
'HLA-B*15:184', 'HLA-B*15:185', 'HLA-B*15:186', 'HLA-B*15:187', 'HLA-B*15:188', 'HLA-B*15:189', 'HLA-B*15:19', 'HLA-B*15:191',
'HLA-B*15:192', 'HLA-B*15:193', 'HLA-B*15:194', 'HLA-B*15:195', 'HLA-B*15:196', 'HLA-B*15:197', 'HLA-B*15:198', 'HLA-B*15:199',
'HLA-B*15:20', 'HLA-B*15:200', 'HLA-B*15:201', 'HLA-B*15:202', 'HLA-B*15:21', 'HLA-B*15:23', 'HLA-B*15:24', 'HLA-B*15:25', 'HLA-B*15:27',
'HLA-B*15:28', 'HLA-B*15:29', 'HLA-B*15:30', 'HLA-B*15:31', 'HLA-B*15:32', 'HLA-B*15:33', 'HLA-B*15:34', 'HLA-B*15:35', 'HLA-B*15:36',
'HLA-B*15:37', 'HLA-B*15:38', 'HLA-B*15:39', 'HLA-B*15:40', 'HLA-B*15:42', 'HLA-B*15:43', 'HLA-B*15:44', 'HLA-B*15:45', 'HLA-B*15:46',
'HLA-B*15:47', 'HLA-B*15:48', 'HLA-B*15:49', 'HLA-B*15:50', 'HLA-B*15:51', 'HLA-B*15:52', 'HLA-B*15:53', 'HLA-B*15:54', 'HLA-B*15:55',
'HLA-B*15:56', 'HLA-B*15:57', 'HLA-B*15:58', 'HLA-B*15:60', 'HLA-B*15:61', 'HLA-B*15:62', 'HLA-B*15:63', 'HLA-B*15:64', 'HLA-B*15:65',
'HLA-B*15:66', 'HLA-B*15:67', 'HLA-B*15:68', 'HLA-B*15:69', 'HLA-B*15:70', 'HLA-B*15:71', 'HLA-B*15:72', 'HLA-B*15:73', 'HLA-B*15:74',
'HLA-B*15:75', 'HLA-B*15:76', 'HLA-B*15:77', 'HLA-B*15:78', 'HLA-B*15:80', 'HLA-B*15:81', 'HLA-B*15:82', 'HLA-B*15:83', 'HLA-B*15:84',
'HLA-B*15:85', 'HLA-B*15:86', 'HLA-B*15:87', 'HLA-B*15:88', 'HLA-B*15:89', 'HLA-B*15:90', 'HLA-B*15:91', 'HLA-B*15:92', 'HLA-B*15:93',
'HLA-B*15:95', 'HLA-B*15:96', 'HLA-B*15:97', 'HLA-B*15:98', 'HLA-B*15:99', 'HLA-B*18:01', 'HLA-B*18:02', 'HLA-B*18:03', 'HLA-B*18:04',
'HLA-B*18:05', 'HLA-B*18:06', 'HLA-B*18:07', 'HLA-B*18:08', 'HLA-B*18:09', 'HLA-B*18:10', 'HLA-B*18:11', 'HLA-B*18:12', 'HLA-B*18:13',
'HLA-B*18:14', 'HLA-B*18:15', 'HLA-B*18:18', 'HLA-B*18:19', 'HLA-B*18:20', 'HLA-B*18:21', 'HLA-B*18:22', 'HLA-B*18:24', 'HLA-B*18:25',
'HLA-B*18:26', 'HLA-B*18:27', 'HLA-B*18:28', 'HLA-B*18:29', 'HLA-B*18:30', 'HLA-B*18:31', 'HLA-B*18:32', 'HLA-B*18:33', 'HLA-B*18:34',
'HLA-B*18:35', 'HLA-B*18:36', 'HLA-B*18:37', 'HLA-B*18:38', 'HLA-B*18:39', 'HLA-B*18:40', 'HLA-B*18:41', 'HLA-B*18:42', 'HLA-B*18:43',
'HLA-B*18:44', 'HLA-B*18:45', 'HLA-B*18:46', 'HLA-B*18:47', 'HLA-B*18:48', 'HLA-B*18:49', 'HLA-B*18:50', 'HLA-B*27:01', 'HLA-B*27:02',
'HLA-B*27:03', 'HLA-B*27:04', 'HLA-B*27:05', 'HLA-B*27:06', 'HLA-B*27:07', 'HLA-B*27:08', 'HLA-B*27:09', 'HLA-B*27:10', 'HLA-B*27:11',
'HLA-B*27:12', 'HLA-B*27:13', 'HLA-B*27:14', 'HLA-B*27:15', 'HLA-B*27:16', 'HLA-B*27:17', 'HLA-B*27:18', 'HLA-B*27:19', 'HLA-B*27:20',
'HLA-B*27:21', 'HLA-B*27:23', 'HLA-B*27:24', 'HLA-B*27:25', 'HLA-B*27:26', 'HLA-B*27:27', 'HLA-B*27:28', 'HLA-B*27:29', 'HLA-B*27:30',
'HLA-B*27:31', 'HLA-B*27:32', 'HLA-B*27:33', 'HLA-B*27:34', 'HLA-B*27:35', 'HLA-B*27:36', 'HLA-B*27:37', 'HLA-B*27:38', 'HLA-B*27:39',
'HLA-B*27:40', 'HLA-B*27:41', 'HLA-B*27:42', 'HLA-B*27:43', 'HLA-B*27:44', 'HLA-B*27:45', 'HLA-B*27:46', 'HLA-B*27:47', 'HLA-B*27:48',
'HLA-B*27:49', 'HLA-B*27:50', 'HLA-B*27:51', 'HLA-B*27:52', 'HLA-B*27:53', 'HLA-B*27:54', 'HLA-B*27:55', 'HLA-B*27:56', 'HLA-B*27:57',
'HLA-B*27:58', 'HLA-B*27:60', 'HLA-B*27:61', 'HLA-B*27:62', 'HLA-B*27:63', 'HLA-B*27:67', 'HLA-B*27:68', 'HLA-B*27:69', 'HLA-B*35:01',
'HLA-B*35:02', 'HLA-B*35:03', 'HLA-B*35:04', 'HLA-B*35:05', 'HLA-B*35:06', 'HLA-B*35:07', 'HLA-B*35:08', 'HLA-B*35:09', 'HLA-B*35:10',
'HLA-B*35:100', 'HLA-B*35:101', 'HLA-B*35:102', 'HLA-B*35:103', 'HLA-B*35:104', 'HLA-B*35:105', 'HLA-B*35:106', 'HLA-B*35:107',
'HLA-B*35:108', 'HLA-B*35:109', 'HLA-B*35:11', 'HLA-B*35:110', 'HLA-B*35:111', 'HLA-B*35:112', 'HLA-B*35:113', 'HLA-B*35:114',
'HLA-B*35:115', 'HLA-B*35:116', 'HLA-B*35:117', 'HLA-B*35:118', 'HLA-B*35:119', 'HLA-B*35:12', 'HLA-B*35:120', 'HLA-B*35:121',
'HLA-B*35:122', 'HLA-B*35:123', 'HLA-B*35:124', 'HLA-B*35:125', 'HLA-B*35:126', 'HLA-B*35:127', 'HLA-B*35:128', 'HLA-B*35:13',
'HLA-B*35:131', 'HLA-B*35:132', 'HLA-B*35:133', 'HLA-B*35:135', 'HLA-B*35:136', 'HLA-B*35:137', 'HLA-B*35:138', 'HLA-B*35:139',
'HLA-B*35:14', 'HLA-B*35:140', 'HLA-B*35:141', 'HLA-B*35:142', 'HLA-B*35:143', 'HLA-B*35:144', 'HLA-B*35:15', 'HLA-B*35:16', 'HLA-B*35:17',
'HLA-B*35:18', 'HLA-B*35:19', 'HLA-B*35:20', 'HLA-B*35:21', 'HLA-B*35:22', 'HLA-B*35:23', 'HLA-B*35:24', 'HLA-B*35:25', 'HLA-B*35:26',
'HLA-B*35:27', 'HLA-B*35:28', 'HLA-B*35:29', 'HLA-B*35:30', 'HLA-B*35:31', 'HLA-B*35:32', 'HLA-B*35:33', 'HLA-B*35:34', 'HLA-B*35:35',
'HLA-B*35:36', 'HLA-B*35:37', 'HLA-B*35:38', 'HLA-B*35:39', 'HLA-B*35:41', 'HLA-B*35:42', 'HLA-B*35:43', 'HLA-B*35:44', 'HLA-B*35:45',
'HLA-B*35:46', 'HLA-B*35:47', 'HLA-B*35:48', 'HLA-B*35:49', 'HLA-B*35:50', 'HLA-B*35:51', 'HLA-B*35:52', 'HLA-B*35:54', 'HLA-B*35:55',
'HLA-B*35:56', 'HLA-B*35:57', 'HLA-B*35:58', 'HLA-B*35:59', 'HLA-B*35:60', 'HLA-B*35:61', 'HLA-B*35:62', 'HLA-B*35:63', 'HLA-B*35:64',
'HLA-B*35:66', 'HLA-B*35:67', 'HLA-B*35:68', 'HLA-B*35:69', 'HLA-B*35:70', 'HLA-B*35:71', 'HLA-B*35:72', 'HLA-B*35:74', 'HLA-B*35:75',
'HLA-B*35:76', 'HLA-B*35:77', 'HLA-B*35:78', 'HLA-B*35:79', 'HLA-B*35:80', 'HLA-B*35:81', 'HLA-B*35:82', 'HLA-B*35:83', 'HLA-B*35:84',
'HLA-B*35:85', 'HLA-B*35:86', 'HLA-B*35:87', 'HLA-B*35:88', 'HLA-B*35:89', 'HLA-B*35:90', 'HLA-B*35:91', 'HLA-B*35:92', 'HLA-B*35:93',
'HLA-B*35:94', 'HLA-B*35:95', 'HLA-B*35:96', 'HLA-B*35:97', 'HLA-B*35:98', 'HLA-B*35:99', 'HLA-B*37:01', 'HLA-B*37:02', 'HLA-B*37:04',
'HLA-B*37:05', 'HLA-B*37:06', 'HLA-B*37:07', 'HLA-B*37:08', 'HLA-B*37:09', 'HLA-B*37:10', 'HLA-B*37:11', 'HLA-B*37:12', 'HLA-B*37:13',
'HLA-B*37:14', 'HLA-B*37:15', 'HLA-B*37:17', 'HLA-B*37:18', 'HLA-B*37:19', 'HLA-B*37:20', 'HLA-B*37:21', 'HLA-B*37:22', 'HLA-B*37:23',
'HLA-B*38:01', 'HLA-B*38:02', 'HLA-B*38:03', 'HLA-B*38:04', 'HLA-B*38:05', 'HLA-B*38:06', 'HLA-B*38:07', 'HLA-B*38:08', 'HLA-B*38:09',
'HLA-B*38:10', 'HLA-B*38:11', 'HLA-B*38:12', 'HLA-B*38:13', 'HLA-B*38:14', 'HLA-B*38:15', 'HLA-B*38:16', 'HLA-B*38:17', 'HLA-B*38:18',
'HLA-B*38:19', 'HLA-B*38:20', 'HLA-B*38:21', 'HLA-B*38:22', 'HLA-B*38:23', 'HLA-B*39:01', 'HLA-B*39:02', 'HLA-B*39:03', 'HLA-B*39:04',
'HLA-B*39:05', 'HLA-B*39:06', 'HLA-B*39:07', 'HLA-B*39:08', 'HLA-B*39:09', 'HLA-B*39:10', 'HLA-B*39:11', 'HLA-B*39:12', 'HLA-B*39:13',
'HLA-B*39:14', 'HLA-B*39:15', 'HLA-B*39:16', 'HLA-B*39:17', 'HLA-B*39:18', 'HLA-B*39:19', 'HLA-B*39:20', 'HLA-B*39:22', 'HLA-B*39:23',
'HLA-B*39:24', 'HLA-B*39:26', 'HLA-B*39:27', 'HLA-B*39:28', 'HLA-B*39:29', 'HLA-B*39:30', 'HLA-B*39:31', 'HLA-B*39:32', 'HLA-B*39:33',
'HLA-B*39:34', 'HLA-B*39:35', 'HLA-B*39:36', 'HLA-B*39:37', 'HLA-B*39:39', 'HLA-B*39:41', 'HLA-B*39:42', 'HLA-B*39:43', 'HLA-B*39:44',
'HLA-B*39:45', 'HLA-B*39:46', 'HLA-B*39:47', 'HLA-B*39:48', 'HLA-B*39:49', 'HLA-B*39:50', 'HLA-B*39:51', 'HLA-B*39:52', 'HLA-B*39:53',
'HLA-B*39:54', 'HLA-B*39:55', 'HLA-B*39:56', 'HLA-B*39:57', 'HLA-B*39:58', 'HLA-B*39:59', 'HLA-B*39:60', 'HLA-B*40:01', 'HLA-B*40:02',
'HLA-B*40:03', 'HLA-B*40:04', 'HLA-B*40:05', 'HLA-B*40:06', 'HLA-B*40:07', 'HLA-B*40:08', 'HLA-B*40:09', 'HLA-B*40:10', 'HLA-B*40:100',
'HLA-B*40:101', 'HLA-B*40:102', 'HLA-B*40:103', 'HLA-B*40:104', 'HLA-B*40:105', 'HLA-B*40:106', 'HLA-B*40:107', 'HLA-B*40:108',
'HLA-B*40:109', 'HLA-B*40:11', 'HLA-B*40:110', 'HLA-B*40:111', 'HLA-B*40:112', 'HLA-B*40:113', 'HLA-B*40:114', 'HLA-B*40:115',
'HLA-B*40:116', 'HLA-B*40:117', 'HLA-B*40:119', 'HLA-B*40:12', 'HLA-B*40:120', 'HLA-B*40:121', 'HLA-B*40:122', 'HLA-B*40:123',
'HLA-B*40:124', 'HLA-B*40:125', 'HLA-B*40:126', 'HLA-B*40:127', 'HLA-B*40:128', 'HLA-B*40:129', 'HLA-B*40:13', 'HLA-B*40:130',
'HLA-B*40:131', 'HLA-B*40:132', 'HLA-B*40:134', 'HLA-B*40:135', 'HLA-B*40:136', 'HLA-B*40:137', 'HLA-B*40:138', 'HLA-B*40:139',
'HLA-B*40:14', 'HLA-B*40:140', 'HLA-B*40:141', 'HLA-B*40:143', 'HLA-B*40:145', 'HLA-B*40:146', 'HLA-B*40:147', 'HLA-B*40:15',
'HLA-B*40:16', 'HLA-B*40:18', 'HLA-B*40:19', 'HLA-B*40:20', 'HLA-B*40:21', 'HLA-B*40:23', 'HLA-B*40:24', 'HLA-B*40:25', 'HLA-B*40:26',
'HLA-B*40:27', 'HLA-B*40:28', 'HLA-B*40:29', 'HLA-B*40:30', 'HLA-B*40:31', 'HLA-B*40:32', 'HLA-B*40:33', 'HLA-B*40:34', 'HLA-B*40:35',
'HLA-B*40:36', 'HLA-B*40:37', 'HLA-B*40:38', 'HLA-B*40:39', 'HLA-B*40:40', 'HLA-B*40:42', 'HLA-B*40:43', 'HLA-B*40:44', 'HLA-B*40:45',
'HLA-B*40:46', 'HLA-B*40:47', 'HLA-B*40:48', 'HLA-B*40:49', 'HLA-B*40:50', 'HLA-B*40:51', 'HLA-B*40:52', 'HLA-B*40:53', 'HLA-B*40:54',
'HLA-B*40:55', 'HLA-B*40:56', 'HLA-B*40:57', 'HLA-B*40:58', 'HLA-B*40:59', 'HLA-B*40:60', 'HLA-B*40:61', 'HLA-B*40:62', 'HLA-B*40:63',
'HLA-B*40:64', 'HLA-B*40:65', 'HLA-B*40:66', 'HLA-B*40:67', 'HLA-B*40:68', 'HLA-B*40:69', 'HLA-B*40:70', 'HLA-B*40:71', 'HLA-B*40:72',
'HLA-B*40:73', 'HLA-B*40:74', 'HLA-B*40:75', 'HLA-B*40:76', 'HLA-B*40:77', 'HLA-B*40:78', 'HLA-B*40:79', 'HLA-B*40:80', 'HLA-B*40:81',
'HLA-B*40:82', 'HLA-B*40:83', 'HLA-B*40:84', 'HLA-B*40:85', 'HLA-B*40:86', 'HLA-B*40:87', 'HLA-B*40:88', 'HLA-B*40:89', 'HLA-B*40:90',
'HLA-B*40:91', 'HLA-B*40:92', 'HLA-B*40:93', 'HLA-B*40:94', 'HLA-B*40:95', 'HLA-B*40:96', 'HLA-B*40:97', 'HLA-B*40:98', 'HLA-B*40:99',
'HLA-B*41:01', 'HLA-B*41:02', 'HLA-B*41:03', 'HLA-B*41:04', 'HLA-B*41:05', 'HLA-B*41:06', 'HLA-B*41:07', 'HLA-B*41:08', 'HLA-B*41:09',
'HLA-B*41:10', 'HLA-B*41:11', 'HLA-B*41:12', 'HLA-B*42:01', 'HLA-B*42:02', 'HLA-B*42:04', 'HLA-B*42:05', 'HLA-B*42:06', 'HLA-B*42:07',
'HLA-B*42:08', 'HLA-B*42:09', 'HLA-B*42:10', 'HLA-B*42:11', 'HLA-B*42:12', 'HLA-B*42:13', 'HLA-B*42:14', 'HLA-B*44:02', 'HLA-B*44:03',
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'HLA-B*44:102', 'HLA-B*44:103', 'HLA-B*44:104', 'HLA-B*44:105', 'HLA-B*44:106', 'HLA-B*44:107', 'HLA-B*44:109', 'HLA-B*44:11',
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'HLA-C*03:70', 'HLA-C*03:71', 'HLA-C*03:72', 'HLA-C*03:73', 'HLA-C*03:74', 'HLA-C*03:75', 'HLA-C*03:76', 'HLA-C*03:77', 'HLA-C*03:78',
'HLA-C*03:79', 'HLA-C*03:80', 'HLA-C*03:81', 'HLA-C*03:82', 'HLA-C*03:83', 'HLA-C*03:84', 'HLA-C*03:85', 'HLA-C*03:86', 'HLA-C*03:87',
'HLA-C*03:88', 'HLA-C*03:89', 'HLA-C*03:90', 'HLA-C*03:91', 'HLA-C*03:92', 'HLA-C*03:93', 'HLA-C*03:94', 'HLA-C*04:01', 'HLA-C*04:03',
'HLA-C*04:04', 'HLA-C*04:05', 'HLA-C*04:06', 'HLA-C*04:07', 'HLA-C*04:08', 'HLA-C*04:10', 'HLA-C*04:11', 'HLA-C*04:12', 'HLA-C*04:13',
'HLA-C*04:14', 'HLA-C*04:15', 'HLA-C*04:16', 'HLA-C*04:17', 'HLA-C*04:18', 'HLA-C*04:19', 'HLA-C*04:20', 'HLA-C*04:23', 'HLA-C*04:24',
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'HLA-C*04:34', 'HLA-C*04:35', 'HLA-C*04:36', 'HLA-C*04:37', 'HLA-C*04:38', 'HLA-C*04:39', 'HLA-C*04:40', 'HLA-C*04:41', 'HLA-C*04:42',
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'HLA-C*04:52', 'HLA-C*04:53', 'HLA-C*04:54', 'HLA-C*04:55', 'HLA-C*04:56', 'HLA-C*04:57', 'HLA-C*04:58', 'HLA-C*04:60', 'HLA-C*04:61',
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'HLA-C*05:01', 'HLA-C*05:03', 'HLA-C*05:04', 'HLA-C*05:05', 'HLA-C*05:06', 'HLA-C*05:08', 'HLA-C*05:09', 'HLA-C*05:10', 'HLA-C*05:11',
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'HLA-C*05:30', 'HLA-C*05:31', 'HLA-C*05:32', 'HLA-C*05:33', 'HLA-C*05:34', 'HLA-C*05:35', 'HLA-C*05:36', 'HLA-C*05:37', 'HLA-C*05:38',
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'HLA-C*06:13', 'HLA-C*06:14', 'HLA-C*06:15', 'HLA-C*06:17', 'HLA-C*06:18', 'HLA-C*06:19', 'HLA-C*06:20', 'HLA-C*06:21', 'HLA-C*06:22',
'HLA-C*06:23', 'HLA-C*06:24', 'HLA-C*06:25', 'HLA-C*06:26', 'HLA-C*06:27', 'HLA-C*06:28', 'HLA-C*06:29', 'HLA-C*06:30', 'HLA-C*06:31',
'HLA-C*06:32', 'HLA-C*06:33', 'HLA-C*06:34', 'HLA-C*06:35', 'HLA-C*06:36', 'HLA-C*06:37', 'HLA-C*06:38', 'HLA-C*06:39', 'HLA-C*06:40',
'HLA-C*06:41', 'HLA-C*06:42', 'HLA-C*06:43', 'HLA-C*06:44', 'HLA-C*06:45', 'HLA-C*07:01', 'HLA-C*07:02', 'HLA-C*07:03', 'HLA-C*07:04',
'HLA-C*07:05', 'HLA-C*07:06', 'HLA-C*07:07', 'HLA-C*07:08', 'HLA-C*07:09', 'HLA-C*07:10', 'HLA-C*07:100', 'HLA-C*07:101', 'HLA-C*07:102',
'HLA-C*07:103', 'HLA-C*07:105', 'HLA-C*07:106', 'HLA-C*07:107', 'HLA-C*07:108', 'HLA-C*07:109', 'HLA-C*07:11', 'HLA-C*07:110',
'HLA-C*07:111', 'HLA-C*07:112', 'HLA-C*07:113', 'HLA-C*07:114', 'HLA-C*07:115', 'HLA-C*07:116', 'HLA-C*07:117', 'HLA-C*07:118',
'HLA-C*07:119', 'HLA-C*07:12', 'HLA-C*07:120', 'HLA-C*07:122', 'HLA-C*07:123', 'HLA-C*07:124', 'HLA-C*07:125', 'HLA-C*07:126',
'HLA-C*07:127', 'HLA-C*07:128', 'HLA-C*07:129', 'HLA-C*07:13', 'HLA-C*07:130', 'HLA-C*07:131', 'HLA-C*07:132', 'HLA-C*07:133',
'HLA-C*07:134', 'HLA-C*07:135', 'HLA-C*07:136', 'HLA-C*07:137', 'HLA-C*07:138', 'HLA-C*07:139', 'HLA-C*07:14', 'HLA-C*07:140',
'HLA-C*07:141', 'HLA-C*07:142', 'HLA-C*07:143', 'HLA-C*07:144', 'HLA-C*07:145', 'HLA-C*07:146', 'HLA-C*07:147', 'HLA-C*07:148',
'HLA-C*07:149', 'HLA-C*07:15', 'HLA-C*07:16', 'HLA-C*07:17', 'HLA-C*07:18', 'HLA-C*07:19', 'HLA-C*07:20', 'HLA-C*07:21', 'HLA-C*07:22',
'HLA-C*07:23', 'HLA-C*07:24', 'HLA-C*07:25', 'HLA-C*07:26', 'HLA-C*07:27', 'HLA-C*07:28', 'HLA-C*07:29', 'HLA-C*07:30', 'HLA-C*07:31',
'HLA-C*07:35', 'HLA-C*07:36', 'HLA-C*07:37', 'HLA-C*07:38', 'HLA-C*07:39', 'HLA-C*07:40', 'HLA-C*07:41', 'HLA-C*07:42', 'HLA-C*07:43',
'HLA-C*07:44', 'HLA-C*07:45', 'HLA-C*07:46', 'HLA-C*07:47', 'HLA-C*07:48', 'HLA-C*07:49', 'HLA-C*07:50', 'HLA-C*07:51', 'HLA-C*07:52',
'HLA-C*07:53', 'HLA-C*07:54', 'HLA-C*07:56', 'HLA-C*07:57', 'HLA-C*07:58', 'HLA-C*07:59', 'HLA-C*07:60', 'HLA-C*07:62', 'HLA-C*07:63',
'HLA-C*07:64', 'HLA-C*07:65', 'HLA-C*07:66', 'HLA-C*07:67', 'HLA-C*07:68', 'HLA-C*07:69', 'HLA-C*07:70', 'HLA-C*07:71', 'HLA-C*07:72',
'HLA-C*07:73', 'HLA-C*07:74', 'HLA-C*07:75', 'HLA-C*07:76', 'HLA-C*07:77', 'HLA-C*07:78', 'HLA-C*07:79', 'HLA-C*07:80', 'HLA-C*07:81',
'HLA-C*07:82', 'HLA-C*07:83', 'HLA-C*07:84', 'HLA-C*07:85', 'HLA-C*07:86', 'HLA-C*07:87', 'HLA-C*07:88', 'HLA-C*07:89', 'HLA-C*07:90',
'HLA-C*07:91', 'HLA-C*07:92', 'HLA-C*07:93', 'HLA-C*07:94', 'HLA-C*07:95', 'HLA-C*07:96', 'HLA-C*07:97', 'HLA-C*07:99', 'HLA-C*08:01',
'HLA-C*08:02', 'HLA-C*08:03', 'HLA-C*08:04', 'HLA-C*08:05', 'HLA-C*08:06', 'HLA-C*08:07', 'HLA-C*08:08', 'HLA-C*08:09', 'HLA-C*08:10',
'HLA-C*08:11', 'HLA-C*08:12', 'HLA-C*08:13', 'HLA-C*08:14', 'HLA-C*08:15', 'HLA-C*08:16', 'HLA-C*08:17', 'HLA-C*08:18', 'HLA-C*08:19',
'HLA-C*08:20', 'HLA-C*08:21', 'HLA-C*08:22', 'HLA-C*08:23', 'HLA-C*08:24', 'HLA-C*08:25', 'HLA-C*08:27', 'HLA-C*08:28', 'HLA-C*08:29',
'HLA-C*08:30', 'HLA-C*08:31', 'HLA-C*08:32', 'HLA-C*08:33', 'HLA-C*08:34', 'HLA-C*08:35', 'HLA-C*12:02', 'HLA-C*12:03', 'HLA-C*12:04',
'HLA-C*12:05', 'HLA-C*12:06', 'HLA-C*12:07', 'HLA-C*12:08', 'HLA-C*12:09', 'HLA-C*12:10', 'HLA-C*12:11', 'HLA-C*12:12', 'HLA-C*12:13',
'HLA-C*12:14', 'HLA-C*12:15', 'HLA-C*12:16', 'HLA-C*12:17', 'HLA-C*12:18', 'HLA-C*12:19', 'HLA-C*12:20', 'HLA-C*12:21', 'HLA-C*12:22',
'HLA-C*12:23', 'HLA-C*12:24', 'HLA-C*12:25', 'HLA-C*12:26', 'HLA-C*12:27', 'HLA-C*12:28', 'HLA-C*12:29', 'HLA-C*12:30', 'HLA-C*12:31',
'HLA-C*12:32', 'HLA-C*12:33', 'HLA-C*12:34', 'HLA-C*12:35', 'HLA-C*12:36', 'HLA-C*12:37', 'HLA-C*12:38', 'HLA-C*12:40', 'HLA-C*12:41',
'HLA-C*12:43', 'HLA-C*12:44', 'HLA-C*14:02', 'HLA-C*14:03', 'HLA-C*14:04', 'HLA-C*14:05', 'HLA-C*14:06', 'HLA-C*14:08', 'HLA-C*14:09',
'HLA-C*14:10', 'HLA-C*14:11', 'HLA-C*14:12', 'HLA-C*14:13', 'HLA-C*14:14', 'HLA-C*14:15', 'HLA-C*14:16', 'HLA-C*14:17', 'HLA-C*14:18',
'HLA-C*14:19', 'HLA-C*14:20', 'HLA-C*15:02', 'HLA-C*15:03', 'HLA-C*15:04', 'HLA-C*15:05', 'HLA-C*15:06', 'HLA-C*15:07', 'HLA-C*15:08',
'HLA-C*15:09', 'HLA-C*15:10', 'HLA-C*15:11', 'HLA-C*15:12', 'HLA-C*15:13', 'HLA-C*15:15', 'HLA-C*15:16', 'HLA-C*15:17', 'HLA-C*15:18',
'HLA-C*15:19', 'HLA-C*15:20', 'HLA-C*15:21', 'HLA-C*15:22', 'HLA-C*15:23', 'HLA-C*15:24', 'HLA-C*15:25', 'HLA-C*15:26', 'HLA-C*15:27',
'HLA-C*15:28', 'HLA-C*15:29', 'HLA-C*15:30', 'HLA-C*15:31', 'HLA-C*15:33', 'HLA-C*15:34', 'HLA-C*15:35', 'HLA-C*16:01', 'HLA-C*16:02',
'HLA-C*16:04', 'HLA-C*16:06', 'HLA-C*16:07', 'HLA-C*16:08', 'HLA-C*16:09', 'HLA-C*16:10', 'HLA-C*16:11', 'HLA-C*16:12', 'HLA-C*16:13',
'HLA-C*16:14', 'HLA-C*16:15', 'HLA-C*16:17', 'HLA-C*16:18', 'HLA-C*16:19', 'HLA-C*16:20', 'HLA-C*16:21', 'HLA-C*16:22', 'HLA-C*16:23',
'HLA-C*16:24', 'HLA-C*16:25', 'HLA-C*16:26', 'HLA-C*17:01', 'HLA-C*17:02', 'HLA-C*17:03', 'HLA-C*17:04', 'HLA-C*17:05', 'HLA-C*17:06',
'HLA-C*17:07', 'HLA-C*18:01', 'HLA-C*18:02', 'HLA-C*18:03', 'HLA-E*01:01', 'HLA-G*01:01', 'HLA-G*01:02', 'HLA-G*01:03', 'HLA-G*01:04',
'HLA-G*01:06', 'HLA-G*01:07', 'HLA-G*01:08', 'HLA-G*01:09',
'H-2-Db', 'H-2-Dd', 'H-2-Kb', 'H-2-Kd', 'H-2-Kk', 'H-2-Ld'])
__version = "2.4"
@property
def version(self):
"""The version of the predictor"""
return self.__version
@property
def supportedAlleles(self):
"""
A list of valid :class:`~epytope.Core.Allele.Allele` models
"""
return self.__alleles
@property
def name(self):
"""The name of the predictor"""
return self.__name
@property
def command(self):
"""
Defines the commandline call for external tool
"""
return self.__command
@property
def supportedLength(self):
"""
A list of supported :class:`~epytope.Core.Peptide.Peptide` lengths
"""
return self.__supported_length
def _represent(self, allele):
"""
Internal function transforming an allele object into its representative string
:param allele: The :class:`~epytope.Core.Allele.Allele` for which the internal predictor representation is
needed
:type alleles: :class:`~epytope.Core.Allele.Allele`
:return: str
"""
if isinstance(allele, MouseAllele):
return "H-2-%s%s%s" % (allele.locus, allele.supertype, allele.subtype)
else:
return "HLA-%s%s:%s" % (allele.locus, allele.supertype, allele.subtype)
def convert_alleles(self, alleles):
"""
Converts :class:`~epytope.Core.Allele.Allele` into the internal :class:`~epytope.Core.Allele.Allele` representation
of the predictor and returns a string representation
:param alleles: The :class:`~epytope.Core.Allele.Allele` for which the internal predictor representation is
needed
:type alleles: :class:`~epytope.Core.Allele.Allele`
:return: Returns a string representation of the input :class:`~epytope.Core.Allele.Allele`
:rtype: list(str)
"""
return [self._represent(a) for a in alleles]
def parse_external_result(self, file):
"""
Parses external results and returns the result containing the predictors string representation
of alleles and peptides.
:param str file: The file path or the external prediction results
:return: A dictionary containing the prediction results
:rtype: dict
"""
f = csv.reader(open(file, "r"), delimiter = '\t')
scores = defaultdict(defaultdict)
alleles = [x for x in next(f) if "HLA" in x]
# Rank is not supported in command line tool of NetMHCpan 2.4
for row in f:
pep_seq = row[PeptideIndex.NETMHCPAN_2_4]
for i, a in enumerate(alleles):
scores[a][pep_seq] = float(row[ScoreIndex.NETMHCPAN_2_4 + i])
# Create dictionary with hierarchy: {'Allele1': {'Score': {'Pep1': Score1, 'Pep2': Score2,..}, 'Allele2':...}
result = {allele: {"Score":(list(scores.values())[j])} for j, allele in enumerate(alleles)}
return result
def get_external_version(self, path=None):
"""
Returns the external version of the tool by executing
>{command} --version
might be dependent on the method and has to be overwritten
therefore it is declared abstract to enforce the user to
overwrite the method. The function in the base class can be called
with super()
:param str path: Optional specification of executable path if deviant from :attr:`self.__command`
:return: The external version of the tool or None if tool does not support versioning
:rtype: str
"""
# can not be determined netmhcpan does not support --version or similar
return None
def prepare_input(self, input, file):
"""
Prepares input for external tools and writes them to file in the specific format
NO return value!
:param: list(str) input: The :class:`~epytope.Core.Peptide.Peptide` sequences to write into file
:param File file: File-handler to input file for external tool
"""
file.write("\n".join(input))
class NetMHCpan_2_8(AExternalEpitopePrediction):
"""
Implements the NetMHC binding (in current form for netMHCpan 2.8).
Supported MHC alleles currently only restricted to HLA alleles.
.. note::
Nielsen, Morten, et al. "NetMHCpan, a method for quantitative predictions of peptide binding to any HLA-A and-B
locus protein of known sequence." PloS one 2.8 (2007): e796.
"""
__version = "2.8"
__supported_length = frozenset([8, 9, 10, 11, 12, 13, 14])
__name = "netmhcpan"
__command = "netMHCpan -p {peptides} -a {alleles} {options} -ic50 -xls -xlsfile {out}"
__alleles = frozenset(
['HLA-A*01:01', 'HLA-A*01:02', 'HLA-A*01:03', 'HLA-A*01:06', 'HLA-A*01:07', 'HLA-A*01:08', 'HLA-A*01:09', 'HLA-A*01:10', 'HLA-A*01:12',
'HLA-A*01:13', 'HLA-A*01:14', 'HLA-A*01:17', 'HLA-A*01:19', 'HLA-A*01:20', 'HLA-A*01:21', 'HLA-A*01:23', 'HLA-A*01:24', 'HLA-A*01:25',
'HLA-A*01:26', 'HLA-A*01:28', 'HLA-A*01:29', 'HLA-A*01:30', 'HLA-A*01:32', 'HLA-A*01:33', 'HLA-A*01:35', 'HLA-A*01:36', 'HLA-A*01:37',
'HLA-A*01:38', 'HLA-A*01:39', 'HLA-A*01:40', 'HLA-A*01:41', 'HLA-A*01:42', 'HLA-A*01:43', 'HLA-A*01:44', 'HLA-A*01:45', 'HLA-A*01:46',
'HLA-A*01:47', 'HLA-A*01:48', 'HLA-A*01:49', 'HLA-A*01:50', 'HLA-A*01:51', 'HLA-A*01:54', 'HLA-A*01:55', 'HLA-A*01:58', 'HLA-A*01:59',
'HLA-A*01:60', 'HLA-A*01:61', 'HLA-A*01:62', 'HLA-A*01:63', 'HLA-A*01:64', 'HLA-A*01:65', 'HLA-A*01:66', 'HLA-A*02:01', 'HLA-A*02:02',
'HLA-A*02:03', 'HLA-A*02:04', 'HLA-A*02:05', 'HLA-A*02:06', 'HLA-A*02:07', 'HLA-A*02:08', 'HLA-A*02:09', 'HLA-A*02:10', 'HLA-A*02:101',
'HLA-A*02:102', 'HLA-A*02:103', 'HLA-A*02:104', 'HLA-A*02:105', 'HLA-A*02:106', 'HLA-A*02:107', 'HLA-A*02:108', 'HLA-A*02:109',
'HLA-A*02:11', 'HLA-A*02:110', 'HLA-A*02:111', 'HLA-A*02:112', 'HLA-A*02:114', 'HLA-A*02:115', 'HLA-A*02:116', 'HLA-A*02:117',
'HLA-A*02:118', 'HLA-A*02:119', 'HLA-A*02:12', 'HLA-A*02:120', 'HLA-A*02:121', 'HLA-A*02:122', 'HLA-A*02:123', 'HLA-A*02:124',
'HLA-A*02:126', 'HLA-A*02:127', 'HLA-A*02:128', 'HLA-A*02:129', 'HLA-A*02:13', 'HLA-A*02:130', 'HLA-A*02:131', 'HLA-A*02:132',
'HLA-A*02:133', 'HLA-A*02:134', 'HLA-A*02:135', 'HLA-A*02:136', 'HLA-A*02:137', 'HLA-A*02:138', 'HLA-A*02:139', 'HLA-A*02:14',
'HLA-A*02:140', 'HLA-A*02:141', 'HLA-A*02:142', 'HLA-A*02:143', 'HLA-A*02:144', 'HLA-A*02:145', 'HLA-A*02:146', 'HLA-A*02:147',
'HLA-A*02:148', 'HLA-A*02:149', 'HLA-A*02:150', 'HLA-A*02:151', 'HLA-A*02:152', 'HLA-A*02:153', 'HLA-A*02:154', 'HLA-A*02:155',
'HLA-A*02:156', 'HLA-A*02:157', 'HLA-A*02:158', 'HLA-A*02:159', 'HLA-A*02:16', 'HLA-A*02:160', 'HLA-A*02:161', 'HLA-A*02:162',
'HLA-A*02:163', 'HLA-A*02:164', 'HLA-A*02:165', 'HLA-A*02:166', 'HLA-A*02:167', 'HLA-A*02:168', 'HLA-A*02:169', 'HLA-A*02:17',
'HLA-A*02:170', 'HLA-A*02:171', 'HLA-A*02:172', 'HLA-A*02:173', 'HLA-A*02:174', 'HLA-A*02:175', 'HLA-A*02:176', 'HLA-A*02:177',
'HLA-A*02:178', 'HLA-A*02:179', 'HLA-A*02:18', 'HLA-A*02:180', 'HLA-A*02:181', 'HLA-A*02:182', 'HLA-A*02:183', 'HLA-A*02:184',
'HLA-A*02:185', 'HLA-A*02:186', 'HLA-A*02:187', 'HLA-A*02:188', 'HLA-A*02:189', 'HLA-A*02:19', 'HLA-A*02:190', 'HLA-A*02:191',
'HLA-A*02:192', 'HLA-A*02:193', 'HLA-A*02:194', 'HLA-A*02:195', 'HLA-A*02:196', 'HLA-A*02:197', 'HLA-A*02:198', 'HLA-A*02:199',
'HLA-A*02:20', 'HLA-A*02:200', 'HLA-A*02:201', 'HLA-A*02:202', 'HLA-A*02:203', 'HLA-A*02:204', 'HLA-A*02:205', 'HLA-A*02:206',
'HLA-A*02:207', 'HLA-A*02:208', 'HLA-A*02:209', 'HLA-A*02:21', 'HLA-A*02:210', 'HLA-A*02:211', 'HLA-A*02:212', 'HLA-A*02:213',
'HLA-A*02:214', 'HLA-A*02:215', 'HLA-A*02:216', 'HLA-A*02:217', 'HLA-A*02:218', 'HLA-A*02:219', 'HLA-A*02:22', 'HLA-A*02:220',
'HLA-A*02:221', 'HLA-A*02:224', 'HLA-A*02:228', 'HLA-A*02:229', 'HLA-A*02:230', 'HLA-A*02:231', 'HLA-A*02:232', 'HLA-A*02:233',
'HLA-A*02:234', 'HLA-A*02:235', 'HLA-A*02:236', 'HLA-A*02:237', 'HLA-A*02:238', 'HLA-A*02:239', 'HLA-A*02:24', 'HLA-A*02:240',
'HLA-A*02:241', 'HLA-A*02:242', 'HLA-A*02:243', 'HLA-A*02:244', 'HLA-A*02:245', 'HLA-A*02:246', 'HLA-A*02:247', 'HLA-A*02:248',
'HLA-A*02:249', 'HLA-A*02:25', 'HLA-A*02:251', 'HLA-A*02:252', 'HLA-A*02:253', 'HLA-A*02:254', 'HLA-A*02:255', 'HLA-A*02:256',
'HLA-A*02:257', 'HLA-A*02:258', 'HLA-A*02:259', 'HLA-A*02:26', 'HLA-A*02:260', 'HLA-A*02:261', 'HLA-A*02:262', 'HLA-A*02:263',
'HLA-A*02:264', 'HLA-A*02:265', 'HLA-A*02:266', 'HLA-A*02:27', 'HLA-A*02:28', 'HLA-A*02:29', 'HLA-A*02:30', 'HLA-A*02:31', 'HLA-A*02:33',
'HLA-A*02:34', 'HLA-A*02:35', 'HLA-A*02:36', 'HLA-A*02:37', 'HLA-A*02:38', 'HLA-A*02:39', 'HLA-A*02:40', 'HLA-A*02:41', 'HLA-A*02:42',
'HLA-A*02:44', 'HLA-A*02:45', 'HLA-A*02:46', 'HLA-A*02:47', 'HLA-A*02:48', 'HLA-A*02:49', 'HLA-A*02:50', 'HLA-A*02:51', 'HLA-A*02:52',
'HLA-A*02:54', 'HLA-A*02:55', 'HLA-A*02:56', 'HLA-A*02:57', 'HLA-A*02:58', 'HLA-A*02:59', 'HLA-A*02:60', 'HLA-A*02:61', 'HLA-A*02:62',
'HLA-A*02:63', 'HLA-A*02:64', 'HLA-A*02:65', 'HLA-A*02:66', 'HLA-A*02:67', 'HLA-A*02:68', 'HLA-A*02:69', 'HLA-A*02:70', 'HLA-A*02:71',
'HLA-A*02:72', 'HLA-A*02:73', 'HLA-A*02:74', 'HLA-A*02:75', 'HLA-A*02:76', 'HLA-A*02:77', 'HLA-A*02:78', 'HLA-A*02:79', 'HLA-A*02:80',
'HLA-A*02:81', 'HLA-A*02:84', 'HLA-A*02:85', 'HLA-A*02:86', 'HLA-A*02:87', 'HLA-A*02:89', 'HLA-A*02:90', 'HLA-A*02:91', 'HLA-A*02:92',
'HLA-A*02:93', 'HLA-A*02:95', 'HLA-A*02:96', 'HLA-A*02:97', 'HLA-A*02:99', 'HLA-A*03:01', 'HLA-A*03:02', 'HLA-A*03:04', 'HLA-A*03:05',
'HLA-A*03:06', 'HLA-A*03:07', 'HLA-A*03:08', 'HLA-A*03:09', 'HLA-A*03:10', 'HLA-A*03:12', 'HLA-A*03:13', 'HLA-A*03:14', 'HLA-A*03:15',
'HLA-A*03:16', 'HLA-A*03:17', 'HLA-A*03:18', 'HLA-A*03:19', 'HLA-A*03:20', 'HLA-A*03:22', 'HLA-A*03:23', 'HLA-A*03:24', 'HLA-A*03:25',
'HLA-A*03:26', 'HLA-A*03:27', 'HLA-A*03:28', 'HLA-A*03:29', 'HLA-A*03:30', 'HLA-A*03:31', 'HLA-A*03:32', 'HLA-A*03:33', 'HLA-A*03:34',
'HLA-A*03:35', 'HLA-A*03:37', 'HLA-A*03:38', 'HLA-A*03:39', 'HLA-A*03:40', 'HLA-A*03:41', 'HLA-A*03:42', 'HLA-A*03:43', 'HLA-A*03:44',
'HLA-A*03:45', 'HLA-A*03:46', 'HLA-A*03:47', 'HLA-A*03:48', 'HLA-A*03:49', 'HLA-A*03:50', 'HLA-A*03:51', 'HLA-A*03:52', 'HLA-A*03:53',
'HLA-A*03:54', 'HLA-A*03:55', 'HLA-A*03:56', 'HLA-A*03:57', 'HLA-A*03:58', 'HLA-A*03:59', 'HLA-A*03:60', 'HLA-A*03:61', 'HLA-A*03:62',
'HLA-A*03:63', 'HLA-A*03:64', 'HLA-A*03:65', 'HLA-A*03:66', 'HLA-A*03:67', 'HLA-A*03:70', 'HLA-A*03:71', 'HLA-A*03:72', 'HLA-A*03:73',
'HLA-A*03:74', 'HLA-A*03:75', 'HLA-A*03:76', 'HLA-A*03:77', 'HLA-A*03:78', 'HLA-A*03:79', 'HLA-A*03:80', 'HLA-A*03:81', 'HLA-A*03:82',
'HLA-A*11:01', 'HLA-A*11:02', 'HLA-A*11:03', 'HLA-A*11:04', 'HLA-A*11:05', 'HLA-A*11:06', 'HLA-A*11:07', 'HLA-A*11:08', 'HLA-A*11:09',
'HLA-A*11:10', 'HLA-A*11:11', 'HLA-A*11:12', 'HLA-A*11:13', 'HLA-A*11:14', 'HLA-A*11:15', 'HLA-A*11:16', 'HLA-A*11:17', 'HLA-A*11:18',
'HLA-A*11:19', 'HLA-A*11:20', 'HLA-A*11:22', 'HLA-A*11:23', 'HLA-A*11:24', 'HLA-A*11:25', 'HLA-A*11:26', 'HLA-A*11:27', 'HLA-A*11:29',
'HLA-A*11:30', 'HLA-A*11:31', 'HLA-A*11:32', 'HLA-A*11:33', 'HLA-A*11:34', 'HLA-A*11:35', 'HLA-A*11:36', 'HLA-A*11:37', 'HLA-A*11:38',
'HLA-A*11:39', 'HLA-A*11:40', 'HLA-A*11:41', 'HLA-A*11:42', 'HLA-A*11:43', 'HLA-A*11:44', 'HLA-A*11:45', 'HLA-A*11:46', 'HLA-A*11:47',
'HLA-A*11:48', 'HLA-A*11:49', 'HLA-A*11:51', 'HLA-A*11:53', 'HLA-A*11:54', 'HLA-A*11:55', 'HLA-A*11:56', 'HLA-A*11:57', 'HLA-A*11:58',
'HLA-A*11:59', 'HLA-A*11:60', 'HLA-A*11:61', 'HLA-A*11:62', 'HLA-A*11:63', 'HLA-A*11:64', 'HLA-A*23:01', 'HLA-A*23:02', 'HLA-A*23:03',
'HLA-A*23:04', 'HLA-A*23:05', 'HLA-A*23:06', 'HLA-A*23:09', 'HLA-A*23:10', 'HLA-A*23:12', 'HLA-A*23:13', 'HLA-A*23:14', 'HLA-A*23:15',
'HLA-A*23:16', 'HLA-A*23:17', 'HLA-A*23:18', 'HLA-A*23:20', 'HLA-A*23:21', 'HLA-A*23:22', 'HLA-A*23:23', 'HLA-A*23:24', 'HLA-A*23:25',
'HLA-A*23:26', 'HLA-A*24:02', 'HLA-A*24:03', 'HLA-A*24:04', 'HLA-A*24:05', 'HLA-A*24:06', 'HLA-A*24:07', 'HLA-A*24:08', 'HLA-A*24:10',
'HLA-A*24:100', 'HLA-A*24:101', 'HLA-A*24:102', 'HLA-A*24:103', 'HLA-A*24:104', 'HLA-A*24:105', 'HLA-A*24:106', 'HLA-A*24:107',
'HLA-A*24:108', 'HLA-A*24:109', 'HLA-A*24:110', 'HLA-A*24:111', 'HLA-A*24:112', 'HLA-A*24:113', 'HLA-A*24:114', 'HLA-A*24:115',
'HLA-A*24:116', 'HLA-A*24:117', 'HLA-A*24:118', 'HLA-A*24:119', 'HLA-A*24:120', 'HLA-A*24:121', 'HLA-A*24:122', 'HLA-A*24:123',
'HLA-A*24:124', 'HLA-A*24:125', 'HLA-A*24:126', 'HLA-A*24:127', 'HLA-A*24:128', 'HLA-A*24:129', 'HLA-A*24:13', 'HLA-A*24:130',
'HLA-A*24:131', 'HLA-A*24:133', 'HLA-A*24:134', 'HLA-A*24:135', 'HLA-A*24:136', 'HLA-A*24:137', 'HLA-A*24:138', 'HLA-A*24:139',
'HLA-A*24:14', 'HLA-A*24:140', 'HLA-A*24:141', 'HLA-A*24:142', 'HLA-A*24:143', 'HLA-A*24:144', 'HLA-A*24:15', 'HLA-A*24:17', 'HLA-A*24:18',
'HLA-A*24:19', 'HLA-A*24:20', 'HLA-A*24:21', 'HLA-A*24:22', 'HLA-A*24:23', 'HLA-A*24:24', 'HLA-A*24:25', 'HLA-A*24:26', 'HLA-A*24:27',
'HLA-A*24:28', 'HLA-A*24:29', 'HLA-A*24:30', 'HLA-A*24:31', 'HLA-A*24:32', 'HLA-A*24:33', 'HLA-A*24:34', 'HLA-A*24:35', 'HLA-A*24:37',
'HLA-A*24:38', 'HLA-A*24:39', 'HLA-A*24:41', 'HLA-A*24:42', 'HLA-A*24:43', 'HLA-A*24:44', 'HLA-A*24:46', 'HLA-A*24:47', 'HLA-A*24:49',
'HLA-A*24:50', 'HLA-A*24:51', 'HLA-A*24:52', 'HLA-A*24:53', 'HLA-A*24:54', 'HLA-A*24:55', 'HLA-A*24:56', 'HLA-A*24:57', 'HLA-A*24:58',
'HLA-A*24:59', 'HLA-A*24:61', 'HLA-A*24:62', 'HLA-A*24:63', 'HLA-A*24:64', 'HLA-A*24:66', 'HLA-A*24:67', 'HLA-A*24:68', 'HLA-A*24:69',
'HLA-A*24:70', 'HLA-A*24:71', 'HLA-A*24:72', 'HLA-A*24:73', 'HLA-A*24:74', 'HLA-A*24:75', 'HLA-A*24:76', 'HLA-A*24:77', 'HLA-A*24:78',
'HLA-A*24:79', 'HLA-A*24:80', 'HLA-A*24:81', 'HLA-A*24:82', 'HLA-A*24:85', 'HLA-A*24:87', 'HLA-A*24:88', 'HLA-A*24:89', 'HLA-A*24:91',
'HLA-A*24:92', 'HLA-A*24:93', 'HLA-A*24:94', 'HLA-A*24:95', 'HLA-A*24:96', 'HLA-A*24:97', 'HLA-A*24:98', 'HLA-A*24:99', 'HLA-A*25:01',
'HLA-A*25:02', 'HLA-A*25:03', 'HLA-A*25:04', 'HLA-A*25:05', 'HLA-A*25:06', 'HLA-A*25:07', 'HLA-A*25:08', 'HLA-A*25:09', 'HLA-A*25:10',
'HLA-A*25:11', 'HLA-A*25:13', 'HLA-A*26:01', 'HLA-A*26:02', 'HLA-A*26:03', 'HLA-A*26:04', 'HLA-A*26:05', 'HLA-A*26:06', 'HLA-A*26:07',
'HLA-A*26:08', 'HLA-A*26:09', 'HLA-A*26:10', 'HLA-A*26:12', 'HLA-A*26:13', 'HLA-A*26:14', 'HLA-A*26:15', 'HLA-A*26:16', 'HLA-A*26:17',
'HLA-A*26:18', 'HLA-A*26:19', 'HLA-A*26:20', 'HLA-A*26:21', 'HLA-A*26:22', 'HLA-A*26:23', 'HLA-A*26:24', 'HLA-A*26:26', 'HLA-A*26:27',
'HLA-A*26:28', 'HLA-A*26:29', 'HLA-A*26:30', 'HLA-A*26:31', 'HLA-A*26:32', 'HLA-A*26:33', 'HLA-A*26:34', 'HLA-A*26:35', 'HLA-A*26:36',
'HLA-A*26:37', 'HLA-A*26:38', 'HLA-A*26:39', 'HLA-A*26:40', 'HLA-A*26:41', 'HLA-A*26:42', 'HLA-A*26:43', 'HLA-A*26:45', 'HLA-A*26:46',
'HLA-A*26:47', 'HLA-A*26:48', 'HLA-A*26:49', 'HLA-A*26:50', 'HLA-A*29:01', 'HLA-A*29:02', 'HLA-A*29:03', 'HLA-A*29:04', 'HLA-A*29:05',
'HLA-A*29:06', 'HLA-A*29:07', 'HLA-A*29:09', 'HLA-A*29:10', 'HLA-A*29:11', 'HLA-A*29:12', 'HLA-A*29:13', 'HLA-A*29:14', 'HLA-A*29:15',
'HLA-A*29:16', 'HLA-A*29:17', 'HLA-A*29:18', 'HLA-A*29:19', 'HLA-A*29:20', 'HLA-A*29:21', 'HLA-A*29:22', 'HLA-A*30:01', 'HLA-A*30:02',
'HLA-A*30:03', 'HLA-A*30:04', 'HLA-A*30:06', 'HLA-A*30:07', 'HLA-A*30:08', 'HLA-A*30:09', 'HLA-A*30:10', 'HLA-A*30:11', 'HLA-A*30:12',
'HLA-A*30:13', 'HLA-A*30:15', 'HLA-A*30:16', 'HLA-A*30:17', 'HLA-A*30:18', 'HLA-A*30:19', 'HLA-A*30:20', 'HLA-A*30:22', 'HLA-A*30:23',
'HLA-A*30:24', 'HLA-A*30:25', 'HLA-A*30:26', 'HLA-A*30:28', 'HLA-A*30:29', 'HLA-A*30:30', 'HLA-A*30:31', 'HLA-A*30:32', 'HLA-A*30:33',
'HLA-A*30:34', 'HLA-A*30:35', 'HLA-A*30:36', 'HLA-A*30:37', 'HLA-A*30:38', 'HLA-A*30:39', 'HLA-A*30:40', 'HLA-A*30:41', 'HLA-A*31:01',
'HLA-A*31:02', 'HLA-A*31:03', 'HLA-A*31:04', 'HLA-A*31:05', 'HLA-A*31:06', 'HLA-A*31:07', 'HLA-A*31:08', 'HLA-A*31:09', 'HLA-A*31:10',
'HLA-A*31:11', 'HLA-A*31:12', 'HLA-A*31:13', 'HLA-A*31:15', 'HLA-A*31:16', 'HLA-A*31:17', 'HLA-A*31:18', 'HLA-A*31:19', 'HLA-A*31:20',
'HLA-A*31:21', 'HLA-A*31:22', 'HLA-A*31:23', 'HLA-A*31:24', 'HLA-A*31:25', 'HLA-A*31:26', 'HLA-A*31:27', 'HLA-A*31:28', 'HLA-A*31:29',
'HLA-A*31:30', 'HLA-A*31:31', 'HLA-A*31:32', 'HLA-A*31:33', 'HLA-A*31:34', 'HLA-A*31:35', 'HLA-A*31:36', 'HLA-A*31:37', 'HLA-A*32:01',
'HLA-A*32:02', 'HLA-A*32:03', 'HLA-A*32:04', 'HLA-A*32:05', 'HLA-A*32:06', 'HLA-A*32:07', 'HLA-A*32:08', 'HLA-A*32:09', 'HLA-A*32:10',
'HLA-A*32:12', 'HLA-A*32:13', 'HLA-A*32:14', 'HLA-A*32:15', 'HLA-A*32:16', 'HLA-A*32:17', 'HLA-A*32:18', 'HLA-A*32:20', 'HLA-A*32:21',
'HLA-A*32:22', 'HLA-A*32:23', 'HLA-A*32:24', 'HLA-A*32:25', 'HLA-A*33:01', 'HLA-A*33:03', 'HLA-A*33:04', 'HLA-A*33:05', 'HLA-A*33:06',
'HLA-A*33:07', 'HLA-A*33:08', 'HLA-A*33:09', 'HLA-A*33:10', 'HLA-A*33:11', 'HLA-A*33:12', 'HLA-A*33:13', 'HLA-A*33:14', 'HLA-A*33:15',
'HLA-A*33:16', 'HLA-A*33:17', 'HLA-A*33:18', 'HLA-A*33:19', 'HLA-A*33:20', 'HLA-A*33:21', 'HLA-A*33:22', 'HLA-A*33:23', 'HLA-A*33:24',
'HLA-A*33:25', 'HLA-A*33:26', 'HLA-A*33:27', 'HLA-A*33:28', 'HLA-A*33:29', 'HLA-A*33:30', 'HLA-A*33:31', 'HLA-A*34:01', 'HLA-A*34:02',
'HLA-A*34:03', 'HLA-A*34:04', 'HLA-A*34:05', 'HLA-A*34:06', 'HLA-A*34:07', 'HLA-A*34:08', 'HLA-A*36:01', 'HLA-A*36:02', 'HLA-A*36:03',
'HLA-A*36:04', 'HLA-A*36:05', 'HLA-A*43:01', 'HLA-A*66:01', 'HLA-A*66:02', 'HLA-A*66:03', 'HLA-A*66:04', 'HLA-A*66:05', 'HLA-A*66:06',
'HLA-A*66:07', 'HLA-A*66:08', 'HLA-A*66:09', 'HLA-A*66:10', 'HLA-A*66:11', 'HLA-A*66:12', 'HLA-A*66:13', 'HLA-A*66:14', 'HLA-A*66:15',
'HLA-A*68:01', 'HLA-A*68:02', 'HLA-A*68:03', 'HLA-A*68:04', 'HLA-A*68:05', 'HLA-A*68:06', 'HLA-A*68:07', 'HLA-A*68:08', 'HLA-A*68:09',
'HLA-A*68:10', 'HLA-A*68:12', 'HLA-A*68:13', 'HLA-A*68:14', 'HLA-A*68:15', 'HLA-A*68:16', 'HLA-A*68:17', 'HLA-A*68:19', 'HLA-A*68:20',
'HLA-A*68:21', 'HLA-A*68:22', 'HLA-A*68:23', 'HLA-A*68:24', 'HLA-A*68:25', 'HLA-A*68:26', 'HLA-A*68:27', 'HLA-A*68:28', 'HLA-A*68:29',
'HLA-A*68:30', 'HLA-A*68:31', 'HLA-A*68:32', 'HLA-A*68:33', 'HLA-A*68:34', 'HLA-A*68:35', 'HLA-A*68:36', 'HLA-A*68:37', 'HLA-A*68:38',
'HLA-A*68:39', 'HLA-A*68:40', 'HLA-A*68:41', 'HLA-A*68:42', 'HLA-A*68:43', 'HLA-A*68:44', 'HLA-A*68:45', 'HLA-A*68:46', 'HLA-A*68:47',
'HLA-A*68:48', 'HLA-A*68:50', 'HLA-A*68:51', 'HLA-A*68:52', 'HLA-A*68:53', 'HLA-A*68:54', 'HLA-A*69:01', 'HLA-A*74:01', 'HLA-A*74:02',
'HLA-A*74:03', 'HLA-A*74:04', 'HLA-A*74:05', 'HLA-A*74:06', 'HLA-A*74:07', 'HLA-A*74:08', 'HLA-A*74:09', 'HLA-A*74:10', 'HLA-A*74:11',
'HLA-A*74:13', 'HLA-A*80:01', 'HLA-A*80:02', 'HLA-B*07:02', 'HLA-B*07:03', 'HLA-B*07:04', 'HLA-B*07:05', 'HLA-B*07:06', 'HLA-B*07:07',
'HLA-B*07:08', 'HLA-B*07:09', 'HLA-B*07:10', 'HLA-B*07:100', 'HLA-B*07:101', 'HLA-B*07:102', 'HLA-B*07:103', 'HLA-B*07:104',
'HLA-B*07:105', 'HLA-B*07:106', 'HLA-B*07:107', 'HLA-B*07:108', 'HLA-B*07:109', 'HLA-B*07:11', 'HLA-B*07:110', 'HLA-B*07:112',
'HLA-B*07:113', 'HLA-B*07:114', 'HLA-B*07:115', 'HLA-B*07:12', 'HLA-B*07:13', 'HLA-B*07:14', 'HLA-B*07:15', 'HLA-B*07:16', 'HLA-B*07:17',
'HLA-B*07:18', 'HLA-B*07:19', 'HLA-B*07:20', 'HLA-B*07:21', 'HLA-B*07:22', 'HLA-B*07:23', 'HLA-B*07:24', 'HLA-B*07:25', 'HLA-B*07:26',
'HLA-B*07:27', 'HLA-B*07:28', 'HLA-B*07:29', 'HLA-B*07:30', 'HLA-B*07:31', 'HLA-B*07:32', 'HLA-B*07:33', 'HLA-B*07:34', 'HLA-B*07:35',
'HLA-B*07:36', 'HLA-B*07:37', 'HLA-B*07:38', 'HLA-B*07:39', 'HLA-B*07:40', 'HLA-B*07:41', 'HLA-B*07:42', 'HLA-B*07:43', 'HLA-B*07:44',
'HLA-B*07:45', 'HLA-B*07:46', 'HLA-B*07:47', 'HLA-B*07:48', 'HLA-B*07:50', 'HLA-B*07:51', 'HLA-B*07:52', 'HLA-B*07:53', 'HLA-B*07:54',
'HLA-B*07:55', 'HLA-B*07:56', 'HLA-B*07:57', 'HLA-B*07:58', 'HLA-B*07:59', 'HLA-B*07:60', 'HLA-B*07:61', 'HLA-B*07:62', 'HLA-B*07:63',
'HLA-B*07:64', 'HLA-B*07:65', 'HLA-B*07:66', 'HLA-B*07:68', 'HLA-B*07:69', 'HLA-B*07:70', 'HLA-B*07:71', 'HLA-B*07:72', 'HLA-B*07:73',
'HLA-B*07:74', 'HLA-B*07:75', 'HLA-B*07:76', 'HLA-B*07:77', 'HLA-B*07:78', 'HLA-B*07:79', 'HLA-B*07:80', 'HLA-B*07:81', 'HLA-B*07:82',
'HLA-B*07:83', 'HLA-B*07:84', 'HLA-B*07:85', 'HLA-B*07:86', 'HLA-B*07:87', 'HLA-B*07:88', 'HLA-B*07:89', 'HLA-B*07:90', 'HLA-B*07:91',
'HLA-B*07:92', 'HLA-B*07:93', 'HLA-B*07:94', 'HLA-B*07:95', 'HLA-B*07:96', 'HLA-B*07:97', 'HLA-B*07:98', 'HLA-B*07:99', 'HLA-B*08:01',
'HLA-B*08:02', 'HLA-B*08:03', 'HLA-B*08:04', 'HLA-B*08:05', 'HLA-B*08:07', 'HLA-B*08:09', 'HLA-B*08:10', 'HLA-B*08:11', 'HLA-B*08:12',
'HLA-B*08:13', 'HLA-B*08:14', 'HLA-B*08:15', 'HLA-B*08:16', 'HLA-B*08:17', 'HLA-B*08:18', 'HLA-B*08:20', 'HLA-B*08:21', 'HLA-B*08:22',
'HLA-B*08:23', 'HLA-B*08:24', 'HLA-B*08:25', 'HLA-B*08:26', 'HLA-B*08:27', 'HLA-B*08:28', 'HLA-B*08:29', 'HLA-B*08:31', 'HLA-B*08:32',
'HLA-B*08:33', 'HLA-B*08:34', 'HLA-B*08:35', 'HLA-B*08:36', 'HLA-B*08:37', 'HLA-B*08:38', 'HLA-B*08:39', 'HLA-B*08:40', 'HLA-B*08:41',
'HLA-B*08:42', 'HLA-B*08:43', 'HLA-B*08:44', 'HLA-B*08:45', 'HLA-B*08:46', 'HLA-B*08:47', 'HLA-B*08:48', 'HLA-B*08:49', 'HLA-B*08:50',
'HLA-B*08:51', 'HLA-B*08:52', 'HLA-B*08:53', 'HLA-B*08:54', 'HLA-B*08:55', 'HLA-B*08:56', 'HLA-B*08:57', 'HLA-B*08:58', 'HLA-B*08:59',
'HLA-B*08:60', 'HLA-B*08:61', 'HLA-B*08:62', 'HLA-B*13:01', 'HLA-B*13:02', 'HLA-B*13:03', 'HLA-B*13:04', 'HLA-B*13:06', 'HLA-B*13:09',
'HLA-B*13:10', 'HLA-B*13:11', 'HLA-B*13:12', 'HLA-B*13:13', 'HLA-B*13:14', 'HLA-B*13:15', 'HLA-B*13:16', 'HLA-B*13:17', 'HLA-B*13:18',
'HLA-B*13:19', 'HLA-B*13:20', 'HLA-B*13:21', 'HLA-B*13:22', 'HLA-B*13:23', 'HLA-B*13:25', 'HLA-B*13:26', 'HLA-B*13:27', 'HLA-B*13:28',
'HLA-B*13:29', 'HLA-B*13:30', 'HLA-B*13:31', 'HLA-B*13:32', 'HLA-B*13:33', 'HLA-B*13:34', 'HLA-B*13:35', 'HLA-B*13:36', 'HLA-B*13:37',
'HLA-B*13:38', 'HLA-B*13:39', 'HLA-B*14:01', 'HLA-B*14:02', 'HLA-B*14:03', 'HLA-B*14:04', 'HLA-B*14:05', 'HLA-B*14:06', 'HLA-B*14:08',
'HLA-B*14:09', 'HLA-B*14:10', 'HLA-B*14:11', 'HLA-B*14:12', 'HLA-B*14:13', 'HLA-B*14:14', 'HLA-B*14:15', 'HLA-B*14:16', 'HLA-B*14:17',
'HLA-B*14:18', 'HLA-B*15:01', 'HLA-B*15:02', 'HLA-B*15:03', 'HLA-B*15:04', 'HLA-B*15:05', 'HLA-B*15:06', 'HLA-B*15:07', 'HLA-B*15:08',
'HLA-B*15:09', 'HLA-B*15:10', 'HLA-B*15:101', 'HLA-B*15:102', 'HLA-B*15:103', 'HLA-B*15:104', 'HLA-B*15:105', 'HLA-B*15:106',
'HLA-B*15:107', 'HLA-B*15:108', 'HLA-B*15:109', 'HLA-B*15:11', 'HLA-B*15:110', 'HLA-B*15:112', 'HLA-B*15:113', 'HLA-B*15:114',
'HLA-B*15:115', 'HLA-B*15:116', 'HLA-B*15:117', 'HLA-B*15:118', 'HLA-B*15:119', 'HLA-B*15:12', 'HLA-B*15:120', 'HLA-B*15:121',
'HLA-B*15:122', 'HLA-B*15:123', 'HLA-B*15:124', 'HLA-B*15:125', 'HLA-B*15:126', 'HLA-B*15:127', 'HLA-B*15:128', 'HLA-B*15:129',
'HLA-B*15:13', 'HLA-B*15:131', 'HLA-B*15:132', 'HLA-B*15:133', 'HLA-B*15:134', 'HLA-B*15:135', 'HLA-B*15:136', 'HLA-B*15:137',
'HLA-B*15:138', 'HLA-B*15:139', 'HLA-B*15:14', 'HLA-B*15:140', 'HLA-B*15:141', 'HLA-B*15:142', 'HLA-B*15:143', 'HLA-B*15:144',
'HLA-B*15:145', 'HLA-B*15:146', 'HLA-B*15:147', 'HLA-B*15:148', 'HLA-B*15:15', 'HLA-B*15:150', 'HLA-B*15:151', 'HLA-B*15:152',
'HLA-B*15:153', 'HLA-B*15:154', 'HLA-B*15:155', 'HLA-B*15:156', 'HLA-B*15:157', 'HLA-B*15:158', 'HLA-B*15:159', 'HLA-B*15:16',
'HLA-B*15:160', 'HLA-B*15:161', 'HLA-B*15:162', 'HLA-B*15:163', 'HLA-B*15:164', 'HLA-B*15:165', 'HLA-B*15:166', 'HLA-B*15:167',
'HLA-B*15:168', 'HLA-B*15:169', 'HLA-B*15:17', 'HLA-B*15:170', 'HLA-B*15:171', 'HLA-B*15:172', 'HLA-B*15:173', 'HLA-B*15:174',
'HLA-B*15:175', 'HLA-B*15:176', 'HLA-B*15:177', 'HLA-B*15:178', 'HLA-B*15:179', 'HLA-B*15:18', 'HLA-B*15:180', 'HLA-B*15:183',
'HLA-B*15:184', 'HLA-B*15:185', 'HLA-B*15:186', 'HLA-B*15:187', 'HLA-B*15:188', 'HLA-B*15:189', 'HLA-B*15:19', 'HLA-B*15:191',
'HLA-B*15:192', 'HLA-B*15:193', 'HLA-B*15:194', 'HLA-B*15:195', 'HLA-B*15:196', 'HLA-B*15:197', 'HLA-B*15:198', 'HLA-B*15:199',
'HLA-B*15:20', 'HLA-B*15:200', 'HLA-B*15:201', 'HLA-B*15:202', 'HLA-B*15:21', 'HLA-B*15:23', 'HLA-B*15:24', 'HLA-B*15:25', 'HLA-B*15:27',
'HLA-B*15:28', 'HLA-B*15:29', 'HLA-B*15:30', 'HLA-B*15:31', 'HLA-B*15:32', 'HLA-B*15:33', 'HLA-B*15:34', 'HLA-B*15:35', 'HLA-B*15:36',
'HLA-B*15:37', 'HLA-B*15:38', 'HLA-B*15:39', 'HLA-B*15:40', 'HLA-B*15:42', 'HLA-B*15:43', 'HLA-B*15:44', 'HLA-B*15:45', 'HLA-B*15:46',
'HLA-B*15:47', 'HLA-B*15:48', 'HLA-B*15:49', 'HLA-B*15:50', 'HLA-B*15:51', 'HLA-B*15:52', 'HLA-B*15:53', 'HLA-B*15:54', 'HLA-B*15:55',
'HLA-B*15:56', 'HLA-B*15:57', 'HLA-B*15:58', 'HLA-B*15:60', 'HLA-B*15:61', 'HLA-B*15:62', 'HLA-B*15:63', 'HLA-B*15:64', 'HLA-B*15:65',
'HLA-B*15:66', 'HLA-B*15:67', 'HLA-B*15:68', 'HLA-B*15:69', 'HLA-B*15:70', 'HLA-B*15:71', 'HLA-B*15:72', 'HLA-B*15:73', 'HLA-B*15:74',
'HLA-B*15:75', 'HLA-B*15:76', 'HLA-B*15:77', 'HLA-B*15:78', 'HLA-B*15:80', 'HLA-B*15:81', 'HLA-B*15:82', 'HLA-B*15:83', 'HLA-B*15:84',
'HLA-B*15:85', 'HLA-B*15:86', 'HLA-B*15:87', 'HLA-B*15:88', 'HLA-B*15:89', 'HLA-B*15:90', 'HLA-B*15:91', 'HLA-B*15:92', 'HLA-B*15:93',
'HLA-B*15:95', 'HLA-B*15:96', 'HLA-B*15:97', 'HLA-B*15:98', 'HLA-B*15:99', 'HLA-B*18:01', 'HLA-B*18:02', 'HLA-B*18:03', 'HLA-B*18:04',
'HLA-B*18:05', 'HLA-B*18:06', 'HLA-B*18:07', 'HLA-B*18:08', 'HLA-B*18:09', 'HLA-B*18:10', 'HLA-B*18:11', 'HLA-B*18:12', 'HLA-B*18:13',
'HLA-B*18:14', 'HLA-B*18:15', 'HLA-B*18:18', 'HLA-B*18:19', 'HLA-B*18:20', 'HLA-B*18:21', 'HLA-B*18:22', 'HLA-B*18:24', 'HLA-B*18:25',
'HLA-B*18:26', 'HLA-B*18:27', 'HLA-B*18:28', 'HLA-B*18:29', 'HLA-B*18:30', 'HLA-B*18:31', 'HLA-B*18:32', 'HLA-B*18:33', 'HLA-B*18:34',
'HLA-B*18:35', 'HLA-B*18:36', 'HLA-B*18:37', 'HLA-B*18:38', 'HLA-B*18:39', 'HLA-B*18:40', 'HLA-B*18:41', 'HLA-B*18:42', 'HLA-B*18:43',
'HLA-B*18:44', 'HLA-B*18:45', 'HLA-B*18:46', 'HLA-B*18:47', 'HLA-B*18:48', 'HLA-B*18:49', 'HLA-B*18:50', 'HLA-B*27:01', 'HLA-B*27:02',
'HLA-B*27:03', 'HLA-B*27:04', 'HLA-B*27:05', 'HLA-B*27:06', 'HLA-B*27:07', 'HLA-B*27:08', 'HLA-B*27:09', 'HLA-B*27:10', 'HLA-B*27:11',
'HLA-B*27:12', 'HLA-B*27:13', 'HLA-B*27:14', 'HLA-B*27:15', 'HLA-B*27:16', 'HLA-B*27:17', 'HLA-B*27:18', 'HLA-B*27:19', 'HLA-B*27:20',
'HLA-B*27:21', 'HLA-B*27:23', 'HLA-B*27:24', 'HLA-B*27:25', 'HLA-B*27:26', 'HLA-B*27:27', 'HLA-B*27:28', 'HLA-B*27:29', 'HLA-B*27:30',
'HLA-B*27:31', 'HLA-B*27:32', 'HLA-B*27:33', 'HLA-B*27:34', 'HLA-B*27:35', 'HLA-B*27:36', 'HLA-B*27:37', 'HLA-B*27:38', 'HLA-B*27:39',
'HLA-B*27:40', 'HLA-B*27:41', 'HLA-B*27:42', 'HLA-B*27:43', 'HLA-B*27:44', 'HLA-B*27:45', 'HLA-B*27:46', 'HLA-B*27:47', 'HLA-B*27:48',
'HLA-B*27:49', 'HLA-B*27:50', 'HLA-B*27:51', 'HLA-B*27:52', 'HLA-B*27:53', 'HLA-B*27:54', 'HLA-B*27:55', 'HLA-B*27:56', 'HLA-B*27:57',
'HLA-B*27:58', 'HLA-B*27:60', 'HLA-B*27:61', 'HLA-B*27:62', 'HLA-B*27:63', 'HLA-B*27:67', 'HLA-B*27:68', 'HLA-B*27:69', 'HLA-B*35:01',
'HLA-B*35:02', 'HLA-B*35:03', 'HLA-B*35:04', 'HLA-B*35:05', 'HLA-B*35:06', 'HLA-B*35:07', 'HLA-B*35:08', 'HLA-B*35:09', 'HLA-B*35:10',
'HLA-B*35:100', 'HLA-B*35:101', 'HLA-B*35:102', 'HLA-B*35:103', 'HLA-B*35:104', 'HLA-B*35:105', 'HLA-B*35:106', 'HLA-B*35:107',
'HLA-B*35:108', 'HLA-B*35:109', 'HLA-B*35:11', 'HLA-B*35:110', 'HLA-B*35:111', 'HLA-B*35:112', 'HLA-B*35:113', 'HLA-B*35:114',
'HLA-B*35:115', 'HLA-B*35:116', 'HLA-B*35:117', 'HLA-B*35:118', 'HLA-B*35:119', 'HLA-B*35:12', 'HLA-B*35:120', 'HLA-B*35:121',
'HLA-B*35:122', 'HLA-B*35:123', 'HLA-B*35:124', 'HLA-B*35:125', 'HLA-B*35:126', 'HLA-B*35:127', 'HLA-B*35:128', 'HLA-B*35:13',
'HLA-B*35:131', 'HLA-B*35:132', 'HLA-B*35:133', 'HLA-B*35:135', 'HLA-B*35:136', 'HLA-B*35:137', 'HLA-B*35:138', 'HLA-B*35:139',
'HLA-B*35:14', 'HLA-B*35:140', 'HLA-B*35:141', 'HLA-B*35:142', 'HLA-B*35:143', 'HLA-B*35:144', 'HLA-B*35:15', 'HLA-B*35:16', 'HLA-B*35:17',
'HLA-B*35:18', 'HLA-B*35:19', 'HLA-B*35:20', 'HLA-B*35:21', 'HLA-B*35:22', 'HLA-B*35:23', 'HLA-B*35:24', 'HLA-B*35:25', 'HLA-B*35:26',
'HLA-B*35:27', 'HLA-B*35:28', 'HLA-B*35:29', 'HLA-B*35:30', 'HLA-B*35:31', 'HLA-B*35:32', 'HLA-B*35:33', 'HLA-B*35:34', 'HLA-B*35:35',
'HLA-B*35:36', 'HLA-B*35:37', 'HLA-B*35:38', 'HLA-B*35:39', 'HLA-B*35:41', 'HLA-B*35:42', 'HLA-B*35:43', 'HLA-B*35:44', 'HLA-B*35:45',
'HLA-B*35:46', 'HLA-B*35:47', 'HLA-B*35:48', 'HLA-B*35:49', 'HLA-B*35:50', 'HLA-B*35:51', 'HLA-B*35:52', 'HLA-B*35:54', 'HLA-B*35:55',
'HLA-B*35:56', 'HLA-B*35:57', 'HLA-B*35:58', 'HLA-B*35:59', 'HLA-B*35:60', 'HLA-B*35:61', 'HLA-B*35:62', 'HLA-B*35:63', 'HLA-B*35:64',
'HLA-B*35:66', 'HLA-B*35:67', 'HLA-B*35:68', 'HLA-B*35:69', 'HLA-B*35:70', 'HLA-B*35:71', 'HLA-B*35:72', 'HLA-B*35:74', 'HLA-B*35:75',
'HLA-B*35:76', 'HLA-B*35:77', 'HLA-B*35:78', 'HLA-B*35:79', 'HLA-B*35:80', 'HLA-B*35:81', 'HLA-B*35:82', 'HLA-B*35:83', 'HLA-B*35:84',
'HLA-B*35:85', 'HLA-B*35:86', 'HLA-B*35:87', 'HLA-B*35:88', 'HLA-B*35:89', 'HLA-B*35:90', 'HLA-B*35:91', 'HLA-B*35:92', 'HLA-B*35:93',
'HLA-B*35:94', 'HLA-B*35:95', 'HLA-B*35:96', 'HLA-B*35:97', 'HLA-B*35:98', 'HLA-B*35:99', 'HLA-B*37:01', 'HLA-B*37:02', 'HLA-B*37:04',
'HLA-B*37:05', 'HLA-B*37:06', 'HLA-B*37:07', 'HLA-B*37:08', 'HLA-B*37:09', 'HLA-B*37:10', 'HLA-B*37:11', 'HLA-B*37:12', 'HLA-B*37:13',
'HLA-B*37:14', 'HLA-B*37:15', 'HLA-B*37:17', 'HLA-B*37:18', 'HLA-B*37:19', 'HLA-B*37:20', 'HLA-B*37:21', 'HLA-B*37:22', 'HLA-B*37:23',
'HLA-B*38:01', 'HLA-B*38:02', 'HLA-B*38:03', 'HLA-B*38:04', 'HLA-B*38:05', 'HLA-B*38:06', 'HLA-B*38:07', 'HLA-B*38:08', 'HLA-B*38:09',
'HLA-B*38:10', 'HLA-B*38:11', 'HLA-B*38:12', 'HLA-B*38:13', 'HLA-B*38:14', 'HLA-B*38:15', 'HLA-B*38:16', 'HLA-B*38:17', 'HLA-B*38:18',
'HLA-B*38:19', 'HLA-B*38:20', 'HLA-B*38:21', 'HLA-B*38:22', 'HLA-B*38:23', 'HLA-B*39:01', 'HLA-B*39:02', 'HLA-B*39:03', 'HLA-B*39:04',
'HLA-B*39:05', 'HLA-B*39:06', 'HLA-B*39:07', 'HLA-B*39:08', 'HLA-B*39:09', 'HLA-B*39:10', 'HLA-B*39:11', 'HLA-B*39:12', 'HLA-B*39:13',
'HLA-B*39:14', 'HLA-B*39:15', 'HLA-B*39:16', 'HLA-B*39:17', 'HLA-B*39:18', 'HLA-B*39:19', 'HLA-B*39:20', 'HLA-B*39:22', 'HLA-B*39:23',
'HLA-B*39:24', 'HLA-B*39:26', 'HLA-B*39:27', 'HLA-B*39:28', 'HLA-B*39:29', 'HLA-B*39:30', 'HLA-B*39:31', 'HLA-B*39:32', 'HLA-B*39:33',
'HLA-B*39:34', 'HLA-B*39:35', 'HLA-B*39:36', 'HLA-B*39:37', 'HLA-B*39:39', 'HLA-B*39:41', 'HLA-B*39:42', 'HLA-B*39:43', 'HLA-B*39:44',
'HLA-B*39:45', 'HLA-B*39:46', 'HLA-B*39:47', 'HLA-B*39:48', 'HLA-B*39:49', 'HLA-B*39:50', 'HLA-B*39:51', 'HLA-B*39:52', 'HLA-B*39:53',
'HLA-B*39:54', 'HLA-B*39:55', 'HLA-B*39:56', 'HLA-B*39:57', 'HLA-B*39:58', 'HLA-B*39:59', 'HLA-B*39:60', 'HLA-B*40:01', 'HLA-B*40:02',
'HLA-B*40:03', 'HLA-B*40:04', 'HLA-B*40:05', 'HLA-B*40:06', 'HLA-B*40:07', 'HLA-B*40:08', 'HLA-B*40:09', 'HLA-B*40:10', 'HLA-B*40:100',
'HLA-B*40:101', 'HLA-B*40:102', 'HLA-B*40:103', 'HLA-B*40:104', 'HLA-B*40:105', 'HLA-B*40:106', 'HLA-B*40:107', 'HLA-B*40:108',
'HLA-B*40:109', 'HLA-B*40:11', 'HLA-B*40:110', 'HLA-B*40:111', 'HLA-B*40:112', 'HLA-B*40:113', 'HLA-B*40:114', 'HLA-B*40:115',
'HLA-B*40:116', 'HLA-B*40:117', 'HLA-B*40:119', 'HLA-B*40:12', 'HLA-B*40:120', 'HLA-B*40:121', 'HLA-B*40:122', 'HLA-B*40:123',
'HLA-B*40:124', 'HLA-B*40:125', 'HLA-B*40:126', 'HLA-B*40:127', 'HLA-B*40:128', 'HLA-B*40:129', 'HLA-B*40:13', 'HLA-B*40:130',
'HLA-B*40:131', 'HLA-B*40:132', 'HLA-B*40:134', 'HLA-B*40:135', 'HLA-B*40:136', 'HLA-B*40:137', 'HLA-B*40:138', 'HLA-B*40:139',
'HLA-B*40:14', 'HLA-B*40:140', 'HLA-B*40:141', 'HLA-B*40:143', 'HLA-B*40:145', 'HLA-B*40:146', 'HLA-B*40:147', 'HLA-B*40:15',
'HLA-B*40:16', 'HLA-B*40:18', 'HLA-B*40:19', 'HLA-B*40:20', 'HLA-B*40:21', 'HLA-B*40:23', 'HLA-B*40:24', 'HLA-B*40:25', 'HLA-B*40:26',
'HLA-B*40:27', 'HLA-B*40:28', 'HLA-B*40:29', 'HLA-B*40:30', 'HLA-B*40:31', 'HLA-B*40:32', 'HLA-B*40:33', 'HLA-B*40:34', 'HLA-B*40:35',
'HLA-B*40:36', 'HLA-B*40:37', 'HLA-B*40:38', 'HLA-B*40:39', 'HLA-B*40:40', 'HLA-B*40:42', 'HLA-B*40:43', 'HLA-B*40:44', 'HLA-B*40:45',
'HLA-B*40:46', 'HLA-B*40:47', 'HLA-B*40:48', 'HLA-B*40:49', 'HLA-B*40:50', 'HLA-B*40:51', 'HLA-B*40:52', 'HLA-B*40:53', 'HLA-B*40:54',
'HLA-B*40:55', 'HLA-B*40:56', 'HLA-B*40:57', 'HLA-B*40:58', 'HLA-B*40:59', 'HLA-B*40:60', 'HLA-B*40:61', 'HLA-B*40:62', 'HLA-B*40:63',
'HLA-B*40:64', 'HLA-B*40:65', 'HLA-B*40:66', 'HLA-B*40:67', 'HLA-B*40:68', 'HLA-B*40:69', 'HLA-B*40:70', 'HLA-B*40:71', 'HLA-B*40:72',
'HLA-B*40:73', 'HLA-B*40:74', 'HLA-B*40:75', 'HLA-B*40:76', 'HLA-B*40:77', 'HLA-B*40:78', 'HLA-B*40:79', 'HLA-B*40:80', 'HLA-B*40:81',
'HLA-B*40:82', 'HLA-B*40:83', 'HLA-B*40:84', 'HLA-B*40:85', 'HLA-B*40:86', 'HLA-B*40:87', 'HLA-B*40:88', 'HLA-B*40:89', 'HLA-B*40:90',
'HLA-B*40:91', 'HLA-B*40:92', 'HLA-B*40:93', 'HLA-B*40:94', 'HLA-B*40:95', 'HLA-B*40:96', 'HLA-B*40:97', 'HLA-B*40:98', 'HLA-B*40:99',
'HLA-B*41:01', 'HLA-B*41:02', 'HLA-B*41:03', 'HLA-B*41:04', 'HLA-B*41:05', 'HLA-B*41:06', 'HLA-B*41:07', 'HLA-B*41:08', 'HLA-B*41:09',
'HLA-B*41:10', 'HLA-B*41:11', 'HLA-B*41:12', 'HLA-B*42:01', 'HLA-B*42:02', 'HLA-B*42:04', 'HLA-B*42:05', 'HLA-B*42:06', 'HLA-B*42:07',
'HLA-B*42:08', 'HLA-B*42:09', 'HLA-B*42:10', 'HLA-B*42:11', 'HLA-B*42:12', 'HLA-B*42:13', 'HLA-B*42:14', 'HLA-B*44:02', 'HLA-B*44:03',
'HLA-B*44:04', 'HLA-B*44:05', 'HLA-B*44:06', 'HLA-B*44:07', 'HLA-B*44:08', 'HLA-B*44:09', 'HLA-B*44:10', 'HLA-B*44:100', 'HLA-B*44:101',
'HLA-B*44:102', 'HLA-B*44:103', 'HLA-B*44:104', 'HLA-B*44:105', 'HLA-B*44:106', 'HLA-B*44:107', 'HLA-B*44:109', 'HLA-B*44:11',
'HLA-B*44:110', 'HLA-B*44:12', 'HLA-B*44:13', 'HLA-B*44:14', 'HLA-B*44:15', 'HLA-B*44:16', 'HLA-B*44:17', 'HLA-B*44:18', 'HLA-B*44:20',
'HLA-B*44:21', 'HLA-B*44:22', 'HLA-B*44:24', 'HLA-B*44:25', 'HLA-B*44:26', 'HLA-B*44:27', 'HLA-B*44:28', 'HLA-B*44:29', 'HLA-B*44:30',
'HLA-B*44:31', 'HLA-B*44:32', 'HLA-B*44:33', 'HLA-B*44:34', 'HLA-B*44:35', 'HLA-B*44:36', 'HLA-B*44:37', 'HLA-B*44:38', 'HLA-B*44:39',
'HLA-B*44:40', 'HLA-B*44:41', 'HLA-B*44:42', 'HLA-B*44:43', 'HLA-B*44:44', 'HLA-B*44:45', 'HLA-B*44:46', 'HLA-B*44:47', 'HLA-B*44:48',
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'HLA-C*07:119', 'HLA-C*07:12', 'HLA-C*07:120', 'HLA-C*07:122', 'HLA-C*07:123', 'HLA-C*07:124', 'HLA-C*07:125', 'HLA-C*07:126',
'HLA-C*07:127', 'HLA-C*07:128', 'HLA-C*07:129', 'HLA-C*07:13', 'HLA-C*07:130', 'HLA-C*07:131', 'HLA-C*07:132', 'HLA-C*07:133',
'HLA-C*07:134', 'HLA-C*07:135', 'HLA-C*07:136', 'HLA-C*07:137', 'HLA-C*07:138', 'HLA-C*07:139', 'HLA-C*07:14', 'HLA-C*07:140',
'HLA-C*07:141', 'HLA-C*07:142', 'HLA-C*07:143', 'HLA-C*07:144', 'HLA-C*07:145', 'HLA-C*07:146', 'HLA-C*07:147', 'HLA-C*07:148',
'HLA-C*07:149', 'HLA-C*07:15', 'HLA-C*07:16', 'HLA-C*07:17', 'HLA-C*07:18', 'HLA-C*07:19', 'HLA-C*07:20', 'HLA-C*07:21', 'HLA-C*07:22',
'HLA-C*07:23', 'HLA-C*07:24', 'HLA-C*07:25', 'HLA-C*07:26', 'HLA-C*07:27', 'HLA-C*07:28', 'HLA-C*07:29', 'HLA-C*07:30', 'HLA-C*07:31',
'HLA-C*07:35', 'HLA-C*07:36', 'HLA-C*07:37', 'HLA-C*07:38', 'HLA-C*07:39', 'HLA-C*07:40', 'HLA-C*07:41', 'HLA-C*07:42', 'HLA-C*07:43',
'HLA-C*07:44', 'HLA-C*07:45', 'HLA-C*07:46', 'HLA-C*07:47', 'HLA-C*07:48', 'HLA-C*07:49', 'HLA-C*07:50', 'HLA-C*07:51', 'HLA-C*07:52',
'HLA-C*07:53', 'HLA-C*07:54', 'HLA-C*07:56', 'HLA-C*07:57', 'HLA-C*07:58', 'HLA-C*07:59', 'HLA-C*07:60', 'HLA-C*07:62', 'HLA-C*07:63',
'HLA-C*07:64', 'HLA-C*07:65', 'HLA-C*07:66', 'HLA-C*07:67', 'HLA-C*07:68', 'HLA-C*07:69', 'HLA-C*07:70', 'HLA-C*07:71', 'HLA-C*07:72',
'HLA-C*07:73', 'HLA-C*07:74', 'HLA-C*07:75', 'HLA-C*07:76', 'HLA-C*07:77', 'HLA-C*07:78', 'HLA-C*07:79', 'HLA-C*07:80', 'HLA-C*07:81',
'HLA-C*07:82', 'HLA-C*07:83', 'HLA-C*07:84', 'HLA-C*07:85', 'HLA-C*07:86', 'HLA-C*07:87', 'HLA-C*07:88', 'HLA-C*07:89', 'HLA-C*07:90',
'HLA-C*07:91', 'HLA-C*07:92', 'HLA-C*07:93', 'HLA-C*07:94', 'HLA-C*07:95', 'HLA-C*07:96', 'HLA-C*07:97', 'HLA-C*07:99', 'HLA-C*08:01',
'HLA-C*08:02', 'HLA-C*08:03', 'HLA-C*08:04', 'HLA-C*08:05', 'HLA-C*08:06', 'HLA-C*08:07', 'HLA-C*08:08', 'HLA-C*08:09', 'HLA-C*08:10',
'HLA-C*08:11', 'HLA-C*08:12', 'HLA-C*08:13', 'HLA-C*08:14', 'HLA-C*08:15', 'HLA-C*08:16', 'HLA-C*08:17', 'HLA-C*08:18', 'HLA-C*08:19',
'HLA-C*08:20', 'HLA-C*08:21', 'HLA-C*08:22', 'HLA-C*08:23', 'HLA-C*08:24', 'HLA-C*08:25', 'HLA-C*08:27', 'HLA-C*08:28', 'HLA-C*08:29',
'HLA-C*08:30', 'HLA-C*08:31', 'HLA-C*08:32', 'HLA-C*08:33', 'HLA-C*08:34', 'HLA-C*08:35', 'HLA-C*12:02', 'HLA-C*12:03', 'HLA-C*12:04',
'HLA-C*12:05', 'HLA-C*12:06', 'HLA-C*12:07', 'HLA-C*12:08', 'HLA-C*12:09', 'HLA-C*12:10', 'HLA-C*12:11', 'HLA-C*12:12', 'HLA-C*12:13',
'HLA-C*12:14', 'HLA-C*12:15', 'HLA-C*12:16', 'HLA-C*12:17', 'HLA-C*12:18', 'HLA-C*12:19', 'HLA-C*12:20', 'HLA-C*12:21', 'HLA-C*12:22',
'HLA-C*12:23', 'HLA-C*12:24', 'HLA-C*12:25', 'HLA-C*12:26', 'HLA-C*12:27', 'HLA-C*12:28', 'HLA-C*12:29', 'HLA-C*12:30', 'HLA-C*12:31',
'HLA-C*12:32', 'HLA-C*12:33', 'HLA-C*12:34', 'HLA-C*12:35', 'HLA-C*12:36', 'HLA-C*12:37', 'HLA-C*12:38', 'HLA-C*12:40', 'HLA-C*12:41',
'HLA-C*12:43', 'HLA-C*12:44', 'HLA-C*14:02', 'HLA-C*14:03', 'HLA-C*14:04', 'HLA-C*14:05', 'HLA-C*14:06', 'HLA-C*14:08', 'HLA-C*14:09',
'HLA-C*14:10', 'HLA-C*14:11', 'HLA-C*14:12', 'HLA-C*14:13', 'HLA-C*14:14', 'HLA-C*14:15', 'HLA-C*14:16', 'HLA-C*14:17', 'HLA-C*14:18',
'HLA-C*14:19', 'HLA-C*14:20', 'HLA-C*15:02', 'HLA-C*15:03', 'HLA-C*15:04', 'HLA-C*15:05', 'HLA-C*15:06', 'HLA-C*15:07', 'HLA-C*15:08',
'HLA-C*15:09', 'HLA-C*15:10', 'HLA-C*15:11', 'HLA-C*15:12', 'HLA-C*15:13', 'HLA-C*15:15', 'HLA-C*15:16', 'HLA-C*15:17', 'HLA-C*15:18',
'HLA-C*15:19', 'HLA-C*15:20', 'HLA-C*15:21', 'HLA-C*15:22', 'HLA-C*15:23', 'HLA-C*15:24', 'HLA-C*15:25', 'HLA-C*15:26', 'HLA-C*15:27',
'HLA-C*15:28', 'HLA-C*15:29', 'HLA-C*15:30', 'HLA-C*15:31', 'HLA-C*15:33', 'HLA-C*15:34', 'HLA-C*15:35', 'HLA-C*16:01', 'HLA-C*16:02',
'HLA-C*16:04', 'HLA-C*16:06', 'HLA-C*16:07', 'HLA-C*16:08', 'HLA-C*16:09', 'HLA-C*16:10', 'HLA-C*16:11', 'HLA-C*16:12', 'HLA-C*16:13',
'HLA-C*16:14', 'HLA-C*16:15', 'HLA-C*16:17', 'HLA-C*16:18', 'HLA-C*16:19', 'HLA-C*16:20', 'HLA-C*16:21', 'HLA-C*16:22', 'HLA-C*16:23',
'HLA-C*16:24', 'HLA-C*16:25', 'HLA-C*16:26', 'HLA-C*17:01', 'HLA-C*17:02', 'HLA-C*17:03', 'HLA-C*17:04', 'HLA-C*17:05', 'HLA-C*17:06',
'HLA-C*17:07', 'HLA-C*18:01', 'HLA-C*18:02', 'HLA-C*18:03', 'HLA-E*01:01', 'HLA-G*01:01', 'HLA-G*01:02', 'HLA-G*01:03', 'HLA-G*01:04',
'HLA-G*01:06', 'HLA-G*01:07', 'HLA-G*01:08', 'HLA-G*01:09',
'H-2-Db', 'H-2-Dd', 'H-2-Kb', 'H-2-Kd', 'H-2-Kk', 'H-2-Ld', "H-2-Qa1", "H-2-Qa2"])
@property
def version(self):
"""The version of the predictor"""
return self.__version
def _represent(self, allele):
"""
Internal function transforming an allele object into its representative string
:param allele: The :class:`~epytope.Core.Allele.Allele` for which the internal predictor representation is
needed
:type alleles: :class:`~epytope.Core.Allele.Allele`
:return: str
"""
if isinstance(allele, MouseAllele):
return "H-2-%s%s%s" % (allele.locus, allele.supertype, allele.subtype)
else:
return "HLA-%s%s:%s" % (allele.locus, allele.supertype, allele.subtype)
def convert_alleles(self, alleles):
"""
Converts :class:`~epytope.Core.Allele.Allele` into the internal :class:`~epytope.Core.Allele.Allele` representation
of the predictor and returns a string representation
:param alleles: The :class:`~epytope.Core.Allele.Allele` for which the internal predictor representation is
needed
:type alleles: :class:`~epytope.Core.Allele.Allele`
:return: Returns a string representation of the input :class:`~epytope.Core.Allele.Allele`
:rtype: list(str)
"""
return [self._represent(a) for a in alleles]
@property
def supportedAlleles(self):
"""A list of valid :class:`~epytope.Core.Allele.Allele` models"""
return self.__alleles
@property
def name(self):
"""The name of the predictor"""
return self.__name
@property
def command(self):
"""
Defines the commandline call for external tool
"""
return self.__command
@property
def supportedLength(self):
"""
A list of supported :class:`~epytope.Core.Peptide.Peptide` lengths
"""
return self.__supported_length
def get_external_version(self, path=None):
"""
Returns the external version of the tool by executing
>{command} --version
might be dependent on the method and has to be overwritten
therefore it is declared abstract to enforce the user to
overwrite the method. The function in the base class can be called
with super()
:param str path: Optional specification of executable path if deviant from :attr:`self.__command`
:return: The external version of the tool or None if tool does not support versioning
:rtype: str
"""
return None
def prepare_input(self, input, file):
"""
Prepares input for external tools
and writes them to file in the specific format
No return value!
:param: list(str) input: The :class:`~epytope.Core.Peptide.Peptide` sequences to write into file
:param File file: File-handler to input file for external tool
"""
file.write("\n".join(input))
def parse_external_result(self, file):
"""
Parses external results and returns the result containing the predictors string representation
of alleles and peptides.
:param str file: The file path or the external prediction results
:return: A dictionary containing the prediction results
:rtype: dict
"""
f = csv.reader(open(file, "r"), delimiter = '\t')
scores = defaultdict(defaultdict)
ranks = defaultdict(defaultdict)
alleles = [x for x in next(f) if x != ""]
next(f)
for row in f:
pep_seq = row[PeptideIndex.NETMHCPAN_2_8]
for i, a in enumerate(alleles):
if row[ScoreIndex.NETMHCPAN_2_8 + i * Offset.NETMHCPAN_2_8] != "1-log50k": # Avoid header column, only access raw and rank scores
scores[a][pep_seq] = float(row[ScoreIndex.NETMHCPAN_2_8 + i * Offset.NETMHCPAN_2_8])
ranks[a][pep_seq] = float(row[RankIndex.NETMHCPAN_2_8 + i * Offset.NETMHCPAN_2_8])
# Create dictionary with hierarchy: {'Allele1': {'Score': {'Pep1': Score1, 'Pep2': Score2,..}, 'Rank': {'Pep1': RankScore1, 'Pep2': RankScore2,..}}, 'Allele2':...}
result = {allele: {metric:(list(scores.values())[j] if metric == "Score" else list(ranks.values())[j]) for metric in ["Score", "Rank"]} for j, allele in enumerate(alleles)}
return result
class NetMHCpan_3_0(NetMHCpan_2_8):
"""
Implements the NetMHC binding version 3.0
Supported MHC alleles currently only restricted to HLA alleles.
.. note::
Nielsen, M., & Andreatta, M. (2016).
NetMHCpan-3.0; improved prediction of binding to MHC class I molecules integrating information from multiple
receptor and peptide length datasets. Genome Medicine, 8(1), 1.
"""
__version = "3.0"
__command = "netMHCpan -p {peptides} -a {alleles} {options} -xls -xlsfile {out}"
@property
def version(self):
return self.__version
@property
def command(self):
return self.__command
def parse_external_result(self, file):
"""
Parses external results and returns the result
:param str file: The file path or the external prediction results
:return: A dictionary containing the prediction results
:rtype: dict
"""
f = csv.reader(open(file, "r"), delimiter = '\t')
scores = defaultdict(defaultdict)
ranks = defaultdict(defaultdict)
alleles = [x for x in next(f) if x != ""]
for row in f:
pep_seq = row[PeptideIndex.NETMHCPAN_3_0]
for i, a in enumerate(alleles):
if row[ScoreIndex.NETMHCPAN_3_0 + i * Offset.NETMHCPAN_3_0] != "1-log50k": # Avoid header column, only access raw and rank scores
scores[a][pep_seq] = float(row[ScoreIndex.NETMHCPAN_3_0 + i * Offset.NETMHCPAN_3_0])
ranks[a][pep_seq] = float(row[RankIndex.NETMHCPAN_3_0 + i * Offset.NETMHCPAN_3_0])
# Create dictionary with hierarchy: {'Allele1': {'Score': {'Pep1': Score1, 'Pep2': Score2,..}, 'Rank': {'Pep1': RankScore1, 'Pep2': RankScore2,..}}, 'Allele2':...}
result = {allele: {metric:(list(scores.values())[j] if metric == "Score" else list(ranks.values())[j]) for metric in ["Score", "Rank"]} for j, allele in enumerate(alleles)}
return result
class NetMHCpan_4_0(NetMHCpan_3_0):
"""
Implements the NetMHC binding version 4.0
Supported MHC alleles currently only restricted to HLA alleles.
"""
__version = "4.0"
__command = "netMHCpan -p {peptides} -a {alleles} {options} -xls -xlsfile {out}"
@property
def version(self):
return self.__version
@property
def command(self):
return self.__command
def parse_external_result(self, file):
"""
Parses external results and returns the result containing the predictors string representation
of alleles and peptides.
:param str file: The file path or the external prediction results
:return: A dictionary containing the prediction results
:rtype: dict
"""
f = csv.reader(open(file, "r"), delimiter = '\t')
scores = defaultdict(defaultdict)
ranks = defaultdict(defaultdict)
alleles = [x for x in next(f) if x != ""]
for row in f:
pep_seq = row[PeptideIndex.NETMHCPAN_4_0]
for i, a in enumerate(alleles):
if row[ScoreIndex.NETMHCPAN_4_0 + i * Offset.NETMHCPAN_4_0] != "1-log50k": # Avoid header column, only access raw and rank scores
scores[a][pep_seq] = float(row[ScoreIndex.NETMHCPAN_4_0 + i * Offset.NETMHCPAN_4_0])
ranks[a][pep_seq] = float(row[RankIndex.NETMHCPAN_4_0 + i * Offset.NETMHCPAN_4_0])
# Create dictionary with hierarchy: {'Allele1': {'Score': {'Pep1': Score1, 'Pep2': Score2,..}, 'Rank': {'Pep1': RankScore1, 'Pep2': RankScore2,..}}, 'Allele2':...}
result = {allele: {metric:(list(scores.values())[j] if metric == "Score" else list(ranks.values())[j]) for metric in ["Score", "Rank"]} for j, allele in enumerate(alleles)}
return result
class NetMHCstabpan_1_0(AExternalEpitopePrediction):
"""
Implements a wrapper to NetMHCstabpan 1.0
.. note:
Pan-specific prediction of peptide-MHC-I complex stability; a correlate of T cell immunogenicity
M Rasmussen, E Fenoy, M Nielsen, Buus S, Accepted JI June, 2016
"""
__name = "netMHCstabpan"
__length = frozenset([8, 9, 10, 11])
__version = "1.0"
__command = "netMHCstabpan -p {peptides} -a {alleles} {options} -xls -xlsfile {out}"
__alleles = frozenset(['HLA-A*01:01', 'HLA-A*01:02', 'HLA-A*01:03', 'HLA-A*01:06', 'HLA-A*01:07', 'HLA-A*01:08', 'HLA-A*01:09', 'HLA-A*01:10', 'HLA-A*01:12',
'HLA-A*01:13', 'HLA-A*01:14', 'HLA-A*01:17', 'HLA-A*01:19', 'HLA-A*01:20', 'HLA-A*01:21', 'HLA-A*01:23', 'HLA-A*01:24',
'HLA-A*01:25', 'HLA-A*01:26', 'HLA-A*01:28', 'HLA-A*01:29', 'HLA-A*01:30', 'HLA-A*01:32', 'HLA-A*01:33', 'HLA-A*01:35',
'HLA-A*01:36', 'HLA-A*01:37', 'HLA-A*01:38', 'HLA-A*01:39', 'HLA-A*01:40', 'HLA-A*01:41', 'HLA-A*01:42', 'HLA-A*01:43',
'HLA-A*01:44', 'HLA-A*01:45', 'HLA-A*01:46', 'HLA-A*01:47', 'HLA-A*01:48', 'HLA-A*01:49', 'HLA-A*01:50', 'HLA-A*01:51',
'HLA-A*01:54', 'HLA-A*01:55', 'HLA-A*01:58', 'HLA-A*01:59', 'HLA-A*01:60', 'HLA-A*01:61', 'HLA-A*01:62', 'HLA-A*01:63',
'HLA-A*01:64', 'HLA-A*01:65', 'HLA-A*01:66', 'HLA-A*02:01', 'HLA-A*02:02', 'HLA-A*02:03', 'HLA-A*02:04', 'HLA-A*02:05',
'HLA-A*02:06', 'HLA-A*02:07', 'HLA-A*02:08', 'HLA-A*02:09', 'HLA-A*02:10', 'HLA-A*02:11', 'HLA-A*02:12', 'HLA-A*02:13',
'HLA-A*02:14', 'HLA-A*02:16', 'HLA-A*02:17', 'HLA-A*02:18', 'HLA-A*02:19', 'HLA-A*02:20', 'HLA-A*02:21', 'HLA-A*02:22',
'HLA-A*02:24', 'HLA-A*02:25', 'HLA-A*02:26', 'HLA-A*02:27', 'HLA-A*02:28', 'HLA-A*02:29', 'HLA-A*02:30', 'HLA-A*02:31',
'HLA-A*02:33', 'HLA-A*02:34', 'HLA-A*02:35', 'HLA-A*02:36', 'HLA-A*02:37', 'HLA-A*02:38', 'HLA-A*02:39', 'HLA-A*02:40',
'HLA-A*02:41', 'HLA-A*02:42', 'HLA-A*02:44', 'HLA-A*02:45', 'HLA-A*02:46', 'HLA-A*02:47', 'HLA-A*02:48', 'HLA-A*02:49',
'HLA-A*02:50', 'HLA-A*02:51', 'HLA-A*02:52', 'HLA-A*02:54', 'HLA-A*02:55', 'HLA-A*02:56', 'HLA-A*02:57', 'HLA-A*02:58',
'HLA-A*02:59', 'HLA-A*02:60', 'HLA-A*02:61', 'HLA-A*02:62', 'HLA-A*02:63', 'HLA-A*02:64', 'HLA-A*02:65', 'HLA-A*02:66',
'HLA-A*02:67', 'HLA-A*02:68', 'HLA-A*02:69', 'HLA-A*02:70', 'HLA-A*02:71', 'HLA-A*02:72', 'HLA-A*02:73', 'HLA-A*02:74',
'HLA-A*02:75', 'HLA-A*02:76', 'HLA-A*02:77', 'HLA-A*02:78', 'HLA-A*02:79', 'HLA-A*02:80', 'HLA-A*02:81', 'HLA-A*02:84',
'HLA-A*02:85', 'HLA-A*02:86', 'HLA-A*02:87', 'HLA-A*02:89', 'HLA-A*02:90', 'HLA-A*02:91', 'HLA-A*02:92', 'HLA-A*02:93',
'HLA-A*02:95', 'HLA-A*02:96', 'HLA-A*02:97', 'HLA-A*02:99', 'HLA-A*02:101', 'HLA-A*02:102', 'HLA-A*02:103', 'HLA-A*02:104',
'HLA-A*02:105', 'HLA-A*02:106', 'HLA-A*02:107', 'HLA-A*02:108', 'HLA-A*02:109', 'HLA-A*02:110', 'HLA-A*02:111',
'HLA-A*02:112', 'HLA-A*02:114', 'HLA-A*02:115', 'HLA-A*02:116', 'HLA-A*02:117', 'HLA-A*02:118', 'HLA-A*02:119',
'HLA-A*02:120', 'HLA-A*02:121', 'HLA-A*02:122', 'HLA-A*02:123', 'HLA-A*02:124', 'HLA-A*02:126', 'HLA-A*02:127',
'HLA-A*02:128', 'HLA-A*02:129', 'HLA-A*02:130', 'HLA-A*02:131', 'HLA-A*02:132', 'HLA-A*02:133', 'HLA-A*02:134',
'HLA-A*02:135', 'HLA-A*02:136', 'HLA-A*02:137', 'HLA-A*02:138', 'HLA-A*02:139', 'HLA-A*02:140', 'HLA-A*02:141',
'HLA-A*02:142', 'HLA-A*02:143', 'HLA-A*02:144', 'HLA-A*02:145', 'HLA-A*02:146', 'HLA-A*02:147', 'HLA-A*02:148',
'HLA-A*02:149', 'HLA-A*02:150', 'HLA-A*02:151', 'HLA-A*02:152', 'HLA-A*02:153', 'HLA-A*02:154', 'HLA-A*02:155',
'HLA-A*02:156', 'HLA-A*02:157', 'HLA-A*02:158', 'HLA-A*02:159', 'HLA-A*02:160', 'HLA-A*02:161', 'HLA-A*02:162',
'HLA-A*02:163', 'HLA-A*02:164', 'HLA-A*02:165', 'HLA-A*02:166', 'HLA-A*02:167', 'HLA-A*02:168', 'HLA-A*02:169',
'HLA-A*02:170', 'HLA-A*02:171', 'HLA-A*02:172', 'HLA-A*02:173', 'HLA-A*02:174', 'HLA-A*02:175', 'HLA-A*02:176',
'HLA-A*02:177', 'HLA-A*02:178', 'HLA-A*02:179', 'HLA-A*02:180', 'HLA-A*02:181', 'HLA-A*02:182', 'HLA-A*02:183',
'HLA-A*02:184', 'HLA-A*02:185', 'HLA-A*02:186', 'HLA-A*02:187', 'HLA-A*02:188', 'HLA-A*02:189', 'HLA-A*02:190',
'HLA-A*02:191', 'HLA-A*02:192', 'HLA-A*02:193', 'HLA-A*02:194', 'HLA-A*02:195', 'HLA-A*02:196', 'HLA-A*02:197',
'HLA-A*02:198', 'HLA-A*02:199', 'HLA-A*02:200', 'HLA-A*02:201', 'HLA-A*02:202', 'HLA-A*02:203', 'HLA-A*02:204',
'HLA-A*02:205', 'HLA-A*02:206', 'HLA-A*02:207', 'HLA-A*02:208', 'HLA-A*02:209', 'HLA-A*02:210', 'HLA-A*02:211',
'HLA-A*02:212', 'HLA-A*02:213', 'HLA-A*02:214', 'HLA-A*02:215', 'HLA-A*02:216', 'HLA-A*02:217', 'HLA-A*02:218',
'HLA-A*02:219', 'HLA-A*02:220', 'HLA-A*02:221', 'HLA-A*02:224', 'HLA-A*02:228', 'HLA-A*02:229', 'HLA-A*02:230',
'HLA-A*02:231', 'HLA-A*02:232', 'HLA-A*02:233', 'HLA-A*02:234', 'HLA-A*02:235', 'HLA-A*02:236', 'HLA-A*02:237',
'HLA-A*02:238', 'HLA-A*02:239', 'HLA-A*02:240', 'HLA-A*02:241', 'HLA-A*02:242', 'HLA-A*02:243', 'HLA-A*02:244',
'HLA-A*02:245', 'HLA-A*02:246', 'HLA-A*02:247', 'HLA-A*02:248', 'HLA-A*02:249', 'HLA-A*02:251', 'HLA-A*02:252',
'HLA-A*02:253', 'HLA-A*02:254', 'HLA-A*02:255', 'HLA-A*02:256', 'HLA-A*02:257', 'HLA-A*02:258', 'HLA-A*02:259',
'HLA-A*02:260', 'HLA-A*02:261', 'HLA-A*02:262', 'HLA-A*02:263', 'HLA-A*02:264', 'HLA-A*02:265', 'HLA-A*02:266',
'HLA-A*03:01', 'HLA-A*03:02', 'HLA-A*03:04', 'HLA-A*03:05', 'HLA-A*03:06', 'HLA-A*03:07', 'HLA-A*03:08', 'HLA-A*03:09',
'HLA-A*03:10', 'HLA-A*03:12', 'HLA-A*03:13', 'HLA-A*03:14', 'HLA-A*03:15', 'HLA-A*03:16', 'HLA-A*03:17', 'HLA-A*03:18',
'HLA-A*03:19', 'HLA-A*03:20', 'HLA-A*03:22', 'HLA-A*03:23', 'HLA-A*03:24', 'HLA-A*03:25', 'HLA-A*03:26', 'HLA-A*03:27',
'HLA-A*03:28', 'HLA-A*03:29', 'HLA-A*03:30', 'HLA-A*03:31', 'HLA-A*03:32', 'HLA-A*03:33', 'HLA-A*03:34', 'HLA-A*03:35',
'HLA-A*03:37', 'HLA-A*03:38', 'HLA-A*03:39', 'HLA-A*03:40', 'HLA-A*03:41', 'HLA-A*03:42', 'HLA-A*03:43', 'HLA-A*03:44',
'HLA-A*03:45', 'HLA-A*03:46', 'HLA-A*03:47', 'HLA-A*03:48', 'HLA-A*03:49', 'HLA-A*03:50', 'HLA-A*03:51', 'HLA-A*03:52',
'HLA-A*03:53', 'HLA-A*03:54', 'HLA-A*03:55', 'HLA-A*03:56', 'HLA-A*03:57', 'HLA-A*03:58', 'HLA-A*03:59', 'HLA-A*03:60',
'HLA-A*03:61', 'HLA-A*03:62', 'HLA-A*03:63', 'HLA-A*03:64', 'HLA-A*03:65', 'HLA-A*03:66', 'HLA-A*03:67', 'HLA-A*03:70',
'HLA-A*03:71', 'HLA-A*03:72', 'HLA-A*03:73', 'HLA-A*03:74', 'HLA-A*03:75', 'HLA-A*03:76', 'HLA-A*03:77', 'HLA-A*03:78',
'HLA-A*03:79', 'HLA-A*03:80', 'HLA-A*03:81', 'HLA-A*03:82', 'HLA-A*11:01', 'HLA-A*11:02', 'HLA-A*11:03', 'HLA-A*11:04',
'HLA-A*11:05', 'HLA-A*11:06', 'HLA-A*11:07', 'HLA-A*11:08', 'HLA-A*11:09', 'HLA-A*11:10', 'HLA-A*11:11', 'HLA-A*11:12',
'HLA-A*11:13', 'HLA-A*11:14', 'HLA-A*11:15', 'HLA-A*11:16', 'HLA-A*11:17', 'HLA-A*11:18', 'HLA-A*11:19', 'HLA-A*11:20',
'HLA-A*11:22', 'HLA-A*11:23', 'HLA-A*11:24', 'HLA-A*11:25', 'HLA-A*11:26', 'HLA-A*11:27', 'HLA-A*11:29', 'HLA-A*11:30',
'HLA-A*11:31', 'HLA-A*11:32', 'HLA-A*11:33', 'HLA-A*11:34', 'HLA-A*11:35', 'HLA-A*11:36', 'HLA-A*11:37', 'HLA-A*11:38',
'HLA-A*11:39', 'HLA-A*11:40', 'HLA-A*11:41', 'HLA-A*11:42', 'HLA-A*11:43', 'HLA-A*11:44', 'HLA-A*11:45', 'HLA-A*11:46',
'HLA-A*11:47', 'HLA-A*11:48', 'HLA-A*11:49', 'HLA-A*11:51', 'HLA-A*11:53', 'HLA-A*11:54', 'HLA-A*11:55', 'HLA-A*11:56',
'HLA-A*11:57', 'HLA-A*11:58', 'HLA-A*11:59', 'HLA-A*11:60', 'HLA-A*11:61', 'HLA-A*11:62', 'HLA-A*11:63', 'HLA-A*11:64',
'HLA-A*23:01', 'HLA-A*23:02', 'HLA-A*23:03', 'HLA-A*23:04', 'HLA-A*23:05', 'HLA-A*23:06', 'HLA-A*23:09', 'HLA-A*23:10',
'HLA-A*23:12', 'HLA-A*23:13', 'HLA-A*23:14', 'HLA-A*23:15', 'HLA-A*23:16', 'HLA-A*23:17', 'HLA-A*23:18', 'HLA-A*23:20',
'HLA-A*23:21', 'HLA-A*23:22', 'HLA-A*23:23', 'HLA-A*23:24', 'HLA-A*23:25', 'HLA-A*23:26', 'HLA-A*24:02', 'HLA-A*24:03',
'HLA-A*24:04', 'HLA-A*24:05', 'HLA-A*24:06', 'HLA-A*24:07', 'HLA-A*24:08', 'HLA-A*24:10', 'HLA-A*24:13', 'HLA-A*24:14',
'HLA-A*24:15', 'HLA-A*24:17', 'HLA-A*24:18', 'HLA-A*24:19', 'HLA-A*24:20', 'HLA-A*24:21', 'HLA-A*24:22', 'HLA-A*24:23',
'HLA-A*24:24', 'HLA-A*24:25', 'HLA-A*24:26', 'HLA-A*24:27', 'HLA-A*24:28', 'HLA-A*24:29', 'HLA-A*24:30', 'HLA-A*24:31',
'HLA-A*24:32', 'HLA-A*24:33', 'HLA-A*24:34', 'HLA-A*24:35', 'HLA-A*24:37', 'HLA-A*24:38', 'HLA-A*24:39', 'HLA-A*24:41',
'HLA-A*24:42', 'HLA-A*24:43', 'HLA-A*24:44', 'HLA-A*24:46', 'HLA-A*24:47', 'HLA-A*24:49', 'HLA-A*24:50', 'HLA-A*24:51',
'HLA-A*24:52', 'HLA-A*24:53', 'HLA-A*24:54', 'HLA-A*24:55', 'HLA-A*24:56', 'HLA-A*24:57', 'HLA-A*24:58', 'HLA-A*24:59',
'HLA-A*24:61', 'HLA-A*24:62', 'HLA-A*24:63', 'HLA-A*24:64', 'HLA-A*24:66', 'HLA-A*24:67', 'HLA-A*24:68', 'HLA-A*24:69',
'HLA-A*24:70', 'HLA-A*24:71', 'HLA-A*24:72', 'HLA-A*24:73', 'HLA-A*24:74', 'HLA-A*24:75', 'HLA-A*24:76', 'HLA-A*24:77',
'HLA-A*24:78', 'HLA-A*24:79', 'HLA-A*24:80', 'HLA-A*24:81', 'HLA-A*24:82', 'HLA-A*24:85', 'HLA-A*24:87', 'HLA-A*24:88',
'HLA-A*24:89', 'HLA-A*24:91', 'HLA-A*24:92', 'HLA-A*24:93', 'HLA-A*24:94', 'HLA-A*24:95', 'HLA-A*24:96', 'HLA-A*24:97',
'HLA-A*24:98', 'HLA-A*24:99', 'HLA-A*24:100', 'HLA-A*24:101', 'HLA-A*24:102', 'HLA-A*24:103', 'HLA-A*24:104',
'HLA-A*24:105', 'HLA-A*24:106', 'HLA-A*24:107', 'HLA-A*24:108', 'HLA-A*24:109', 'HLA-A*24:110', 'HLA-A*24:111',
'HLA-A*24:112', 'HLA-A*24:113', 'HLA-A*24:114', 'HLA-A*24:115', 'HLA-A*24:116', 'HLA-A*24:117', 'HLA-A*24:118',
'HLA-A*24:119', 'HLA-A*24:120', 'HLA-A*24:121', 'HLA-A*24:122', 'HLA-A*24:123', 'HLA-A*24:124', 'HLA-A*24:125',
'HLA-A*24:126', 'HLA-A*24:127', 'HLA-A*24:128', 'HLA-A*24:129', 'HLA-A*24:130', 'HLA-A*24:131', 'HLA-A*24:133',
'HLA-A*24:134', 'HLA-A*24:135', 'HLA-A*24:136', 'HLA-A*24:137', 'HLA-A*24:138', 'HLA-A*24:139', 'HLA-A*24:140',
'HLA-A*24:141', 'HLA-A*24:142', 'HLA-A*24:143', 'HLA-A*24:144', 'HLA-A*25:01', 'HLA-A*25:02', 'HLA-A*25:03', 'HLA-A*25:04',
'HLA-A*25:05', 'HLA-A*25:06', 'HLA-A*25:07', 'HLA-A*25:08', 'HLA-A*25:09', 'HLA-A*25:10', 'HLA-A*25:11', 'HLA-A*25:13',
'HLA-A*26:01', 'HLA-A*26:02', 'HLA-A*26:03', 'HLA-A*26:04', 'HLA-A*26:05', 'HLA-A*26:06', 'HLA-A*26:07', 'HLA-A*26:08',
'HLA-A*26:09', 'HLA-A*26:10', 'HLA-A*26:12', 'HLA-A*26:13', 'HLA-A*26:14', 'HLA-A*26:15', 'HLA-A*26:16', 'HLA-A*26:17',
'HLA-A*26:18', 'HLA-A*26:19', 'HLA-A*26:20', 'HLA-A*26:21', 'HLA-A*26:22', 'HLA-A*26:23', 'HLA-A*26:24', 'HLA-A*26:26',
'HLA-A*26:27', 'HLA-A*26:28', 'HLA-A*26:29', 'HLA-A*26:30', 'HLA-A*26:31', 'HLA-A*26:32', 'HLA-A*26:33', 'HLA-A*26:34',
'HLA-A*26:35', 'HLA-A*26:36', 'HLA-A*26:37', 'HLA-A*26:38', 'HLA-A*26:39', 'HLA-A*26:40', 'HLA-A*26:41', 'HLA-A*26:42',
'HLA-A*26:43', 'HLA-A*26:45', 'HLA-A*26:46', 'HLA-A*26:47', 'HLA-A*26:48', 'HLA-A*26:49', 'HLA-A*26:50', 'HLA-A*29:01',
'HLA-A*29:02', 'HLA-A*29:03', 'HLA-A*29:04', 'HLA-A*29:05', 'HLA-A*29:06', 'HLA-A*29:07', 'HLA-A*29:09', 'HLA-A*29:10',
'HLA-A*29:11', 'HLA-A*29:12', 'HLA-A*29:13', 'HLA-A*29:14', 'HLA-A*29:15', 'HLA-A*29:16', 'HLA-A*29:17', 'HLA-A*29:18',
'HLA-A*29:19', 'HLA-A*29:20', 'HLA-A*29:21', 'HLA-A*29:22', 'HLA-A*30:01', 'HLA-A*30:02', 'HLA-A*30:03', 'HLA-A*30:04',
'HLA-A*30:06', 'HLA-A*30:07', 'HLA-A*30:08', 'HLA-A*30:09', 'HLA-A*30:10', 'HLA-A*30:11', 'HLA-A*30:12', 'HLA-A*30:13',
'HLA-A*30:15', 'HLA-A*30:16', 'HLA-A*30:17', 'HLA-A*30:18', 'HLA-A*30:19', 'HLA-A*30:20', 'HLA-A*30:22', 'HLA-A*30:23',
'HLA-A*30:24', 'HLA-A*30:25', 'HLA-A*30:26', 'HLA-A*30:28', 'HLA-A*30:29', 'HLA-A*30:30', 'HLA-A*30:31', 'HLA-A*30:32',
'HLA-A*30:33', 'HLA-A*30:34', 'HLA-A*30:35', 'HLA-A*30:36', 'HLA-A*30:37', 'HLA-A*30:38', 'HLA-A*30:39', 'HLA-A*30:40',
'HLA-A*30:41', 'HLA-A*31:01', 'HLA-A*31:02', 'HLA-A*31:03', 'HLA-A*31:04', 'HLA-A*31:05', 'HLA-A*31:06', 'HLA-A*31:07',
'HLA-A*31:08', 'HLA-A*31:09', 'HLA-A*31:10', 'HLA-A*31:11', 'HLA-A*31:12', 'HLA-A*31:13', 'HLA-A*31:15', 'HLA-A*31:16',
'HLA-A*31:17', 'HLA-A*31:18', 'HLA-A*31:19', 'HLA-A*31:20', 'HLA-A*31:21', 'HLA-A*31:22', 'HLA-A*31:23', 'HLA-A*31:24',
'HLA-A*31:25', 'HLA-A*31:26', 'HLA-A*31:27', 'HLA-A*31:28', 'HLA-A*31:29', 'HLA-A*31:30', 'HLA-A*31:31', 'HLA-A*31:32',
'HLA-A*31:33', 'HLA-A*31:34', 'HLA-A*31:35', 'HLA-A*31:36', 'HLA-A*31:37', 'HLA-A*32:01', 'HLA-A*32:02', 'HLA-A*32:03',
'HLA-A*32:04', 'HLA-A*32:05', 'HLA-A*32:06', 'HLA-A*32:07', 'HLA-A*32:08', 'HLA-A*32:09', 'HLA-A*32:10', 'HLA-A*32:12',
'HLA-A*32:13', 'HLA-A*32:14', 'HLA-A*32:15', 'HLA-A*32:16', 'HLA-A*32:17', 'HLA-A*32:18', 'HLA-A*32:20', 'HLA-A*32:21',
'HLA-A*32:22', 'HLA-A*32:23', 'HLA-A*32:24', 'HLA-A*32:25', 'HLA-A*33:01', 'HLA-A*33:03', 'HLA-A*33:04', 'HLA-A*33:05',
'HLA-A*33:06', 'HLA-A*33:07', 'HLA-A*33:08', 'HLA-A*33:09', 'HLA-A*33:10', 'HLA-A*33:11', 'HLA-A*33:12', 'HLA-A*33:13',
'HLA-A*33:14', 'HLA-A*33:15', 'HLA-A*33:16', 'HLA-A*33:17', 'HLA-A*33:18', 'HLA-A*33:19', 'HLA-A*33:20', 'HLA-A*33:21',
'HLA-A*33:22', 'HLA-A*33:23', 'HLA-A*33:24', 'HLA-A*33:25', 'HLA-A*33:26', 'HLA-A*33:27', 'HLA-A*33:28', 'HLA-A*33:29',
'HLA-A*33:30', 'HLA-A*33:31', 'HLA-A*34:01', 'HLA-A*34:02', 'HLA-A*34:03', 'HLA-A*34:04', 'HLA-A*34:05', 'HLA-A*34:06',
'HLA-A*34:07', 'HLA-A*34:08', 'HLA-A*36:01', 'HLA-A*36:02', 'HLA-A*36:03', 'HLA-A*36:04', 'HLA-A*36:05', 'HLA-A*43:01',
'HLA-A*66:01', 'HLA-A*66:02', 'HLA-A*66:03', 'HLA-A*66:04', 'HLA-A*66:05', 'HLA-A*66:06', 'HLA-A*66:07', 'HLA-A*66:08',
'HLA-A*66:09', 'HLA-A*66:10', 'HLA-A*66:11', 'HLA-A*66:12', 'HLA-A*66:13', 'HLA-A*66:14', 'HLA-A*66:15', 'HLA-A*68:01',
'HLA-A*68:02', 'HLA-A*68:03', 'HLA-A*68:04', 'HLA-A*68:05', 'HLA-A*68:06', 'HLA-A*68:07', 'HLA-A*68:08', 'HLA-A*68:09',
'HLA-A*68:10', 'HLA-A*68:12', 'HLA-A*68:13', 'HLA-A*68:14', 'HLA-A*68:15', 'HLA-A*68:16', 'HLA-A*68:17', 'HLA-A*68:19',
'HLA-A*68:20', 'HLA-A*68:21', 'HLA-A*68:22', 'HLA-A*68:23', 'HLA-A*68:24', 'HLA-A*68:25', 'HLA-A*68:26', 'HLA-A*68:27',
'HLA-A*68:28', 'HLA-A*68:29', 'HLA-A*68:30', 'HLA-A*68:31', 'HLA-A*68:32', 'HLA-A*68:33', 'HLA-A*68:34', 'HLA-A*68:35',
'HLA-A*68:36', 'HLA-A*68:37', 'HLA-A*68:38', 'HLA-A*68:39', 'HLA-A*68:40', 'HLA-A*68:41', 'HLA-A*68:42', 'HLA-A*68:43',
'HLA-A*68:44', 'HLA-A*68:45', 'HLA-A*68:46', 'HLA-A*68:47', 'HLA-A*68:48', 'HLA-A*68:50', 'HLA-A*68:51', 'HLA-A*68:52',
'HLA-A*68:53', 'HLA-A*68:54', 'HLA-A*69:01', 'HLA-A*74:01', 'HLA-A*74:02', 'HLA-A*74:03', 'HLA-A*74:04', 'HLA-A*74:05',
'HLA-A*74:06', 'HLA-A*74:07', 'HLA-A*74:08', 'HLA-A*74:09', 'HLA-A*74:10', 'HLA-A*74:11', 'HLA-A*74:13', 'HLA-A*80:01',
'HLA-A*80:02', 'HLA-B*07:02', 'HLA-B*07:03', 'HLA-B*07:04', 'HLA-B*07:05', 'HLA-B*07:06', 'HLA-B*07:07', 'HLA-B*07:08',
'HLA-B*07:09', 'HLA-B*07:10', 'HLA-B*07:11', 'HLA-B*07:12', 'HLA-B*07:13', 'HLA-B*07:14', 'HLA-B*07:15', 'HLA-B*07:16',
'HLA-B*07:17', 'HLA-B*07:18', 'HLA-B*07:19', 'HLA-B*07:20', 'HLA-B*07:21', 'HLA-B*07:22', 'HLA-B*07:23', 'HLA-B*07:24',
'HLA-B*07:25', 'HLA-B*07:26', 'HLA-B*07:27', 'HLA-B*07:28', 'HLA-B*07:29', 'HLA-B*07:30', 'HLA-B*07:31', 'HLA-B*07:32',
'HLA-B*07:33', 'HLA-B*07:34', 'HLA-B*07:35', 'HLA-B*07:36', 'HLA-B*07:37', 'HLA-B*07:38', 'HLA-B*07:39', 'HLA-B*07:40',
'HLA-B*07:41', 'HLA-B*07:42', 'HLA-B*07:43', 'HLA-B*07:44', 'HLA-B*07:45', 'HLA-B*07:46', 'HLA-B*07:47', 'HLA-B*07:48',
'HLA-B*07:50', 'HLA-B*07:51', 'HLA-B*07:52', 'HLA-B*07:53', 'HLA-B*07:54', 'HLA-B*07:55', 'HLA-B*07:56', 'HLA-B*07:57',
'HLA-B*07:58', 'HLA-B*07:59', 'HLA-B*07:60', 'HLA-B*07:61', 'HLA-B*07:62', 'HLA-B*07:63', 'HLA-B*07:64', 'HLA-B*07:65',
'HLA-B*07:66', 'HLA-B*07:68', 'HLA-B*07:69', 'HLA-B*07:70', 'HLA-B*07:71', 'HLA-B*07:72', 'HLA-B*07:73', 'HLA-B*07:74',
'HLA-B*07:75', 'HLA-B*07:76', 'HLA-B*07:77', 'HLA-B*07:78', 'HLA-B*07:79', 'HLA-B*07:80', 'HLA-B*07:81', 'HLA-B*07:82',
'HLA-B*07:83', 'HLA-B*07:84', 'HLA-B*07:85', 'HLA-B*07:86', 'HLA-B*07:87', 'HLA-B*07:88', 'HLA-B*07:89', 'HLA-B*07:90',
'HLA-B*07:91', 'HLA-B*07:92', 'HLA-B*07:93', 'HLA-B*07:94', 'HLA-B*07:95', 'HLA-B*07:96', 'HLA-B*07:97', 'HLA-B*07:98',
'HLA-B*07:99', 'HLA-B*07:100', 'HLA-B*07:101', 'HLA-B*07:102', 'HLA-B*07:103', 'HLA-B*07:104', 'HLA-B*07:105',
'HLA-B*07:106', 'HLA-B*07:107', 'HLA-B*07:108', 'HLA-B*07:109', 'HLA-B*07:110', 'HLA-B*07:112', 'HLA-B*07:113',
'HLA-B*07:114', 'HLA-B*07:115', 'HLA-B*08:01', 'HLA-B*08:02', 'HLA-B*08:03', 'HLA-B*08:04', 'HLA-B*08:05', 'HLA-B*08:07',
'HLA-B*08:09', 'HLA-B*08:10', 'HLA-B*08:11', 'HLA-B*08:12', 'HLA-B*08:13', 'HLA-B*08:14', 'HLA-B*08:15', 'HLA-B*08:16',
'HLA-B*08:17', 'HLA-B*08:18', 'HLA-B*08:20', 'HLA-B*08:21', 'HLA-B*08:22', 'HLA-B*08:23', 'HLA-B*08:24', 'HLA-B*08:25',
'HLA-B*08:26', 'HLA-B*08:27', 'HLA-B*08:28', 'HLA-B*08:29', 'HLA-B*08:31', 'HLA-B*08:32', 'HLA-B*08:33', 'HLA-B*08:34',
'HLA-B*08:35', 'HLA-B*08:36', 'HLA-B*08:37', 'HLA-B*08:38', 'HLA-B*08:39', 'HLA-B*08:40', 'HLA-B*08:41', 'HLA-B*08:42',
'HLA-B*08:43', 'HLA-B*08:44', 'HLA-B*08:45', 'HLA-B*08:46', 'HLA-B*08:47', 'HLA-B*08:48', 'HLA-B*08:49', 'HLA-B*08:50',
'HLA-B*08:51', 'HLA-B*08:52', 'HLA-B*08:53', 'HLA-B*08:54', 'HLA-B*08:55', 'HLA-B*08:56', 'HLA-B*08:57', 'HLA-B*08:58',
'HLA-B*08:59', 'HLA-B*08:60', 'HLA-B*08:61', 'HLA-B*08:62', 'HLA-B*13:01', 'HLA-B*13:02', 'HLA-B*13:03', 'HLA-B*13:04',
'HLA-B*13:06', 'HLA-B*13:09', 'HLA-B*13:10', 'HLA-B*13:11', 'HLA-B*13:12', 'HLA-B*13:13', 'HLA-B*13:14', 'HLA-B*13:15',
'HLA-B*13:16', 'HLA-B*13:17', 'HLA-B*13:18', 'HLA-B*13:19', 'HLA-B*13:20', 'HLA-B*13:21', 'HLA-B*13:22', 'HLA-B*13:23',
'HLA-B*13:25', 'HLA-B*13:26', 'HLA-B*13:27', 'HLA-B*13:28', 'HLA-B*13:29', 'HLA-B*13:30', 'HLA-B*13:31', 'HLA-B*13:32',
'HLA-B*13:33', 'HLA-B*13:34', 'HLA-B*13:35', 'HLA-B*13:36', 'HLA-B*13:37', 'HLA-B*13:38', 'HLA-B*13:39', 'HLA-B*14:01',
'HLA-B*14:02', 'HLA-B*14:03', 'HLA-B*14:04', 'HLA-B*14:05', 'HLA-B*14:06', 'HLA-B*14:08', 'HLA-B*14:09', 'HLA-B*14:10',
'HLA-B*14:11', 'HLA-B*14:12', 'HLA-B*14:13', 'HLA-B*14:14', 'HLA-B*14:15', 'HLA-B*14:16', 'HLA-B*14:17', 'HLA-B*14:18',
'HLA-B*15:01', 'HLA-B*15:02', 'HLA-B*15:03', 'HLA-B*15:04', 'HLA-B*15:05', 'HLA-B*15:06', 'HLA-B*15:07', 'HLA-B*15:08',
'HLA-B*15:09', 'HLA-B*15:10', 'HLA-B*15:11', 'HLA-B*15:12', 'HLA-B*15:13', 'HLA-B*15:14', 'HLA-B*15:15', 'HLA-B*15:16',
'HLA-B*15:17', 'HLA-B*15:18', 'HLA-B*15:19', 'HLA-B*15:20', 'HLA-B*15:21', 'HLA-B*15:23', 'HLA-B*15:24', 'HLA-B*15:25',
'HLA-B*15:27', 'HLA-B*15:28', 'HLA-B*15:29', 'HLA-B*15:30', 'HLA-B*15:31', 'HLA-B*15:32', 'HLA-B*15:33', 'HLA-B*15:34',
'HLA-B*15:35', 'HLA-B*15:36', 'HLA-B*15:37', 'HLA-B*15:38', 'HLA-B*15:39', 'HLA-B*15:40', 'HLA-B*15:42', 'HLA-B*15:43',
'HLA-B*15:44', 'HLA-B*15:45', 'HLA-B*15:46', 'HLA-B*15:47', 'HLA-B*15:48', 'HLA-B*15:49', 'HLA-B*15:50', 'HLA-B*15:51',
'HLA-B*15:52', 'HLA-B*15:53', 'HLA-B*15:54', 'HLA-B*15:55', 'HLA-B*15:56', 'HLA-B*15:57', 'HLA-B*15:58', 'HLA-B*15:60',
'HLA-B*15:61', 'HLA-B*15:62', 'HLA-B*15:63', 'HLA-B*15:64', 'HLA-B*15:65', 'HLA-B*15:66', 'HLA-B*15:67', 'HLA-B*15:68',
'HLA-B*15:69', 'HLA-B*15:70', 'HLA-B*15:71', 'HLA-B*15:72', 'HLA-B*15:73', 'HLA-B*15:74', 'HLA-B*15:75', 'HLA-B*15:76',
'HLA-B*15:77', 'HLA-B*15:78', 'HLA-B*15:80', 'HLA-B*15:81', 'HLA-B*15:82', 'HLA-B*15:83', 'HLA-B*15:84', 'HLA-B*15:85',
'HLA-B*15:86', 'HLA-B*15:87', 'HLA-B*15:88', 'HLA-B*15:89', 'HLA-B*15:90', 'HLA-B*15:91', 'HLA-B*15:92', 'HLA-B*15:93',
'HLA-B*15:95', 'HLA-B*15:96', 'HLA-B*15:97', 'HLA-B*15:98', 'HLA-B*15:99', 'HLA-B*15:101', 'HLA-B*15:102', 'HLA-B*15:103',
'HLA-B*15:104', 'HLA-B*15:105', 'HLA-B*15:106', 'HLA-B*15:107', 'HLA-B*15:108', 'HLA-B*15:109', 'HLA-B*15:110',
'HLA-B*15:112', 'HLA-B*15:113', 'HLA-B*15:114', 'HLA-B*15:115', 'HLA-B*15:116', 'HLA-B*15:117', 'HLA-B*15:118',
'HLA-B*15:119', 'HLA-B*15:120', 'HLA-B*15:121', 'HLA-B*15:122', 'HLA-B*15:123', 'HLA-B*15:124', 'HLA-B*15:125',
'HLA-B*15:126', 'HLA-B*15:127', 'HLA-B*15:128', 'HLA-B*15:129', 'HLA-B*15:131', 'HLA-B*15:132', 'HLA-B*15:133',
'HLA-B*15:134', 'HLA-B*15:135', 'HLA-B*15:136', 'HLA-B*15:137', 'HLA-B*15:138', 'HLA-B*15:139', 'HLA-B*15:140',
'HLA-B*15:141', 'HLA-B*15:142', 'HLA-B*15:143', 'HLA-B*15:144', 'HLA-B*15:145', 'HLA-B*15:146', 'HLA-B*15:147',
'HLA-B*15:148', 'HLA-B*15:150', 'HLA-B*15:151', 'HLA-B*15:152', 'HLA-B*15:153', 'HLA-B*15:154', 'HLA-B*15:155',
'HLA-B*15:156', 'HLA-B*15:157', 'HLA-B*15:158', 'HLA-B*15:159', 'HLA-B*15:160', 'HLA-B*15:161', 'HLA-B*15:162',
'HLA-B*15:163', 'HLA-B*15:164', 'HLA-B*15:165', 'HLA-B*15:166', 'HLA-B*15:167', 'HLA-B*15:168', 'HLA-B*15:169',
'HLA-B*15:170', 'HLA-B*15:171', 'HLA-B*15:172', 'HLA-B*15:173', 'HLA-B*15:174', 'HLA-B*15:175', 'HLA-B*15:176',
'HLA-B*15:177', 'HLA-B*15:178', 'HLA-B*15:179', 'HLA-B*15:180', 'HLA-B*15:183', 'HLA-B*15:184', 'HLA-B*15:185',
'HLA-B*15:186', 'HLA-B*15:187', 'HLA-B*15:188', 'HLA-B*15:189', 'HLA-B*15:191', 'HLA-B*15:192', 'HLA-B*15:193',
'HLA-B*15:194', 'HLA-B*15:195', 'HLA-B*15:196', 'HLA-B*15:197', 'HLA-B*15:198', 'HLA-B*15:199', 'HLA-B*15:200',
'HLA-B*15:201', 'HLA-B*15:202', 'HLA-B*18:01', 'HLA-B*18:02', 'HLA-B*18:03', 'HLA-B*18:04', 'HLA-B*18:05', 'HLA-B*18:06',
'HLA-B*18:07', 'HLA-B*18:08', 'HLA-B*18:09', 'HLA-B*18:10', 'HLA-B*18:11', 'HLA-B*18:12', 'HLA-B*18:13', 'HLA-B*18:14',
'HLA-B*18:15', 'HLA-B*18:18', 'HLA-B*18:19', 'HLA-B*18:20', 'HLA-B*18:21', 'HLA-B*18:22', 'HLA-B*18:24', 'HLA-B*18:25',
'HLA-B*18:26', 'HLA-B*18:27', 'HLA-B*18:28', 'HLA-B*18:29', 'HLA-B*18:30', 'HLA-B*18:31', 'HLA-B*18:32', 'HLA-B*18:33',
'HLA-B*18:34', 'HLA-B*18:35', 'HLA-B*18:36', 'HLA-B*18:37', 'HLA-B*18:38', 'HLA-B*18:39', 'HLA-B*18:40', 'HLA-B*18:41',
'HLA-B*18:42', 'HLA-B*18:43', 'HLA-B*18:44', 'HLA-B*18:45', 'HLA-B*18:46', 'HLA-B*18:47', 'HLA-B*18:48', 'HLA-B*18:49',
'HLA-B*18:50', 'HLA-B*27:01', 'HLA-B*27:02', 'HLA-B*27:03', 'HLA-B*27:04', 'HLA-B*27:05', 'HLA-B*27:06', 'HLA-B*27:07',
'HLA-B*27:08', 'HLA-B*27:09', 'HLA-B*27:10', 'HLA-B*27:11', 'HLA-B*27:12', 'HLA-B*27:13', 'HLA-B*27:14', 'HLA-B*27:15',
'HLA-B*27:16', 'HLA-B*27:17', 'HLA-B*27:18', 'HLA-B*27:19', 'HLA-B*27:20', 'HLA-B*27:21', 'HLA-B*27:23', 'HLA-B*27:24',
'HLA-B*27:25', 'HLA-B*27:26', 'HLA-B*27:27', 'HLA-B*27:28', 'HLA-B*27:29', 'HLA-B*27:30', 'HLA-B*27:31', 'HLA-B*27:32',
'HLA-B*27:33', 'HLA-B*27:34', 'HLA-B*27:35', 'HLA-B*27:36', 'HLA-B*27:37', 'HLA-B*27:38', 'HLA-B*27:39', 'HLA-B*27:40',
'HLA-B*27:41', 'HLA-B*27:42', 'HLA-B*27:43', 'HLA-B*27:44', 'HLA-B*27:45', 'HLA-B*27:46', 'HLA-B*27:47', 'HLA-B*27:48',
'HLA-B*27:49', 'HLA-B*27:50', 'HLA-B*27:51', 'HLA-B*27:52', 'HLA-B*27:53', 'HLA-B*27:54', 'HLA-B*27:55', 'HLA-B*27:56',
'HLA-B*27:57', 'HLA-B*27:58', 'HLA-B*27:60', 'HLA-B*27:61', 'HLA-B*27:62', 'HLA-B*27:63', 'HLA-B*27:67', 'HLA-B*27:68',
'HLA-B*27:69', 'HLA-B*35:01', 'HLA-B*35:02', 'HLA-B*35:03', 'HLA-B*35:04', 'HLA-B*35:05', 'HLA-B*35:06', 'HLA-B*35:07',
'HLA-B*35:08', 'HLA-B*35:09', 'HLA-B*35:10', 'HLA-B*35:11', 'HLA-B*35:12', 'HLA-B*35:13', 'HLA-B*35:14', 'HLA-B*35:15',
'HLA-B*35:16', 'HLA-B*35:17', 'HLA-B*35:18', 'HLA-B*35:19', 'HLA-B*35:20', 'HLA-B*35:21', 'HLA-B*35:22', 'HLA-B*35:23',
'HLA-B*35:24', 'HLA-B*35:25', 'HLA-B*35:26', 'HLA-B*35:27', 'HLA-B*35:28', 'HLA-B*35:29', 'HLA-B*35:30', 'HLA-B*35:31',
'HLA-B*35:32', 'HLA-B*35:33', 'HLA-B*35:34', 'HLA-B*35:35', 'HLA-B*35:36', 'HLA-B*35:37', 'HLA-B*35:38', 'HLA-B*35:39',
'HLA-B*35:41', 'HLA-B*35:42', 'HLA-B*35:43', 'HLA-B*35:44', 'HLA-B*35:45', 'HLA-B*35:46', 'HLA-B*35:47', 'HLA-B*35:48',
'HLA-B*35:49', 'HLA-B*35:50', 'HLA-B*35:51', 'HLA-B*35:52', 'HLA-B*35:54', 'HLA-B*35:55', 'HLA-B*35:56', 'HLA-B*35:57',
'HLA-B*35:58', 'HLA-B*35:59', 'HLA-B*35:60', 'HLA-B*35:61', 'HLA-B*35:62', 'HLA-B*35:63', 'HLA-B*35:64', 'HLA-B*35:66',
'HLA-B*35:67', 'HLA-B*35:68', 'HLA-B*35:69', 'HLA-B*35:70', 'HLA-B*35:71', 'HLA-B*35:72', 'HLA-B*35:74', 'HLA-B*35:75',
'HLA-B*35:76', 'HLA-B*35:77', 'HLA-B*35:78', 'HLA-B*35:79', 'HLA-B*35:80', 'HLA-B*35:81', 'HLA-B*35:82', 'HLA-B*35:83',
'HLA-B*35:84', 'HLA-B*35:85', 'HLA-B*35:86', 'HLA-B*35:87', 'HLA-B*35:88', 'HLA-B*35:89', 'HLA-B*35:90', 'HLA-B*35:91',
'HLA-B*35:92', 'HLA-B*35:93', 'HLA-B*35:94', 'HLA-B*35:95', 'HLA-B*35:96', 'HLA-B*35:97', 'HLA-B*35:98', 'HLA-B*35:99',
'HLA-B*35:100', 'HLA-B*35:101', 'HLA-B*35:102', 'HLA-B*35:103', 'HLA-B*35:104', 'HLA-B*35:105', 'HLA-B*35:106',
'HLA-B*35:107', 'HLA-B*35:108', 'HLA-B*35:109', 'HLA-B*35:110', 'HLA-B*35:111', 'HLA-B*35:112', 'HLA-B*35:113',
'HLA-B*35:114', 'HLA-B*35:115', 'HLA-B*35:116', 'HLA-B*35:117', 'HLA-B*35:118', 'HLA-B*35:119', 'HLA-B*35:120',
'HLA-B*35:121', 'HLA-B*35:122', 'HLA-B*35:123', 'HLA-B*35:124', 'HLA-B*35:125', 'HLA-B*35:126', 'HLA-B*35:127',
'HLA-B*35:128', 'HLA-B*35:131', 'HLA-B*35:132', 'HLA-B*35:133', 'HLA-B*35:135', 'HLA-B*35:136', 'HLA-B*35:137',
'HLA-B*35:138', 'HLA-B*35:139', 'HLA-B*35:140', 'HLA-B*35:141', 'HLA-B*35:142', 'HLA-B*35:143', 'HLA-B*35:144',
'HLA-B*37:01', 'HLA-B*37:02', 'HLA-B*37:04', 'HLA-B*37:05', 'HLA-B*37:06', 'HLA-B*37:07', 'HLA-B*37:08', 'HLA-B*37:09',
'HLA-B*37:10', 'HLA-B*37:11', 'HLA-B*37:12', 'HLA-B*37:13', 'HLA-B*37:14', 'HLA-B*37:15', 'HLA-B*37:17', 'HLA-B*37:18',
'HLA-B*37:19', 'HLA-B*37:20', 'HLA-B*37:21', 'HLA-B*37:22', 'HLA-B*37:23', 'HLA-B*38:01', 'HLA-B*38:02', 'HLA-B*38:03',
'HLA-B*38:04', 'HLA-B*38:05', 'HLA-B*38:06', 'HLA-B*38:07', 'HLA-B*38:08', 'HLA-B*38:09', 'HLA-B*38:10', 'HLA-B*38:11',
'HLA-B*38:12', 'HLA-B*38:13', 'HLA-B*38:14', 'HLA-B*38:15', 'HLA-B*38:16', 'HLA-B*38:17', 'HLA-B*38:18', 'HLA-B*38:19',
'HLA-B*38:20', 'HLA-B*38:21', 'HLA-B*38:22', 'HLA-B*38:23', 'HLA-B*39:01', 'HLA-B*39:02', 'HLA-B*39:03', 'HLA-B*39:04',
'HLA-B*39:05', 'HLA-B*39:06', 'HLA-B*39:07', 'HLA-B*39:08', 'HLA-B*39:09', 'HLA-B*39:10', 'HLA-B*39:11', 'HLA-B*39:12',
'HLA-B*39:13', 'HLA-B*39:14', 'HLA-B*39:15', 'HLA-B*39:16', 'HLA-B*39:17', 'HLA-B*39:18', 'HLA-B*39:19', 'HLA-B*39:20',
'HLA-B*39:22', 'HLA-B*39:23', 'HLA-B*39:24', 'HLA-B*39:26', 'HLA-B*39:27', 'HLA-B*39:28', 'HLA-B*39:29', 'HLA-B*39:30',
'HLA-B*39:31', 'HLA-B*39:32', 'HLA-B*39:33', 'HLA-B*39:34', 'HLA-B*39:35', 'HLA-B*39:36', 'HLA-B*39:37', 'HLA-B*39:39',
'HLA-B*39:41', 'HLA-B*39:42', 'HLA-B*39:43', 'HLA-B*39:44', 'HLA-B*39:45', 'HLA-B*39:46', 'HLA-B*39:47', 'HLA-B*39:48',
'HLA-B*39:49', 'HLA-B*39:50', 'HLA-B*39:51', 'HLA-B*39:52', 'HLA-B*39:53', 'HLA-B*39:54', 'HLA-B*39:55', 'HLA-B*39:56',
'HLA-B*39:57', 'HLA-B*39:58', 'HLA-B*39:59', 'HLA-B*39:60', 'HLA-B*40:01', 'HLA-B*40:02', 'HLA-B*40:03', 'HLA-B*40:04',
'HLA-B*40:05', 'HLA-B*40:06', 'HLA-B*40:07', 'HLA-B*40:08', 'HLA-B*40:09', 'HLA-B*40:10', 'HLA-B*40:11', 'HLA-B*40:12',
'HLA-B*40:13', 'HLA-B*40:14', 'HLA-B*40:15', 'HLA-B*40:16', 'HLA-B*40:18', 'HLA-B*40:19', 'HLA-B*40:20', 'HLA-B*40:21',
'HLA-B*40:23', 'HLA-B*40:24', 'HLA-B*40:25', 'HLA-B*40:26', 'HLA-B*40:27', 'HLA-B*40:28', 'HLA-B*40:29', 'HLA-B*40:30',
'HLA-B*40:31', 'HLA-B*40:32', 'HLA-B*40:33', 'HLA-B*40:34', 'HLA-B*40:35', 'HLA-B*40:36', 'HLA-B*40:37', 'HLA-B*40:38',
'HLA-B*40:39', 'HLA-B*40:40', 'HLA-B*40:42', 'HLA-B*40:43', 'HLA-B*40:44', 'HLA-B*40:45', 'HLA-B*40:46', 'HLA-B*40:47',
'HLA-B*40:48', 'HLA-B*40:49', 'HLA-B*40:50', 'HLA-B*40:51', 'HLA-B*40:52', 'HLA-B*40:53', 'HLA-B*40:54', 'HLA-B*40:55',
'HLA-B*40:56', 'HLA-B*40:57', 'HLA-B*40:58', 'HLA-B*40:59', 'HLA-B*40:60', 'HLA-B*40:61', 'HLA-B*40:62', 'HLA-B*40:63',
'HLA-B*40:64', 'HLA-B*40:65', 'HLA-B*40:66', 'HLA-B*40:67', 'HLA-B*40:68', 'HLA-B*40:69', 'HLA-B*40:70', 'HLA-B*40:71',
'HLA-B*40:72', 'HLA-B*40:73', 'HLA-B*40:74', 'HLA-B*40:75', 'HLA-B*40:76', 'HLA-B*40:77', 'HLA-B*40:78', 'HLA-B*40:79',
'HLA-B*40:80', 'HLA-B*40:81', 'HLA-B*40:82', 'HLA-B*40:83', 'HLA-B*40:84', 'HLA-B*40:85', 'HLA-B*40:86', 'HLA-B*40:87',
'HLA-B*40:88', 'HLA-B*40:89', 'HLA-B*40:90', 'HLA-B*40:91', 'HLA-B*40:92', 'HLA-B*40:93', 'HLA-B*40:94', 'HLA-B*40:95',
'HLA-B*40:96', 'HLA-B*40:97', 'HLA-B*40:98', 'HLA-B*40:99', 'HLA-B*40:100', 'HLA-B*40:101', 'HLA-B*40:102', 'HLA-B*40:103',
'HLA-B*40:104', 'HLA-B*40:105', 'HLA-B*40:106', 'HLA-B*40:107', 'HLA-B*40:108', 'HLA-B*40:109', 'HLA-B*40:110',
'HLA-B*40:111', 'HLA-B*40:112', 'HLA-B*40:113', 'HLA-B*40:114', 'HLA-B*40:115', 'HLA-B*40:116', 'HLA-B*40:117',
'HLA-B*40:119', 'HLA-B*40:120', 'HLA-B*40:121', 'HLA-B*40:122', 'HLA-B*40:123', 'HLA-B*40:124', 'HLA-B*40:125',
'HLA-B*40:126', 'HLA-B*40:127', 'HLA-B*40:128', 'HLA-B*40:129', 'HLA-B*40:130', 'HLA-B*40:131', 'HLA-B*40:132',
'HLA-B*40:134', 'HLA-B*40:135', 'HLA-B*40:136', 'HLA-B*40:137', 'HLA-B*40:138', 'HLA-B*40:139', 'HLA-B*40:140',
'HLA-B*40:141', 'HLA-B*40:143', 'HLA-B*40:145', 'HLA-B*40:146', 'HLA-B*40:147', 'HLA-B*41:01', 'HLA-B*41:02', 'HLA-B*41:03',
'HLA-B*41:04', 'HLA-B*41:05', 'HLA-B*41:06', 'HLA-B*41:07', 'HLA-B*41:08', 'HLA-B*41:09', 'HLA-B*41:10', 'HLA-B*41:11',
'HLA-B*41:12', 'HLA-B*42:01', 'HLA-B*42:02', 'HLA-B*42:04', 'HLA-B*42:05', 'HLA-B*42:06', 'HLA-B*42:07', 'HLA-B*42:08',
'HLA-B*42:09', 'HLA-B*42:10', 'HLA-B*42:11', 'HLA-B*42:12', 'HLA-B*42:13', 'HLA-B*42:14', 'HLA-B*44:02', 'HLA-B*44:03',
'HLA-B*44:04', 'HLA-B*44:05', 'HLA-B*44:06', 'HLA-B*44:07', 'HLA-B*44:08', 'HLA-B*44:09', 'HLA-B*44:10', 'HLA-B*44:11',
'HLA-B*44:12', 'HLA-B*44:13', 'HLA-B*44:14', 'HLA-B*44:15', 'HLA-B*44:16', 'HLA-B*44:17', 'HLA-B*44:18', 'HLA-B*44:20',
'HLA-B*44:21', 'HLA-B*44:22', 'HLA-B*44:24', 'HLA-B*44:25', 'HLA-B*44:26', 'HLA-B*44:27', 'HLA-B*44:28', 'HLA-B*44:29',
'HLA-B*44:30', 'HLA-B*44:31', 'HLA-B*44:32', 'HLA-B*44:33', 'HLA-B*44:34', 'HLA-B*44:35', 'HLA-B*44:36', 'HLA-B*44:37',
'HLA-B*44:38', 'HLA-B*44:39', 'HLA-B*44:40', 'HLA-B*44:41', 'HLA-B*44:42', 'HLA-B*44:43', 'HLA-B*44:44', 'HLA-B*44:45',
'HLA-B*44:46', 'HLA-B*44:47', 'HLA-B*44:48', 'HLA-B*44:49', 'HLA-B*44:50', 'HLA-B*44:51', 'HLA-B*44:53', 'HLA-B*44:54',
'HLA-B*44:55', 'HLA-B*44:57', 'HLA-B*44:59', 'HLA-B*44:60', 'HLA-B*44:62', 'HLA-B*44:63', 'HLA-B*44:64', 'HLA-B*44:65',
'HLA-B*44:66', 'HLA-B*44:67', 'HLA-B*44:68', 'HLA-B*44:69', 'HLA-B*44:70', 'HLA-B*44:71', 'HLA-B*44:72', 'HLA-B*44:73',
'HLA-B*44:74', 'HLA-B*44:75', 'HLA-B*44:76', 'HLA-B*44:77', 'HLA-B*44:78', 'HLA-B*44:79', 'HLA-B*44:80', 'HLA-B*44:81',
'HLA-B*44:82', 'HLA-B*44:83', 'HLA-B*44:84', 'HLA-B*44:85', 'HLA-B*44:86', 'HLA-B*44:87', 'HLA-B*44:88', 'HLA-B*44:89',
'HLA-B*44:90', 'HLA-B*44:91', 'HLA-B*44:92', 'HLA-B*44:93', 'HLA-B*44:94', 'HLA-B*44:95', 'HLA-B*44:96', 'HLA-B*44:97',
'HLA-B*44:98', 'HLA-B*44:99', 'HLA-B*44:100', 'HLA-B*44:101', 'HLA-B*44:102', 'HLA-B*44:103', 'HLA-B*44:104',
'HLA-B*44:105', 'HLA-B*44:106', 'HLA-B*44:107', 'HLA-B*44:109', 'HLA-B*44:110', 'HLA-B*45:01', 'HLA-B*45:02', 'HLA-B*45:03',
'HLA-B*45:04', 'HLA-B*45:05', 'HLA-B*45:06', 'HLA-B*45:07', 'HLA-B*45:08', 'HLA-B*45:09', 'HLA-B*45:10', 'HLA-B*45:11',
'HLA-B*45:12', 'HLA-B*46:01', 'HLA-B*46:02', 'HLA-B*46:03', 'HLA-B*46:04', 'HLA-B*46:05', 'HLA-B*46:06', 'HLA-B*46:08',
'HLA-B*46:09', 'HLA-B*46:10', 'HLA-B*46:11', 'HLA-B*46:12', 'HLA-B*46:13', 'HLA-B*46:14', 'HLA-B*46:16', 'HLA-B*46:17',
'HLA-B*46:18', 'HLA-B*46:19', 'HLA-B*46:20', 'HLA-B*46:21', 'HLA-B*46:22', 'HLA-B*46:23', 'HLA-B*46:24', 'HLA-B*47:01',
'HLA-B*47:02', 'HLA-B*47:03', 'HLA-B*47:04', 'HLA-B*47:05', 'HLA-B*47:06', 'HLA-B*47:07', 'HLA-B*48:01', 'HLA-B*48:02',
'HLA-B*48:03', 'HLA-B*48:04', 'HLA-B*48:05', 'HLA-B*48:06', 'HLA-B*48:07', 'HLA-B*48:08', 'HLA-B*48:09', 'HLA-B*48:10',
'HLA-B*48:11', 'HLA-B*48:12', 'HLA-B*48:13', 'HLA-B*48:14', 'HLA-B*48:15', 'HLA-B*48:16', 'HLA-B*48:17', 'HLA-B*48:18',
'HLA-B*48:19', 'HLA-B*48:20', 'HLA-B*48:21', 'HLA-B*48:22', 'HLA-B*48:23', 'HLA-B*49:01', 'HLA-B*49:02', 'HLA-B*49:03',
'HLA-B*49:04', 'HLA-B*49:05', 'HLA-B*49:06', 'HLA-B*49:07', 'HLA-B*49:08', 'HLA-B*49:09', 'HLA-B*49:10', 'HLA-B*50:01',
'HLA-B*50:02', 'HLA-B*50:04', 'HLA-B*50:05', 'HLA-B*50:06', 'HLA-B*50:07', 'HLA-B*50:08', 'HLA-B*50:09', 'HLA-B*51:01',
'HLA-B*51:02', 'HLA-B*51:03', 'HLA-B*51:04', 'HLA-B*51:05', 'HLA-B*51:06', 'HLA-B*51:07', 'HLA-B*51:08', 'HLA-B*51:09',
'HLA-B*51:12', 'HLA-B*51:13', 'HLA-B*51:14', 'HLA-B*51:15', 'HLA-B*51:16', 'HLA-B*51:17', 'HLA-B*51:18', 'HLA-B*51:19',
'HLA-B*51:20', 'HLA-B*51:21', 'HLA-B*51:22', 'HLA-B*51:23', 'HLA-B*51:24', 'HLA-B*51:26', 'HLA-B*51:28', 'HLA-B*51:29',
'HLA-B*51:30', 'HLA-B*51:31', 'HLA-B*51:32', 'HLA-B*51:33', 'HLA-B*51:34', 'HLA-B*51:35', 'HLA-B*51:36', 'HLA-B*51:37',
'HLA-B*51:38', 'HLA-B*51:39', 'HLA-B*51:40', 'HLA-B*51:42', 'HLA-B*51:43', 'HLA-B*51:45', 'HLA-B*51:46', 'HLA-B*51:48',
'HLA-B*51:49', 'HLA-B*51:50', 'HLA-B*51:51', 'HLA-B*51:52', 'HLA-B*51:53', 'HLA-B*51:54', 'HLA-B*51:55', 'HLA-B*51:56',
'HLA-B*51:57', 'HLA-B*51:58', 'HLA-B*51:59', 'HLA-B*51:60', 'HLA-B*51:61', 'HLA-B*51:62', 'HLA-B*51:63', 'HLA-B*51:64',
'HLA-B*51:65', 'HLA-B*51:66', 'HLA-B*51:67', 'HLA-B*51:68', 'HLA-B*51:69', 'HLA-B*51:70', 'HLA-B*51:71', 'HLA-B*51:72',
'HLA-B*51:73', 'HLA-B*51:74', 'HLA-B*51:75', 'HLA-B*51:76', 'HLA-B*51:77', 'HLA-B*51:78', 'HLA-B*51:79', 'HLA-B*51:80',
'HLA-B*51:81', 'HLA-B*51:82', 'HLA-B*51:83', 'HLA-B*51:84', 'HLA-B*51:85', 'HLA-B*51:86', 'HLA-B*51:87', 'HLA-B*51:88',
'HLA-B*51:89', 'HLA-B*51:90', 'HLA-B*51:91', 'HLA-B*51:92', 'HLA-B*51:93', 'HLA-B*51:94', 'HLA-B*51:95', 'HLA-B*51:96',
'HLA-B*52:01', 'HLA-B*52:02', 'HLA-B*52:03', 'HLA-B*52:04', 'HLA-B*52:05', 'HLA-B*52:06', 'HLA-B*52:07', 'HLA-B*52:08',
'HLA-B*52:09', 'HLA-B*52:10', 'HLA-B*52:11', 'HLA-B*52:12', 'HLA-B*52:13', 'HLA-B*52:14', 'HLA-B*52:15', 'HLA-B*52:16',
'HLA-B*52:17', 'HLA-B*52:18', 'HLA-B*52:19', 'HLA-B*52:20', 'HLA-B*52:21', 'HLA-B*53:01', 'HLA-B*53:02', 'HLA-B*53:03',
'HLA-B*53:04', 'HLA-B*53:05', 'HLA-B*53:06', 'HLA-B*53:07', 'HLA-B*53:08', 'HLA-B*53:09', 'HLA-B*53:10', 'HLA-B*53:11',
'HLA-B*53:12', 'HLA-B*53:13', 'HLA-B*53:14', 'HLA-B*53:15', 'HLA-B*53:16', 'HLA-B*53:17', 'HLA-B*53:18', 'HLA-B*53:19',
'HLA-B*53:20', 'HLA-B*53:21', 'HLA-B*53:22', 'HLA-B*53:23', 'HLA-B*54:01', 'HLA-B*54:02', 'HLA-B*54:03', 'HLA-B*54:04',
'HLA-B*54:06', 'HLA-B*54:07', 'HLA-B*54:09', 'HLA-B*54:10', 'HLA-B*54:11', 'HLA-B*54:12', 'HLA-B*54:13', 'HLA-B*54:14',
'HLA-B*54:15', 'HLA-B*54:16', 'HLA-B*54:17', 'HLA-B*54:18', 'HLA-B*54:19', 'HLA-B*54:20', 'HLA-B*54:21', 'HLA-B*54:22',
'HLA-B*54:23', 'HLA-B*55:01', 'HLA-B*55:02', 'HLA-B*55:03', 'HLA-B*55:04', 'HLA-B*55:05', 'HLA-B*55:07', 'HLA-B*55:08',
'HLA-B*55:09', 'HLA-B*55:10', 'HLA-B*55:11', 'HLA-B*55:12', 'HLA-B*55:13', 'HLA-B*55:14', 'HLA-B*55:15', 'HLA-B*55:16',
'HLA-B*55:17', 'HLA-B*55:18', 'HLA-B*55:19', 'HLA-B*55:20', 'HLA-B*55:21', 'HLA-B*55:22', 'HLA-B*55:23', 'HLA-B*55:24',
'HLA-B*55:25', 'HLA-B*55:26', 'HLA-B*55:27', 'HLA-B*55:28', 'HLA-B*55:29', 'HLA-B*55:30', 'HLA-B*55:31', 'HLA-B*55:32',
'HLA-B*55:33', 'HLA-B*55:34', 'HLA-B*55:35', 'HLA-B*55:36', 'HLA-B*55:37', 'HLA-B*55:38', 'HLA-B*55:39', 'HLA-B*55:40',
'HLA-B*55:41', 'HLA-B*55:42', 'HLA-B*55:43', 'HLA-B*56:01', 'HLA-B*56:02', 'HLA-B*56:03', 'HLA-B*56:04', 'HLA-B*56:05',
'HLA-B*56:06', 'HLA-B*56:07', 'HLA-B*56:08', 'HLA-B*56:09', 'HLA-B*56:10', 'HLA-B*56:11', 'HLA-B*56:12', 'HLA-B*56:13',
'HLA-B*56:14', 'HLA-B*56:15', 'HLA-B*56:16', 'HLA-B*56:17', 'HLA-B*56:18', 'HLA-B*56:20', 'HLA-B*56:21', 'HLA-B*56:22',
'HLA-B*56:23', 'HLA-B*56:24', 'HLA-B*56:25', 'HLA-B*56:26', 'HLA-B*56:27', 'HLA-B*56:29', 'HLA-B*57:01', 'HLA-B*57:02',
'HLA-B*57:03', 'HLA-B*57:04', 'HLA-B*57:05', 'HLA-B*57:06', 'HLA-B*57:07', 'HLA-B*57:08', 'HLA-B*57:09', 'HLA-B*57:10',
'HLA-B*57:11', 'HLA-B*57:12', 'HLA-B*57:13', 'HLA-B*57:14', 'HLA-B*57:15', 'HLA-B*57:16', 'HLA-B*57:17', 'HLA-B*57:18',
'HLA-B*57:19', 'HLA-B*57:20', 'HLA-B*57:21', 'HLA-B*57:22', 'HLA-B*57:23', 'HLA-B*57:24', 'HLA-B*57:25', 'HLA-B*57:26',
'HLA-B*57:27', 'HLA-B*57:29', 'HLA-B*57:30', 'HLA-B*57:31', 'HLA-B*57:32', 'HLA-B*58:01', 'HLA-B*58:02', 'HLA-B*58:04',
'HLA-B*58:05', 'HLA-B*58:06', 'HLA-B*58:07', 'HLA-B*58:08', 'HLA-B*58:09', 'HLA-B*58:11', 'HLA-B*58:12', 'HLA-B*58:13',
'HLA-B*58:14', 'HLA-B*58:15', 'HLA-B*58:16', 'HLA-B*58:18', 'HLA-B*58:19', 'HLA-B*58:20', 'HLA-B*58:21', 'HLA-B*58:22',
'HLA-B*58:23', 'HLA-B*58:24', 'HLA-B*58:25', 'HLA-B*58:26', 'HLA-B*58:27', 'HLA-B*58:28', 'HLA-B*58:29', 'HLA-B*58:30',
'HLA-B*59:01', 'HLA-B*59:02', 'HLA-B*59:03', 'HLA-B*59:04', 'HLA-B*59:05', 'HLA-B*67:01', 'HLA-B*67:02', 'HLA-B*73:01',
'HLA-B*73:02', 'HLA-B*78:01', 'HLA-B*78:02', 'HLA-B*78:03', 'HLA-B*78:04', 'HLA-B*78:05', 'HLA-B*78:06', 'HLA-B*78:07',
'HLA-B*81:01', 'HLA-B*81:02', 'HLA-B*81:03', 'HLA-B*81:05', 'HLA-B*82:01', 'HLA-B*82:02', 'HLA-B*82:03', 'HLA-B*83:01',
'HLA-C*01:02', 'HLA-C*01:03', 'HLA-C*01:04', 'HLA-C*01:05', 'HLA-C*01:06', 'HLA-C*01:07', 'HLA-C*01:08', 'HLA-C*01:09',
'HLA-C*01:10', 'HLA-C*01:11', 'HLA-C*01:12', 'HLA-C*01:13', 'HLA-C*01:14', 'HLA-C*01:15', 'HLA-C*01:16', 'HLA-C*01:17',
'HLA-C*01:18', 'HLA-C*01:19', 'HLA-C*01:20', 'HLA-C*01:21', 'HLA-C*01:22', 'HLA-C*01:23', 'HLA-C*01:24', 'HLA-C*01:25',
'HLA-C*01:26', 'HLA-C*01:27', 'HLA-C*01:28', 'HLA-C*01:29', 'HLA-C*01:30', 'HLA-C*01:31', 'HLA-C*01:32', 'HLA-C*01:33',
'HLA-C*01:34', 'HLA-C*01:35', 'HLA-C*01:36', 'HLA-C*01:38', 'HLA-C*01:39', 'HLA-C*01:40', 'HLA-C*02:02', 'HLA-C*02:03',
'HLA-C*02:04', 'HLA-C*02:05', 'HLA-C*02:06', 'HLA-C*02:07', 'HLA-C*02:08', 'HLA-C*02:09', 'HLA-C*02:10', 'HLA-C*02:11',
'HLA-C*02:12', 'HLA-C*02:13', 'HLA-C*02:14', 'HLA-C*02:15', 'HLA-C*02:16', 'HLA-C*02:17', 'HLA-C*02:18', 'HLA-C*02:19',
'HLA-C*02:20', 'HLA-C*02:21', 'HLA-C*02:22', 'HLA-C*02:23', 'HLA-C*02:24', 'HLA-C*02:26', 'HLA-C*02:27', 'HLA-C*02:28',
'HLA-C*02:29', 'HLA-C*02:30', 'HLA-C*02:31', 'HLA-C*02:32', 'HLA-C*02:33', 'HLA-C*02:34', 'HLA-C*02:35', 'HLA-C*02:36',
'HLA-C*02:37', 'HLA-C*02:39', 'HLA-C*02:40', 'HLA-C*03:01', 'HLA-C*03:02', 'HLA-C*03:03', 'HLA-C*03:04', 'HLA-C*03:05',
'HLA-C*03:06', 'HLA-C*03:07', 'HLA-C*03:08', 'HLA-C*03:09', 'HLA-C*03:10', 'HLA-C*03:11', 'HLA-C*03:12', 'HLA-C*03:13',
'HLA-C*03:14', 'HLA-C*03:15', 'HLA-C*03:16', 'HLA-C*03:17', 'HLA-C*03:18', 'HLA-C*03:19', 'HLA-C*03:21', 'HLA-C*03:23',
'HLA-C*03:24', 'HLA-C*03:25', 'HLA-C*03:26', 'HLA-C*03:27', 'HLA-C*03:28', 'HLA-C*03:29', 'HLA-C*03:30', 'HLA-C*03:31',
'HLA-C*03:32', 'HLA-C*03:33', 'HLA-C*03:34', 'HLA-C*03:35', 'HLA-C*03:36', 'HLA-C*03:37', 'HLA-C*03:38', 'HLA-C*03:39',
'HLA-C*03:40', 'HLA-C*03:41', 'HLA-C*03:42', 'HLA-C*03:43', 'HLA-C*03:44', 'HLA-C*03:45', 'HLA-C*03:46', 'HLA-C*03:47',
'HLA-C*03:48', 'HLA-C*03:49', 'HLA-C*03:50', 'HLA-C*03:51', 'HLA-C*03:52', 'HLA-C*03:53', 'HLA-C*03:54', 'HLA-C*03:55',
'HLA-C*03:56', 'HLA-C*03:57', 'HLA-C*03:58', 'HLA-C*03:59', 'HLA-C*03:60', 'HLA-C*03:61', 'HLA-C*03:62', 'HLA-C*03:63',
'HLA-C*03:64', 'HLA-C*03:65', 'HLA-C*03:66', 'HLA-C*03:67', 'HLA-C*03:68', 'HLA-C*03:69', 'HLA-C*03:70', 'HLA-C*03:71',
'HLA-C*03:72', 'HLA-C*03:73', 'HLA-C*03:74', 'HLA-C*03:75', 'HLA-C*03:76', 'HLA-C*03:77', 'HLA-C*03:78', 'HLA-C*03:79',
'HLA-C*03:80', 'HLA-C*03:81', 'HLA-C*03:82', 'HLA-C*03:83', 'HLA-C*03:84', 'HLA-C*03:85', 'HLA-C*03:86', 'HLA-C*03:87',
'HLA-C*03:88', 'HLA-C*03:89', 'HLA-C*03:90', 'HLA-C*03:91', 'HLA-C*03:92', 'HLA-C*03:93', 'HLA-C*03:94', 'HLA-C*04:01',
'HLA-C*04:03', 'HLA-C*04:04', 'HLA-C*04:05', 'HLA-C*04:06', 'HLA-C*04:07', 'HLA-C*04:08', 'HLA-C*04:10', 'HLA-C*04:11',
'HLA-C*04:12', 'HLA-C*04:13', 'HLA-C*04:14', 'HLA-C*04:15', 'HLA-C*04:16', 'HLA-C*04:17', 'HLA-C*04:18', 'HLA-C*04:19',
'HLA-C*04:20', 'HLA-C*04:23', 'HLA-C*04:24', 'HLA-C*04:25', 'HLA-C*04:26', 'HLA-C*04:27', 'HLA-C*04:28', 'HLA-C*04:29',
'HLA-C*04:30', 'HLA-C*04:31', 'HLA-C*04:32', 'HLA-C*04:33', 'HLA-C*04:34', 'HLA-C*04:35', 'HLA-C*04:36', 'HLA-C*04:37',
'HLA-C*04:38', 'HLA-C*04:39', 'HLA-C*04:40', 'HLA-C*04:41', 'HLA-C*04:42', 'HLA-C*04:43', 'HLA-C*04:44', 'HLA-C*04:45',
'HLA-C*04:46', 'HLA-C*04:47', 'HLA-C*04:48', 'HLA-C*04:49', 'HLA-C*04:50', 'HLA-C*04:51', 'HLA-C*04:52', 'HLA-C*04:53',
'HLA-C*04:54', 'HLA-C*04:55', 'HLA-C*04:56', 'HLA-C*04:57', 'HLA-C*04:58', 'HLA-C*04:60', 'HLA-C*04:61', 'HLA-C*04:62',
'HLA-C*04:63', 'HLA-C*04:64', 'HLA-C*04:65', 'HLA-C*04:66', 'HLA-C*04:67', 'HLA-C*04:68', 'HLA-C*04:69', 'HLA-C*04:70',
'HLA-C*05:01', 'HLA-C*05:03', 'HLA-C*05:04', 'HLA-C*05:05', 'HLA-C*05:06', 'HLA-C*05:08', 'HLA-C*05:09', 'HLA-C*05:10',
'HLA-C*05:11', 'HLA-C*05:12', 'HLA-C*05:13', 'HLA-C*05:14', 'HLA-C*05:15', 'HLA-C*05:16', 'HLA-C*05:17', 'HLA-C*05:18',
'HLA-C*05:19', 'HLA-C*05:20', 'HLA-C*05:21', 'HLA-C*05:22', 'HLA-C*05:23', 'HLA-C*05:24', 'HLA-C*05:25', 'HLA-C*05:26',
'HLA-C*05:27', 'HLA-C*05:28', 'HLA-C*05:29', 'HLA-C*05:30', 'HLA-C*05:31', 'HLA-C*05:32', 'HLA-C*05:33', 'HLA-C*05:34',
'HLA-C*05:35', 'HLA-C*05:36', 'HLA-C*05:37', 'HLA-C*05:38', 'HLA-C*05:39', 'HLA-C*05:40', 'HLA-C*05:41', 'HLA-C*05:42',
'HLA-C*05:43', 'HLA-C*05:44', 'HLA-C*05:45', 'HLA-C*06:02', 'HLA-C*06:03', 'HLA-C*06:04', 'HLA-C*06:05', 'HLA-C*06:06',
'HLA-C*06:07', 'HLA-C*06:08', 'HLA-C*06:09', 'HLA-C*06:10', 'HLA-C*06:11', 'HLA-C*06:12', 'HLA-C*06:13', 'HLA-C*06:14',
'HLA-C*06:15', 'HLA-C*06:17', 'HLA-C*06:18', 'HLA-C*06:19', 'HLA-C*06:20', 'HLA-C*06:21', 'HLA-C*06:22', 'HLA-C*06:23',
'HLA-C*06:24', 'HLA-C*06:25', 'HLA-C*06:26', 'HLA-C*06:27', 'HLA-C*06:28', 'HLA-C*06:29', 'HLA-C*06:30', 'HLA-C*06:31',
'HLA-C*06:32', 'HLA-C*06:33', 'HLA-C*06:34', 'HLA-C*06:35', 'HLA-C*06:36', 'HLA-C*06:37', 'HLA-C*06:38', 'HLA-C*06:39',
'HLA-C*06:40', 'HLA-C*06:41', 'HLA-C*06:42', 'HLA-C*06:43', 'HLA-C*06:44', 'HLA-C*06:45', 'HLA-C*07:01', 'HLA-C*07:02',
'HLA-C*07:03', 'HLA-C*07:04', 'HLA-C*07:05', 'HLA-C*07:06', 'HLA-C*07:07', 'HLA-C*07:08', 'HLA-C*07:09', 'HLA-C*07:10',
'HLA-C*07:11', 'HLA-C*07:12', 'HLA-C*07:13', 'HLA-C*07:14', 'HLA-C*07:15', 'HLA-C*07:16', 'HLA-C*07:17', 'HLA-C*07:18',
'HLA-C*07:19', 'HLA-C*07:20', 'HLA-C*07:21', 'HLA-C*07:22', 'HLA-C*07:23', 'HLA-C*07:24', 'HLA-C*07:25', 'HLA-C*07:26',
'HLA-C*07:27', 'HLA-C*07:28', 'HLA-C*07:29', 'HLA-C*07:30', 'HLA-C*07:31', 'HLA-C*07:35', 'HLA-C*07:36', 'HLA-C*07:37',
'HLA-C*07:38', 'HLA-C*07:39', 'HLA-C*07:40', 'HLA-C*07:41', 'HLA-C*07:42', 'HLA-C*07:43', 'HLA-C*07:44', 'HLA-C*07:45',
'HLA-C*07:46', 'HLA-C*07:47', 'HLA-C*07:48', 'HLA-C*07:49', 'HLA-C*07:50', 'HLA-C*07:51', 'HLA-C*07:52', 'HLA-C*07:53',
'HLA-C*07:54', 'HLA-C*07:56', 'HLA-C*07:57', 'HLA-C*07:58', 'HLA-C*07:59', 'HLA-C*07:60', 'HLA-C*07:62', 'HLA-C*07:63',
'HLA-C*07:64', 'HLA-C*07:65', 'HLA-C*07:66', 'HLA-C*07:67', 'HLA-C*07:68', 'HLA-C*07:69', 'HLA-C*07:70', 'HLA-C*07:71',
'HLA-C*07:72', 'HLA-C*07:73', 'HLA-C*07:74', 'HLA-C*07:75', 'HLA-C*07:76', 'HLA-C*07:77', 'HLA-C*07:78', 'HLA-C*07:79',
'HLA-C*07:80', 'HLA-C*07:81', 'HLA-C*07:82', 'HLA-C*07:83', 'HLA-C*07:84', 'HLA-C*07:85', 'HLA-C*07:86', 'HLA-C*07:87',
'HLA-C*07:88', 'HLA-C*07:89', 'HLA-C*07:90', 'HLA-C*07:91', 'HLA-C*07:92', 'HLA-C*07:93', 'HLA-C*07:94', 'HLA-C*07:95',
'HLA-C*07:96', 'HLA-C*07:97', 'HLA-C*07:99', 'HLA-C*07:100', 'HLA-C*07:101', 'HLA-C*07:102', 'HLA-C*07:103', 'HLA-C*07:105',
'HLA-C*07:106', 'HLA-C*07:107', 'HLA-C*07:108', 'HLA-C*07:109', 'HLA-C*07:110', 'HLA-C*07:111', 'HLA-C*07:112',
'HLA-C*07:113', 'HLA-C*07:114', 'HLA-C*07:115', 'HLA-C*07:116', 'HLA-C*07:117', 'HLA-C*07:118', 'HLA-C*07:119',
'HLA-C*07:120', 'HLA-C*07:122', 'HLA-C*07:123', 'HLA-C*07:124', 'HLA-C*07:125', 'HLA-C*07:126', 'HLA-C*07:127',
'HLA-C*07:128', 'HLA-C*07:129', 'HLA-C*07:130', 'HLA-C*07:131', 'HLA-C*07:132', 'HLA-C*07:133', 'HLA-C*07:134',
'HLA-C*07:135', 'HLA-C*07:136', 'HLA-C*07:137', 'HLA-C*07:138', 'HLA-C*07:139', 'HLA-C*07:140', 'HLA-C*07:141',
'HLA-C*07:142', 'HLA-C*07:143', 'HLA-C*07:144', 'HLA-C*07:145', 'HLA-C*07:146', 'HLA-C*07:147', 'HLA-C*07:148',
'HLA-C*07:149', 'HLA-C*08:01', 'HLA-C*08:02', 'HLA-C*08:03', 'HLA-C*08:04', 'HLA-C*08:05', 'HLA-C*08:06', 'HLA-C*08:07',
'HLA-C*08:08', 'HLA-C*08:09', 'HLA-C*08:10', 'HLA-C*08:11', 'HLA-C*08:12', 'HLA-C*08:13', 'HLA-C*08:14', 'HLA-C*08:15',
'HLA-C*08:16', 'HLA-C*08:17', 'HLA-C*08:18', 'HLA-C*08:19', 'HLA-C*08:20', 'HLA-C*08:21', 'HLA-C*08:22', 'HLA-C*08:23',
'HLA-C*08:24', 'HLA-C*08:25', 'HLA-C*08:27', 'HLA-C*08:28', 'HLA-C*08:29', 'HLA-C*08:30', 'HLA-C*08:31', 'HLA-C*08:32',
'HLA-C*08:33', 'HLA-C*08:34', 'HLA-C*08:35', 'HLA-C*12:02', 'HLA-C*12:03', 'HLA-C*12:04', 'HLA-C*12:05', 'HLA-C*12:06',
'HLA-C*12:07', 'HLA-C*12:08', 'HLA-C*12:09', 'HLA-C*12:10', 'HLA-C*12:11', 'HLA-C*12:12', 'HLA-C*12:13', 'HLA-C*12:14',
'HLA-C*12:15', 'HLA-C*12:16', 'HLA-C*12:17', 'HLA-C*12:18', 'HLA-C*12:19', 'HLA-C*12:20', 'HLA-C*12:21', 'HLA-C*12:22',
'HLA-C*12:23', 'HLA-C*12:24', 'HLA-C*12:25', 'HLA-C*12:26', 'HLA-C*12:27', 'HLA-C*12:28', 'HLA-C*12:29', 'HLA-C*12:30',
'HLA-C*12:31', 'HLA-C*12:32', 'HLA-C*12:33', 'HLA-C*12:34', 'HLA-C*12:35', 'HLA-C*12:36', 'HLA-C*12:37', 'HLA-C*12:38',
'HLA-C*12:40', 'HLA-C*12:41', 'HLA-C*12:43', 'HLA-C*12:44', 'HLA-C*14:02', 'HLA-C*14:03', 'HLA-C*14:04', 'HLA-C*14:05',
'HLA-C*14:06', 'HLA-C*14:08', 'HLA-C*14:09', 'HLA-C*14:10', 'HLA-C*14:11', 'HLA-C*14:12', 'HLA-C*14:13', 'HLA-C*14:14',
'HLA-C*14:15', 'HLA-C*14:16', 'HLA-C*14:17', 'HLA-C*14:18', 'HLA-C*14:19', 'HLA-C*14:20', 'HLA-C*15:02', 'HLA-C*15:03',
'HLA-C*15:04', 'HLA-C*15:05', 'HLA-C*15:06', 'HLA-C*15:07', 'HLA-C*15:08', 'HLA-C*15:09', 'HLA-C*15:10', 'HLA-C*15:11',
'HLA-C*15:12', 'HLA-C*15:13', 'HLA-C*15:15', 'HLA-C*15:16', 'HLA-C*15:17', 'HLA-C*15:18', 'HLA-C*15:19', 'HLA-C*15:20',
'HLA-C*15:21', 'HLA-C*15:22', 'HLA-C*15:23', 'HLA-C*15:24', 'HLA-C*15:25', 'HLA-C*15:26', 'HLA-C*15:27', 'HLA-C*15:28',
'HLA-C*15:29', 'HLA-C*15:30', 'HLA-C*15:31', 'HLA-C*15:33', 'HLA-C*15:34', 'HLA-C*15:35', 'HLA-C*16:01', 'HLA-C*16:02',
'HLA-C*16:04', 'HLA-C*16:06', 'HLA-C*16:07', 'HLA-C*16:08', 'HLA-C*16:09', 'HLA-C*16:10', 'HLA-C*16:11', 'HLA-C*16:12',
'HLA-C*16:13', 'HLA-C*16:14', 'HLA-C*16:15', 'HLA-C*16:17', 'HLA-C*16:18', 'HLA-C*16:19', 'HLA-C*16:20', 'HLA-C*16:21',
'HLA-C*16:22', 'HLA-C*16:23', 'HLA-C*16:24', 'HLA-C*16:25', 'HLA-C*16:26', 'HLA-C*17:01', 'HLA-C*17:02', 'HLA-C*17:03',
'HLA-C*17:04', 'HLA-C*17:05', 'HLA-C*17:06', 'HLA-C*17:07', 'HLA-C*18:01', 'HLA-C*18:02', 'HLA-C*18:03', 'HLA-G*01:01',
'HLA-G*01:02', 'HLA-G*01:03', 'HLA-G*01:04', 'HLA-G*01:06', 'HLA-G*01:07', 'HLA-G*01:08', 'HLA-G*01:09', 'HLA-E*01:01'])
@property
def command(self):
return self.__command
@property
def name(self):
return self.__name
@property
def version(self):
return self.__version
@property
def supportedAlleles(self):
return self.__alleles
@property
def supportedLength(self):
return self.__length
def convert_alleles(self, alleles):
"""
Converts :class:`~epytope.Core.Allele.Allele` into the internal allele representation of the predictor
and returns a string representation
:param alleles: The :class:`~epytope.Core.Allele.Allele` for which the
internal predictor representation is needed
:type alleles: list(:class:`~epytope.Core.Allele.Allele`)
:return: Returns a string representation of the input :class:`~epytope.Core.Allele.Allele`
:rtype: list(str)
"""
return ["HLA-%s%s:%s" % (a.locus, a.supertype, a.subtype) for a in alleles]
def parse_external_result(self, file):
"""
Parses external results and returns the result containing the predictors string representation
of alleles and peptides.
:param str file: The file path or the external prediction results
:return: A dictionary containing the prediction results
:rtype: dict
"""
result = defaultdict(dict)
with open(file, "r") as f:
f = csv.reader(f, delimiter='\t')
alleles = [x for x in next(f) if x != ""]
ranks = defaultdict(defaultdict)
rank_pos = 5
offset = 3
header = next(f)
if "Aff(nM)" in header: # With option command line option '-ia', which includes prediction score in output file
scores = defaultdict(defaultdict)
for row in f:
pep_seq = row[PeptideIndex.NETMHCSTABPAN_1_0]
for i, a in enumerate(alleles):
scores[a][pep_seq] = float(row[ScoreIndex.NETMHCSTABPAN_1_0 + i * Offset.NETMHCSTABPAN_1_0_W_SCORE])
ranks[a][pep_seq] = float(row[RankIndex.NETMHCSTABPAN_1_0 + i * Offset.NETMHCSTABPAN_1_0_W_SCORE])
# Create dictionary with hierarchy: {'Allele1': {'Score': {'Pep1': Score1, 'Pep2': Score2,..}, 'Rank': {'Pep1': RankScore1, 'Pep2': RankScore2,..}}, 'Allele2':...}
result = {allele: {metric:(list(scores.values())[j] if metric == "Score" else list(ranks.values())[j]) for metric in ["Score", "Rank"]} for j, allele in enumerate(alleles)}
else:
for row in f:
pep_seq = row[PeptideIndex.NETMHCSTABPAN_1_0]
for i, a in enumerate(alleles):
ranks[a][pep_seq] = float(row[RankIndex.NETMHCSTABPAN_1_0 + i * Offset.NETMHCSTABPAN_1_0_WO_SCORE])
# Create dictionary with hierarchy: {'Allele1':{'Rank': {'Pep1': RankScore1, 'Pep2': RankScore2,..}}, 'Allele2':...}
result = {allele: {"Rank":list(ranks.values())[j]} for j, allele in enumerate(alleles)}
return result
def get_external_version(self, path=None):
"""
Returns the external version of the tool by executing
>{command} --version
might be dependent on the method and has to be overwritten
therefore it is declared abstract to enforce the user to
overwrite the method. The function in the base class can be called
with super()
:param str path: Optional specification of executable path if deviant from :attr:`self.__command`
:return: The external version of the tool or None if tool does not support versioning
:rtype: str
"""
# can not be determined netmhcpan does not support --version or similar
return None
def prepare_input(self, input, file):
"""
Prepares input for external tools and writes them to file in the specific format
NO return value!
:param: list(str) input: The :class:`~epytope.Core.Peptide.Peptide` sequences to write into file
:param File file: File-handler to input file for external tool
"""
file.write("\n".join(input))
class NetMHCII_2_2(AExternalEpitopePrediction):
"""
Implements a wrapper for NetMHCII
.. note::
Nielsen, M., & Lund, O. (2009). NN-align. An artificial neural network-based alignment algorithm for MHC class
II peptide binding prediction. BMC Bioinformatics, 10(1), 296.
Nielsen, M., Lundegaard, C., & Lund, O. (2007). Prediction of MHC class II binding affinity using SMM-align,
a novel stabilization matrix alignment method. BMC Bioinformatics, 8(1), 238.
"""
__supported_length = frozenset([15])
__name = "netmhcII"
__command = 'netMHCII {peptides} -a {alleles} {options} | grep -v "#" > {out}'
__alleles = frozenset(
['HLA-DRB1*01:01', 'HLA-DRB1*03:01', 'HLA-DRB1*04:01', 'HLA-DRB1*04:04', 'HLA-DRB1*04:05', 'HLA-DRB1*07:01', 'HLA-DRB1*08:02', 'HLA-DRB1*09:01',
'HLA-DRB1*11:01', 'HLA-DRB1*13:02', 'HLA-DRB1*15:01', 'HLA-DRB3*01:01', 'HLA-DRB4*01:01', 'HLA-DRB5*01:01',
'H-2-Iab', 'H-2-Iad'])
__version = "2.2"
@property
def version(self):
"""The version of the predictor"""
return self.__version
@property
def command(self):
"""
Defines the commandline call for external tool
"""
return self.__command
@property
def supportedLength(self):
"""
A list of supported :class:`~epytope.Core.Peptide.Peptide` lengths
"""
return self.__supported_length
@property
def supportedAlleles(self):
"""A list of valid :class:`~epytope.Core.Allele.Allele` models"""
return self.__alleles
@property
def name(self):
"""The name of the predictor"""
return self.__name
def _represent(self, allele):
"""
Internal function transforming an allele object into its representative string
:param allele: The :class:`~epytope.Core.Allele.Allele` for which the internal predictor representation is
needed
:type alleles: :class:`~epytope.Core.Allele.Allele`
:return: str
"""
if isinstance(allele, MouseAllele):
return "H-2-%s%s%s" % (allele.locus, allele.supertype, allele.subtype)
else:
return "HLA-%s%s%s" % (allele.locus, allele.supertype, allele.subtype)
def convert_alleles(self, alleles):
"""
Converts :class:`~epytope.Core.Allele.Allele` into the internal :class:`~epytope.Core.Allele.Allele` representation
of the predictor and returns a string representation
:param alleles: The :class:`~epytope.Core.Allele.Allele` for which the internal predictor representation is
needed
:type alleles: :class:`~epytope.Core.Allele.Allele`
:return: Returns a string representation of the input :class:`~epytope.Core.Allele.Allele`
:rtype: list(str)
"""
return [self._represent(a) for a in alleles]
def parse_external_result(self, file):
"""
Parses external results and returns the result containing the predictors string representation
of alleles and peptides.
:param str file: The file path or the external prediction results
:return: A dictionary containing the prediction results
:rtype: dict
"""
f = csv.reader(open(file, "r"), delimiter='\t')
scores = defaultdict(defaultdict)
for r in f:
if not r:
continue
row = r[0].split()
if not len(row):
continue
if "HLA" not in row[HLAIndex.NETMHCII_2_2]:
continue
allele = row[HLAIndex.NETMHCII_2_2]
pep = row[PeptideIndex.NETMHCII_2_2]
scores[allele][pep] = float(row[ScoreIndex.NETMHCII_2_2])
result = {allele: {"Score":list(scores.values())[j]} for j, allele in enumerate(scores.keys())}
return result
def get_external_version(self, path=None):
"""
Returns the external version of the tool by executing
>{command} --version
might be dependent on the method and has to be overwritten
therefore it is declared abstract to enforce the user to
overwrite the method. The function in the base class can be called
with super()
:param str path: Optional specification of executable path if deviant from :attr:`self.__command`
:return: The external version of the tool or None if tool does not support versioning
:rtype: str
"""
return None
def prepare_input(self, input, file):
"""
Prepares input for external tools
and writes them to _file in the specific format
No return value!
:param: list(str) input: The :class:`~epytope.Core.Peptide.Peptide` sequences to write into file
:param File file: File-handler to input file for external tool
"""
file.write("\n".join(">pepe_%i\n%s" % (i, p) for i, p in enumerate(input)))
class NetMHCII_2_3(NetMHCII_2_2):
"""
Implements a wrapper for NetMHCII 2.3
.. note::
Jensen KK, Andreatta M, Marcatili P, Buus S, Greenbaum JA, Yan Z, Sette A, Peters B, and Nielsen M. (2018)
Improved methods for predicting peptide binding affinity to MHC class II molecules.
"""
__supported_length = frozenset([15])
__name = "netmhcII"
__command = 'netMHCII {peptides} -a {alleles} {options} | grep -v "#" > {out}'
__alleles = frozenset(
['HLA-DRB1*01:01', 'HLA-DRB1*01:03', 'HLA-DRB1*03:01', 'HLA-DRB1*04:01', 'HLA-DRB1*04:02',
'HLA-DRB1*04:03', 'HLA-DRB1*04:04', 'HLA-DRB1*04:05', 'HLA-DRB1*07:01', 'HLA-DRB1*08:01',
'HLA-DRB1*08:02', 'HLA-DRB1*09:01', 'HLA-DRB1*10:01', 'HLA-DRB1*11:01', 'HLA-DRB1*12:01',
'HLA-DRB1*13:01', 'HLA-DRB1*13:02', 'HLA-DRB1*15:01', 'HLA-DRB1*16:02', 'HLA-DRB3*01:01',
'HLA-DRB3*02:02', 'HLA-DRB3*03:01', 'HLA-DRB4*01:01', 'HLA-DRB4*01:03', 'HLA-DRB5*01:01',
'HLA-DPA1*01:03-DPB1*02:01', 'HLA-DPA1*01:03-DPB1*03:01', 'HLA-DPA1*01:03-DPB1*04:01',
'HLA-DPA1*01:03-DPB1*04:02', 'HLA-DPA1*01:03-DPB1*06:01', 'HLA-DPA1*02:01-DPB1*01:01', 'HLA-DPA1*02:01-DPB1*05:01', 'HLA-DPA1*02:01-DPB1*14:01',
'HLA-DPA1*03:01-DPB1*04:02', 'HLA-DQA1*01:01-DQB1*05:01', 'HLA-DQA1*01:02-DQB1*05:01', 'HLA-DQA1*01:02-DQB1*05:02', 'HLA-DQA1*01:02-DQB1*06:02',
'HLA-DQA1*01:03-DQB1*06:03', 'HLA-DQA1*01:04-DQB1*05:03', 'HLA-DQA1*02:01-DQB1*02:02', 'HLA-DQA1*02:01-DQB1*03:01', 'HLA-DQA1*02:01-DQB1*03:03',
'HLA-DQA1*02:01-DQB1*04:02', 'HLA-DQA1*03:01-DQB1*03:01', 'HLA-DQA1*03:01-DQB1*03:02', 'HLA-DQA1*03:03-DQB1*04:02', 'HLA-DQA1*04:01-DQB1*04:02',
'HLA-DQA1*05:01-DQB1*02:01', 'HLA-DQA1*05:01-DQB1*03:01', 'HLA-DQA1*05:01-DQB1*03:02', 'HLA-DQA1*05:01-DQB1*03:03', 'HLA-DQA1*05:01-DQB1*04:02',
'HLA-DQA1*06:01-DQB1*04:02', 'H-2-Iab', 'H-2-Iad', 'H-2-Iak', 'H-2-Ias', 'H-2-Iau', 'H-2-Iad', 'H-2-Iak'])
__version = "2.3"
@property
def version(self):
"""The version of the predictor"""
return self.__version
@property
def command(self):
"""
Defines the commandline call for external tool
"""
return self.__command
@property
def supportedLength(self):
"""
A list of supported :class:`~epytope.Core.Peptide.Peptide` lengths
"""
return self.__supported_length
@property
def supportedAlleles(self):
"""A list of valid :class:`~epytope.Core.Allele.Allele` models"""
return self.__alleles
@property
def name(self):
"""The name of the predictor"""
return self.__name
def _represent(self, allele):
"""
Internal function transforming an allele object into its representative string
:param allele: The :class:`~epytope.Core.Allele.Allele` for which the internal predictor representation is
needed
:type alleles: :class:`~epytope.Core.Allele.Allele`
:return: str
"""
if isinstance(allele, MouseAllele):
return "H-2-%s%s%s" % (allele.locus, allele.supertype, allele.subtype)
elif isinstance(allele, CombinedAllele):
return '%s-%s%s%s-%s%s%s' % (allele.organism, allele.alpha_locus, allele.alpha_supertype, allele.alpha_subtype,
allele.beta_locus, allele.beta_supertype, allele.beta_subtype)
else:
return "%s_%s%s" % (allele.locus, allele.supertype, allele.subtype)
def parse_external_result(self, file):
"""
Parses external results and returns the result containing the predictors string representation
of alleles and peptides.
:param str file: The file path or the external prediction results
:return: A dictionary containing the prediction results
:rtype: dict
"""
f = csv.reader(open(file, "r"), delimiter='\t')
scores = defaultdict(defaultdict)
ranks = defaultdict(defaultdict)
for r in f:
if not r:
continue
row = r[0].split()
if not len(row):
continue
if all(prefix not in row[HLAIndex.NETMHCII_2_3] for prefix in ['HLA-', 'H-2', 'D']):
continue
allele = row[HLAIndex.NETMHCII_2_3]
pep = row[PeptideIndex.NETMHCII_2_3]
scores[allele][pep] = float(row[ScoreIndex.NETMHCII_2_3])
ranks[allele][pep] = float(row[ScoreIndex.NETMHCII_2_3])
result = {allele: {metric:(list(scores.values())[j] if metric == "Score" else list(ranks.values())[j]) for metric in ["Score", "Rank"]} for j, allele in enumerate(scores.keys())}
return result
class NetMHCIIpan_3_0(AExternalEpitopePrediction):
"""
Implements a wrapper for NetMHCIIpan.
.. note::
Andreatta, M., Karosiene, E., Rasmussen, M., Stryhn, A., Buus, S., & Nielsen, M. (2015).
Accurate pan-specific prediction of peptide-MHC class II binding affinity with improved binding
core identification. Immunogenetics, 1-10.
"""
__supported_length = frozenset([9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20])
__name = "netmhcIIpan"
__command = "netMHCIIpan -f {peptides} -inptype 1 -a {alleles} {options} -xls -xlsfile {out}"
__alleles = frozenset(
['HLA-DRB1*01:01', 'HLA-DRB1*01:02', 'HLA-DRB1*01:03', 'HLA-DRB1*01:04', 'HLA-DRB1*01:05', 'HLA-DRB1*01:06',
'HLA-DRB1*01:07', 'HLA-DRB1*01:08', 'HLA-DRB1*01:09', 'HLA-DRB1*01:10', 'HLA-DRB1*01:11', 'HLA-DRB1*01:12',
'HLA-DRB1*01:13', 'HLA-DRB1*01:14', 'HLA-DRB1*01:15', 'HLA-DRB1*01:16', 'HLA-DRB1*01:17', 'HLA-DRB1*01:18',
'HLA-DRB1*01:19', 'HLA-DRB1*01:20', 'HLA-DRB1*01:21', 'HLA-DRB1*01:22', 'HLA-DRB1*01:23', 'HLA-DRB1*01:24',
'HLA-DRB1*01:25', 'HLA-DRB1*01:26', 'HLA-DRB1*01:27', 'HLA-DRB1*01:28', 'HLA-DRB1*01:29', 'HLA-DRB1*01:30',
'HLA-DRB1*01:31', 'HLA-DRB1*01:32', 'HLA-DRB1*03:01', 'HLA-DRB1*03:02', 'HLA-DRB1*03:03', 'HLA-DRB1*03:04',
'HLA-DRB1*03:05', 'HLA-DRB1*03:06', 'HLA-DRB1*03:07', 'HLA-DRB1*03:08', 'HLA-DRB1*03:10', 'HLA-DRB1*03:11',
'HLA-DRB1*03:13', 'HLA-DRB1*03:14', 'HLA-DRB1*03:15', 'HLA-DRB1*03:17', 'HLA-DRB1*03:18', 'HLA-DRB1*03:19',
'HLA-DRB1*03:20', 'HLA-DRB1*03:21', 'HLA-DRB1*03:22', 'HLA-DRB1*03:23', 'HLA-DRB1*03:24', 'HLA-DRB1*03:25',
'HLA-DRB1*03:26', 'HLA-DRB1*03:27', 'HLA-DRB1*03:28', 'HLA-DRB1*03:29', 'HLA-DRB1*03:30', 'HLA-DRB1*03:31',
'HLA-DRB1*03:32', 'HLA-DRB1*03:33', 'HLA-DRB1*03:34', 'HLA-DRB1*03:35', 'HLA-DRB1*03:36', 'HLA-DRB1*03:37',
'HLA-DRB1*03:38', 'HLA-DRB1*03:39', 'HLA-DRB1*03:40', 'HLA-DRB1*03:41', 'HLA-DRB1*03:42', 'HLA-DRB1*03:43',
'HLA-DRB1*03:44', 'HLA-DRB1*03:45', 'HLA-DRB1*03:46', 'HLA-DRB1*03:47', 'HLA-DRB1*03:48', 'HLA-DRB1*03:49',
'HLA-DRB1*03:50', 'HLA-DRB1*03:51', 'HLA-DRB1*03:52', 'HLA-DRB1*03:53', 'HLA-DRB1*03:54', 'HLA-DRB1*03:55',
'HLA-DRB1*04:01', 'HLA-DRB1*04:02', 'HLA-DRB1*04:03', 'HLA-DRB1*04:04', 'HLA-DRB1*04:05', 'HLA-DRB1*04:06',
'HLA-DRB1*04:07', 'HLA-DRB1*04:08', 'HLA-DRB1*04:09', 'HLA-DRB1*04:10', 'HLA-DRB1*04:11', 'HLA-DRB1*04:12',
'HLA-DRB1*04:13', 'HLA-DRB1*04:14', 'HLA-DRB1*04:15', 'HLA-DRB1*04:16', 'HLA-DRB1*04:17', 'HLA-DRB1*04:18',
'HLA-DRB1*04:19', 'HLA-DRB1*04:21', 'HLA-DRB1*04:22', 'HLA-DRB1*04:23', 'HLA-DRB1*04:24', 'HLA-DRB1*04:26',
'HLA-DRB1*04:27', 'HLA-DRB1*04:28', 'HLA-DRB1*04:29', 'HLA-DRB1*04:30', 'HLA-DRB1*04:31', 'HLA-DRB1*04:33',
'HLA-DRB1*04:34', 'HLA-DRB1*04:35', 'HLA-DRB1*04:36', 'HLA-DRB1*04:37', 'HLA-DRB1*04:38', 'HLA-DRB1*04:39',
'HLA-DRB1*04:40', 'HLA-DRB1*04:41', 'HLA-DRB1*04:42', 'HLA-DRB1*04:43', 'HLA-DRB1*04:44', 'HLA-DRB1*04:45',
'HLA-DRB1*04:46', 'HLA-DRB1*04:47', 'HLA-DRB1*04:48', 'HLA-DRB1*04:49', 'HLA-DRB1*04:50', 'HLA-DRB1*04:51',
'HLA-DRB1*04:52', 'HLA-DRB1*04:53', 'HLA-DRB1*04:54', 'HLA-DRB1*04:55', 'HLA-DRB1*04:56', 'HLA-DRB1*04:57',
'HLA-DRB1*04:58', 'HLA-DRB1*04:59', 'HLA-DRB1*04:60', 'HLA-DRB1*04:61', 'HLA-DRB1*04:62', 'HLA-DRB1*04:63',
'HLA-DRB1*04:64', 'HLA-DRB1*04:65', 'HLA-DRB1*04:66', 'HLA-DRB1*04:67', 'HLA-DRB1*04:68', 'HLA-DRB1*04:69',
'HLA-DRB1*04:70', 'HLA-DRB1*04:71', 'HLA-DRB1*04:72', 'HLA-DRB1*04:73', 'HLA-DRB1*04:74', 'HLA-DRB1*04:75',
'HLA-DRB1*04:76', 'HLA-DRB1*04:77', 'HLA-DRB1*04:78', 'HLA-DRB1*04:79', 'HLA-DRB1*04:80', 'HLA-DRB1*04:82',
'HLA-DRB1*04:83', 'HLA-DRB1*04:84', 'HLA-DRB1*04:85', 'HLA-DRB1*04:86', 'HLA-DRB1*04:87', 'HLA-DRB1*04:88',
'HLA-DRB1*04:89', 'HLA-DRB1*04:91', 'HLA-DRB1*07:01', 'HLA-DRB1*07:03', 'HLA-DRB1*07:04', 'HLA-DRB1*07:05',
'HLA-DRB1*07:06', 'HLA-DRB1*07:07', 'HLA-DRB1*07:08', 'HLA-DRB1*07:09', 'HLA-DRB1*07:11', 'HLA-DRB1*07:12',
'HLA-DRB1*07:13', 'HLA-DRB1*07:14', 'HLA-DRB1*07:15', 'HLA-DRB1*07:16', 'HLA-DRB1*07:17', 'HLA-DRB1*07:19',
'HLA-DRB1*08:01', 'HLA-DRB1*08:02', 'HLA-DRB1*08:03', 'HLA-DRB1*08:04', 'HLA-DRB1*08:05', 'HLA-DRB1*08:06',
'HLA-DRB1*08:07', 'HLA-DRB1*08:08', 'HLA-DRB1*08:09', 'HLA-DRB1*08:10', 'HLA-DRB1*08:11', 'HLA-DRB1*08:12',
'HLA-DRB1*08:13', 'HLA-DRB1*08:14', 'HLA-DRB1*08:15', 'HLA-DRB1*08:16', 'HLA-DRB1*08:18', 'HLA-DRB1*08:19',
'HLA-DRB1*08:20', 'HLA-DRB1*08:21', 'HLA-DRB1*08:22', 'HLA-DRB1*08:23', 'HLA-DRB1*08:24', 'HLA-DRB1*08:25',
'HLA-DRB1*08:26', 'HLA-DRB1*08:27', 'HLA-DRB1*08:28', 'HLA-DRB1*08:29', 'HLA-DRB1*08:30', 'HLA-DRB1*08:31',
'HLA-DRB1*08:32', 'HLA-DRB1*08:33', 'HLA-DRB1*08:34', 'HLA-DRB1*08:35', 'HLA-DRB1*08:36', 'HLA-DRB1*08:37',
'HLA-DRB1*08:38', 'HLA-DRB1*08:39', 'HLA-DRB1*08:40', 'HLA-DRB1*09:01', 'HLA-DRB1*09:02', 'HLA-DRB1*09:03',
'HLA-DRB1*09:04', 'HLA-DRB1*09:05', 'HLA-DRB1*09:06', 'HLA-DRB1*09:07', 'HLA-DRB1*09:08', 'HLA-DRB1*09:09',
'HLA-DRB1*10:01', 'HLA-DRB1*10:02', 'HLA-DRB1*10:03', 'HLA-DRB1*11:01', 'HLA-DRB1*11:02', 'HLA-DRB1*11:03',
'HLA-DRB1*11:04', 'HLA-DRB1*11:05', 'HLA-DRB1*11:06', 'HLA-DRB1*11:07', 'HLA-DRB1*11:08', 'HLA-DRB1*11:09',
'HLA-DRB1*11:10', 'HLA-DRB1*11:11', 'HLA-DRB1*11:12', 'HLA-DRB1*11:13', 'HLA-DRB1*11:14', 'HLA-DRB1*11:15',
'HLA-DRB1*11:16', 'HLA-DRB1*11:17', 'HLA-DRB1*11:18', 'HLA-DRB1*11:19', 'HLA-DRB1*11:20', 'HLA-DRB1*11:21',
'HLA-DRB1*11:24', 'HLA-DRB1*11:25', 'HLA-DRB1*11:27', 'HLA-DRB1*11:28', 'HLA-DRB1*11:29', 'HLA-DRB1*11:30',
'HLA-DRB1*11:31', 'HLA-DRB1*11:32', 'HLA-DRB1*11:33', 'HLA-DRB1*11:34', 'HLA-DRB1*11:35', 'HLA-DRB1*11:36',
'HLA-DRB1*11:37', 'HLA-DRB1*11:38', 'HLA-DRB1*11:39', 'HLA-DRB1*11:41', 'HLA-DRB1*11:42', 'HLA-DRB1*11:43',
'HLA-DRB1*11:44', 'HLA-DRB1*11:45', 'HLA-DRB1*11:46', 'HLA-DRB1*11:47', 'HLA-DRB1*11:48', 'HLA-DRB1*11:49',
'HLA-DRB1*11:50', 'HLA-DRB1*11:51', 'HLA-DRB1*11:52', 'HLA-DRB1*11:53', 'HLA-DRB1*11:54', 'HLA-DRB1*11:55',
'HLA-DRB1*11:56', 'HLA-DRB1*11:57', 'HLA-DRB1*11:58', 'HLA-DRB1*11:59', 'HLA-DRB1*11:60', 'HLA-DRB1*11:61',
'HLA-DRB1*11:62', 'HLA-DRB1*11:63', 'HLA-DRB1*11:64', 'HLA-DRB1*11:65', 'HLA-DRB1*11:66', 'HLA-DRB1*11:67',
'HLA-DRB1*11:68', 'HLA-DRB1*11:69', 'HLA-DRB1*11:70', 'HLA-DRB1*11:72', 'HLA-DRB1*11:73', 'HLA-DRB1*11:74',
'HLA-DRB1*11:75', 'HLA-DRB1*11:76', 'HLA-DRB1*11:77', 'HLA-DRB1*11:78', 'HLA-DRB1*11:79', 'HLA-DRB1*11:80',
'HLA-DRB1*11:81', 'HLA-DRB1*11:82', 'HLA-DRB1*11:83', 'HLA-DRB1*11:84', 'HLA-DRB1*11:85', 'HLA-DRB1*11:86',
'HLA-DRB1*11:87', 'HLA-DRB1*11:88', 'HLA-DRB1*11:89', 'HLA-DRB1*11:90', 'HLA-DRB1*11:91', 'HLA-DRB1*11:92',
'HLA-DRB1*11:93', 'HLA-DRB1*11:94', 'HLA-DRB1*11:95', 'HLA-DRB1*11:96', 'HLA-DRB1*12:01', 'HLA-DRB1*12:02',
'HLA-DRB1*12:03', 'HLA-DRB1*12:04', 'HLA-DRB1*12:05', 'HLA-DRB1*12:06', 'HLA-DRB1*12:07', 'HLA-DRB1*12:08',
'HLA-DRB1*12:09', 'HLA-DRB1*12:10', 'HLA-DRB1*12:11', 'HLA-DRB1*12:12', 'HLA-DRB1*12:13', 'HLA-DRB1*12:14',
'HLA-DRB1*12:15', 'HLA-DRB1*12:16', 'HLA-DRB1*12:17', 'HLA-DRB1*12:18', 'HLA-DRB1*12:19', 'HLA-DRB1*12:20',
'HLA-DRB1*12:21', 'HLA-DRB1*12:22', 'HLA-DRB1*12:23', 'HLA-DRB1*13:01', 'HLA-DRB1*13:02', 'HLA-DRB1*13:03',
'HLA-DRB1*13:04', 'HLA-DRB1*13:05', 'HLA-DRB1*13:06', 'HLA-DRB1*13:07', 'HLA-DRB1*13:08', 'HLA-DRB1*13:09',
'HLA-DRB1*13:10', 'HLA-DRB1*13:100', 'HLA-DRB1*13:101', 'HLA-DRB1*13:11', 'HLA-DRB1*13:12', 'HLA-DRB1*13:13',
'HLA-DRB1*13:14', 'HLA-DRB1*13:15', 'HLA-DRB1*13:16', 'HLA-DRB1*13:17', 'HLA-DRB1*13:18', 'HLA-DRB1*13:19',
'HLA-DRB1*13:20', 'HLA-DRB1*13:21', 'HLA-DRB1*13:22', 'HLA-DRB1*13:23', 'HLA-DRB1*13:24', 'HLA-DRB1*13:26',
'HLA-DRB1*13:27', 'HLA-DRB1*13:29', 'HLA-DRB1*13:30', 'HLA-DRB1*13:31', 'HLA-DRB1*13:32', 'HLA-DRB1*13:33',
'HLA-DRB1*13:34', 'HLA-DRB1*13:35', 'HLA-DRB1*13:36', 'HLA-DRB1*13:37', 'HLA-DRB1*13:38', 'HLA-DRB1*13:39',
'HLA-DRB1*13:41', 'HLA-DRB1*13:42', 'HLA-DRB1*13:43', 'HLA-DRB1*13:44', 'HLA-DRB1*13:46', 'HLA-DRB1*13:47',
'HLA-DRB1*13:48', 'HLA-DRB1*13:49', 'HLA-DRB1*13:50', 'HLA-DRB1*13:51', 'HLA-DRB1*13:52', 'HLA-DRB1*13:53',
'HLA-DRB1*13:54', 'HLA-DRB1*13:55', 'HLA-DRB1*13:56', 'HLA-DRB1*13:57', 'HLA-DRB1*13:58', 'HLA-DRB1*13:59',
'HLA-DRB1*13:60', 'HLA-DRB1*13:61', 'HLA-DRB1*13:62', 'HLA-DRB1*13:63', 'HLA-DRB1*13:64', 'HLA-DRB1*13:65',
'HLA-DRB1*13:66', 'HLA-DRB1*13:67', 'HLA-DRB1*13:68', 'HLA-DRB1*13:69', 'HLA-DRB1*13:70', 'HLA-DRB1*13:71',
'HLA-DRB1*13:72', 'HLA-DRB1*13:73', 'HLA-DRB1*13:74', 'HLA-DRB1*13:75', 'HLA-DRB1*13:76', 'HLA-DRB1*13:77',
'HLA-DRB1*13:78', 'HLA-DRB1*13:79', 'HLA-DRB1*13:80', 'HLA-DRB1*13:81', 'HLA-DRB1*13:82', 'HLA-DRB1*13:83',
'HLA-DRB1*13:84', 'HLA-DRB1*13:85', 'HLA-DRB1*13:86', 'HLA-DRB1*13:87', 'HLA-DRB1*13:88', 'HLA-DRB1*13:89',
'HLA-DRB1*13:90', 'HLA-DRB1*13:91', 'HLA-DRB1*13:92', 'HLA-DRB1*13:93', 'HLA-DRB1*13:94', 'HLA-DRB1*13:95',
'HLA-DRB1*13:96', 'HLA-DRB1*13:97', 'HLA-DRB1*13:98', 'HLA-DRB1*13:99', 'HLA-DRB1*14:01', 'HLA-DRB1*14:02',
'HLA-DRB1*14:03', 'HLA-DRB1*14:04', 'HLA-DRB1*14:05', 'HLA-DRB1*14:06', 'HLA-DRB1*14:07', 'HLA-DRB1*14:08',
'HLA-DRB1*14:09', 'HLA-DRB1*14:10', 'HLA-DRB1*14:11', 'HLA-DRB1*14:12', 'HLA-DRB1*14:13', 'HLA-DRB1*14:14',
'HLA-DRB1*14:15', 'HLA-DRB1*14:16', 'HLA-DRB1*14:17', 'HLA-DRB1*14:18', 'HLA-DRB1*14:19', 'HLA-DRB1*14:20',
'HLA-DRB1*14:21', 'HLA-DRB1*14:22', 'HLA-DRB1*14:23', 'HLA-DRB1*14:24', 'HLA-DRB1*14:25', 'HLA-DRB1*14:26',
'HLA-DRB1*14:27', 'HLA-DRB1*14:28', 'HLA-DRB1*14:29', 'HLA-DRB1*14:30', 'HLA-DRB1*14:31', 'HLA-DRB1*14:32',
'HLA-DRB1*14:33', 'HLA-DRB1*14:34', 'HLA-DRB1*14:35', 'HLA-DRB1*14:36', 'HLA-DRB1*14:37', 'HLA-DRB1*14:38',
'HLA-DRB1*14:39', 'HLA-DRB1*14:40', 'HLA-DRB1*14:41', 'HLA-DRB1*14:42', 'HLA-DRB1*14:43', 'HLA-DRB1*14:44',
'HLA-DRB1*14:45', 'HLA-DRB1*14:46', 'HLA-DRB1*14:47', 'HLA-DRB1*14:48', 'HLA-DRB1*14:49', 'HLA-DRB1*14:50',
'HLA-DRB1*14:51', 'HLA-DRB1*14:52', 'HLA-DRB1*14:53', 'HLA-DRB1*14:54', 'HLA-DRB1*14:55', 'HLA-DRB1*14:56',
'HLA-DRB1*14:57', 'HLA-DRB1*14:58', 'HLA-DRB1*14:59', 'HLA-DRB1*14:60', 'HLA-DRB1*14:61', 'HLA-DRB1*14:62',
'HLA-DRB1*14:63', 'HLA-DRB1*14:64', 'HLA-DRB1*14:65', 'HLA-DRB1*14:67', 'HLA-DRB1*14:68', 'HLA-DRB1*14:69',
'HLA-DRB1*14:70', 'HLA-DRB1*14:71', 'HLA-DRB1*14:72', 'HLA-DRB1*14:73', 'HLA-DRB1*14:74', 'HLA-DRB1*14:75',
'HLA-DRB1*14:76', 'HLA-DRB1*14:77', 'HLA-DRB1*14:78', 'HLA-DRB1*14:79', 'HLA-DRB1*14:80', 'HLA-DRB1*14:81',
'HLA-DRB1*14:82', 'HLA-DRB1*14:83', 'HLA-DRB1*14:84', 'HLA-DRB1*14:85', 'HLA-DRB1*14:86', 'HLA-DRB1*14:87',
'HLA-DRB1*14:88', 'HLA-DRB1*14:89', 'HLA-DRB1*14:90', 'HLA-DRB1*14:91', 'HLA-DRB1*14:93', 'HLA-DRB1*14:94',
'HLA-DRB1*14:95', 'HLA-DRB1*14:96', 'HLA-DRB1*14:97', 'HLA-DRB1*14:98', 'HLA-DRB1*14:99', 'HLA-DRB1*15:01',
'HLA-DRB1*15:02', 'HLA-DRB1*15:03', 'HLA-DRB1*15:04', 'HLA-DRB1*15:05', 'HLA-DRB1*15:06', 'HLA-DRB1*15:07',
'HLA-DRB1*15:08', 'HLA-DRB1*15:09', 'HLA-DRB1*15:10', 'HLA-DRB1*15:11', 'HLA-DRB1*15:12', 'HLA-DRB1*15:13',
'HLA-DRB1*15:14', 'HLA-DRB1*15:15', 'HLA-DRB1*15:16', 'HLA-DRB1*15:18', 'HLA-DRB1*15:19', 'HLA-DRB1*15:20',
'HLA-DRB1*15:21', 'HLA-DRB1*15:22', 'HLA-DRB1*15:23', 'HLA-DRB1*15:24', 'HLA-DRB1*15:25', 'HLA-DRB1*15:26',
'HLA-DRB1*15:27', 'HLA-DRB1*15:28', 'HLA-DRB1*15:29', 'HLA-DRB1*15:30', 'HLA-DRB1*15:31', 'HLA-DRB1*15:32',
'HLA-DRB1*15:33', 'HLA-DRB1*15:34', 'HLA-DRB1*15:35', 'HLA-DRB1*15:36', 'HLA-DRB1*15:37', 'HLA-DRB1*15:38',
'HLA-DRB1*15:39', 'HLA-DRB1*15:40', 'HLA-DRB1*15:41', 'HLA-DRB1*15:42', 'HLA-DRB1*15:43', 'HLA-DRB1*15:44',
'HLA-DRB1*15:45', 'HLA-DRB1*15:46', 'HLA-DRB1*15:47', 'HLA-DRB1*15:48', 'HLA-DRB1*15:49', 'HLA-DRB1*16:01',
'HLA-DRB1*16:02', 'HLA-DRB1*16:03', 'HLA-DRB1*16:04', 'HLA-DRB1*16:05', 'HLA-DRB1*16:07', 'HLA-DRB1*16:08',
'HLA-DRB1*16:09', 'HLA-DRB1*16:10', 'HLA-DRB1*16:11', 'HLA-DRB1*16:12', 'HLA-DRB1*16:14', 'HLA-DRB1*16:15',
'HLA-DRB1*16:16', 'HLA-DRB3*01:01', 'HLA-DRB3*01:04', 'HLA-DRB3*01:05', 'HLA-DRB3*01:08', 'HLA-DRB3*01:09',
'HLA-DRB3*01:11', 'HLA-DRB3*01:12', 'HLA-DRB3*01:13', 'HLA-DRB3*01:14', 'HLA-DRB3*02:01', 'HLA-DRB3*02:02',
'HLA-DRB3*02:04', 'HLA-DRB3*02:05', 'HLA-DRB3*02:09', 'HLA-DRB3*02:10', 'HLA-DRB3*02:11', 'HLA-DRB3*02:12',
'HLA-DRB3*02:13', 'HLA-DRB3*02:14', 'HLA-DRB3*02:15', 'HLA-DRB3*02:16', 'HLA-DRB3*02:17', 'HLA-DRB3*02:18',
'HLA-DRB3*02:19', 'HLA-DRB3*02:20', 'HLA-DRB3*02:21', 'HLA-DRB3*02:22', 'HLA-DRB3*02:23', 'HLA-DRB3*02:24',
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'HLA-DQA1*05:11-DQB1*03:05', 'HLA-DQA1*05:11-DQB1*03:06',
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'HLA-DQA1*05:11-DQB1*03:11', 'HLA-DQA1*05:11-DQB1*03:12',
'HLA-DQA1*05:11-DQB1*03:13', 'HLA-DQA1*05:11-DQB1*03:14', 'HLA-DQA1*05:11-DQB1*03:15', 'HLA-DQA1*05:11-DQB1*03:16',
'HLA-DQA1*05:11-DQB1*03:17', 'HLA-DQA1*05:11-DQB1*03:18',
'HLA-DQA1*05:11-DQB1*03:19', 'HLA-DQA1*05:11-DQB1*03:20', 'HLA-DQA1*05:11-DQB1*03:21', 'HLA-DQA1*05:11-DQB1*03:22',
'HLA-DQA1*05:11-DQB1*03:23', 'HLA-DQA1*05:11-DQB1*03:24',
'HLA-DQA1*05:11-DQB1*03:25', 'HLA-DQA1*05:11-DQB1*03:26', 'HLA-DQA1*05:11-DQB1*03:27', 'HLA-DQA1*05:11-DQB1*03:28',
'HLA-DQA1*05:11-DQB1*03:29', 'HLA-DQA1*05:11-DQB1*03:30',
'HLA-DQA1*05:11-DQB1*03:31', 'HLA-DQA1*05:11-DQB1*03:32', 'HLA-DQA1*05:11-DQB1*03:33', 'HLA-DQA1*05:11-DQB1*03:34',
'HLA-DQA1*05:11-DQB1*03:35', 'HLA-DQA1*05:11-DQB1*03:36',
'HLA-DQA1*05:11-DQB1*03:37', 'HLA-DQA1*05:11-DQB1*03:38', 'HLA-DQA1*05:11-DQB1*04:01', 'HLA-DQA1*05:11-DQB1*04:02',
'HLA-DQA1*05:11-DQB1*04:03', 'HLA-DQA1*05:11-DQB1*04:04',
'HLA-DQA1*05:11-DQB1*04:05', 'HLA-DQA1*05:11-DQB1*04:06', 'HLA-DQA1*05:11-DQB1*04:07', 'HLA-DQA1*05:11-DQB1*04:08',
'HLA-DQA1*05:11-DQB1*05:01', 'HLA-DQA1*05:11-DQB1*05:02',
'HLA-DQA1*05:11-DQB1*05:03', 'HLA-DQA1*05:11-DQB1*05:05', 'HLA-DQA1*05:11-DQB1*05:06', 'HLA-DQA1*05:11-DQB1*05:07',
'HLA-DQA1*05:11-DQB1*05:08', 'HLA-DQA1*05:11-DQB1*05:09',
'HLA-DQA1*05:11-DQB1*05:10', 'HLA-DQA1*05:11-DQB1*05:11', 'HLA-DQA1*05:11-DQB1*05:12', 'HLA-DQA1*05:11-DQB1*05:13',
'HLA-DQA1*05:11-DQB1*05:14', 'HLA-DQA1*05:11-DQB1*06:01',
'HLA-DQA1*05:11-DQB1*06:02', 'HLA-DQA1*05:11-DQB1*06:03', 'HLA-DQA1*05:11-DQB1*06:04', 'HLA-DQA1*05:11-DQB1*06:07',
'HLA-DQA1*05:11-DQB1*06:08', 'HLA-DQA1*05:11-DQB1*06:09',
'HLA-DQA1*05:11-DQB1*06:10', 'HLA-DQA1*05:11-DQB1*06:11', 'HLA-DQA1*05:11-DQB1*06:12', 'HLA-DQA1*05:11-DQB1*06:14',
'HLA-DQA1*05:11-DQB1*06:15', 'HLA-DQA1*05:11-DQB1*06:16',
'HLA-DQA1*05:11-DQB1*06:17', 'HLA-DQA1*05:11-DQB1*06:18', 'HLA-DQA1*05:11-DQB1*06:19', 'HLA-DQA1*05:11-DQB1*06:21',
'HLA-DQA1*05:11-DQB1*06:22', 'HLA-DQA1*05:11-DQB1*06:23',
'HLA-DQA1*05:11-DQB1*06:24', 'HLA-DQA1*05:11-DQB1*06:25', 'HLA-DQA1*05:11-DQB1*06:27', 'HLA-DQA1*05:11-DQB1*06:28',
'HLA-DQA1*05:11-DQB1*06:29', 'HLA-DQA1*05:11-DQB1*06:30',
'HLA-DQA1*05:11-DQB1*06:31', 'HLA-DQA1*05:11-DQB1*06:32', 'HLA-DQA1*05:11-DQB1*06:33', 'HLA-DQA1*05:11-DQB1*06:34',
'HLA-DQA1*05:11-DQB1*06:35', 'HLA-DQA1*05:11-DQB1*06:36',
'HLA-DQA1*05:11-DQB1*06:37', 'HLA-DQA1*05:11-DQB1*06:38', 'HLA-DQA1*05:11-DQB1*06:39', 'HLA-DQA1*05:11-DQB1*06:40',
'HLA-DQA1*05:11-DQB1*06:41', 'HLA-DQA1*05:11-DQB1*06:42',
'HLA-DQA1*05:11-DQB1*06:43', 'HLA-DQA1*05:11-DQB1*06:44', 'HLA-DQA1*06:01-DQB1*02:01', 'HLA-DQA1*06:01-DQB1*02:02',
'HLA-DQA1*06:01-DQB1*02:03', 'HLA-DQA1*06:01-DQB1*02:04',
'HLA-DQA1*06:01-DQB1*02:05', 'HLA-DQA1*06:01-DQB1*02:06', 'HLA-DQA1*06:01-DQB1*03:01', 'HLA-DQA1*06:01-DQB1*03:02',
'HLA-DQA1*06:01-DQB1*03:03', 'HLA-DQA1*06:01-DQB1*03:04',
'HLA-DQA1*06:01-DQB1*03:05', 'HLA-DQA1*06:01-DQB1*03:06', 'HLA-DQA1*06:01-DQB1*03:07', 'HLA-DQA1*06:01-DQB1*03:08',
'HLA-DQA1*06:01-DQB1*03:09', 'HLA-DQA1*06:01-DQB1*03:10',
'HLA-DQA1*06:01-DQB1*03:11', 'HLA-DQA1*06:01-DQB1*03:12', 'HLA-DQA1*06:01-DQB1*03:13', 'HLA-DQA1*06:01-DQB1*03:14',
'HLA-DQA1*06:01-DQB1*03:15', 'HLA-DQA1*06:01-DQB1*03:16',
'HLA-DQA1*06:01-DQB1*03:17', 'HLA-DQA1*06:01-DQB1*03:18', 'HLA-DQA1*06:01-DQB1*03:19', 'HLA-DQA1*06:01-DQB1*03:20',
'HLA-DQA1*06:01-DQB1*03:21', 'HLA-DQA1*06:01-DQB1*03:22',
'HLA-DQA1*06:01-DQB1*03:23', 'HLA-DQA1*06:01-DQB1*03:24', 'HLA-DQA1*06:01-DQB1*03:25', 'HLA-DQA1*06:01-DQB1*03:26',
'HLA-DQA1*06:01-DQB1*03:27', 'HLA-DQA1*06:01-DQB1*03:28',
'HLA-DQA1*06:01-DQB1*03:29', 'HLA-DQA1*06:01-DQB1*03:30', 'HLA-DQA1*06:01-DQB1*03:31', 'HLA-DQA1*06:01-DQB1*03:32',
'HLA-DQA1*06:01-DQB1*03:33', 'HLA-DQA1*06:01-DQB1*03:34',
'HLA-DQA1*06:01-DQB1*03:35', 'HLA-DQA1*06:01-DQB1*03:36', 'HLA-DQA1*06:01-DQB1*03:37', 'HLA-DQA1*06:01-DQB1*03:38',
'HLA-DQA1*06:01-DQB1*04:01', 'HLA-DQA1*06:01-DQB1*04:02',
'HLA-DQA1*06:01-DQB1*04:03', 'HLA-DQA1*06:01-DQB1*04:04', 'HLA-DQA1*06:01-DQB1*04:05', 'HLA-DQA1*06:01-DQB1*04:06',
'HLA-DQA1*06:01-DQB1*04:07', 'HLA-DQA1*06:01-DQB1*04:08',
'HLA-DQA1*06:01-DQB1*05:01', 'HLA-DQA1*06:01-DQB1*05:02', 'HLA-DQA1*06:01-DQB1*05:03', 'HLA-DQA1*06:01-DQB1*05:05',
'HLA-DQA1*06:01-DQB1*05:06', 'HLA-DQA1*06:01-DQB1*05:07',
'HLA-DQA1*06:01-DQB1*05:08', 'HLA-DQA1*06:01-DQB1*05:09', 'HLA-DQA1*06:01-DQB1*05:10', 'HLA-DQA1*06:01-DQB1*05:11',
'HLA-DQA1*06:01-DQB1*05:12', 'HLA-DQA1*06:01-DQB1*05:13',
'HLA-DQA1*06:01-DQB1*05:14', 'HLA-DQA1*06:01-DQB1*06:01', 'HLA-DQA1*06:01-DQB1*06:02', 'HLA-DQA1*06:01-DQB1*06:03',
'HLA-DQA1*06:01-DQB1*06:04', 'HLA-DQA1*06:01-DQB1*06:07',
'HLA-DQA1*06:01-DQB1*06:08', 'HLA-DQA1*06:01-DQB1*06:09', 'HLA-DQA1*06:01-DQB1*06:10', 'HLA-DQA1*06:01-DQB1*06:11',
'HLA-DQA1*06:01-DQB1*06:12', 'HLA-DQA1*06:01-DQB1*06:14',
'HLA-DQA1*06:01-DQB1*06:15', 'HLA-DQA1*06:01-DQB1*06:16', 'HLA-DQA1*06:01-DQB1*06:17', 'HLA-DQA1*06:01-DQB1*06:18',
'HLA-DQA1*06:01-DQB1*06:19', 'HLA-DQA1*06:01-DQB1*06:21',
'HLA-DQA1*06:01-DQB1*06:22', 'HLA-DQA1*06:01-DQB1*06:23', 'HLA-DQA1*06:01-DQB1*06:24', 'HLA-DQA1*06:01-DQB1*06:25',
'HLA-DQA1*06:01-DQB1*06:27', 'HLA-DQA1*06:01-DQB1*06:28',
'HLA-DQA1*06:01-DQB1*06:29', 'HLA-DQA1*06:01-DQB1*06:30', 'HLA-DQA1*06:01-DQB1*06:31', 'HLA-DQA1*06:01-DQB1*06:32',
'HLA-DQA1*06:01-DQB1*06:33', 'HLA-DQA1*06:01-DQB1*06:34',
'HLA-DQA1*06:01-DQB1*06:35', 'HLA-DQA1*06:01-DQB1*06:36', 'HLA-DQA1*06:01-DQB1*06:37', 'HLA-DQA1*06:01-DQB1*06:38',
'HLA-DQA1*06:01-DQB1*06:39', 'HLA-DQA1*06:01-DQB1*06:40',
'HLA-DQA1*06:01-DQB1*06:41', 'HLA-DQA1*06:01-DQB1*06:42', 'HLA-DQA1*06:01-DQB1*06:43', 'HLA-DQA1*06:01-DQB1*06:44',
'HLA-DQA1*06:02-DQB1*02:01', 'HLA-DQA1*06:02-DQB1*02:02',
'HLA-DQA1*06:02-DQB1*02:03', 'HLA-DQA1*06:02-DQB1*02:04', 'HLA-DQA1*06:02-DQB1*02:05', 'HLA-DQA1*06:02-DQB1*02:06',
'HLA-DQA1*06:02-DQB1*03:01', 'HLA-DQA1*06:02-DQB1*03:02',
'HLA-DQA1*06:02-DQB1*03:03', 'HLA-DQA1*06:02-DQB1*03:04', 'HLA-DQA1*06:02-DQB1*03:05', 'HLA-DQA1*06:02-DQB1*03:06',
'HLA-DQA1*06:02-DQB1*03:07', 'HLA-DQA1*06:02-DQB1*03:08',
'HLA-DQA1*06:02-DQB1*03:09', 'HLA-DQA1*06:02-DQB1*03:10', 'HLA-DQA1*06:02-DQB1*03:11', 'HLA-DQA1*06:02-DQB1*03:12',
'HLA-DQA1*06:02-DQB1*03:13', 'HLA-DQA1*06:02-DQB1*03:14',
'HLA-DQA1*06:02-DQB1*03:15', 'HLA-DQA1*06:02-DQB1*03:16', 'HLA-DQA1*06:02-DQB1*03:17', 'HLA-DQA1*06:02-DQB1*03:18',
'HLA-DQA1*06:02-DQB1*03:19', 'HLA-DQA1*06:02-DQB1*03:20',
'HLA-DQA1*06:02-DQB1*03:21', 'HLA-DQA1*06:02-DQB1*03:22', 'HLA-DQA1*06:02-DQB1*03:23', 'HLA-DQA1*06:02-DQB1*03:24',
'HLA-DQA1*06:02-DQB1*03:25', 'HLA-DQA1*06:02-DQB1*03:26',
'HLA-DQA1*06:02-DQB1*03:27', 'HLA-DQA1*06:02-DQB1*03:28', 'HLA-DQA1*06:02-DQB1*03:29', 'HLA-DQA1*06:02-DQB1*03:30',
'HLA-DQA1*06:02-DQB1*03:31', 'HLA-DQA1*06:02-DQB1*03:32',
'HLA-DQA1*06:02-DQB1*03:33', 'HLA-DQA1*06:02-DQB1*03:34', 'HLA-DQA1*06:02-DQB1*03:35', 'HLA-DQA1*06:02-DQB1*03:36',
'HLA-DQA1*06:02-DQB1*03:37', 'HLA-DQA1*06:02-DQB1*03:38',
'HLA-DQA1*06:02-DQB1*04:01', 'HLA-DQA1*06:02-DQB1*04:02', 'HLA-DQA1*06:02-DQB1*04:03', 'HLA-DQA1*06:02-DQB1*04:04',
'HLA-DQA1*06:02-DQB1*04:05', 'HLA-DQA1*06:02-DQB1*04:06',
'HLA-DQA1*06:02-DQB1*04:07', 'HLA-DQA1*06:02-DQB1*04:08', 'HLA-DQA1*06:02-DQB1*05:01', 'HLA-DQA1*06:02-DQB1*05:02',
'HLA-DQA1*06:02-DQB1*05:03', 'HLA-DQA1*06:02-DQB1*05:05',
'HLA-DQA1*06:02-DQB1*05:06', 'HLA-DQA1*06:02-DQB1*05:07', 'HLA-DQA1*06:02-DQB1*05:08', 'HLA-DQA1*06:02-DQB1*05:09',
'HLA-DQA1*06:02-DQB1*05:10', 'HLA-DQA1*06:02-DQB1*05:11',
'HLA-DQA1*06:02-DQB1*05:12', 'HLA-DQA1*06:02-DQB1*05:13', 'HLA-DQA1*06:02-DQB1*05:14', 'HLA-DQA1*06:02-DQB1*06:01',
'HLA-DQA1*06:02-DQB1*06:02', 'HLA-DQA1*06:02-DQB1*06:03',
'HLA-DQA1*06:02-DQB1*06:04', 'HLA-DQA1*06:02-DQB1*06:07', 'HLA-DQA1*06:02-DQB1*06:08', 'HLA-DQA1*06:02-DQB1*06:09',
'HLA-DQA1*06:02-DQB1*06:10', 'HLA-DQA1*06:02-DQB1*06:11',
'HLA-DQA1*06:02-DQB1*06:12', 'HLA-DQA1*06:02-DQB1*06:14', 'HLA-DQA1*06:02-DQB1*06:15', 'HLA-DQA1*06:02-DQB1*06:16',
'HLA-DQA1*06:02-DQB1*06:17', 'HLA-DQA1*06:02-DQB1*06:18',
'HLA-DQA1*06:02-DQB1*06:19', 'HLA-DQA1*06:02-DQB1*06:21', 'HLA-DQA1*06:02-DQB1*06:22', 'HLA-DQA1*06:02-DQB1*06:23',
'HLA-DQA1*06:02-DQB1*06:24', 'HLA-DQA1*06:02-DQB1*06:25',
'HLA-DQA1*06:02-DQB1*06:27', 'HLA-DQA1*06:02-DQB1*06:28', 'HLA-DQA1*06:02-DQB1*06:29', 'HLA-DQA1*06:02-DQB1*06:30',
'HLA-DQA1*06:02-DQB1*06:31', 'HLA-DQA1*06:02-DQB1*06:32',
'HLA-DQA1*06:02-DQB1*06:33', 'HLA-DQA1*06:02-DQB1*06:34', 'HLA-DQA1*06:02-DQB1*06:35', 'HLA-DQA1*06:02-DQB1*06:36',
'HLA-DQA1*06:02-DQB1*06:37', 'HLA-DQA1*06:02-DQB1*06:38',
'HLA-DQA1*06:02-DQB1*06:39', 'HLA-DQA1*06:02-DQB1*06:40', 'HLA-DQA1*06:02-DQB1*06:41', 'HLA-DQA1*06:02-DQB1*06:42',
'HLA-DQA1*06:02-DQB1*06:43', 'HLA-DQA1*06:02-DQB1*06:44',
'H-2-Iab', 'H-2-Iad'])
__version = "3.0"
@property
def version(self):
"""The version of the predictor"""
return self.__version
@property
def command(self):
"""
Defines the commandline call for external tool
"""
return self.__command
@property
def supportedLength(self):
"""
A list of supported :class:`~epytope.Core.Peptide.Peptide` lengths
"""
return self.__supported_length
@property
def supportedAlleles(self):
"""A list of valid :class:`~epytope.Core.Allele.Allele` models"""
return self.__alleles
@property
def name(self):
"""The name of the predictor"""
return self.__name
def _represent(self, allele):
"""
Internal function transforming an allele object into its representative string
:param allele: The :class:`~epytope.Core.Allele.Allele` for which the internal predictor representation is
needed
:type alleles: :class:`~epytope.Core.Allele.Allele`
:return: str
"""
if isinstance(allele, MouseAllele):
return "H-2-%s%s%s" % (allele.locus, allele.supertype.upper(), allele.subtype)
elif isinstance(allele, CombinedAllele):
return "HLA-%s%s%s-%s%s%s" % (allele.alpha_locus, allele.alpha_supertype, allele.alpha_subtype,
allele.beta_locus, allele.beta_supertype, allele.beta_subtype)
else:
return "%s_%s%s" % (allele.locus, allele.supertype, allele.subtype)
def convert_alleles(self, alleles):
"""
Converts :class:`~epytope.Core.Allele.Allele` into the internal :class:`~epytope.Core.Allele.Allele` representation
of the predictor and returns a string representation
:param alleles: The :class:`~epytope.Core.Allele.Allele` for which the internal predictor representation is
needed
:type alleles: :class:`~epytope.Core.Allele.Allele`
:return: Returns a string representation of the input :class:`~epytope.Core.Allele.Allele`
:rtype: list(str)
"""
return [self._represent(a) for a in alleles]
def parse_external_result(self, file):
"""
Parses external results and returns the result containing the predictors string representation
of alleles and peptides.
:param str file: The file path or the external prediction results
:return: A dictionary containing the prediction results
:rtype: dict
"""
def parse_allele_from_external_result(alleles_str_out):
"""
Parses allele string from external result to allele string representation of input
:param str allele_str_out: The allele string representation from the external result output
:return: str allele_str_in: The allele string representation from the external result input
:rtype: str
"""
alleles_str_in = []
for allele_str_out in alleles_str_out:
if allele_str_out.startswith('HLA-'):
allele_str_in = allele_str_out.replace('*','').replace(':','')
elif allele_str_out.startswith('D'):
allele_str_in = allele_str_out.replace('*','_').replace(':','')
else:
allele_str_in = allele_str_out
alleles_str_in.append(allele_str_in)
return(alleles_str_in)
f = csv.reader(open(file, "r"), delimiter='\t')
scores = defaultdict(defaultdict)
ranks = defaultdict(defaultdict)
alleles = [x for x in set([x for x in next(f) if x != ""])]
# Convert output representation of allele to input representation of allele, because they differ
alleles = parse_allele_from_external_result(alleles)
next(f)
for row in f:
pep_seq = row[PeptideIndex.NETMHCIIPAN_3_0]
for i, a in enumerate(alleles):
scores[a][pep_seq] = float(row[ScoreIndex.NETMHCIIPAN_3_0 + i * Offset.NETMHCIIPAN_3_0])
ranks[a][pep_seq] = float(row[RankIndex.NETMHCIIPAN_3_0 + i * Offset.NETMHCIIPAN_3_0])
# Create dictionary with hierarchy: {'Allele1': {'Score': {'Pep1': Score1, 'Pep2': Score2,..}, 'Rank': {'Pep1': RankScore1, 'Pep2': RankScore2,..}}, 'Allele2':...}
result = {allele: {metric:(list(scores.values())[j] if metric == "Score" else list(ranks.values())[j]) for metric in ["Score", "Rank"]} for j, allele in enumerate(alleles)}
return result
def get_external_version(self, path=None):
"""
Returns the external version of the tool by executing
>{command} --version
might be dependent on the method and has to be overwritten
therefore it is declared abstract to enforce the user to
overwrite the method. The function in the base class can be called
with super()
:param str path: Optional specification of executable path if deviant from :attr:`self.__command`
:return: The external version of the tool or None if tool does not support versioning
:rtype: str
"""
return None
def prepare_input(self, input, file):
"""
Prepares input for external tools
and writes them to _file in the specific format
No return value!
:param: list(str) input: The :class:`~epytope.Core.Peptide.Peptide` sequences to write into file
:param File file: File-handler to input file for external tool
"""
file.write("\n".join(input))
class NetMHCIIpan_3_1(NetMHCIIpan_3_0):
"""
Implementation of NetMHCIIpan 3.1 adapter.
.. note::
Andreatta, M., Karosiene, E., Rasmussen, M., Stryhn, A., Buus, S., & Nielsen, M. (2015). Accurate pan-specific
prediction of peptide-MHC class II binding affinity with improved binding core identification.
Immunogenetics, 1-10.
"""
__supported_length = frozenset([9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20])
__name = "netmhcIIpan"
__command = "netMHCIIpan -f {peptides} -inptype 1 -a {alleles} {options} -xls -xlsfile {out}"
__alleles = frozenset(
['HLA-DRB1*01:01', 'HLA-DRB1*01:02', 'HLA-DRB1*01:03', 'HLA-DRB1*01:04', 'HLA-DRB1*01:05', 'HLA-DRB1*01:06',
'HLA-DRB1*01:07', 'HLA-DRB1*01:08', 'HLA-DRB1*01:09', 'HLA-DRB1*01:10', 'HLA-DRB1*01:11', 'HLA-DRB1*01:12',
'HLA-DRB1*01:13', 'HLA-DRB1*01:14', 'HLA-DRB1*01:15', 'HLA-DRB1*01:16', 'HLA-DRB1*01:17', 'HLA-DRB1*01:18',
'HLA-DRB1*01:19', 'HLA-DRB1*01:20', 'HLA-DRB1*01:21', 'HLA-DRB1*01:22', 'HLA-DRB1*01:23', 'HLA-DRB1*01:24',
'HLA-DRB1*01:25', 'HLA-DRB1*01:26', 'HLA-DRB1*01:27', 'HLA-DRB1*01:28', 'HLA-DRB1*01:29', 'HLA-DRB1*01:30',
'HLA-DRB1*01:31', 'HLA-DRB1*01:32', 'HLA-DRB1*03:01', 'HLA-DRB1*03:02', 'HLA-DRB1*03:03', 'HLA-DRB1*03:04',
'HLA-DRB1*03:05', 'HLA-DRB1*03:06', 'HLA-DRB1*03:07', 'HLA-DRB1*03:08', 'HLA-DRB1*03:10', 'HLA-DRB1*03:11',
'HLA-DRB1*03:13', 'HLA-DRB1*03:14', 'HLA-DRB1*03:15', 'HLA-DRB1*03:17', 'HLA-DRB1*03:18', 'HLA-DRB1*03:19',
'HLA-DRB1*03:20', 'HLA-DRB1*03:21', 'HLA-DRB1*03:22', 'HLA-DRB1*03:23', 'HLA-DRB1*03:24', 'HLA-DRB1*03:25',
'HLA-DRB1*03:26', 'HLA-DRB1*03:27', 'HLA-DRB1*03:28', 'HLA-DRB1*03:29', 'HLA-DRB1*03:30', 'HLA-DRB1*03:31',
'HLA-DRB1*03:32', 'HLA-DRB1*03:33', 'HLA-DRB1*03:34', 'HLA-DRB1*03:35', 'HLA-DRB1*03:36', 'HLA-DRB1*03:37',
'HLA-DRB1*03:38', 'HLA-DRB1*03:39', 'HLA-DRB1*03:40', 'HLA-DRB1*03:41', 'HLA-DRB1*03:42', 'HLA-DRB1*03:43',
'HLA-DRB1*03:44', 'HLA-DRB1*03:45', 'HLA-DRB1*03:46', 'HLA-DRB1*03:47', 'HLA-DRB1*03:48', 'HLA-DRB1*03:49',
'HLA-DRB1*03:50', 'HLA-DRB1*03:51', 'HLA-DRB1*03:52', 'HLA-DRB1*03:53', 'HLA-DRB1*03:54', 'HLA-DRB1*03:55',
'HLA-DRB1*04:01', 'HLA-DRB1*04:02', 'HLA-DRB1*04:03', 'HLA-DRB1*04:04', 'HLA-DRB1*04:05', 'HLA-DRB1*04:06',
'HLA-DRB1*04:07', 'HLA-DRB1*04:08', 'HLA-DRB1*04:09', 'HLA-DRB1*04:10', 'HLA-DRB1*04:11', 'HLA-DRB1*04:12',
'HLA-DRB1*04:13', 'HLA-DRB1*04:14', 'HLA-DRB1*04:15', 'HLA-DRB1*04:16', 'HLA-DRB1*04:17', 'HLA-DRB1*04:18',
'HLA-DRB1*04:19', 'HLA-DRB1*04:21', 'HLA-DRB1*04:22', 'HLA-DRB1*04:23', 'HLA-DRB1*04:24', 'HLA-DRB1*04:26',
'HLA-DRB1*04:27', 'HLA-DRB1*04:28', 'HLA-DRB1*04:29', 'HLA-DRB1*04:30', 'HLA-DRB1*04:31', 'HLA-DRB1*04:33',
'HLA-DRB1*04:34', 'HLA-DRB1*04:35', 'HLA-DRB1*04:36', 'HLA-DRB1*04:37', 'HLA-DRB1*04:38', 'HLA-DRB1*04:39',
'HLA-DRB1*04:40', 'HLA-DRB1*04:41', 'HLA-DRB1*04:42', 'HLA-DRB1*04:43', 'HLA-DRB1*04:44', 'HLA-DRB1*04:45',
'HLA-DRB1*04:46', 'HLA-DRB1*04:47', 'HLA-DRB1*04:48', 'HLA-DRB1*04:49', 'HLA-DRB1*04:50', 'HLA-DRB1*04:51',
'HLA-DRB1*04:52', 'HLA-DRB1*04:53', 'HLA-DRB1*04:54', 'HLA-DRB1*04:55', 'HLA-DRB1*04:56', 'HLA-DRB1*04:57',
'HLA-DRB1*04:58', 'HLA-DRB1*04:59', 'HLA-DRB1*04:60', 'HLA-DRB1*04:61', 'HLA-DRB1*04:62', 'HLA-DRB1*04:63',
'HLA-DRB1*04:64', 'HLA-DRB1*04:65', 'HLA-DRB1*04:66', 'HLA-DRB1*04:67', 'HLA-DRB1*04:68', 'HLA-DRB1*04:69',
'HLA-DRB1*04:70', 'HLA-DRB1*04:71', 'HLA-DRB1*04:72', 'HLA-DRB1*04:73', 'HLA-DRB1*04:74', 'HLA-DRB1*04:75',
'HLA-DRB1*04:76', 'HLA-DRB1*04:77', 'HLA-DRB1*04:78', 'HLA-DRB1*04:79', 'HLA-DRB1*04:80', 'HLA-DRB1*04:82',
'HLA-DRB1*04:83', 'HLA-DRB1*04:84', 'HLA-DRB1*04:85', 'HLA-DRB1*04:86', 'HLA-DRB1*04:87', 'HLA-DRB1*04:88',
'HLA-DRB1*04:89', 'HLA-DRB1*04:91', 'HLA-DRB1*07:01', 'HLA-DRB1*07:03', 'HLA-DRB1*07:04', 'HLA-DRB1*07:05',
'HLA-DRB1*07:06', 'HLA-DRB1*07:07', 'HLA-DRB1*07:08', 'HLA-DRB1*07:09', 'HLA-DRB1*07:11', 'HLA-DRB1*07:12',
'HLA-DRB1*07:13', 'HLA-DRB1*07:14', 'HLA-DRB1*07:15', 'HLA-DRB1*07:16', 'HLA-DRB1*07:17', 'HLA-DRB1*07:19',
'HLA-DRB1*08:01', 'HLA-DRB1*08:02', 'HLA-DRB1*08:03', 'HLA-DRB1*08:04', 'HLA-DRB1*08:05', 'HLA-DRB1*08:06',
'HLA-DRB1*08:07', 'HLA-DRB1*08:08', 'HLA-DRB1*08:09', 'HLA-DRB1*08:10', 'HLA-DRB1*08:11', 'HLA-DRB1*08:12',
'HLA-DRB1*08:13', 'HLA-DRB1*08:14', 'HLA-DRB1*08:15', 'HLA-DRB1*08:16', 'HLA-DRB1*08:18', 'HLA-DRB1*08:19',
'HLA-DRB1*08:20', 'HLA-DRB1*08:21', 'HLA-DRB1*08:22', 'HLA-DRB1*08:23', 'HLA-DRB1*08:24', 'HLA-DRB1*08:25',
'HLA-DRB1*08:26', 'HLA-DRB1*08:27', 'HLA-DRB1*08:28', 'HLA-DRB1*08:29', 'HLA-DRB1*08:30', 'HLA-DRB1*08:31',
'HLA-DRB1*08:32', 'HLA-DRB1*08:33', 'HLA-DRB1*08:34', 'HLA-DRB1*08:35', 'HLA-DRB1*08:36', 'HLA-DRB1*08:37',
'HLA-DRB1*08:38', 'HLA-DRB1*08:39', 'HLA-DRB1*08:40', 'HLA-DRB1*09:01', 'HLA-DRB1*09:02', 'HLA-DRB1*09:03',
'HLA-DRB1*09:04', 'HLA-DRB1*09:05', 'HLA-DRB1*09:06', 'HLA-DRB1*09:07', 'HLA-DRB1*09:08', 'HLA-DRB1*09:09',
'HLA-DRB1*10:01', 'HLA-DRB1*10:02', 'HLA-DRB1*10:03', 'HLA-DRB1*11:01', 'HLA-DRB1*11:02', 'HLA-DRB1*11:03',
'HLA-DRB1*11:04', 'HLA-DRB1*11:05', 'HLA-DRB1*11:06', 'HLA-DRB1*11:07', 'HLA-DRB1*11:08', 'HLA-DRB1*11:09',
'HLA-DRB1*11:10', 'HLA-DRB1*11:11', 'HLA-DRB1*11:12', 'HLA-DRB1*11:13', 'HLA-DRB1*11:14', 'HLA-DRB1*11:15',
'HLA-DRB1*11:16', 'HLA-DRB1*11:17', 'HLA-DRB1*11:18', 'HLA-DRB1*11:19', 'HLA-DRB1*11:20', 'HLA-DRB1*11:21',
'HLA-DRB1*11:24', 'HLA-DRB1*11:25', 'HLA-DRB1*11:27', 'HLA-DRB1*11:28', 'HLA-DRB1*11:29', 'HLA-DRB1*11:30',
'HLA-DRB1*11:31', 'HLA-DRB1*11:32', 'HLA-DRB1*11:33', 'HLA-DRB1*11:34', 'HLA-DRB1*11:35', 'HLA-DRB1*11:36',
'HLA-DRB1*11:37', 'HLA-DRB1*11:38', 'HLA-DRB1*11:39', 'HLA-DRB1*11:41', 'HLA-DRB1*11:42', 'HLA-DRB1*11:43',
'HLA-DRB1*11:44', 'HLA-DRB1*11:45', 'HLA-DRB1*11:46', 'HLA-DRB1*11:47', 'HLA-DRB1*11:48', 'HLA-DRB1*11:49',
'HLA-DRB1*11:50', 'HLA-DRB1*11:51', 'HLA-DRB1*11:52', 'HLA-DRB1*11:53', 'HLA-DRB1*11:54', 'HLA-DRB1*11:55',
'HLA-DRB1*11:56', 'HLA-DRB1*11:57', 'HLA-DRB1*11:58', 'HLA-DRB1*11:59', 'HLA-DRB1*11:60', 'HLA-DRB1*11:61',
'HLA-DRB1*11:62', 'HLA-DRB1*11:63', 'HLA-DRB1*11:64', 'HLA-DRB1*11:65', 'HLA-DRB1*11:66', 'HLA-DRB1*11:67',
'HLA-DRB1*11:68', 'HLA-DRB1*11:69', 'HLA-DRB1*11:70', 'HLA-DRB1*11:72', 'HLA-DRB1*11:73', 'HLA-DRB1*11:74',
'HLA-DRB1*11:75', 'HLA-DRB1*11:76', 'HLA-DRB1*11:77', 'HLA-DRB1*11:78', 'HLA-DRB1*11:79', 'HLA-DRB1*11:80',
'HLA-DRB1*11:81', 'HLA-DRB1*11:82', 'HLA-DRB1*11:83', 'HLA-DRB1*11:84', 'HLA-DRB1*11:85', 'HLA-DRB1*11:86',
'HLA-DRB1*11:87', 'HLA-DRB1*11:88', 'HLA-DRB1*11:89', 'HLA-DRB1*11:90', 'HLA-DRB1*11:91', 'HLA-DRB1*11:92',
'HLA-DRB1*11:93', 'HLA-DRB1*11:94', 'HLA-DRB1*11:95', 'HLA-DRB1*11:96', 'HLA-DRB1*12:01', 'HLA-DRB1*12:02',
'HLA-DRB1*12:03', 'HLA-DRB1*12:04', 'HLA-DRB1*12:05', 'HLA-DRB1*12:06', 'HLA-DRB1*12:07', 'HLA-DRB1*12:08',
'HLA-DRB1*12:09', 'HLA-DRB1*12:10', 'HLA-DRB1*12:11', 'HLA-DRB1*12:12', 'HLA-DRB1*12:13', 'HLA-DRB1*12:14',
'HLA-DRB1*12:15', 'HLA-DRB1*12:16', 'HLA-DRB1*12:17', 'HLA-DRB1*12:18', 'HLA-DRB1*12:19', 'HLA-DRB1*12:20',
'HLA-DRB1*12:21', 'HLA-DRB1*12:22', 'HLA-DRB1*12:23', 'HLA-DRB1*13:01', 'HLA-DRB1*13:02', 'HLA-DRB1*13:03',
'HLA-DRB1*13:04', 'HLA-DRB1*13:05', 'HLA-DRB1*13:06', 'HLA-DRB1*13:07', 'HLA-DRB1*13:08', 'HLA-DRB1*13:09',
'HLA-DRB1*13:10', 'HLA-DRB1*13:100', 'HLA-DRB1*13:101', 'HLA-DRB1*13:11', 'HLA-DRB1*13:12', 'HLA-DRB1*13:13',
'HLA-DRB1*13:14', 'HLA-DRB1*13:15', 'HLA-DRB1*13:16', 'HLA-DRB1*13:17', 'HLA-DRB1*13:18', 'HLA-DRB1*13:19',
'HLA-DRB1*13:20', 'HLA-DRB1*13:21', 'HLA-DRB1*13:22', 'HLA-DRB1*13:23', 'HLA-DRB1*13:24', 'HLA-DRB1*13:26',
'HLA-DRB1*13:27', 'HLA-DRB1*13:29', 'HLA-DRB1*13:30', 'HLA-DRB1*13:31', 'HLA-DRB1*13:32', 'HLA-DRB1*13:33',
'HLA-DRB1*13:34', 'HLA-DRB1*13:35', 'HLA-DRB1*13:36', 'HLA-DRB1*13:37', 'HLA-DRB1*13:38', 'HLA-DRB1*13:39',
'HLA-DRB1*13:41', 'HLA-DRB1*13:42', 'HLA-DRB1*13:43', 'HLA-DRB1*13:44', 'HLA-DRB1*13:46', 'HLA-DRB1*13:47',
'HLA-DRB1*13:48', 'HLA-DRB1*13:49', 'HLA-DRB1*13:50', 'HLA-DRB1*13:51', 'HLA-DRB1*13:52', 'HLA-DRB1*13:53',
'HLA-DRB1*13:54', 'HLA-DRB1*13:55', 'HLA-DRB1*13:56', 'HLA-DRB1*13:57', 'HLA-DRB1*13:58', 'HLA-DRB1*13:59',
'HLA-DRB1*13:60', 'HLA-DRB1*13:61', 'HLA-DRB1*13:62', 'HLA-DRB1*13:63', 'HLA-DRB1*13:64', 'HLA-DRB1*13:65',
'HLA-DRB1*13:66', 'HLA-DRB1*13:67', 'HLA-DRB1*13:68', 'HLA-DRB1*13:69', 'HLA-DRB1*13:70', 'HLA-DRB1*13:71',
'HLA-DRB1*13:72', 'HLA-DRB1*13:73', 'HLA-DRB1*13:74', 'HLA-DRB1*13:75', 'HLA-DRB1*13:76', 'HLA-DRB1*13:77',
'HLA-DRB1*13:78', 'HLA-DRB1*13:79', 'HLA-DRB1*13:80', 'HLA-DRB1*13:81', 'HLA-DRB1*13:82', 'HLA-DRB1*13:83',
'HLA-DRB1*13:84', 'HLA-DRB1*13:85', 'HLA-DRB1*13:86', 'HLA-DRB1*13:87', 'HLA-DRB1*13:88', 'HLA-DRB1*13:89',
'HLA-DRB1*13:90', 'HLA-DRB1*13:91', 'HLA-DRB1*13:92', 'HLA-DRB1*13:93', 'HLA-DRB1*13:94', 'HLA-DRB1*13:95',
'HLA-DRB1*13:96', 'HLA-DRB1*13:97', 'HLA-DRB1*13:98', 'HLA-DRB1*13:99', 'HLA-DRB1*14:01', 'HLA-DRB1*14:02',
'HLA-DRB1*14:03', 'HLA-DRB1*14:04', 'HLA-DRB1*14:05', 'HLA-DRB1*14:06', 'HLA-DRB1*14:07', 'HLA-DRB1*14:08',
'HLA-DRB1*14:09', 'HLA-DRB1*14:10', 'HLA-DRB1*14:11', 'HLA-DRB1*14:12', 'HLA-DRB1*14:13', 'HLA-DRB1*14:14',
'HLA-DRB1*14:15', 'HLA-DRB1*14:16', 'HLA-DRB1*14:17', 'HLA-DRB1*14:18', 'HLA-DRB1*14:19', 'HLA-DRB1*14:20',
'HLA-DRB1*14:21', 'HLA-DRB1*14:22', 'HLA-DRB1*14:23', 'HLA-DRB1*14:24', 'HLA-DRB1*14:25', 'HLA-DRB1*14:26',
'HLA-DRB1*14:27', 'HLA-DRB1*14:28', 'HLA-DRB1*14:29', 'HLA-DRB1*14:30', 'HLA-DRB1*14:31', 'HLA-DRB1*14:32',
'HLA-DRB1*14:33', 'HLA-DRB1*14:34', 'HLA-DRB1*14:35', 'HLA-DRB1*14:36', 'HLA-DRB1*14:37', 'HLA-DRB1*14:38',
'HLA-DRB1*14:39', 'HLA-DRB1*14:40', 'HLA-DRB1*14:41', 'HLA-DRB1*14:42', 'HLA-DRB1*14:43', 'HLA-DRB1*14:44',
'HLA-DRB1*14:45', 'HLA-DRB1*14:46', 'HLA-DRB1*14:47', 'HLA-DRB1*14:48', 'HLA-DRB1*14:49', 'HLA-DRB1*14:50',
'HLA-DRB1*14:51', 'HLA-DRB1*14:52', 'HLA-DRB1*14:53', 'HLA-DRB1*14:54', 'HLA-DRB1*14:55', 'HLA-DRB1*14:56',
'HLA-DRB1*14:57', 'HLA-DRB1*14:58', 'HLA-DRB1*14:59', 'HLA-DRB1*14:60', 'HLA-DRB1*14:61', 'HLA-DRB1*14:62',
'HLA-DRB1*14:63', 'HLA-DRB1*14:64', 'HLA-DRB1*14:65', 'HLA-DRB1*14:67', 'HLA-DRB1*14:68', 'HLA-DRB1*14:69',
'HLA-DRB1*14:70', 'HLA-DRB1*14:71', 'HLA-DRB1*14:72', 'HLA-DRB1*14:73', 'HLA-DRB1*14:74', 'HLA-DRB1*14:75',
'HLA-DRB1*14:76', 'HLA-DRB1*14:77', 'HLA-DRB1*14:78', 'HLA-DRB1*14:79', 'HLA-DRB1*14:80', 'HLA-DRB1*14:81',
'HLA-DRB1*14:82', 'HLA-DRB1*14:83', 'HLA-DRB1*14:84', 'HLA-DRB1*14:85', 'HLA-DRB1*14:86', 'HLA-DRB1*14:87',
'HLA-DRB1*14:88', 'HLA-DRB1*14:89', 'HLA-DRB1*14:90', 'HLA-DRB1*14:91', 'HLA-DRB1*14:93', 'HLA-DRB1*14:94',
'HLA-DRB1*14:95', 'HLA-DRB1*14:96', 'HLA-DRB1*14:97', 'HLA-DRB1*14:98', 'HLA-DRB1*14:99', 'HLA-DRB1*15:01',
'HLA-DRB1*15:02', 'HLA-DRB1*15:03', 'HLA-DRB1*15:04', 'HLA-DRB1*15:05', 'HLA-DRB1*15:06', 'HLA-DRB1*15:07',
'HLA-DRB1*15:08', 'HLA-DRB1*15:09', 'HLA-DRB1*15:10', 'HLA-DRB1*15:11', 'HLA-DRB1*15:12', 'HLA-DRB1*15:13',
'HLA-DRB1*15:14', 'HLA-DRB1*15:15', 'HLA-DRB1*15:16', 'HLA-DRB1*15:18', 'HLA-DRB1*15:19', 'HLA-DRB1*15:20',
'HLA-DRB1*15:21', 'HLA-DRB1*15:22', 'HLA-DRB1*15:23', 'HLA-DRB1*15:24', 'HLA-DRB1*15:25', 'HLA-DRB1*15:26',
'HLA-DRB1*15:27', 'HLA-DRB1*15:28', 'HLA-DRB1*15:29', 'HLA-DRB1*15:30', 'HLA-DRB1*15:31', 'HLA-DRB1*15:32',
'HLA-DRB1*15:33', 'HLA-DRB1*15:34', 'HLA-DRB1*15:35', 'HLA-DRB1*15:36', 'HLA-DRB1*15:37', 'HLA-DRB1*15:38',
'HLA-DRB1*15:39', 'HLA-DRB1*15:40', 'HLA-DRB1*15:41', 'HLA-DRB1*15:42', 'HLA-DRB1*15:43', 'HLA-DRB1*15:44',
'HLA-DRB1*15:45', 'HLA-DRB1*15:46', 'HLA-DRB1*15:47', 'HLA-DRB1*15:48', 'HLA-DRB1*15:49', 'HLA-DRB1*16:01',
'HLA-DRB1*16:02', 'HLA-DRB1*16:03', 'HLA-DRB1*16:04', 'HLA-DRB1*16:05', 'HLA-DRB1*16:07', 'HLA-DRB1*16:08',
'HLA-DRB1*16:09', 'HLA-DRB1*16:10', 'HLA-DRB1*16:11', 'HLA-DRB1*16:12', 'HLA-DRB1*16:14', 'HLA-DRB1*16:15',
'HLA-DRB1*16:16', 'HLA-DRB3*01:01', 'HLA-DRB3*01:04', 'HLA-DRB3*01:05', 'HLA-DRB3*01:08', 'HLA-DRB3*01:09',
'HLA-DRB3*01:11', 'HLA-DRB3*01:12', 'HLA-DRB3*01:13', 'HLA-DRB3*01:14', 'HLA-DRB3*02:01', 'HLA-DRB3*02:02',
'HLA-DRB3*02:04', 'HLA-DRB3*02:05', 'HLA-DRB3*02:09', 'HLA-DRB3*02:10', 'HLA-DRB3*02:11', 'HLA-DRB3*02:12',
'HLA-DRB3*02:13', 'HLA-DRB3*02:14', 'HLA-DRB3*02:15', 'HLA-DRB3*02:16', 'HLA-DRB3*02:17', 'HLA-DRB3*02:18',
'HLA-DRB3*02:19', 'HLA-DRB3*02:20', 'HLA-DRB3*02:21', 'HLA-DRB3*02:22', 'HLA-DRB3*02:23', 'HLA-DRB3*02:24',
'HLA-DRB3*02:25', 'HLA-DRB3*03:01', 'HLA-DRB3*03:03', 'HLA-DRB4*01:01', 'HLA-DRB4*01:03', 'HLA-DRB4*01:04',
'HLA-DRB4*01:06', 'HLA-DRB4*01:07', 'HLA-DRB4*01:08', 'HLA-DRB5*01:01', 'HLA-DRB5*01:02', 'HLA-DRB5*01:03',
'HLA-DRB5*01:04', 'HLA-DRB5*01:05', 'HLA-DRB5*01:06', 'HLA-DRB5*01:08N', 'HLA-DRB5*01:11', 'HLA-DRB5*01:12',
'HLA-DRB5*01:13', 'HLA-DRB5*01:14', 'HLA-DRB5*02:02', 'HLA-DRB5*02:03', 'HLA-DRB5*02:04', 'HLA-DRB5*02:05',
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'HLA-DQA1*05:11-DQB1*03:25', 'HLA-DQA1*05:11-DQB1*03:26', 'HLA-DQA1*05:11-DQB1*03:27', 'HLA-DQA1*05:11-DQB1*03:28',
'HLA-DQA1*05:11-DQB1*03:29', 'HLA-DQA1*05:11-DQB1*03:30',
'HLA-DQA1*05:11-DQB1*03:31', 'HLA-DQA1*05:11-DQB1*03:32', 'HLA-DQA1*05:11-DQB1*03:33', 'HLA-DQA1*05:11-DQB1*03:34',
'HLA-DQA1*05:11-DQB1*03:35', 'HLA-DQA1*05:11-DQB1*03:36',
'HLA-DQA1*05:11-DQB1*03:37', 'HLA-DQA1*05:11-DQB1*03:38', 'HLA-DQA1*05:11-DQB1*04:01', 'HLA-DQA1*05:11-DQB1*04:02',
'HLA-DQA1*05:11-DQB1*04:03', 'HLA-DQA1*05:11-DQB1*04:04',
'HLA-DQA1*05:11-DQB1*04:05', 'HLA-DQA1*05:11-DQB1*04:06', 'HLA-DQA1*05:11-DQB1*04:07', 'HLA-DQA1*05:11-DQB1*04:08',
'HLA-DQA1*05:11-DQB1*05:01', 'HLA-DQA1*05:11-DQB1*05:02',
'HLA-DQA1*05:11-DQB1*05:03', 'HLA-DQA1*05:11-DQB1*05:05', 'HLA-DQA1*05:11-DQB1*05:06', 'HLA-DQA1*05:11-DQB1*05:07',
'HLA-DQA1*05:11-DQB1*05:08', 'HLA-DQA1*05:11-DQB1*05:09',
'HLA-DQA1*05:11-DQB1*05:10', 'HLA-DQA1*05:11-DQB1*05:11', 'HLA-DQA1*05:11-DQB1*05:12', 'HLA-DQA1*05:11-DQB1*05:13',
'HLA-DQA1*05:11-DQB1*05:14', 'HLA-DQA1*05:11-DQB1*06:01',
'HLA-DQA1*05:11-DQB1*06:02', 'HLA-DQA1*05:11-DQB1*06:03', 'HLA-DQA1*05:11-DQB1*06:04', 'HLA-DQA1*05:11-DQB1*06:07',
'HLA-DQA1*05:11-DQB1*06:08', 'HLA-DQA1*05:11-DQB1*06:09',
'HLA-DQA1*05:11-DQB1*06:10', 'HLA-DQA1*05:11-DQB1*06:11', 'HLA-DQA1*05:11-DQB1*06:12', 'HLA-DQA1*05:11-DQB1*06:14',
'HLA-DQA1*05:11-DQB1*06:15', 'HLA-DQA1*05:11-DQB1*06:16',
'HLA-DQA1*05:11-DQB1*06:17', 'HLA-DQA1*05:11-DQB1*06:18', 'HLA-DQA1*05:11-DQB1*06:19', 'HLA-DQA1*05:11-DQB1*06:21',
'HLA-DQA1*05:11-DQB1*06:22', 'HLA-DQA1*05:11-DQB1*06:23',
'HLA-DQA1*05:11-DQB1*06:24', 'HLA-DQA1*05:11-DQB1*06:25', 'HLA-DQA1*05:11-DQB1*06:27', 'HLA-DQA1*05:11-DQB1*06:28',
'HLA-DQA1*05:11-DQB1*06:29', 'HLA-DQA1*05:11-DQB1*06:30',
'HLA-DQA1*05:11-DQB1*06:31', 'HLA-DQA1*05:11-DQB1*06:32', 'HLA-DQA1*05:11-DQB1*06:33', 'HLA-DQA1*05:11-DQB1*06:34',
'HLA-DQA1*05:11-DQB1*06:35', 'HLA-DQA1*05:11-DQB1*06:36',
'HLA-DQA1*05:11-DQB1*06:37', 'HLA-DQA1*05:11-DQB1*06:38', 'HLA-DQA1*05:11-DQB1*06:39', 'HLA-DQA1*05:11-DQB1*06:40',
'HLA-DQA1*05:11-DQB1*06:41', 'HLA-DQA1*05:11-DQB1*06:42',
'HLA-DQA1*05:11-DQB1*06:43', 'HLA-DQA1*05:11-DQB1*06:44', 'HLA-DQA1*06:01-DQB1*02:01', 'HLA-DQA1*06:01-DQB1*02:02',
'HLA-DQA1*06:01-DQB1*02:03', 'HLA-DQA1*06:01-DQB1*02:04',
'HLA-DQA1*06:01-DQB1*02:05', 'HLA-DQA1*06:01-DQB1*02:06', 'HLA-DQA1*06:01-DQB1*03:01', 'HLA-DQA1*06:01-DQB1*03:02',
'HLA-DQA1*06:01-DQB1*03:03', 'HLA-DQA1*06:01-DQB1*03:04',
'HLA-DQA1*06:01-DQB1*03:05', 'HLA-DQA1*06:01-DQB1*03:06', 'HLA-DQA1*06:01-DQB1*03:07', 'HLA-DQA1*06:01-DQB1*03:08',
'HLA-DQA1*06:01-DQB1*03:09', 'HLA-DQA1*06:01-DQB1*03:10',
'HLA-DQA1*06:01-DQB1*03:11', 'HLA-DQA1*06:01-DQB1*03:12', 'HLA-DQA1*06:01-DQB1*03:13', 'HLA-DQA1*06:01-DQB1*03:14',
'HLA-DQA1*06:01-DQB1*03:15', 'HLA-DQA1*06:01-DQB1*03:16',
'HLA-DQA1*06:01-DQB1*03:17', 'HLA-DQA1*06:01-DQB1*03:18', 'HLA-DQA1*06:01-DQB1*03:19', 'HLA-DQA1*06:01-DQB1*03:20',
'HLA-DQA1*06:01-DQB1*03:21', 'HLA-DQA1*06:01-DQB1*03:22',
'HLA-DQA1*06:01-DQB1*03:23', 'HLA-DQA1*06:01-DQB1*03:24', 'HLA-DQA1*06:01-DQB1*03:25', 'HLA-DQA1*06:01-DQB1*03:26',
'HLA-DQA1*06:01-DQB1*03:27', 'HLA-DQA1*06:01-DQB1*03:28',
'HLA-DQA1*06:01-DQB1*03:29', 'HLA-DQA1*06:01-DQB1*03:30', 'HLA-DQA1*06:01-DQB1*03:31', 'HLA-DQA1*06:01-DQB1*03:32',
'HLA-DQA1*06:01-DQB1*03:33', 'HLA-DQA1*06:01-DQB1*03:34',
'HLA-DQA1*06:01-DQB1*03:35', 'HLA-DQA1*06:01-DQB1*03:36', 'HLA-DQA1*06:01-DQB1*03:37', 'HLA-DQA1*06:01-DQB1*03:38',
'HLA-DQA1*06:01-DQB1*04:01', 'HLA-DQA1*06:01-DQB1*04:02',
'HLA-DQA1*06:01-DQB1*04:03', 'HLA-DQA1*06:01-DQB1*04:04', 'HLA-DQA1*06:01-DQB1*04:05', 'HLA-DQA1*06:01-DQB1*04:06',
'HLA-DQA1*06:01-DQB1*04:07', 'HLA-DQA1*06:01-DQB1*04:08',
'HLA-DQA1*06:01-DQB1*05:01', 'HLA-DQA1*06:01-DQB1*05:02', 'HLA-DQA1*06:01-DQB1*05:03', 'HLA-DQA1*06:01-DQB1*05:05',
'HLA-DQA1*06:01-DQB1*05:06', 'HLA-DQA1*06:01-DQB1*05:07',
'HLA-DQA1*06:01-DQB1*05:08', 'HLA-DQA1*06:01-DQB1*05:09', 'HLA-DQA1*06:01-DQB1*05:10', 'HLA-DQA1*06:01-DQB1*05:11',
'HLA-DQA1*06:01-DQB1*05:12', 'HLA-DQA1*06:01-DQB1*05:13',
'HLA-DQA1*06:01-DQB1*05:14', 'HLA-DQA1*06:01-DQB1*06:01', 'HLA-DQA1*06:01-DQB1*06:02', 'HLA-DQA1*06:01-DQB1*06:03',
'HLA-DQA1*06:01-DQB1*06:04', 'HLA-DQA1*06:01-DQB1*06:07',
'HLA-DQA1*06:01-DQB1*06:08', 'HLA-DQA1*06:01-DQB1*06:09', 'HLA-DQA1*06:01-DQB1*06:10', 'HLA-DQA1*06:01-DQB1*06:11',
'HLA-DQA1*06:01-DQB1*06:12', 'HLA-DQA1*06:01-DQB1*06:14',
'HLA-DQA1*06:01-DQB1*06:15', 'HLA-DQA1*06:01-DQB1*06:16', 'HLA-DQA1*06:01-DQB1*06:17', 'HLA-DQA1*06:01-DQB1*06:18',
'HLA-DQA1*06:01-DQB1*06:19', 'HLA-DQA1*06:01-DQB1*06:21',
'HLA-DQA1*06:01-DQB1*06:22', 'HLA-DQA1*06:01-DQB1*06:23', 'HLA-DQA1*06:01-DQB1*06:24', 'HLA-DQA1*06:01-DQB1*06:25',
'HLA-DQA1*06:01-DQB1*06:27', 'HLA-DQA1*06:01-DQB1*06:28',
'HLA-DQA1*06:01-DQB1*06:29', 'HLA-DQA1*06:01-DQB1*06:30', 'HLA-DQA1*06:01-DQB1*06:31', 'HLA-DQA1*06:01-DQB1*06:32',
'HLA-DQA1*06:01-DQB1*06:33', 'HLA-DQA1*06:01-DQB1*06:34',
'HLA-DQA1*06:01-DQB1*06:35', 'HLA-DQA1*06:01-DQB1*06:36', 'HLA-DQA1*06:01-DQB1*06:37', 'HLA-DQA1*06:01-DQB1*06:38',
'HLA-DQA1*06:01-DQB1*06:39', 'HLA-DQA1*06:01-DQB1*06:40',
'HLA-DQA1*06:01-DQB1*06:41', 'HLA-DQA1*06:01-DQB1*06:42', 'HLA-DQA1*06:01-DQB1*06:43', 'HLA-DQA1*06:01-DQB1*06:44',
'HLA-DQA1*06:02-DQB1*02:01', 'HLA-DQA1*06:02-DQB1*02:02',
'HLA-DQA1*06:02-DQB1*02:03', 'HLA-DQA1*06:02-DQB1*02:04', 'HLA-DQA1*06:02-DQB1*02:05', 'HLA-DQA1*06:02-DQB1*02:06',
'HLA-DQA1*06:02-DQB1*03:01', 'HLA-DQA1*06:02-DQB1*03:02',
'HLA-DQA1*06:02-DQB1*03:03', 'HLA-DQA1*06:02-DQB1*03:04', 'HLA-DQA1*06:02-DQB1*03:05', 'HLA-DQA1*06:02-DQB1*03:06',
'HLA-DQA1*06:02-DQB1*03:07', 'HLA-DQA1*06:02-DQB1*03:08',
'HLA-DQA1*06:02-DQB1*03:09', 'HLA-DQA1*06:02-DQB1*03:10', 'HLA-DQA1*06:02-DQB1*03:11', 'HLA-DQA1*06:02-DQB1*03:12',
'HLA-DQA1*06:02-DQB1*03:13', 'HLA-DQA1*06:02-DQB1*03:14',
'HLA-DQA1*06:02-DQB1*03:15', 'HLA-DQA1*06:02-DQB1*03:16', 'HLA-DQA1*06:02-DQB1*03:17', 'HLA-DQA1*06:02-DQB1*03:18',
'HLA-DQA1*06:02-DQB1*03:19', 'HLA-DQA1*06:02-DQB1*03:20',
'HLA-DQA1*06:02-DQB1*03:21', 'HLA-DQA1*06:02-DQB1*03:22', 'HLA-DQA1*06:02-DQB1*03:23', 'HLA-DQA1*06:02-DQB1*03:24',
'HLA-DQA1*06:02-DQB1*03:25', 'HLA-DQA1*06:02-DQB1*03:26',
'HLA-DQA1*06:02-DQB1*03:27', 'HLA-DQA1*06:02-DQB1*03:28', 'HLA-DQA1*06:02-DQB1*03:29', 'HLA-DQA1*06:02-DQB1*03:30',
'HLA-DQA1*06:02-DQB1*03:31', 'HLA-DQA1*06:02-DQB1*03:32',
'HLA-DQA1*06:02-DQB1*03:33', 'HLA-DQA1*06:02-DQB1*03:34', 'HLA-DQA1*06:02-DQB1*03:35', 'HLA-DQA1*06:02-DQB1*03:36',
'HLA-DQA1*06:02-DQB1*03:37', 'HLA-DQA1*06:02-DQB1*03:38',
'HLA-DQA1*06:02-DQB1*04:01', 'HLA-DQA1*06:02-DQB1*04:02', 'HLA-DQA1*06:02-DQB1*04:03', 'HLA-DQA1*06:02-DQB1*04:04',
'HLA-DQA1*06:02-DQB1*04:05', 'HLA-DQA1*06:02-DQB1*04:06',
'HLA-DQA1*06:02-DQB1*04:07', 'HLA-DQA1*06:02-DQB1*04:08', 'HLA-DQA1*06:02-DQB1*05:01', 'HLA-DQA1*06:02-DQB1*05:02',
'HLA-DQA1*06:02-DQB1*05:03', 'HLA-DQA1*06:02-DQB1*05:05',
'HLA-DQA1*06:02-DQB1*05:06', 'HLA-DQA1*06:02-DQB1*05:07', 'HLA-DQA1*06:02-DQB1*05:08', 'HLA-DQA1*06:02-DQB1*05:09',
'HLA-DQA1*06:02-DQB1*05:10', 'HLA-DQA1*06:02-DQB1*05:11',
'HLA-DQA1*06:02-DQB1*05:12', 'HLA-DQA1*06:02-DQB1*05:13', 'HLA-DQA1*06:02-DQB1*05:14', 'HLA-DQA1*06:02-DQB1*06:01',
'HLA-DQA1*06:02-DQB1*06:02', 'HLA-DQA1*06:02-DQB1*06:03',
'HLA-DQA1*06:02-DQB1*06:04', 'HLA-DQA1*06:02-DQB1*06:07', 'HLA-DQA1*06:02-DQB1*06:08', 'HLA-DQA1*06:02-DQB1*06:09',
'HLA-DQA1*06:02-DQB1*06:10', 'HLA-DQA1*06:02-DQB1*06:11',
'HLA-DQA1*06:02-DQB1*06:12', 'HLA-DQA1*06:02-DQB1*06:14', 'HLA-DQA1*06:02-DQB1*06:15', 'HLA-DQA1*06:02-DQB1*06:16',
'HLA-DQA1*06:02-DQB1*06:17', 'HLA-DQA1*06:02-DQB1*06:18',
'HLA-DQA1*06:02-DQB1*06:19', 'HLA-DQA1*06:02-DQB1*06:21', 'HLA-DQA1*06:02-DQB1*06:22', 'HLA-DQA1*06:02-DQB1*06:23',
'HLA-DQA1*06:02-DQB1*06:24', 'HLA-DQA1*06:02-DQB1*06:25',
'HLA-DQA1*06:02-DQB1*06:27', 'HLA-DQA1*06:02-DQB1*06:28', 'HLA-DQA1*06:02-DQB1*06:29', 'HLA-DQA1*06:02-DQB1*06:30',
'HLA-DQA1*06:02-DQB1*06:31', 'HLA-DQA1*06:02-DQB1*06:32',
'HLA-DQA1*06:02-DQB1*06:33', 'HLA-DQA1*06:02-DQB1*06:34', 'HLA-DQA1*06:02-DQB1*06:35', 'HLA-DQA1*06:02-DQB1*06:36',
'HLA-DQA1*06:02-DQB1*06:37', 'HLA-DQA1*06:02-DQB1*06:38',
'HLA-DQA1*06:02-DQB1*06:39', 'HLA-DQA1*06:02-DQB1*06:40', 'HLA-DQA1*06:02-DQB1*06:41', 'HLA-DQA1*06:02-DQB1*06:42',
'HLA-DQA1*06:02-DQB1*06:43', 'HLA-DQA1*06:02-DQB1*06:44',
'H-2-Iad', 'H-2-Iab'])
__version = "3.1"
@property
def version(self):
"""The version of the predictor"""
return self.__version
@property
def command(self):
"""
Defines the commandline call for external tool
"""
return self.__command
@property
def supportedLength(self):
"""
A list of supported :class:`~epytope.Core.Peptide.Peptide` lengths
"""
return self.__supported_length
@property
def supportedAlleles(self):
"""A list of valid :class:`~epytope.Core.Allele.Allele` models"""
return self.__alleles
@property
def name(self):
"""The name of the predictor"""
return self.__name
def parse_external_result(self, file):
"""
Parses external results and returns the result containing the predictors string representation
of alleles and peptides.
:param str file: The file path or the external prediction results
:return: A dictionary containing the prediction results
:rtype: dict
"""
f = csv.reader(open(file, "r"), delimiter='\t')
scores = defaultdict(defaultdict)
ranks = defaultdict(defaultdict)
alleles = [x for x in set([x for x in next(f) if x != ""])]
next(f)
for row in f:
pep_seq = row[PeptideIndex.NETMHCIIPAN_3_1]
for i, a in enumerate(alleles):
scores[a][pep_seq] = float(row[ScoreIndex.NETMHCIIPAN_3_1 + i * Offset.NETMHCIIPAN_3_1])
ranks[a][pep_seq] = float(row[RankIndex.NETMHCIIPAN_3_1 + i * Offset.NETMHCIIPAN_3_1])
# Create dictionary with hierarchy: {'Allele1': {'Score': {'Pep1': Score1, 'Pep2': Score2,..}, 'Rank': {'Pep1': RankScore1, 'Pep2': RankScore2,..}}, 'Allele2':...}
result = {allele: {metric:(list(scores.values())[j] if metric == "Score" else list(ranks.values())[j]) for metric in ["Score", "Rank"]} for j, allele in enumerate(alleles)}
return result
class NetMHCIIpan_4_0(NetMHCIIpan_3_1):
"""
Implementation of NetMHCIIpan 4.0 adapter.
.. note::
Reynisson B, Barra C, Kaabinejadian S, Hildebrand WH, Peters B, Nielsen M (2020). Improved prediction of MHC II antigen presentation
through integration and motif deconvolution of mass spectrometry MHC eluted ligand data.
Immunogenetics, 1-10.
"""
__supported_length = frozenset([9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20])
__name = "netmhcIIpan"
__command = "netMHCIIpan -f {peptides} -inptype 1 -a {alleles} {options} -xls -xlsfile {out}"
__alleles = frozenset(['HLA-DRB1*01:01', 'HLA-DRB1*01:02', 'HLA-DRB1*01:03', 'HLA-DRB1*01:04', 'HLA-DRB1*01:05',
'HLA-DRB1*01:06', 'HLA-DRB1*01:07', 'HLA-DRB1*01:08', 'HLA-DRB1*01:09', 'HLA-DRB1*01:10',
'HLA-DRB1*01:11', 'HLA-DRB1*01:12', 'HLA-DRB1*01:13', 'HLA-DRB1*01:14', 'HLA-DRB1*01:15',
'HLA-DRB1*01:16', 'HLA-DRB1*01:17', 'HLA-DRB1*01:18', 'HLA-DRB1*01:19', 'HLA-DRB1*01:20',
'HLA-DRB1*01:21', 'HLA-DRB1*01:22', 'HLA-DRB1*01:23', 'HLA-DRB1*01:24', 'HLA-DRB1*01:25',
'HLA-DRB1*01:26', 'HLA-DRB1*01:27', 'HLA-DRB1*01:28', 'HLA-DRB1*01:29', 'HLA-DRB1*01:30',
'HLA-DRB1*01:31', 'HLA-DRB1*01:32', 'HLA-DRB1*03:01', 'HLA-DRB1*03:02', 'HLA-DRB1*03:03',
'HLA-DRB1*03:04', 'HLA-DRB1*03:05', 'HLA-DRB1*03:06', 'HLA-DRB1*03:07', 'HLA-DRB1*03:08',
'HLA-DRB1*03:10', 'HLA-DRB1*03:11', 'HLA-DRB1*03:13', 'HLA-DRB1*03:14', 'HLA-DRB1*03:15',
'HLA-DRB1*03:17', 'HLA-DRB1*03:18', 'HLA-DRB1*03:19', 'HLA-DRB1*03:20', 'HLA-DRB1*03:21',
'HLA-DRB1*03:22', 'HLA-DRB1*03:23', 'HLA-DRB1*03:24', 'HLA-DRB1*03:25', 'HLA-DRB1*03:26',
'HLA-DRB1*03:27', 'HLA-DRB1*03:28', 'HLA-DRB1*03:29', 'HLA-DRB1*03:30', 'HLA-DRB1*03:31',
'HLA-DRB1*03:32', 'HLA-DRB1*03:33', 'HLA-DRB1*03:34', 'HLA-DRB1*03:35', 'HLA-DRB1*03:36',
'HLA-DRB1*03:37', 'HLA-DRB1*03:38', 'HLA-DRB1*03:39', 'HLA-DRB1*03:40', 'HLA-DRB1*03:41',
'HLA-DRB1*03:42', 'HLA-DRB1*03:43', 'HLA-DRB1*03:44', 'HLA-DRB1*03:45', 'HLA-DRB1*03:46',
'HLA-DRB1*03:47', 'HLA-DRB1*03:48', 'HLA-DRB1*03:49', 'HLA-DRB1*03:50', 'HLA-DRB1*03:51',
'HLA-DRB1*03:52', 'HLA-DRB1*03:53', 'HLA-DRB1*03:54', 'HLA-DRB1*03:55', 'HLA-DRB1*04:01',
'HLA-DRB1*04:02', 'HLA-DRB1*04:03', 'HLA-DRB1*04:04', 'HLA-DRB1*04:05', 'HLA-DRB1*04:06',
'HLA-DRB1*04:07', 'HLA-DRB1*04:08', 'HLA-DRB1*04:09', 'HLA-DRB1*04:10', 'HLA-DRB1*04:11',
'HLA-DRB1*04:12', 'HLA-DRB1*04:13', 'HLA-DRB1*04:14', 'HLA-DRB1*04:15', 'HLA-DRB1*04:16',
'HLA-DRB1*04:17', 'HLA-DRB1*04:18', 'HLA-DRB1*04:19', 'HLA-DRB1*04:21', 'HLA-DRB1*04:22',
'HLA-DRB1*04:23', 'HLA-DRB1*04:24', 'HLA-DRB1*04:26', 'HLA-DRB1*04:27', 'HLA-DRB1*04:28',
'HLA-DRB1*04:29', 'HLA-DRB1*04:30', 'HLA-DRB1*04:31', 'HLA-DRB1*04:33', 'HLA-DRB1*04:34',
'HLA-DRB1*04:35', 'HLA-DRB1*04:36', 'HLA-DRB1*04:37', 'HLA-DRB1*04:38', 'HLA-DRB1*04:39',
'HLA-DRB1*04:40', 'HLA-DRB1*04:41', 'HLA-DRB1*04:42', 'HLA-DRB1*04:43', 'HLA-DRB1*04:44',
'HLA-DRB1*04:45', 'HLA-DRB1*04:46', 'HLA-DRB1*04:47', 'HLA-DRB1*04:48', 'HLA-DRB1*04:49',
'HLA-DRB1*04:50', 'HLA-DRB1*04:51', 'HLA-DRB1*04:52', 'HLA-DRB1*04:53', 'HLA-DRB1*04:54',
'HLA-DRB1*04:55', 'HLA-DRB1*04:56', 'HLA-DRB1*04:57', 'HLA-DRB1*04:58', 'HLA-DRB1*04:59',
'HLA-DRB1*04:60', 'HLA-DRB1*04:61', 'HLA-DRB1*04:62', 'HLA-DRB1*04:63', 'HLA-DRB1*04:64',
'HLA-DRB1*04:65', 'HLA-DRB1*04:66', 'HLA-DRB1*04:67', 'HLA-DRB1*04:68', 'HLA-DRB1*04:69',
'HLA-DRB1*04:70', 'HLA-DRB1*04:71', 'HLA-DRB1*04:72', 'HLA-DRB1*04:73', 'HLA-DRB1*04:74',
'HLA-DRB1*04:75', 'HLA-DRB1*04:76', 'HLA-DRB1*04:77', 'HLA-DRB1*04:78', 'HLA-DRB1*04:79',
'HLA-DRB1*04:80', 'HLA-DRB1*04:82', 'HLA-DRB1*04:83', 'HLA-DRB1*04:84', 'HLA-DRB1*04:85',
'HLA-DRB1*04:86', 'HLA-DRB1*04:87', 'HLA-DRB1*04:88', 'HLA-DRB1*04:89', 'HLA-DRB1*04:91',
'HLA-DRB1*07:01', 'HLA-DRB1*07:03', 'HLA-DRB1*07:04', 'HLA-DRB1*07:05', 'HLA-DRB1*07:06',
'HLA-DRB1*07:07', 'HLA-DRB1*07:08', 'HLA-DRB1*07:09', 'HLA-DRB1*07:11', 'HLA-DRB1*07:12',
'HLA-DRB1*07:13', 'HLA-DRB1*07:14', 'HLA-DRB1*07:15', 'HLA-DRB1*07:16', 'HLA-DRB1*07:17',
'HLA-DRB1*07:19', 'HLA-DRB1*08:01', 'HLA-DRB1*08:02', 'HLA-DRB1*08:03', 'HLA-DRB1*08:04',
'HLA-DRB1*08:05', 'HLA-DRB1*08:06', 'HLA-DRB1*08:07', 'HLA-DRB1*08:08', 'HLA-DRB1*08:09',
'HLA-DRB1*08:10', 'HLA-DRB1*08:11', 'HLA-DRB1*08:12', 'HLA-DRB1*08:13', 'HLA-DRB1*08:14',
'HLA-DRB1*08:15', 'HLA-DRB1*08:16', 'HLA-DRB1*08:18', 'HLA-DRB1*08:19', 'HLA-DRB1*08:20',
'HLA-DRB1*08:21', 'HLA-DRB1*08:22', 'HLA-DRB1*08:23', 'HLA-DRB1*08:24', 'HLA-DRB1*08:25',
'HLA-DRB1*08:26', 'HLA-DRB1*08:27', 'HLA-DRB1*08:28', 'HLA-DRB1*08:29', 'HLA-DRB1*08:30',
'HLA-DRB1*08:31', 'HLA-DRB1*08:32', 'HLA-DRB1*08:33', 'HLA-DRB1*08:34', 'HLA-DRB1*08:35',
'HLA-DRB1*08:36', 'HLA-DRB1*08:37', 'HLA-DRB1*08:38', 'HLA-DRB1*08:39', 'HLA-DRB1*08:40',
'HLA-DRB1*09:01', 'HLA-DRB1*09:02', 'HLA-DRB1*09:03', 'HLA-DRB1*09:04', 'HLA-DRB1*09:05',
'HLA-DRB1*09:06', 'HLA-DRB1*09:07', 'HLA-DRB1*09:08', 'HLA-DRB1*09:09', 'HLA-DRB1*10:01',
'HLA-DRB1*10:02', 'HLA-DRB1*10:03', 'HLA-DRB1*11:01', 'HLA-DRB1*11:02', 'HLA-DRB1*11:03',
'HLA-DRB1*11:04', 'HLA-DRB1*11:05', 'HLA-DRB1*11:06', 'HLA-DRB1*11:07', 'HLA-DRB1*11:08',
'HLA-DRB1*11:09', 'HLA-DRB1*11:10', 'HLA-DRB1*11:11', 'HLA-DRB1*11:12', 'HLA-DRB1*11:13',
'HLA-DRB1*11:14', 'HLA-DRB1*11:15', 'HLA-DRB1*11:16', 'HLA-DRB1*11:17', 'HLA-DRB1*11:18',
'HLA-DRB1*11:19', 'HLA-DRB1*11:20', 'HLA-DRB1*11:21', 'HLA-DRB1*11:24', 'HLA-DRB1*11:25',
'HLA-DRB1*11:27', 'HLA-DRB1*11:28', 'HLA-DRB1*11:29', 'HLA-DRB1*11:30', 'HLA-DRB1*11:31',
'HLA-DRB1*11:32', 'HLA-DRB1*11:33', 'HLA-DRB1*11:34', 'HLA-DRB1*11:35', 'HLA-DRB1*11:36',
'HLA-DRB1*11:37', 'HLA-DRB1*11:38', 'HLA-DRB1*11:39', 'HLA-DRB1*11:41', 'HLA-DRB1*11:42',
'HLA-DRB1*11:43', 'HLA-DRB1*11:44', 'HLA-DRB1*11:45', 'HLA-DRB1*11:46', 'HLA-DRB1*11:47',
'HLA-DRB1*11:48', 'HLA-DRB1*11:49', 'HLA-DRB1*11:50', 'HLA-DRB1*11:51', 'HLA-DRB1*11:52',
'HLA-DRB1*11:53', 'HLA-DRB1*11:54', 'HLA-DRB1*11:55', 'HLA-DRB1*11:56', 'HLA-DRB1*11:57',
'HLA-DRB1*11:58', 'HLA-DRB1*11:59', 'HLA-DRB1*11:60', 'HLA-DRB1*11:61', 'HLA-DRB1*11:62',
'HLA-DRB1*11:63', 'HLA-DRB1*11:64', 'HLA-DRB1*11:65', 'HLA-DRB1*11:66', 'HLA-DRB1*11:67',
'HLA-DRB1*11:68', 'HLA-DRB1*11:69', 'HLA-DRB1*11:70', 'HLA-DRB1*11:72', 'HLA-DRB1*11:73',
'HLA-DRB1*11:74', 'HLA-DRB1*11:75', 'HLA-DRB1*11:76', 'HLA-DRB1*11:77', 'HLA-DRB1*11:78',
'HLA-DRB1*11:79', 'HLA-DRB1*11:80', 'HLA-DRB1*11:81', 'HLA-DRB1*11:82', 'HLA-DRB1*11:83',
'HLA-DRB1*11:84', 'HLA-DRB1*11:85', 'HLA-DRB1*11:86', 'HLA-DRB1*11:87', 'HLA-DRB1*11:88',
'HLA-DRB1*11:89', 'HLA-DRB1*11:90', 'HLA-DRB1*11:91', 'HLA-DRB1*11:92', 'HLA-DRB1*11:93',
'HLA-DRB1*11:94', 'HLA-DRB1*11:95', 'HLA-DRB1*11:96', 'HLA-DRB1*12:01', 'HLA-DRB1*12:02',
'HLA-DRB1*12:03', 'HLA-DRB1*12:04', 'HLA-DRB1*12:05', 'HLA-DRB1*12:06', 'HLA-DRB1*12:07',
'HLA-DRB1*12:08', 'HLA-DRB1*12:09', 'HLA-DRB1*12:10', 'HLA-DRB1*12:11', 'HLA-DRB1*12:12',
'HLA-DRB1*12:13', 'HLA-DRB1*12:14', 'HLA-DRB1*12:15', 'HLA-DRB1*12:16', 'HLA-DRB1*12:17',
'HLA-DRB1*12:18', 'HLA-DRB1*12:19', 'HLA-DRB1*12:20', 'HLA-DRB1*12:21', 'HLA-DRB1*12:22',
'HLA-DRB1*12:23', 'HLA-DRB1*13:01', 'HLA-DRB1*13:02', 'HLA-DRB1*13:03', 'HLA-DRB1*13:04',
'HLA-DRB1*13:05', 'HLA-DRB1*13:06', 'HLA-DRB1*13:07', 'HLA-DRB1*13:08', 'HLA-DRB1*13:09',
'HLA-DRB1*13:10', 'HLA-DRB1*13:100', 'HLA-DRB1*13:101', 'HLA-DRB1*13:11', 'HLA-DRB1*13:12',
'HLA-DRB1*13:13', 'HLA-DRB1*13:14', 'HLA-DRB1*13:15', 'HLA-DRB1*13:16', 'HLA-DRB1*13:17',
'HLA-DRB1*13:18', 'HLA-DRB1*13:19', 'HLA-DRB1*13:20', 'HLA-DRB1*13:21', 'HLA-DRB1*13:22',
'HLA-DRB1*13:23', 'HLA-DRB1*13:24', 'HLA-DRB1*13:26', 'HLA-DRB1*13:27', 'HLA-DRB1*13:29',
'HLA-DRB1*13:30', 'HLA-DRB1*13:31', 'HLA-DRB1*13:32', 'HLA-DRB1*13:33', 'HLA-DRB1*13:34',
'HLA-DRB1*13:35', 'HLA-DRB1*13:36', 'HLA-DRB1*13:37', 'HLA-DRB1*13:38', 'HLA-DRB1*13:39',
'HLA-DRB1*13:41', 'HLA-DRB1*13:42', 'HLA-DRB1*13:43', 'HLA-DRB1*13:44', 'HLA-DRB1*13:46',
'HLA-DRB1*13:47', 'HLA-DRB1*13:48', 'HLA-DRB1*13:49', 'HLA-DRB1*13:50', 'HLA-DRB1*13:51',
'HLA-DRB1*13:52', 'HLA-DRB1*13:53', 'HLA-DRB1*13:54', 'HLA-DRB1*13:55', 'HLA-DRB1*13:56',
'HLA-DRB1*13:57', 'HLA-DRB1*13:58', 'HLA-DRB1*13:59', 'HLA-DRB1*13:60', 'HLA-DRB1*13:61',
'HLA-DRB1*13:62', 'HLA-DRB1*13:63', 'HLA-DRB1*13:64', 'HLA-DRB1*13:65', 'HLA-DRB1*13:66',
'HLA-DRB1*13:67', 'HLA-DRB1*13:68', 'HLA-DRB1*13:69', 'HLA-DRB1*13:70', 'HLA-DRB1*13:71',
'HLA-DRB1*13:72', 'HLA-DRB1*13:73', 'HLA-DRB1*13:74', 'HLA-DRB1*13:75', 'HLA-DRB1*13:76',
'HLA-DRB1*13:77', 'HLA-DRB1*13:78', 'HLA-DRB1*13:79', 'HLA-DRB1*13:80', 'HLA-DRB1*13:81',
'HLA-DRB1*13:82', 'HLA-DRB1*13:83', 'HLA-DRB1*13:84', 'HLA-DRB1*13:85', 'HLA-DRB1*13:86',
'HLA-DRB1*13:87', 'HLA-DRB1*13:88', 'HLA-DRB1*13:89', 'HLA-DRB1*13:90', 'HLA-DRB1*13:91',
'HLA-DRB1*13:92', 'HLA-DRB1*13:93', 'HLA-DRB1*13:94', 'HLA-DRB1*13:95', 'HLA-DRB1*13:96',
'HLA-DRB1*13:97', 'HLA-DRB1*13:98', 'HLA-DRB1*13:99', 'HLA-DRB1*14:01', 'HLA-DRB1*14:02',
'HLA-DRB1*14:03', 'HLA-DRB1*14:04', 'HLA-DRB1*14:05', 'HLA-DRB1*14:06', 'HLA-DRB1*14:07',
'HLA-DRB1*14:08', 'HLA-DRB1*14:09', 'HLA-DRB1*14:10', 'HLA-DRB1*14:11', 'HLA-DRB1*14:12',
'HLA-DRB1*14:13', 'HLA-DRB1*14:14', 'HLA-DRB1*14:15', 'HLA-DRB1*14:16', 'HLA-DRB1*14:17',
'HLA-DRB1*14:18', 'HLA-DRB1*14:19', 'HLA-DRB1*14:20', 'HLA-DRB1*14:21', 'HLA-DRB1*14:22',
'HLA-DRB1*14:23', 'HLA-DRB1*14:24', 'HLA-DRB1*14:25', 'HLA-DRB1*14:26', 'HLA-DRB1*14:27',
'HLA-DRB1*14:28', 'HLA-DRB1*14:29', 'HLA-DRB1*14:30', 'HLA-DRB1*14:31', 'HLA-DRB1*14:32',
'HLA-DRB1*14:33', 'HLA-DRB1*14:34', 'HLA-DRB1*14:35', 'HLA-DRB1*14:36', 'HLA-DRB1*14:37',
'HLA-DRB1*14:38', 'HLA-DRB1*14:39', 'HLA-DRB1*14:40', 'HLA-DRB1*14:41', 'HLA-DRB1*14:42',
'HLA-DRB1*14:43', 'HLA-DRB1*14:44', 'HLA-DRB1*14:45', 'HLA-DRB1*14:46', 'HLA-DRB1*14:47',
'HLA-DRB1*14:48', 'HLA-DRB1*14:49', 'HLA-DRB1*14:50', 'HLA-DRB1*14:51', 'HLA-DRB1*14:52',
'HLA-DRB1*14:53', 'HLA-DRB1*14:54', 'HLA-DRB1*14:55', 'HLA-DRB1*14:56', 'HLA-DRB1*14:57',
'HLA-DRB1*14:58', 'HLA-DRB1*14:59', 'HLA-DRB1*14:60', 'HLA-DRB1*14:61', 'HLA-DRB1*14:62',
'HLA-DRB1*14:63', 'HLA-DRB1*14:64', 'HLA-DRB1*14:65', 'HLA-DRB1*14:67', 'HLA-DRB1*14:68',
'HLA-DRB1*14:69', 'HLA-DRB1*14:70', 'HLA-DRB1*14:71', 'HLA-DRB1*14:72', 'HLA-DRB1*14:73',
'HLA-DRB1*14:74', 'HLA-DRB1*14:75', 'HLA-DRB1*14:76', 'HLA-DRB1*14:77', 'HLA-DRB1*14:78',
'HLA-DRB1*14:79', 'HLA-DRB1*14:80', 'HLA-DRB1*14:81', 'HLA-DRB1*14:82', 'HLA-DRB1*14:83',
'HLA-DRB1*14:84', 'HLA-DRB1*14:85', 'HLA-DRB1*14:86', 'HLA-DRB1*14:87', 'HLA-DRB1*14:88',
'HLA-DRB1*14:89', 'HLA-DRB1*14:90', 'HLA-DRB1*14:91', 'HLA-DRB1*14:93', 'HLA-DRB1*14:94',
'HLA-DRB1*14:95', 'HLA-DRB1*14:96', 'HLA-DRB1*14:97', 'HLA-DRB1*14:98', 'HLA-DRB1*14:99',
'HLA-DRB1*15:01', 'HLA-DRB1*15:02', 'HLA-DRB1*15:03', 'HLA-DRB1*15:04', 'HLA-DRB1*15:05',
'HLA-DRB1*15:06', 'HLA-DRB1*15:07', 'HLA-DRB1*15:08', 'HLA-DRB1*15:09', 'HLA-DRB1*15:10',
'HLA-DRB1*15:11', 'HLA-DRB1*15:12', 'HLA-DRB1*15:13', 'HLA-DRB1*15:14', 'HLA-DRB1*15:15',
'HLA-DRB1*15:16', 'HLA-DRB1*15:18', 'HLA-DRB1*15:19', 'HLA-DRB1*15:20', 'HLA-DRB1*15:21',
'HLA-DRB1*15:22', 'HLA-DRB1*15:23', 'HLA-DRB1*15:24', 'HLA-DRB1*15:25', 'HLA-DRB1*15:26',
'HLA-DRB1*15:27', 'HLA-DRB1*15:28', 'HLA-DRB1*15:29', 'HLA-DRB1*15:30', 'HLA-DRB1*15:31',
'HLA-DRB1*15:32', 'HLA-DRB1*15:33', 'HLA-DRB1*15:34', 'HLA-DRB1*15:35', 'HLA-DRB1*15:36',
'HLA-DRB1*15:37', 'HLA-DRB1*15:38', 'HLA-DRB1*15:39', 'HLA-DRB1*15:40', 'HLA-DRB1*15:41',
'HLA-DRB1*15:42', 'HLA-DRB1*15:43', 'HLA-DRB1*15:44', 'HLA-DRB1*15:45', 'HLA-DRB1*15:46',
'HLA-DRB1*15:47', 'HLA-DRB1*15:48', 'HLA-DRB1*15:49', 'HLA-DRB1*16:01', 'HLA-DRB1*16:02',
'HLA-DRB1*16:03', 'HLA-DRB1*16:04', 'HLA-DRB1*16:05', 'HLA-DRB1*16:07', 'HLA-DRB1*16:08',
'HLA-DRB1*16:09', 'HLA-DRB1*16:10', 'HLA-DRB1*16:11', 'HLA-DRB1*16:12', 'HLA-DRB1*16:14',
'HLA-DRB1*16:15', 'HLA-DRB1*16:16', 'HLA-DRB3*01:01', 'HLA-DRB3*01:04', 'HLA-DRB3*01:05',
'HLA-DRB3*01:08', 'HLA-DRB3*01:09', 'HLA-DRB3*01:11', 'HLA-DRB3*01:12', 'HLA-DRB3*01:13',
'HLA-DRB3*01:14', 'HLA-DRB3*02:01', 'HLA-DRB3*02:02', 'HLA-DRB3*02:04', 'HLA-DRB3*02:05',
'HLA-DRB3*02:09', 'HLA-DRB3*02:10', 'HLA-DRB3*02:11', 'HLA-DRB3*02:12', 'HLA-DRB3*02:13',
'HLA-DRB3*02:14', 'HLA-DRB3*02:15', 'HLA-DRB3*02:16', 'HLA-DRB3*02:17', 'HLA-DRB3*02:18',
'HLA-DRB3*02:19', 'HLA-DRB3*02:20', 'HLA-DRB3*02:21', 'HLA-DRB3*02:22', 'HLA-DRB3*02:23',
'HLA-DRB3*02:24', 'HLA-DRB3*02:25', 'HLA-DRB3*03:01', 'HLA-DRB3*03:03', 'HLA-DRB4*01:01',
'HLA-DRB4*01:03', 'HLA-DRB4*01:04', 'HLA-DRB4*01:06', 'HLA-DRB4*01:07', 'HLA-DRB4*01:08',
'HLA-DRB5*01:01', 'HLA-DRB5*01:02', 'HLA-DRB5*01:03', 'HLA-DRB5*01:04', 'HLA-DRB5*01:05',
'HLA-DRB5*01:06', 'HLA-DRB5*01:08N', 'HLA-DRB5*01:11', 'HLA-DRB5*01:12', 'HLA-DRB5*01:13',
'HLA-DRB5*01:14', 'HLA-DRB5*02:02', 'HLA-DRB5*02:03', 'HLA-DRB5*02:04', 'HLA-DRB5*02:05',
'HLA-DPA1*01:03-DPB1*01:01', 'HLA-DPA1*01:03-DPB1*02:01', 'HLA-DPA1*01:03-DPB1*02:02', 'HLA-DPA1*01:03-DPB1*03:01', 'HLA-DPA1*01:03-DPB1*04:01',
'HLA-DPA1*01:03-DPB1*04:02', 'HLA-DPA1*01:03-DPB1*05:01', 'HLA-DPA1*01:03-DPB1*06:01', 'HLA-DPA1*01:03-DPB1*08:01', 'HLA-DPA1*01:03-DPB1*09:01',
'HLA-DPA1*01:03-DPB1*10:001', 'HLA-DPA1*01:03-DPB1*10:01', 'HLA-DPA1*01:03-DPB1*10:101', 'HLA-DPA1*01:03-DPB1*10:201', 'HLA-DPA1*01:03-DPB1*10:301',
'HLA-DPA1*01:03-DPB1*10:401', 'HLA-DPA1*01:03-DPB1*10:501', 'HLA-DPA1*01:03-DPB1*10:601', 'HLA-DPA1*01:03-DPB1*10:701', 'HLA-DPA1*01:03-DPB1*10:801',
'HLA-DPA1*01:03-DPB1*10:901', 'HLA-DPA1*01:03-DPB1*11:001', 'HLA-DPA1*01:03-DPB1*11:01', 'HLA-DPA1*01:03-DPB1*11:101', 'HLA-DPA1*01:03-DPB1*11:201',
'HLA-DPA1*01:03-DPB1*11:301', 'HLA-DPA1*01:03-DPB1*11:401', 'HLA-DPA1*01:03-DPB1*11:501', 'HLA-DPA1*01:03-DPB1*11:601', 'HLA-DPA1*01:03-DPB1*11:701',
'HLA-DPA1*01:03-DPB1*11:801', 'HLA-DPA1*01:03-DPB1*11:901', 'HLA-DPA1*01:03-DPB1*12:101', 'HLA-DPA1*01:03-DPB1*12:201', 'HLA-DPA1*01:03-DPB1*12:301',
'HLA-DPA1*01:03-DPB1*12:401', 'HLA-DPA1*01:03-DPB1*12:501', 'HLA-DPA1*01:03-DPB1*12:601', 'HLA-DPA1*01:03-DPB1*12:701', 'HLA-DPA1*01:03-DPB1*12:801',
'HLA-DPA1*01:03-DPB1*12:901', 'HLA-DPA1*01:03-DPB1*13:001', 'HLA-DPA1*01:03-DPB1*13:01', 'HLA-DPA1*01:03-DPB1*13:101', 'HLA-DPA1*01:03-DPB1*13:201',
'HLA-DPA1*01:03-DPB1*13:301', 'HLA-DPA1*01:03-DPB1*13:401', 'HLA-DPA1*01:03-DPB1*14:01', 'HLA-DPA1*01:03-DPB1*15:01', 'HLA-DPA1*01:03-DPB1*16:01',
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'HLA-DQA1*05:11-DQB1*06:22', 'HLA-DQA1*05:11-DQB1*06:23', 'HLA-DQA1*05:11-DQB1*06:24', 'HLA-DQA1*05:11-DQB1*06:25', 'HLA-DQA1*05:11-DQB1*06:27',
'HLA-DQA1*05:11-DQB1*06:28', 'HLA-DQA1*05:11-DQB1*06:29', 'HLA-DQA1*05:11-DQB1*06:30', 'HLA-DQA1*05:11-DQB1*06:31', 'HLA-DQA1*05:11-DQB1*06:32',
'HLA-DQA1*05:11-DQB1*06:33', 'HLA-DQA1*05:11-DQB1*06:34', 'HLA-DQA1*05:11-DQB1*06:35', 'HLA-DQA1*05:11-DQB1*06:36', 'HLA-DQA1*05:11-DQB1*06:37',
'HLA-DQA1*05:11-DQB1*06:38', 'HLA-DQA1*05:11-DQB1*06:39', 'HLA-DQA1*05:11-DQB1*06:40', 'HLA-DQA1*05:11-DQB1*06:41', 'HLA-DQA1*05:11-DQB1*06:42',
'HLA-DQA1*05:11-DQB1*06:43', 'HLA-DQA1*05:11-DQB1*06:44', 'HLA-DQA1*06:01-DQB1*02:01', 'HLA-DQA1*06:01-DQB1*02:02', 'HLA-DQA1*06:01-DQB1*02:03',
'HLA-DQA1*06:01-DQB1*02:04', 'HLA-DQA1*06:01-DQB1*02:05', 'HLA-DQA1*06:01-DQB1*02:06', 'HLA-DQA1*06:01-DQB1*03:01', 'HLA-DQA1*06:01-DQB1*03:02',
'HLA-DQA1*06:01-DQB1*03:03', 'HLA-DQA1*06:01-DQB1*03:04', 'HLA-DQA1*06:01-DQB1*03:05', 'HLA-DQA1*06:01-DQB1*03:06', 'HLA-DQA1*06:01-DQB1*03:07',
'HLA-DQA1*06:01-DQB1*03:08', 'HLA-DQA1*06:01-DQB1*03:09', 'HLA-DQA1*06:01-DQB1*03:10', 'HLA-DQA1*06:01-DQB1*03:11', 'HLA-DQA1*06:01-DQB1*03:12',
'HLA-DQA1*06:01-DQB1*03:13', 'HLA-DQA1*06:01-DQB1*03:14', 'HLA-DQA1*06:01-DQB1*03:15', 'HLA-DQA1*06:01-DQB1*03:16', 'HLA-DQA1*06:01-DQB1*03:17',
'HLA-DQA1*06:01-DQB1*03:18', 'HLA-DQA1*06:01-DQB1*03:19', 'HLA-DQA1*06:01-DQB1*03:20', 'HLA-DQA1*06:01-DQB1*03:21', 'HLA-DQA1*06:01-DQB1*03:22',
'HLA-DQA1*06:01-DQB1*03:23', 'HLA-DQA1*06:01-DQB1*03:24', 'HLA-DQA1*06:01-DQB1*03:25', 'HLA-DQA1*06:01-DQB1*03:26', 'HLA-DQA1*06:01-DQB1*03:27',
'HLA-DQA1*06:01-DQB1*03:28', 'HLA-DQA1*06:01-DQB1*03:29', 'HLA-DQA1*06:01-DQB1*03:30', 'HLA-DQA1*06:01-DQB1*03:31', 'HLA-DQA1*06:01-DQB1*03:32',
'HLA-DQA1*06:01-DQB1*03:33', 'HLA-DQA1*06:01-DQB1*03:34', 'HLA-DQA1*06:01-DQB1*03:35', 'HLA-DQA1*06:01-DQB1*03:36', 'HLA-DQA1*06:01-DQB1*03:37',
'HLA-DQA1*06:01-DQB1*03:38', 'HLA-DQA1*06:01-DQB1*04:01', 'HLA-DQA1*06:01-DQB1*04:02', 'HLA-DQA1*06:01-DQB1*04:03', 'HLA-DQA1*06:01-DQB1*04:04',
'HLA-DQA1*06:01-DQB1*04:05', 'HLA-DQA1*06:01-DQB1*04:06', 'HLA-DQA1*06:01-DQB1*04:07', 'HLA-DQA1*06:01-DQB1*04:08', 'HLA-DQA1*06:01-DQB1*05:01',
'HLA-DQA1*06:01-DQB1*05:02', 'HLA-DQA1*06:01-DQB1*05:03', 'HLA-DQA1*06:01-DQB1*05:05', 'HLA-DQA1*06:01-DQB1*05:06', 'HLA-DQA1*06:01-DQB1*05:07',
'HLA-DQA1*06:01-DQB1*05:08', 'HLA-DQA1*06:01-DQB1*05:09', 'HLA-DQA1*06:01-DQB1*05:10', 'HLA-DQA1*06:01-DQB1*05:11', 'HLA-DQA1*06:01-DQB1*05:12',
'HLA-DQA1*06:01-DQB1*05:13', 'HLA-DQA1*06:01-DQB1*05:14', 'HLA-DQA1*06:01-DQB1*06:01', 'HLA-DQA1*06:01-DQB1*06:02', 'HLA-DQA1*06:01-DQB1*06:03',
'HLA-DQA1*06:01-DQB1*06:04', 'HLA-DQA1*06:01-DQB1*06:07', 'HLA-DQA1*06:01-DQB1*06:08', 'HLA-DQA1*06:01-DQB1*06:09', 'HLA-DQA1*06:01-DQB1*06:10',
'HLA-DQA1*06:01-DQB1*06:11', 'HLA-DQA1*06:01-DQB1*06:12', 'HLA-DQA1*06:01-DQB1*06:14', 'HLA-DQA1*06:01-DQB1*06:15', 'HLA-DQA1*06:01-DQB1*06:16',
'HLA-DQA1*06:01-DQB1*06:17', 'HLA-DQA1*06:01-DQB1*06:18', 'HLA-DQA1*06:01-DQB1*06:19', 'HLA-DQA1*06:01-DQB1*06:21', 'HLA-DQA1*06:01-DQB1*06:22',
'HLA-DQA1*06:01-DQB1*06:23', 'HLA-DQA1*06:01-DQB1*06:24', 'HLA-DQA1*06:01-DQB1*06:25', 'HLA-DQA1*06:01-DQB1*06:27', 'HLA-DQA1*06:01-DQB1*06:28',
'HLA-DQA1*06:01-DQB1*06:29', 'HLA-DQA1*06:01-DQB1*06:30', 'HLA-DQA1*06:01-DQB1*06:31', 'HLA-DQA1*06:01-DQB1*06:32', 'HLA-DQA1*06:01-DQB1*06:33',
'HLA-DQA1*06:01-DQB1*06:34', 'HLA-DQA1*06:01-DQB1*06:35', 'HLA-DQA1*06:01-DQB1*06:36', 'HLA-DQA1*06:01-DQB1*06:37', 'HLA-DQA1*06:01-DQB1*06:38',
'HLA-DQA1*06:01-DQB1*06:39', 'HLA-DQA1*06:01-DQB1*06:40', 'HLA-DQA1*06:01-DQB1*06:41', 'HLA-DQA1*06:01-DQB1*06:42', 'HLA-DQA1*06:01-DQB1*06:43',
'HLA-DQA1*06:01-DQB1*06:44', 'HLA-DQA1*06:02-DQB1*02:01', 'HLA-DQA1*06:02-DQB1*02:02', 'HLA-DQA1*06:02-DQB1*02:03', 'HLA-DQA1*06:02-DQB1*02:04',
'HLA-DQA1*06:02-DQB1*02:05', 'HLA-DQA1*06:02-DQB1*02:06', 'HLA-DQA1*06:02-DQB1*03:01', 'HLA-DQA1*06:02-DQB1*03:02', 'HLA-DQA1*06:02-DQB1*03:03',
'HLA-DQA1*06:02-DQB1*03:04', 'HLA-DQA1*06:02-DQB1*03:05', 'HLA-DQA1*06:02-DQB1*03:06', 'HLA-DQA1*06:02-DQB1*03:07', 'HLA-DQA1*06:02-DQB1*03:08',
'HLA-DQA1*06:02-DQB1*03:09', 'HLA-DQA1*06:02-DQB1*03:10', 'HLA-DQA1*06:02-DQB1*03:11', 'HLA-DQA1*06:02-DQB1*03:12', 'HLA-DQA1*06:02-DQB1*03:13',
'HLA-DQA1*06:02-DQB1*03:14', 'HLA-DQA1*06:02-DQB1*03:15', 'HLA-DQA1*06:02-DQB1*03:16', 'HLA-DQA1*06:02-DQB1*03:17', 'HLA-DQA1*06:02-DQB1*03:18',
'HLA-DQA1*06:02-DQB1*03:19', 'HLA-DQA1*06:02-DQB1*03:20', 'HLA-DQA1*06:02-DQB1*03:21', 'HLA-DQA1*06:02-DQB1*03:22', 'HLA-DQA1*06:02-DQB1*03:23',
'HLA-DQA1*06:02-DQB1*03:24', 'HLA-DQA1*06:02-DQB1*03:25', 'HLA-DQA1*06:02-DQB1*03:26', 'HLA-DQA1*06:02-DQB1*03:27', 'HLA-DQA1*06:02-DQB1*03:28',
'HLA-DQA1*06:02-DQB1*03:29', 'HLA-DQA1*06:02-DQB1*03:30', 'HLA-DQA1*06:02-DQB1*03:31', 'HLA-DQA1*06:02-DQB1*03:32', 'HLA-DQA1*06:02-DQB1*03:33',
'HLA-DQA1*06:02-DQB1*03:34', 'HLA-DQA1*06:02-DQB1*03:35', 'HLA-DQA1*06:02-DQB1*03:36', 'HLA-DQA1*06:02-DQB1*03:37', 'HLA-DQA1*06:02-DQB1*03:38',
'HLA-DQA1*06:02-DQB1*04:01', 'HLA-DQA1*06:02-DQB1*04:02', 'HLA-DQA1*06:02-DQB1*04:03', 'HLA-DQA1*06:02-DQB1*04:04', 'HLA-DQA1*06:02-DQB1*04:05',
'HLA-DQA1*06:02-DQB1*04:06', 'HLA-DQA1*06:02-DQB1*04:07', 'HLA-DQA1*06:02-DQB1*04:08', 'HLA-DQA1*06:02-DQB1*05:01', 'HLA-DQA1*06:02-DQB1*05:02',
'HLA-DQA1*06:02-DQB1*05:03', 'HLA-DQA1*06:02-DQB1*05:05', 'HLA-DQA1*06:02-DQB1*05:06', 'HLA-DQA1*06:02-DQB1*05:07', 'HLA-DQA1*06:02-DQB1*05:08',
'HLA-DQA1*06:02-DQB1*05:09', 'HLA-DQA1*06:02-DQB1*05:10', 'HLA-DQA1*06:02-DQB1*05:11', 'HLA-DQA1*06:02-DQB1*05:12', 'HLA-DQA1*06:02-DQB1*05:13',
'HLA-DQA1*06:02-DQB1*05:14', 'HLA-DQA1*06:02-DQB1*06:01', 'HLA-DQA1*06:02-DQB1*06:02', 'HLA-DQA1*06:02-DQB1*06:03', 'HLA-DQA1*06:02-DQB1*06:04',
'HLA-DQA1*06:02-DQB1*06:07', 'HLA-DQA1*06:02-DQB1*06:08', 'HLA-DQA1*06:02-DQB1*06:09', 'HLA-DQA1*06:02-DQB1*06:10', 'HLA-DQA1*06:02-DQB1*06:11',
'HLA-DQA1*06:02-DQB1*06:12', 'HLA-DQA1*06:02-DQB1*06:14', 'HLA-DQA1*06:02-DQB1*06:15', 'HLA-DQA1*06:02-DQB1*06:16', 'HLA-DQA1*06:02-DQB1*06:17',
'HLA-DQA1*06:02-DQB1*06:18', 'HLA-DQA1*06:02-DQB1*06:19', 'HLA-DQA1*06:02-DQB1*06:21', 'HLA-DQA1*06:02-DQB1*06:22', 'HLA-DQA1*06:02-DQB1*06:23',
'HLA-DQA1*06:02-DQB1*06:24', 'HLA-DQA1*06:02-DQB1*06:25', 'HLA-DQA1*06:02-DQB1*06:27', 'HLA-DQA1*06:02-DQB1*06:28', 'HLA-DQA1*06:02-DQB1*06:29',
'HLA-DQA1*06:02-DQB1*06:30', 'HLA-DQA1*06:02-DQB1*06:31', 'HLA-DQA1*06:02-DQB1*06:32', 'HLA-DQA1*06:02-DQB1*06:33', 'HLA-DQA1*06:02-DQB1*06:34',
'HLA-DQA1*06:02-DQB1*06:35', 'HLA-DQA1*06:02-DQB1*06:36', 'HLA-DQA1*06:02-DQB1*06:37', 'HLA-DQA1*06:02-DQB1*06:38', 'HLA-DQA1*06:02-DQB1*06:39',
'HLA-DQA1*06:02-DQB1*06:40', 'HLA-DQA1*06:02-DQB1*06:41', 'HLA-DQA1*06:02-DQB1*06:42', 'HLA-DQA1*06:02-DQB1*06:43', 'HLA-DQA1*06:02-DQB1*06:44',
'H-2-Iab', 'H-2-Iad', 'H-2-Iak', 'H-2-Iaq', 'H-2-Ias',
'H-2-Iau', 'H-2-Iad', 'H-2-Iak'])
__version = "4.0"
@property
def version(self):
"""The version of the predictor"""
return self.__version
@property
def command(self):
"""
Defines the commandline call for external tool
"""
return self.__command
@property
def supportedLength(self):
"""
A list of supported :class:`~epytope.Core.Peptide.Peptide` lengths
"""
return self.__supported_length
@property
def supportedAlleles(self):
"""A list of valid :class:`~epytope.Core.Allele.Allele` models"""
return self.__alleles
@property
def name(self):
"""The name of the predictor"""
return self.__name
def parse_external_result(self, file):
"""
Parses external results and returns the result containing the predictors string representation
of alleles and peptides.
:param str file: The file path or the external prediction results
:return: A dictionary containing the prediction results
:rtype: dict
"""
f = csv.reader(open(file, "r"), delimiter='\t')
scores = defaultdict(defaultdict)
ranks = defaultdict(defaultdict)
alleles = [x for x in set([x for x in next(f) if x != ""])]
next(f)
for row in f:
pep_seq = row[PeptideIndex.NETMHCIIPAN_4_0]
for i, a in enumerate(alleles):
scores[a][pep_seq] = float(row[ScoreIndex.NETMHCIIPAN_4_0 + i * Offset.NETMHCIIPAN_4_0])
ranks[a][pep_seq] = float(row[RankIndex.NETMHCIIPAN_4_0 + i * Offset.NETMHCIIPAN_4_0])
# Create dictionary with hierarchy: {'Allele1': {'Score': {'Pep1': Score1, 'Pep2': Score2,..}, 'Rank': {'Pep1': RankScore1, 'Pep2': RankScore2,..}}, 'Allele2':...}
result = {allele: {metric:(list(scores.values())[j] if metric == "Score" else list(ranks.values())[j]) for metric in ["Score", "Rank"]} for j, allele in enumerate(alleles)}
return result
class PickPocket_1_1(AExternalEpitopePrediction):
"""
Implementation of PickPocket adapter.
.. note::
Zhang, H., Lund, O., & Nielsen, M. (2009). The PickPocket method for predicting binding specificities
for receptors based on receptor pocket similarities: application to MHC-peptide binding.
Bioinformatics, 25(10), 1293-1299.
"""
__name = "pickpocket"
__supported_length = frozenset([8, 9, 10, 11])
__command = 'PickPocket -p {peptides} -a {alleles} {options} | grep -v "#" > {out}'
__supported_alleles = frozenset(['HLA-A*01:01', 'HLA-A*01:02', 'HLA-A*01:03', 'HLA-A*01:06', 'HLA-A*01:07', 'HLA-A*01:08', 'HLA-A*01:09',
'HLA-A*01:10', 'HLA-A*01:12', 'HLA-A*01:13', 'HLA-A*01:14', 'HLA-A*01:17', 'HLA-A*01:19', 'HLA-A*01:20',
'HLA-A*01:21', 'HLA-A*01:23', 'HLA-A*01:24',
'HLA-A*01:25', 'HLA-A*01:26', 'HLA-A*01:28', 'HLA-A*01:29', 'HLA-A*01:30', 'HLA-A*01:32', 'HLA-A*01:33',
'HLA-A*01:35', 'HLA-A*01:36', 'HLA-A*01:37',
'HLA-A*01:38', 'HLA-A*01:39', 'HLA-A*01:40', 'HLA-A*01:41', 'HLA-A*01:42', 'HLA-A*01:43', 'HLA-A*01:44',
'HLA-A*01:45', 'HLA-A*01:46', 'HLA-A*01:47',
'HLA-A*01:48', 'HLA-A*01:49', 'HLA-A*01:50', 'HLA-A*01:51', 'HLA-A*01:54', 'HLA-A*01:55', 'HLA-A*01:58',
'HLA-A*01:59', 'HLA-A*01:60', 'HLA-A*01:61',
'HLA-A*01:62', 'HLA-A*01:63', 'HLA-A*01:64', 'HLA-A*01:65', 'HLA-A*01:66', 'HLA-A*02:01', 'HLA-A*02:02',
'HLA-A*02:03', 'HLA-A*02:04', 'HLA-A*02:05',
'HLA-A*02:06', 'HLA-A*02:07', 'HLA-A*02:08', 'HLA-A*02:09', 'HLA-A*02:10', 'HLA-A*02:11', 'HLA-A*02:12',
'HLA-A*02:13', 'HLA-A*02:14', 'HLA-A*02:16',
'HLA-A*02:17', 'HLA-A*02:18', 'HLA-A*02:19', 'HLA-A*02:20', 'HLA-A*02:21', 'HLA-A*02:22', 'HLA-A*02:24',
'HLA-A*02:25', 'HLA-A*02:26', 'HLA-A*02:27',
'HLA-A*02:28', 'HLA-A*02:29', 'HLA-A*02:30', 'HLA-A*02:31', 'HLA-A*02:33', 'HLA-A*02:34', 'HLA-A*02:35',
'HLA-A*02:36', 'HLA-A*02:37', 'HLA-A*02:38',
'HLA-A*02:39', 'HLA-A*02:40', 'HLA-A*02:41', 'HLA-A*02:42', 'HLA-A*02:44', 'HLA-A*02:45', 'HLA-A*02:46',
'HLA-A*02:47', 'HLA-A*02:48', 'HLA-A*02:49',
'HLA-A*02:50', 'HLA-A*02:51', 'HLA-A*02:52', 'HLA-A*02:54', 'HLA-A*02:55', 'HLA-A*02:56', 'HLA-A*02:57',
'HLA-A*02:58', 'HLA-A*02:59', 'HLA-A*02:60',
'HLA-A*02:61', 'HLA-A*02:62', 'HLA-A*02:63', 'HLA-A*02:64', 'HLA-A*02:65', 'HLA-A*02:66', 'HLA-A*02:67',
'HLA-A*02:68', 'HLA-A*02:69', 'HLA-A*02:70',
'HLA-A*02:71', 'HLA-A*02:72', 'HLA-A*02:73', 'HLA-A*02:74', 'HLA-A*02:75', 'HLA-A*02:76', 'HLA-A*02:77',
'HLA-A*02:78', 'HLA-A*02:79', 'HLA-A*02:80',
'HLA-A*02:81', 'HLA-A*02:84', 'HLA-A*02:85', 'HLA-A*02:86', 'HLA-A*02:87', 'HLA-A*02:89', 'HLA-A*02:90',
'HLA-A*02:91', 'HLA-A*02:92', 'HLA-A*02:93',
'HLA-A*02:95', 'HLA-A*02:96', 'HLA-A*02:97', 'HLA-A*02:99', 'HLA-A*02:101', 'HLA-A*02:102', 'HLA-A*02:103',
'HLA-A*02:104', 'HLA-A*02:105',
'HLA-A*02:106', 'HLA-A*02:107', 'HLA-A*02:108', 'HLA-A*02:109', 'HLA-A*02:110', 'HLA-A*02:111', 'HLA-A*02:112',
'HLA-A*02:114', 'HLA-A*02:115',
'HLA-A*02:116', 'HLA-A*02:117', 'HLA-A*02:118', 'HLA-A*02:119', 'HLA-A*02:120', 'HLA-A*02:121', 'HLA-A*02:122',
'HLA-A*02:123', 'HLA-A*02:124',
'HLA-A*02:126', 'HLA-A*02:127', 'HLA-A*02:128', 'HLA-A*02:129', 'HLA-A*02:130', 'HLA-A*02:131', 'HLA-A*02:132',
'HLA-A*02:133', 'HLA-A*02:134',
'HLA-A*02:135', 'HLA-A*02:136', 'HLA-A*02:137', 'HLA-A*02:138', 'HLA-A*02:139', 'HLA-A*02:140', 'HLA-A*02:141',
'HLA-A*02:142', 'HLA-A*02:143',
'HLA-A*02:144', 'HLA-A*02:145', 'HLA-A*02:146', 'HLA-A*02:147', 'HLA-A*02:148', 'HLA-A*02:149', 'HLA-A*02:150',
'HLA-A*02:151', 'HLA-A*02:152',
'HLA-A*02:153', 'HLA-A*02:154', 'HLA-A*02:155', 'HLA-A*02:156', 'HLA-A*02:157', 'HLA-A*02:158', 'HLA-A*02:159',
'HLA-A*02:160', 'HLA-A*02:161',
'HLA-A*02:162', 'HLA-A*02:163', 'HLA-A*02:164', 'HLA-A*02:165', 'HLA-A*02:166', 'HLA-A*02:167', 'HLA-A*02:168',
'HLA-A*02:169', 'HLA-A*02:170',
'HLA-A*02:171', 'HLA-A*02:172', 'HLA-A*02:173', 'HLA-A*02:174', 'HLA-A*02:175', 'HLA-A*02:176', 'HLA-A*02:177',
'HLA-A*02:178', 'HLA-A*02:179',
'HLA-A*02:180', 'HLA-A*02:181', 'HLA-A*02:182', 'HLA-A*02:183', 'HLA-A*02:184', 'HLA-A*02:185', 'HLA-A*02:186',
'HLA-A*02:187', 'HLA-A*02:188',
'HLA-A*02:189', 'HLA-A*02:190', 'HLA-A*02:191', 'HLA-A*02:192', 'HLA-A*02:193', 'HLA-A*02:194', 'HLA-A*02:195',
'HLA-A*02:196', 'HLA-A*02:197',
'HLA-A*02:198', 'HLA-A*02:199', 'HLA-A*02:200', 'HLA-A*02:201', 'HLA-A*02:202', 'HLA-A*02:203', 'HLA-A*02:204',
'HLA-A*02:205', 'HLA-A*02:206',
'HLA-A*02:207', 'HLA-A*02:208', 'HLA-A*02:209', 'HLA-A*02:210', 'HLA-A*02:211', 'HLA-A*02:212', 'HLA-A*02:213',
'HLA-A*02:214', 'HLA-A*02:215',
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'HLA-C*05:43', 'HLA-C*05:44', 'HLA-C*05:45', 'HLA-C*06:02', 'HLA-C*06:03', 'HLA-C*06:04', 'HLA-C*06:05',
'HLA-C*06:06', 'HLA-C*06:07', 'HLA-C*06:08',
'HLA-C*06:09', 'HLA-C*06:10', 'HLA-C*06:11', 'HLA-C*06:12', 'HLA-C*06:13', 'HLA-C*06:14', 'HLA-C*06:15',
'HLA-C*06:17', 'HLA-C*06:18', 'HLA-C*06:19',
'HLA-C*06:20', 'HLA-C*06:21', 'HLA-C*06:22', 'HLA-C*06:23', 'HLA-C*06:24', 'HLA-C*06:25', 'HLA-C*06:26',
'HLA-C*06:27', 'HLA-C*06:28', 'HLA-C*06:29',
'HLA-C*06:30', 'HLA-C*06:31', 'HLA-C*06:32', 'HLA-C*06:33', 'HLA-C*06:34', 'HLA-C*06:35', 'HLA-C*06:36',
'HLA-C*06:37', 'HLA-C*06:38', 'HLA-C*06:39',
'HLA-C*06:40', 'HLA-C*06:41', 'HLA-C*06:42', 'HLA-C*06:43', 'HLA-C*06:44', 'HLA-C*06:45', 'HLA-C*07:01',
'HLA-C*07:02', 'HLA-C*07:03', 'HLA-C*07:04',
'HLA-C*07:05', 'HLA-C*07:06', 'HLA-C*07:07', 'HLA-C*07:08', 'HLA-C*07:09', 'HLA-C*07:10', 'HLA-C*07:11',
'HLA-C*07:12', 'HLA-C*07:13', 'HLA-C*07:14',
'HLA-C*07:15', 'HLA-C*07:16', 'HLA-C*07:17', 'HLA-C*07:18', 'HLA-C*07:19', 'HLA-C*07:20', 'HLA-C*07:21',
'HLA-C*07:22', 'HLA-C*07:23', 'HLA-C*07:24',
'HLA-C*07:25', 'HLA-C*07:26', 'HLA-C*07:27', 'HLA-C*07:28', 'HLA-C*07:29', 'HLA-C*07:30', 'HLA-C*07:31',
'HLA-C*07:35', 'HLA-C*07:36', 'HLA-C*07:37',
'HLA-C*07:38', 'HLA-C*07:39', 'HLA-C*07:40', 'HLA-C*07:41', 'HLA-C*07:42', 'HLA-C*07:43', 'HLA-C*07:44',
'HLA-C*07:45', 'HLA-C*07:46', 'HLA-C*07:47',
'HLA-C*07:48', 'HLA-C*07:49', 'HLA-C*07:50', 'HLA-C*07:51', 'HLA-C*07:52', 'HLA-C*07:53', 'HLA-C*07:54',
'HLA-C*07:56', 'HLA-C*07:57', 'HLA-C*07:58',
'HLA-C*07:59', 'HLA-C*07:60', 'HLA-C*07:62', 'HLA-C*07:63', 'HLA-C*07:64', 'HLA-C*07:65', 'HLA-C*07:66',
'HLA-C*07:67', 'HLA-C*07:68', 'HLA-C*07:69',
'HLA-C*07:70', 'HLA-C*07:71', 'HLA-C*07:72', 'HLA-C*07:73', 'HLA-C*07:74', 'HLA-C*07:75', 'HLA-C*07:76',
'HLA-C*07:77', 'HLA-C*07:78', 'HLA-C*07:79',
'HLA-C*07:80', 'HLA-C*07:81', 'HLA-C*07:82', 'HLA-C*07:83', 'HLA-C*07:84', 'HLA-C*07:85', 'HLA-C*07:86',
'HLA-C*07:87', 'HLA-C*07:88', 'HLA-C*07:89',
'HLA-C*07:90', 'HLA-C*07:91', 'HLA-C*07:92', 'HLA-C*07:93', 'HLA-C*07:94', 'HLA-C*07:95', 'HLA-C*07:96',
'HLA-C*07:97', 'HLA-C*07:99', 'HLA-C*07:100',
'HLA-C*07:101', 'HLA-C*07:102', 'HLA-C*07:103', 'HLA-C*07:105', 'HLA-C*07:106', 'HLA-C*07:107', 'HLA-C*07:108',
'HLA-C*07:109', 'HLA-C*07:110',
'HLA-C*07:111', 'HLA-C*07:112', 'HLA-C*07:113', 'HLA-C*07:114', 'HLA-C*07:115', 'HLA-C*07:116', 'HLA-C*07:117',
'HLA-C*07:118', 'HLA-C*07:119',
'HLA-C*07:120', 'HLA-C*07:122', 'HLA-C*07:123', 'HLA-C*07:124', 'HLA-C*07:125', 'HLA-C*07:126', 'HLA-C*07:127',
'HLA-C*07:128', 'HLA-C*07:129',
'HLA-C*07:130', 'HLA-C*07:131', 'HLA-C*07:132', 'HLA-C*07:133', 'HLA-C*07:134', 'HLA-C*07:135', 'HLA-C*07:136',
'HLA-C*07:137', 'HLA-C*07:138',
'HLA-C*07:139', 'HLA-C*07:140', 'HLA-C*07:141', 'HLA-C*07:142', 'HLA-C*07:143', 'HLA-C*07:144', 'HLA-C*07:145',
'HLA-C*07:146', 'HLA-C*07:147',
'HLA-C*07:148', 'HLA-C*07:149', 'HLA-C*08:01', 'HLA-C*08:02', 'HLA-C*08:03', 'HLA-C*08:04', 'HLA-C*08:05',
'HLA-C*08:06', 'HLA-C*08:07', 'HLA-C*08:08',
'HLA-C*08:09', 'HLA-C*08:10', 'HLA-C*08:11', 'HLA-C*08:12', 'HLA-C*08:13', 'HLA-C*08:14', 'HLA-C*08:15',
'HLA-C*08:16', 'HLA-C*08:17', 'HLA-C*08:18',
'HLA-C*08:19', 'HLA-C*08:20', 'HLA-C*08:21', 'HLA-C*08:22', 'HLA-C*08:23', 'HLA-C*08:24', 'HLA-C*08:25',
'HLA-C*08:27', 'HLA-C*08:28', 'HLA-C*08:29',
'HLA-C*08:30', 'HLA-C*08:31', 'HLA-C*08:32', 'HLA-C*08:33', 'HLA-C*08:34', 'HLA-C*08:35', 'HLA-C*12:02',
'HLA-C*12:03', 'HLA-C*12:04', 'HLA-C*12:05',
'HLA-C*12:06', 'HLA-C*12:07', 'HLA-C*12:08', 'HLA-C*12:09', 'HLA-C*12:10', 'HLA-C*12:11', 'HLA-C*12:12',
'HLA-C*12:13', 'HLA-C*12:14', 'HLA-C*12:15',
'HLA-C*12:16', 'HLA-C*12:17', 'HLA-C*12:18', 'HLA-C*12:19', 'HLA-C*12:20', 'HLA-C*12:21', 'HLA-C*12:22',
'HLA-C*12:23', 'HLA-C*12:24', 'HLA-C*12:25',
'HLA-C*12:26', 'HLA-C*12:27', 'HLA-C*12:28', 'HLA-C*12:29', 'HLA-C*12:30', 'HLA-C*12:31', 'HLA-C*12:32',
'HLA-C*12:33', 'HLA-C*12:34', 'HLA-C*12:35',
'HLA-C*12:36', 'HLA-C*12:37', 'HLA-C*12:38', 'HLA-C*12:40', 'HLA-C*12:41', 'HLA-C*12:43', 'HLA-C*12:44',
'HLA-C*14:02', 'HLA-C*14:03', 'HLA-C*14:04',
'HLA-C*14:05', 'HLA-C*14:06', 'HLA-C*14:08', 'HLA-C*14:09', 'HLA-C*14:10', 'HLA-C*14:11', 'HLA-C*14:12',
'HLA-C*14:13', 'HLA-C*14:14', 'HLA-C*14:15',
'HLA-C*14:16', 'HLA-C*14:17', 'HLA-C*14:18', 'HLA-C*14:19', 'HLA-C*14:20', 'HLA-C*15:02', 'HLA-C*15:03',
'HLA-C*15:04', 'HLA-C*15:05', 'HLA-C*15:06',
'HLA-C*15:07', 'HLA-C*15:08', 'HLA-C*15:09', 'HLA-C*15:10', 'HLA-C*15:11', 'HLA-C*15:12', 'HLA-C*15:13',
'HLA-C*15:15', 'HLA-C*15:16', 'HLA-C*15:17',
'HLA-C*15:18', 'HLA-C*15:19', 'HLA-C*15:20', 'HLA-C*15:21', 'HLA-C*15:22', 'HLA-C*15:23', 'HLA-C*15:24',
'HLA-C*15:25', 'HLA-C*15:26', 'HLA-C*15:27',
'HLA-C*15:28', 'HLA-C*15:29', 'HLA-C*15:30', 'HLA-C*15:31', 'HLA-C*15:33', 'HLA-C*15:34', 'HLA-C*15:35',
'HLA-C*16:01', 'HLA-C*16:02', 'HLA-C*16:04',
'HLA-C*16:06', 'HLA-C*16:07', 'HLA-C*16:08', 'HLA-C*16:09', 'HLA-C*16:10', 'HLA-C*16:11', 'HLA-C*16:12',
'HLA-C*16:13', 'HLA-C*16:14', 'HLA-C*16:15',
'HLA-C*16:17', 'HLA-C*16:18', 'HLA-C*16:19', 'HLA-C*16:20', 'HLA-C*16:21', 'HLA-C*16:22', 'HLA-C*16:23',
'HLA-C*16:24', 'HLA-C*16:25', 'HLA-C*16:26',
'HLA-C*17:01', 'HLA-C*17:02', 'HLA-C*17:03', 'HLA-C*17:04', 'HLA-C*17:05', 'HLA-C*17:06', 'HLA-C*17:07',
'HLA-C*18:01', 'HLA-C*18:02', 'HLA-C*18:03',
'HLA-G*01:01', 'HLA-G*01:02', 'HLA-G*01:03', 'HLA-G*01:04', 'HLA-G*01:06', 'HLA-G*01:07', 'HLA-G*01:08',
'HLA-G*01:09', 'HLA-E*01:01',
'H2-Db', 'H2-Dd', 'H2-Kb', 'H2-Kd', 'H2-Kk', 'H2-Ld'])
__version = "1.1"
@property
def version(self):
"""The version of the predictor"""
return self.__version
@property
def command(self):
"""
Defines the commandline call for external tool
"""
return self.__command
@property
def supportedLength(self):
"""
A list of supported :class:`~epytope.Core.Peptide.Peptide` lengths
"""
return self.__supported_length
@property
def supportedAlleles(self):
"""
A list of supported :class:`~epytope.Core.Allele.Allele`
"""
return self.__supported_alleles
@property
def name(self):
"""The name of the predictor"""
return self.__name
def _represent(self, allele):
"""
Internal function transforming an allele object into its representative string
:param allele: The :class:`~epytope.Core.Allele.Allele` for which the internal predictor representation is
needed
:type alleles: :class:`~epytope.Core.Allele.Allele`
:return: str
"""
if isinstance(allele, MouseAllele):
return "H-2-%s%s%s" % (allele.locus, allele.supertype, allele.subtype)
else:
return "HLA-%s%s:%s" % (allele.locus, allele.supertype, allele.subtype)
def convert_alleles(self, alleles):
"""
Converts :class:`~epytope.Core.Allele.Allele` into the internal :class:`~epytope.Core.Allele.Allele` representation
of the predictor and returns a string representation
:param alleles: The :class:`~epytope.Core.Allele.Allele` for which the internal predictor representation is
needed
:type alleles: :class:`~epytope.Core.Allele.Allele`
:return: Returns a string representation of the input :class:`~epytope.Core.Allele.Allele`
:rtype: list(str)
"""
return [self._represent(a) for a in alleles]
def parse_external_result(self, file):
"""
Parses external results and returns the result containing the predictors string representation
of alleles and peptides.
:param str file: The file path or the external prediction results
:return: A dictionary containing the prediction results
:rtype: dict
"""
scores = defaultdict(defaultdict)
alleles = []
with open(file, "r") as f:
for row in f:
if row[0] in ["#", "-"] or row.strip() == "" or "pos" in row:
continue
else:
allele = row.split()[HLAIndex.PICKPOCKET_1_1].replace('*','')
pep = row.split()[PeptideIndex.PICKPOCKET_1_1]
score = float(row.split()[ScoreIndex.PICKPOCKET_1_1])
if allele not in alleles:
alleles.append(allele)
scores[allele][pep] = score
result = {allele: {"Score": list(scores.values())[j]} for j, allele in enumerate(alleles)}
return result
def get_external_version(self, path=None):
"""
Returns the external version of the tool by executing
>{command} --version
might be dependent on the method and has to be overwritten
therefore it is declared abstract to enforce the user to
overwrite the method. The function in the base class can be called
with super()
:param str path: Optional specification of executable path if deviant from :attr:`elf.__command`
:return: The external version of the tool or None if tool does not support versioning
:rtype: str
"""
return None
def prepare_input(self, input, file):
"""
Prepares input for external tools and writes them to file in the specific format
No return value!
:param: list(str) input: The :class:`~epytope.Core.Peptide.Peptide` sequences to write into _file
:param File file: File-handler to input file for external tool
"""
file.write("\n".join(input))
class NetCTLpan_1_1(AExternalEpitopePrediction):
"""
Interface for NetCTLpan 1.1.
.. note::
NetCTLpan - Pan-specific MHC class I epitope predictions Stranzl T., Larsen M. V., Lundegaard C., Nielsen M.
Immunogenetics. 2010 Apr 9. [Epub ahead of print]
"""
__name = "netctlpan"
__command = "netCTLpan -f {peptides} -a {alleles} {options} > {out}"
__supported_length = frozenset([8, 9, 10, 11])
__alleles = frozenset(
['HLA-A*01:01', 'HLA-A*01:02', 'HLA-A*01:03', 'HLA-A*01:06', 'HLA-A*01:07', 'HLA-A*01:08', 'HLA-A*01:09', 'HLA-A*01:10', 'HLA-A*01:12',
'HLA-A*01:13', 'HLA-A*01:14', 'HLA-A*01:17', 'HLA-A*01:19', 'HLA-A*01:20', 'HLA-A*01:21', 'HLA-A*01:23', 'HLA-A*01:24', 'HLA-A*01:25',
'HLA-A*01:26', 'HLA-A*01:28', 'HLA-A*01:29', 'HLA-A*01:30', 'HLA-A*01:32', 'HLA-A*01:33', 'HLA-A*01:35', 'HLA-A*01:36', 'HLA-A*01:37',
'HLA-A*01:38', 'HLA-A*01:39', 'HLA-A*01:40', 'HLA-A*01:41', 'HLA-A*01:42', 'HLA-A*01:43', 'HLA-A*01:44', 'HLA-A*01:45', 'HLA-A*01:46',
'HLA-A*01:47', 'HLA-A*01:48', 'HLA-A*01:49', 'HLA-A*01:50', 'HLA-A*01:51', 'HLA-A*01:54', 'HLA-A*01:55', 'HLA-A*01:58', 'HLA-A*01:59',
'HLA-A*01:60', 'HLA-A*01:61', 'HLA-A*01:62', 'HLA-A*01:63', 'HLA-A*01:64', 'HLA-A*01:65', 'HLA-A*01:66', 'HLA-A*02:01', 'HLA-A*02:02',
'HLA-A*02:03', 'HLA-A*02:04', 'HLA-A*02:05', 'HLA-A*02:06', 'HLA-A*02:07', 'HLA-A*02:08', 'HLA-A*02:09', 'HLA-A*02:10', 'HLA-A*02:101',
'HLA-A*02:102', 'HLA-A*02:103', 'HLA-A*02:104', 'HLA-A*02:105', 'HLA-A*02:106', 'HLA-A*02:107', 'HLA-A*02:108', 'HLA-A*02:109',
'HLA-A*02:11', 'HLA-A*02:110', 'HLA-A*02:111', 'HLA-A*02:112', 'HLA-A*02:114', 'HLA-A*02:115', 'HLA-A*02:116', 'HLA-A*02:117',
'HLA-A*02:118', 'HLA-A*02:119', 'HLA-A*02:12', 'HLA-A*02:120', 'HLA-A*02:121', 'HLA-A*02:122', 'HLA-A*02:123', 'HLA-A*02:124',
'HLA-A*02:126', 'HLA-A*02:127', 'HLA-A*02:128', 'HLA-A*02:129', 'HLA-A*02:13', 'HLA-A*02:130', 'HLA-A*02:131', 'HLA-A*02:132',
'HLA-A*02:133', 'HLA-A*02:134', 'HLA-A*02:135', 'HLA-A*02:136', 'HLA-A*02:137', 'HLA-A*02:138', 'HLA-A*02:139', 'HLA-A*02:14',
'HLA-A*02:140', 'HLA-A*02:141', 'HLA-A*02:142', 'HLA-A*02:143', 'HLA-A*02:144', 'HLA-A*02:145', 'HLA-A*02:146', 'HLA-A*02:147',
'HLA-A*02:148', 'HLA-A*02:149', 'HLA-A*02:150', 'HLA-A*02:151', 'HLA-A*02:152', 'HLA-A*02:153', 'HLA-A*02:154', 'HLA-A*02:155',
'HLA-A*02:156', 'HLA-A*02:157', 'HLA-A*02:158', 'HLA-A*02:159', 'HLA-A*02:16', 'HLA-A*02:160', 'HLA-A*02:161', 'HLA-A*02:162',
'HLA-A*02:163', 'HLA-A*02:164', 'HLA-A*02:165', 'HLA-A*02:166', 'HLA-A*02:167', 'HLA-A*02:168', 'HLA-A*02:169', 'HLA-A*02:17',
'HLA-A*02:170', 'HLA-A*02:171', 'HLA-A*02:172', 'HLA-A*02:173', 'HLA-A*02:174', 'HLA-A*02:175', 'HLA-A*02:176', 'HLA-A*02:177',
'HLA-A*02:178', 'HLA-A*02:179', 'HLA-A*02:18', 'HLA-A*02:180', 'HLA-A*02:181', 'HLA-A*02:182', 'HLA-A*02:183', 'HLA-A*02:184',
'HLA-A*02:185', 'HLA-A*02:186', 'HLA-A*02:187', 'HLA-A*02:188', 'HLA-A*02:189', 'HLA-A*02:19', 'HLA-A*02:190', 'HLA-A*02:191',
'HLA-A*02:192', 'HLA-A*02:193', 'HLA-A*02:194', 'HLA-A*02:195', 'HLA-A*02:196', 'HLA-A*02:197', 'HLA-A*02:198', 'HLA-A*02:199',
'HLA-A*02:20', 'HLA-A*02:200', 'HLA-A*02:201', 'HLA-A*02:202', 'HLA-A*02:203', 'HLA-A*02:204', 'HLA-A*02:205', 'HLA-A*02:206',
'HLA-A*02:207', 'HLA-A*02:208', 'HLA-A*02:209', 'HLA-A*02:21', 'HLA-A*02:210', 'HLA-A*02:211', 'HLA-A*02:212', 'HLA-A*02:213',
'HLA-A*02:214', 'HLA-A*02:215', 'HLA-A*02:216', 'HLA-A*02:217', 'HLA-A*02:218', 'HLA-A*02:219', 'HLA-A*02:22', 'HLA-A*02:220',
'HLA-A*02:221', 'HLA-A*02:224', 'HLA-A*02:228', 'HLA-A*02:229', 'HLA-A*02:230', 'HLA-A*02:231', 'HLA-A*02:232', 'HLA-A*02:233',
'HLA-A*02:234', 'HLA-A*02:235', 'HLA-A*02:236', 'HLA-A*02:237', 'HLA-A*02:238', 'HLA-A*02:239', 'HLA-A*02:24', 'HLA-A*02:240',
'HLA-A*02:241', 'HLA-A*02:242', 'HLA-A*02:243', 'HLA-A*02:244', 'HLA-A*02:245', 'HLA-A*02:246', 'HLA-A*02:247', 'HLA-A*02:248',
'HLA-A*02:249', 'HLA-A*02:25', 'HLA-A*02:251', 'HLA-A*02:252', 'HLA-A*02:253', 'HLA-A*02:254', 'HLA-A*02:255', 'HLA-A*02:256',
'HLA-A*02:257', 'HLA-A*02:258', 'HLA-A*02:259', 'HLA-A*02:26', 'HLA-A*02:260', 'HLA-A*02:261', 'HLA-A*02:262', 'HLA-A*02:263',
'HLA-A*02:264', 'HLA-A*02:265', 'HLA-A*02:266', 'HLA-A*02:27', 'HLA-A*02:28', 'HLA-A*02:29', 'HLA-A*02:30', 'HLA-A*02:31', 'HLA-A*02:33',
'HLA-A*02:34', 'HLA-A*02:35', 'HLA-A*02:36', 'HLA-A*02:37', 'HLA-A*02:38', 'HLA-A*02:39', 'HLA-A*02:40', 'HLA-A*02:41', 'HLA-A*02:42',
'HLA-A*02:44', 'HLA-A*02:45', 'HLA-A*02:46', 'HLA-A*02:47', 'HLA-A*02:48', 'HLA-A*02:49', 'HLA-A*02:50', 'HLA-A*02:51', 'HLA-A*02:52',
'HLA-A*02:54', 'HLA-A*02:55', 'HLA-A*02:56', 'HLA-A*02:57', 'HLA-A*02:58', 'HLA-A*02:59', 'HLA-A*02:60', 'HLA-A*02:61', 'HLA-A*02:62',
'HLA-A*02:63', 'HLA-A*02:64', 'HLA-A*02:65', 'HLA-A*02:66', 'HLA-A*02:67', 'HLA-A*02:68', 'HLA-A*02:69', 'HLA-A*02:70', 'HLA-A*02:71',
'HLA-A*02:72', 'HLA-A*02:73', 'HLA-A*02:74', 'HLA-A*02:75', 'HLA-A*02:76', 'HLA-A*02:77', 'HLA-A*02:78', 'HLA-A*02:79', 'HLA-A*02:80',
'HLA-A*02:81', 'HLA-A*02:84', 'HLA-A*02:85', 'HLA-A*02:86', 'HLA-A*02:87', 'HLA-A*02:89', 'HLA-A*02:90', 'HLA-A*02:91', 'HLA-A*02:92',
'HLA-A*02:93', 'HLA-A*02:95', 'HLA-A*02:96', 'HLA-A*02:97', 'HLA-A*02:99', 'HLA-A*03:01', 'HLA-A*03:02', 'HLA-A*03:04', 'HLA-A*03:05',
'HLA-A*03:06', 'HLA-A*03:07', 'HLA-A*03:08', 'HLA-A*03:09', 'HLA-A*03:10', 'HLA-A*03:12', 'HLA-A*03:13', 'HLA-A*03:14', 'HLA-A*03:15',
'HLA-A*03:16', 'HLA-A*03:17', 'HLA-A*03:18', 'HLA-A*03:19', 'HLA-A*03:20', 'HLA-A*03:22', 'HLA-A*03:23', 'HLA-A*03:24', 'HLA-A*03:25',
'HLA-A*03:26', 'HLA-A*03:27', 'HLA-A*03:28', 'HLA-A*03:29', 'HLA-A*03:30', 'HLA-A*03:31', 'HLA-A*03:32', 'HLA-A*03:33', 'HLA-A*03:34',
'HLA-A*03:35', 'HLA-A*03:37', 'HLA-A*03:38', 'HLA-A*03:39', 'HLA-A*03:40', 'HLA-A*03:41', 'HLA-A*03:42', 'HLA-A*03:43', 'HLA-A*03:44',
'HLA-A*03:45', 'HLA-A*03:46', 'HLA-A*03:47', 'HLA-A*03:48', 'HLA-A*03:49', 'HLA-A*03:50', 'HLA-A*03:51', 'HLA-A*03:52', 'HLA-A*03:53',
'HLA-A*03:54', 'HLA-A*03:55', 'HLA-A*03:56', 'HLA-A*03:57', 'HLA-A*03:58', 'HLA-A*03:59', 'HLA-A*03:60', 'HLA-A*03:61', 'HLA-A*03:62',
'HLA-A*03:63', 'HLA-A*03:64', 'HLA-A*03:65', 'HLA-A*03:66', 'HLA-A*03:67', 'HLA-A*03:70', 'HLA-A*03:71', 'HLA-A*03:72', 'HLA-A*03:73',
'HLA-A*03:74', 'HLA-A*03:75', 'HLA-A*03:76', 'HLA-A*03:77', 'HLA-A*03:78', 'HLA-A*03:79', 'HLA-A*03:80', 'HLA-A*03:81', 'HLA-A*03:82',
'HLA-A*11:01', 'HLA-A*11:02', 'HLA-A*11:03', 'HLA-A*11:04', 'HLA-A*11:05', 'HLA-A*11:06', 'HLA-A*11:07', 'HLA-A*11:08', 'HLA-A*11:09',
'HLA-A*11:10', 'HLA-A*11:11', 'HLA-A*11:12', 'HLA-A*11:13', 'HLA-A*11:14', 'HLA-A*11:15', 'HLA-A*11:16', 'HLA-A*11:17', 'HLA-A*11:18',
'HLA-A*11:19', 'HLA-A*11:20', 'HLA-A*11:22', 'HLA-A*11:23', 'HLA-A*11:24', 'HLA-A*11:25', 'HLA-A*11:26', 'HLA-A*11:27', 'HLA-A*11:29',
'HLA-A*11:30', 'HLA-A*11:31', 'HLA-A*11:32', 'HLA-A*11:33', 'HLA-A*11:34', 'HLA-A*11:35', 'HLA-A*11:36', 'HLA-A*11:37', 'HLA-A*11:38',
'HLA-A*11:39', 'HLA-A*11:40', 'HLA-A*11:41', 'HLA-A*11:42', 'HLA-A*11:43', 'HLA-A*11:44', 'HLA-A*11:45', 'HLA-A*11:46', 'HLA-A*11:47',
'HLA-A*11:48', 'HLA-A*11:49', 'HLA-A*11:51', 'HLA-A*11:53', 'HLA-A*11:54', 'HLA-A*11:55', 'HLA-A*11:56', 'HLA-A*11:57', 'HLA-A*11:58',
'HLA-A*11:59', 'HLA-A*11:60', 'HLA-A*11:61', 'HLA-A*11:62', 'HLA-A*11:63', 'HLA-A*11:64', 'HLA-A*23:01', 'HLA-A*23:02', 'HLA-A*23:03',
'HLA-A*23:04', 'HLA-A*23:05', 'HLA-A*23:06', 'HLA-A*23:09', 'HLA-A*23:10', 'HLA-A*23:12', 'HLA-A*23:13', 'HLA-A*23:14', 'HLA-A*23:15',
'HLA-A*23:16', 'HLA-A*23:17', 'HLA-A*23:18', 'HLA-A*23:20', 'HLA-A*23:21', 'HLA-A*23:22', 'HLA-A*23:23', 'HLA-A*23:24', 'HLA-A*23:25',
'HLA-A*23:26', 'HLA-A*24:02', 'HLA-A*24:03', 'HLA-A*24:04', 'HLA-A*24:05', 'HLA-A*24:06', 'HLA-A*24:07', 'HLA-A*24:08', 'HLA-A*24:10',
'HLA-A*24:100', 'HLA-A*24:101', 'HLA-A*24:102', 'HLA-A*24:103', 'HLA-A*24:104', 'HLA-A*24:105', 'HLA-A*24:106', 'HLA-A*24:107',
'HLA-A*24:108', 'HLA-A*24:109', 'HLA-A*24:110', 'HLA-A*24:111', 'HLA-A*24:112', 'HLA-A*24:113', 'HLA-A*24:114', 'HLA-A*24:115',
'HLA-A*24:116', 'HLA-A*24:117', 'HLA-A*24:118', 'HLA-A*24:119', 'HLA-A*24:120', 'HLA-A*24:121', 'HLA-A*24:122', 'HLA-A*24:123',
'HLA-A*24:124', 'HLA-A*24:125', 'HLA-A*24:126', 'HLA-A*24:127', 'HLA-A*24:128', 'HLA-A*24:129', 'HLA-A*24:13', 'HLA-A*24:130',
'HLA-A*24:131', 'HLA-A*24:133', 'HLA-A*24:134', 'HLA-A*24:135', 'HLA-A*24:136', 'HLA-A*24:137', 'HLA-A*24:138', 'HLA-A*24:139',
'HLA-A*24:14', 'HLA-A*24:140', 'HLA-A*24:141', 'HLA-A*24:142', 'HLA-A*24:143', 'HLA-A*24:144', 'HLA-A*24:15', 'HLA-A*24:17', 'HLA-A*24:18',
'HLA-A*24:19', 'HLA-A*24:20', 'HLA-A*24:21', 'HLA-A*24:22', 'HLA-A*24:23', 'HLA-A*24:24', 'HLA-A*24:25', 'HLA-A*24:26', 'HLA-A*24:27',
'HLA-A*24:28', 'HLA-A*24:29', 'HLA-A*24:30', 'HLA-A*24:31', 'HLA-A*24:32', 'HLA-A*24:33', 'HLA-A*24:34', 'HLA-A*24:35', 'HLA-A*24:37',
'HLA-A*24:38', 'HLA-A*24:39', 'HLA-A*24:41', 'HLA-A*24:42', 'HLA-A*24:43', 'HLA-A*24:44', 'HLA-A*24:46', 'HLA-A*24:47', 'HLA-A*24:49',
'HLA-A*24:50', 'HLA-A*24:51', 'HLA-A*24:52', 'HLA-A*24:53', 'HLA-A*24:54', 'HLA-A*24:55', 'HLA-A*24:56', 'HLA-A*24:57', 'HLA-A*24:58',
'HLA-A*24:59', 'HLA-A*24:61', 'HLA-A*24:62', 'HLA-A*24:63', 'HLA-A*24:64', 'HLA-A*24:66', 'HLA-A*24:67', 'HLA-A*24:68', 'HLA-A*24:69',
'HLA-A*24:70', 'HLA-A*24:71', 'HLA-A*24:72', 'HLA-A*24:73', 'HLA-A*24:74', 'HLA-A*24:75', 'HLA-A*24:76', 'HLA-A*24:77', 'HLA-A*24:78',
'HLA-A*24:79', 'HLA-A*24:80', 'HLA-A*24:81', 'HLA-A*24:82', 'HLA-A*24:85', 'HLA-A*24:87', 'HLA-A*24:88', 'HLA-A*24:89', 'HLA-A*24:91',
'HLA-A*24:92', 'HLA-A*24:93', 'HLA-A*24:94', 'HLA-A*24:95', 'HLA-A*24:96', 'HLA-A*24:97', 'HLA-A*24:98', 'HLA-A*24:99', 'HLA-A*25:01',
'HLA-A*25:02', 'HLA-A*25:03', 'HLA-A*25:04', 'HLA-A*25:05', 'HLA-A*25:06', 'HLA-A*25:07', 'HLA-A*25:08', 'HLA-A*25:09', 'HLA-A*25:10',
'HLA-A*25:11', 'HLA-A*25:13', 'HLA-A*26:01', 'HLA-A*26:02', 'HLA-A*26:03', 'HLA-A*26:04', 'HLA-A*26:05', 'HLA-A*26:06', 'HLA-A*26:07',
'HLA-A*26:08', 'HLA-A*26:09', 'HLA-A*26:10', 'HLA-A*26:12', 'HLA-A*26:13', 'HLA-A*26:14', 'HLA-A*26:15', 'HLA-A*26:16', 'HLA-A*26:17',
'HLA-A*26:18', 'HLA-A*26:19', 'HLA-A*26:20', 'HLA-A*26:21', 'HLA-A*26:22', 'HLA-A*26:23', 'HLA-A*26:24', 'HLA-A*26:26', 'HLA-A*26:27',
'HLA-A*26:28', 'HLA-A*26:29', 'HLA-A*26:30', 'HLA-A*26:31', 'HLA-A*26:32', 'HLA-A*26:33', 'HLA-A*26:34', 'HLA-A*26:35', 'HLA-A*26:36',
'HLA-A*26:37', 'HLA-A*26:38', 'HLA-A*26:39', 'HLA-A*26:40', 'HLA-A*26:41', 'HLA-A*26:42', 'HLA-A*26:43', 'HLA-A*26:45', 'HLA-A*26:46',
'HLA-A*26:47', 'HLA-A*26:48', 'HLA-A*26:49', 'HLA-A*26:50', 'HLA-A*29:01', 'HLA-A*29:02', 'HLA-A*29:03', 'HLA-A*29:04', 'HLA-A*29:05',
'HLA-A*29:06', 'HLA-A*29:07', 'HLA-A*29:09', 'HLA-A*29:10', 'HLA-A*29:11', 'HLA-A*29:12', 'HLA-A*29:13', 'HLA-A*29:14', 'HLA-A*29:15',
'HLA-A*29:16', 'HLA-A*29:17', 'HLA-A*29:18', 'HLA-A*29:19', 'HLA-A*29:20', 'HLA-A*29:21', 'HLA-A*29:22', 'HLA-A*30:01', 'HLA-A*30:02',
'HLA-A*30:03', 'HLA-A*30:04', 'HLA-A*30:06', 'HLA-A*30:07', 'HLA-A*30:08', 'HLA-A*30:09', 'HLA-A*30:10', 'HLA-A*30:11', 'HLA-A*30:12',
'HLA-A*30:13', 'HLA-A*30:15', 'HLA-A*30:16', 'HLA-A*30:17', 'HLA-A*30:18', 'HLA-A*30:19', 'HLA-A*30:20', 'HLA-A*30:22', 'HLA-A*30:23',
'HLA-A*30:24', 'HLA-A*30:25', 'HLA-A*30:26', 'HLA-A*30:28', 'HLA-A*30:29', 'HLA-A*30:30', 'HLA-A*30:31', 'HLA-A*30:32', 'HLA-A*30:33',
'HLA-A*30:34', 'HLA-A*30:35', 'HLA-A*30:36', 'HLA-A*30:37', 'HLA-A*30:38', 'HLA-A*30:39', 'HLA-A*30:40', 'HLA-A*30:41', 'HLA-A*31:01',
'HLA-A*31:02', 'HLA-A*31:03', 'HLA-A*31:04', 'HLA-A*31:05', 'HLA-A*31:06', 'HLA-A*31:07', 'HLA-A*31:08', 'HLA-A*31:09', 'HLA-A*31:10',
'HLA-A*31:11', 'HLA-A*31:12', 'HLA-A*31:13', 'HLA-A*31:15', 'HLA-A*31:16', 'HLA-A*31:17', 'HLA-A*31:18', 'HLA-A*31:19', 'HLA-A*31:20',
'HLA-A*31:21', 'HLA-A*31:22', 'HLA-A*31:23', 'HLA-A*31:24', 'HLA-A*31:25', 'HLA-A*31:26', 'HLA-A*31:27', 'HLA-A*31:28', 'HLA-A*31:29',
'HLA-A*31:30', 'HLA-A*31:31', 'HLA-A*31:32', 'HLA-A*31:33', 'HLA-A*31:34', 'HLA-A*31:35', 'HLA-A*31:36', 'HLA-A*31:37', 'HLA-A*32:01',
'HLA-A*32:02', 'HLA-A*32:03', 'HLA-A*32:04', 'HLA-A*32:05', 'HLA-A*32:06', 'HLA-A*32:07', 'HLA-A*32:08', 'HLA-A*32:09', 'HLA-A*32:10',
'HLA-A*32:12', 'HLA-A*32:13', 'HLA-A*32:14', 'HLA-A*32:15', 'HLA-A*32:16', 'HLA-A*32:17', 'HLA-A*32:18', 'HLA-A*32:20', 'HLA-A*32:21',
'HLA-A*32:22', 'HLA-A*32:23', 'HLA-A*32:24', 'HLA-A*32:25', 'HLA-A*33:01', 'HLA-A*33:03', 'HLA-A*33:04', 'HLA-A*33:05', 'HLA-A*33:06',
'HLA-A*33:07', 'HLA-A*33:08', 'HLA-A*33:09', 'HLA-A*33:10', 'HLA-A*33:11', 'HLA-A*33:12', 'HLA-A*33:13', 'HLA-A*33:14', 'HLA-A*33:15',
'HLA-A*33:16', 'HLA-A*33:17', 'HLA-A*33:18', 'HLA-A*33:19', 'HLA-A*33:20', 'HLA-A*33:21', 'HLA-A*33:22', 'HLA-A*33:23', 'HLA-A*33:24',
'HLA-A*33:25', 'HLA-A*33:26', 'HLA-A*33:27', 'HLA-A*33:28', 'HLA-A*33:29', 'HLA-A*33:30', 'HLA-A*33:31', 'HLA-A*34:01', 'HLA-A*34:02',
'HLA-A*34:03', 'HLA-A*34:04', 'HLA-A*34:05', 'HLA-A*34:06', 'HLA-A*34:07', 'HLA-A*34:08', 'HLA-A*36:01', 'HLA-A*36:02', 'HLA-A*36:03',
'HLA-A*36:04', 'HLA-A*36:05', 'HLA-A*43:01', 'HLA-A*66:01', 'HLA-A*66:02', 'HLA-A*66:03', 'HLA-A*66:04', 'HLA-A*66:05', 'HLA-A*66:06',
'HLA-A*66:07', 'HLA-A*66:08', 'HLA-A*66:09', 'HLA-A*66:10', 'HLA-A*66:11', 'HLA-A*66:12', 'HLA-A*66:13', 'HLA-A*66:14', 'HLA-A*66:15',
'HLA-A*68:01', 'HLA-A*68:02', 'HLA-A*68:03', 'HLA-A*68:04', 'HLA-A*68:05', 'HLA-A*68:06', 'HLA-A*68:07', 'HLA-A*68:08', 'HLA-A*68:09',
'HLA-A*68:10', 'HLA-A*68:12', 'HLA-A*68:13', 'HLA-A*68:14', 'HLA-A*68:15', 'HLA-A*68:16', 'HLA-A*68:17', 'HLA-A*68:19', 'HLA-A*68:20',
'HLA-A*68:21', 'HLA-A*68:22', 'HLA-A*68:23', 'HLA-A*68:24', 'HLA-A*68:25', 'HLA-A*68:26', 'HLA-A*68:27', 'HLA-A*68:28', 'HLA-A*68:29',
'HLA-A*68:30', 'HLA-A*68:31', 'HLA-A*68:32', 'HLA-A*68:33', 'HLA-A*68:34', 'HLA-A*68:35', 'HLA-A*68:36', 'HLA-A*68:37', 'HLA-A*68:38',
'HLA-A*68:39', 'HLA-A*68:40', 'HLA-A*68:41', 'HLA-A*68:42', 'HLA-A*68:43', 'HLA-A*68:44', 'HLA-A*68:45', 'HLA-A*68:46', 'HLA-A*68:47',
'HLA-A*68:48', 'HLA-A*68:50', 'HLA-A*68:51', 'HLA-A*68:52', 'HLA-A*68:53', 'HLA-A*68:54', 'HLA-A*69:01', 'HLA-A*74:01', 'HLA-A*74:02',
'HLA-A*74:03', 'HLA-A*74:04', 'HLA-A*74:05', 'HLA-A*74:06', 'HLA-A*74:07', 'HLA-A*74:08', 'HLA-A*74:09', 'HLA-A*74:10', 'HLA-A*74:11',
'HLA-A*74:13', 'HLA-A*80:01', 'HLA-A*80:02', 'HLA-B*07:02', 'HLA-B*07:03', 'HLA-B*07:04', 'HLA-B*07:05', 'HLA-B*07:06', 'HLA-B*07:07',
'HLA-B*07:08', 'HLA-B*07:09', 'HLA-B*07:10', 'HLA-B*07:100', 'HLA-B*07:101', 'HLA-B*07:102', 'HLA-B*07:103', 'HLA-B*07:104',
'HLA-B*07:105', 'HLA-B*07:106', 'HLA-B*07:107', 'HLA-B*07:108', 'HLA-B*07:109', 'HLA-B*07:11', 'HLA-B*07:110', 'HLA-B*07:112',
'HLA-B*07:113', 'HLA-B*07:114', 'HLA-B*07:115', 'HLA-B*07:12', 'HLA-B*07:13', 'HLA-B*07:14', 'HLA-B*07:15', 'HLA-B*07:16', 'HLA-B*07:17',
'HLA-B*07:18', 'HLA-B*07:19', 'HLA-B*07:20', 'HLA-B*07:21', 'HLA-B*07:22', 'HLA-B*07:23', 'HLA-B*07:24', 'HLA-B*07:25', 'HLA-B*07:26',
'HLA-B*07:27', 'HLA-B*07:28', 'HLA-B*07:29', 'HLA-B*07:30', 'HLA-B*07:31', 'HLA-B*07:32', 'HLA-B*07:33', 'HLA-B*07:34', 'HLA-B*07:35',
'HLA-B*07:36', 'HLA-B*07:37', 'HLA-B*07:38', 'HLA-B*07:39', 'HLA-B*07:40', 'HLA-B*07:41', 'HLA-B*07:42', 'HLA-B*07:43', 'HLA-B*07:44',
'HLA-B*07:45', 'HLA-B*07:46', 'HLA-B*07:47', 'HLA-B*07:48', 'HLA-B*07:50', 'HLA-B*07:51', 'HLA-B*07:52', 'HLA-B*07:53', 'HLA-B*07:54',
'HLA-B*07:55', 'HLA-B*07:56', 'HLA-B*07:57', 'HLA-B*07:58', 'HLA-B*07:59', 'HLA-B*07:60', 'HLA-B*07:61', 'HLA-B*07:62', 'HLA-B*07:63',
'HLA-B*07:64', 'HLA-B*07:65', 'HLA-B*07:66', 'HLA-B*07:68', 'HLA-B*07:69', 'HLA-B*07:70', 'HLA-B*07:71', 'HLA-B*07:72', 'HLA-B*07:73',
'HLA-B*07:74', 'HLA-B*07:75', 'HLA-B*07:76', 'HLA-B*07:77', 'HLA-B*07:78', 'HLA-B*07:79', 'HLA-B*07:80', 'HLA-B*07:81', 'HLA-B*07:82',
'HLA-B*07:83', 'HLA-B*07:84', 'HLA-B*07:85', 'HLA-B*07:86', 'HLA-B*07:87', 'HLA-B*07:88', 'HLA-B*07:89', 'HLA-B*07:90', 'HLA-B*07:91',
'HLA-B*07:92', 'HLA-B*07:93', 'HLA-B*07:94', 'HLA-B*07:95', 'HLA-B*07:96', 'HLA-B*07:97', 'HLA-B*07:98', 'HLA-B*07:99', 'HLA-B*08:01',
'HLA-B*08:02', 'HLA-B*08:03', 'HLA-B*08:04', 'HLA-B*08:05', 'HLA-B*08:07', 'HLA-B*08:09', 'HLA-B*08:10', 'HLA-B*08:11', 'HLA-B*08:12',
'HLA-B*08:13', 'HLA-B*08:14', 'HLA-B*08:15', 'HLA-B*08:16', 'HLA-B*08:17', 'HLA-B*08:18', 'HLA-B*08:20', 'HLA-B*08:21', 'HLA-B*08:22',
'HLA-B*08:23', 'HLA-B*08:24', 'HLA-B*08:25', 'HLA-B*08:26', 'HLA-B*08:27', 'HLA-B*08:28', 'HLA-B*08:29', 'HLA-B*08:31', 'HLA-B*08:32',
'HLA-B*08:33', 'HLA-B*08:34', 'HLA-B*08:35', 'HLA-B*08:36', 'HLA-B*08:37', 'HLA-B*08:38', 'HLA-B*08:39', 'HLA-B*08:40', 'HLA-B*08:41',
'HLA-B*08:42', 'HLA-B*08:43', 'HLA-B*08:44', 'HLA-B*08:45', 'HLA-B*08:46', 'HLA-B*08:47', 'HLA-B*08:48', 'HLA-B*08:49', 'HLA-B*08:50',
'HLA-B*08:51', 'HLA-B*08:52', 'HLA-B*08:53', 'HLA-B*08:54', 'HLA-B*08:55', 'HLA-B*08:56', 'HLA-B*08:57', 'HLA-B*08:58', 'HLA-B*08:59',
'HLA-B*08:60', 'HLA-B*08:61', 'HLA-B*08:62', 'HLA-B*13:01', 'HLA-B*13:02', 'HLA-B*13:03', 'HLA-B*13:04', 'HLA-B*13:06', 'HLA-B*13:09',
'HLA-B*13:10', 'HLA-B*13:11', 'HLA-B*13:12', 'HLA-B*13:13', 'HLA-B*13:14', 'HLA-B*13:15', 'HLA-B*13:16', 'HLA-B*13:17', 'HLA-B*13:18',
'HLA-B*13:19', 'HLA-B*13:20', 'HLA-B*13:21', 'HLA-B*13:22', 'HLA-B*13:23', 'HLA-B*13:25', 'HLA-B*13:26', 'HLA-B*13:27', 'HLA-B*13:28',
'HLA-B*13:29', 'HLA-B*13:30', 'HLA-B*13:31', 'HLA-B*13:32', 'HLA-B*13:33', 'HLA-B*13:34', 'HLA-B*13:35', 'HLA-B*13:36', 'HLA-B*13:37',
'HLA-B*13:38', 'HLA-B*13:39', 'HLA-B*14:01', 'HLA-B*14:02', 'HLA-B*14:03', 'HLA-B*14:04', 'HLA-B*14:05', 'HLA-B*14:06', 'HLA-B*14:08',
'HLA-B*14:09', 'HLA-B*14:10', 'HLA-B*14:11', 'HLA-B*14:12', 'HLA-B*14:13', 'HLA-B*14:14', 'HLA-B*14:15', 'HLA-B*14:16', 'HLA-B*14:17',
'HLA-B*14:18', 'HLA-B*15:01', 'HLA-B*15:02', 'HLA-B*15:03', 'HLA-B*15:04', 'HLA-B*15:05', 'HLA-B*15:06', 'HLA-B*15:07', 'HLA-B*15:08',
'HLA-B*15:09', 'HLA-B*15:10', 'HLA-B*15:101', 'HLA-B*15:102', 'HLA-B*15:103', 'HLA-B*15:104', 'HLA-B*15:105', 'HLA-B*15:106',
'HLA-B*15:107', 'HLA-B*15:108', 'HLA-B*15:109', 'HLA-B*15:11', 'HLA-B*15:110', 'HLA-B*15:112', 'HLA-B*15:113', 'HLA-B*15:114',
'HLA-B*15:115', 'HLA-B*15:116', 'HLA-B*15:117', 'HLA-B*15:118', 'HLA-B*15:119', 'HLA-B*15:12', 'HLA-B*15:120', 'HLA-B*15:121',
'HLA-B*15:122', 'HLA-B*15:123', 'HLA-B*15:124', 'HLA-B*15:125', 'HLA-B*15:126', 'HLA-B*15:127', 'HLA-B*15:128', 'HLA-B*15:129',
'HLA-B*15:13', 'HLA-B*15:131', 'HLA-B*15:132', 'HLA-B*15:133', 'HLA-B*15:134', 'HLA-B*15:135', 'HLA-B*15:136', 'HLA-B*15:137',
'HLA-B*15:138', 'HLA-B*15:139', 'HLA-B*15:14', 'HLA-B*15:140', 'HLA-B*15:141', 'HLA-B*15:142', 'HLA-B*15:143', 'HLA-B*15:144',
'HLA-B*15:145', 'HLA-B*15:146', 'HLA-B*15:147', 'HLA-B*15:148', 'HLA-B*15:15', 'HLA-B*15:150', 'HLA-B*15:151', 'HLA-B*15:152',
'HLA-B*15:153', 'HLA-B*15:154', 'HLA-B*15:155', 'HLA-B*15:156', 'HLA-B*15:157', 'HLA-B*15:158', 'HLA-B*15:159', 'HLA-B*15:16',
'HLA-B*15:160', 'HLA-B*15:161', 'HLA-B*15:162', 'HLA-B*15:163', 'HLA-B*15:164', 'HLA-B*15:165', 'HLA-B*15:166', 'HLA-B*15:167',
'HLA-B*15:168', 'HLA-B*15:169', 'HLA-B*15:17', 'HLA-B*15:170', 'HLA-B*15:171', 'HLA-B*15:172', 'HLA-B*15:173', 'HLA-B*15:174',
'HLA-B*15:175', 'HLA-B*15:176', 'HLA-B*15:177', 'HLA-B*15:178', 'HLA-B*15:179', 'HLA-B*15:18', 'HLA-B*15:180', 'HLA-B*15:183',
'HLA-B*15:184', 'HLA-B*15:185', 'HLA-B*15:186', 'HLA-B*15:187', 'HLA-B*15:188', 'HLA-B*15:189', 'HLA-B*15:19', 'HLA-B*15:191',
'HLA-B*15:192', 'HLA-B*15:193', 'HLA-B*15:194', 'HLA-B*15:195', 'HLA-B*15:196', 'HLA-B*15:197', 'HLA-B*15:198', 'HLA-B*15:199',
'HLA-B*15:20', 'HLA-B*15:200', 'HLA-B*15:201', 'HLA-B*15:202', 'HLA-B*15:21', 'HLA-B*15:23', 'HLA-B*15:24', 'HLA-B*15:25', 'HLA-B*15:27',
'HLA-B*15:28', 'HLA-B*15:29', 'HLA-B*15:30', 'HLA-B*15:31', 'HLA-B*15:32', 'HLA-B*15:33', 'HLA-B*15:34', 'HLA-B*15:35', 'HLA-B*15:36',
'HLA-B*15:37', 'HLA-B*15:38', 'HLA-B*15:39', 'HLA-B*15:40', 'HLA-B*15:42', 'HLA-B*15:43', 'HLA-B*15:44', 'HLA-B*15:45', 'HLA-B*15:46',
'HLA-B*15:47', 'HLA-B*15:48', 'HLA-B*15:49', 'HLA-B*15:50', 'HLA-B*15:51', 'HLA-B*15:52', 'HLA-B*15:53', 'HLA-B*15:54', 'HLA-B*15:55',
'HLA-B*15:56', 'HLA-B*15:57', 'HLA-B*15:58', 'HLA-B*15:60', 'HLA-B*15:61', 'HLA-B*15:62', 'HLA-B*15:63', 'HLA-B*15:64', 'HLA-B*15:65',
'HLA-B*15:66', 'HLA-B*15:67', 'HLA-B*15:68', 'HLA-B*15:69', 'HLA-B*15:70', 'HLA-B*15:71', 'HLA-B*15:72', 'HLA-B*15:73', 'HLA-B*15:74',
'HLA-B*15:75', 'HLA-B*15:76', 'HLA-B*15:77', 'HLA-B*15:78', 'HLA-B*15:80', 'HLA-B*15:81', 'HLA-B*15:82', 'HLA-B*15:83', 'HLA-B*15:84',
'HLA-B*15:85', 'HLA-B*15:86', 'HLA-B*15:87', 'HLA-B*15:88', 'HLA-B*15:89', 'HLA-B*15:90', 'HLA-B*15:91', 'HLA-B*15:92', 'HLA-B*15:93',
'HLA-B*15:95', 'HLA-B*15:96', 'HLA-B*15:97', 'HLA-B*15:98', 'HLA-B*15:99', 'HLA-B*18:01', 'HLA-B*18:02', 'HLA-B*18:03', 'HLA-B*18:04',
'HLA-B*18:05', 'HLA-B*18:06', 'HLA-B*18:07', 'HLA-B*18:08', 'HLA-B*18:09', 'HLA-B*18:10', 'HLA-B*18:11', 'HLA-B*18:12', 'HLA-B*18:13',
'HLA-B*18:14', 'HLA-B*18:15', 'HLA-B*18:18', 'HLA-B*18:19', 'HLA-B*18:20', 'HLA-B*18:21', 'HLA-B*18:22', 'HLA-B*18:24', 'HLA-B*18:25',
'HLA-B*18:26', 'HLA-B*18:27', 'HLA-B*18:28', 'HLA-B*18:29', 'HLA-B*18:30', 'HLA-B*18:31', 'HLA-B*18:32', 'HLA-B*18:33', 'HLA-B*18:34',
'HLA-B*18:35', 'HLA-B*18:36', 'HLA-B*18:37', 'HLA-B*18:38', 'HLA-B*18:39', 'HLA-B*18:40', 'HLA-B*18:41', 'HLA-B*18:42', 'HLA-B*18:43',
'HLA-B*18:44', 'HLA-B*18:45', 'HLA-B*18:46', 'HLA-B*18:47', 'HLA-B*18:48', 'HLA-B*18:49', 'HLA-B*18:50', 'HLA-B*27:01', 'HLA-B*27:02',
'HLA-B*27:03', 'HLA-B*27:04', 'HLA-B*27:05', 'HLA-B*27:06', 'HLA-B*27:07', 'HLA-B*27:08', 'HLA-B*27:09', 'HLA-B*27:10', 'HLA-B*27:11',
'HLA-B*27:12', 'HLA-B*27:13', 'HLA-B*27:14', 'HLA-B*27:15', 'HLA-B*27:16', 'HLA-B*27:17', 'HLA-B*27:18', 'HLA-B*27:19', 'HLA-B*27:20',
'HLA-B*27:21', 'HLA-B*27:23', 'HLA-B*27:24', 'HLA-B*27:25', 'HLA-B*27:26', 'HLA-B*27:27', 'HLA-B*27:28', 'HLA-B*27:29', 'HLA-B*27:30',
'HLA-B*27:31', 'HLA-B*27:32', 'HLA-B*27:33', 'HLA-B*27:34', 'HLA-B*27:35', 'HLA-B*27:36', 'HLA-B*27:37', 'HLA-B*27:38', 'HLA-B*27:39',
'HLA-B*27:40', 'HLA-B*27:41', 'HLA-B*27:42', 'HLA-B*27:43', 'HLA-B*27:44', 'HLA-B*27:45', 'HLA-B*27:46', 'HLA-B*27:47', 'HLA-B*27:48',
'HLA-B*27:49', 'HLA-B*27:50', 'HLA-B*27:51', 'HLA-B*27:52', 'HLA-B*27:53', 'HLA-B*27:54', 'HLA-B*27:55', 'HLA-B*27:56', 'HLA-B*27:57',
'HLA-B*27:58', 'HLA-B*27:60', 'HLA-B*27:61', 'HLA-B*27:62', 'HLA-B*27:63', 'HLA-B*27:67', 'HLA-B*27:68', 'HLA-B*27:69', 'HLA-B*35:01',
'HLA-B*35:02', 'HLA-B*35:03', 'HLA-B*35:04', 'HLA-B*35:05', 'HLA-B*35:06', 'HLA-B*35:07', 'HLA-B*35:08', 'HLA-B*35:09', 'HLA-B*35:10',
'HLA-B*35:100', 'HLA-B*35:101', 'HLA-B*35:102', 'HLA-B*35:103', 'HLA-B*35:104', 'HLA-B*35:105', 'HLA-B*35:106', 'HLA-B*35:107',
'HLA-B*35:108', 'HLA-B*35:109', 'HLA-B*35:11', 'HLA-B*35:110', 'HLA-B*35:111', 'HLA-B*35:112', 'HLA-B*35:113', 'HLA-B*35:114',
'HLA-B*35:115', 'HLA-B*35:116', 'HLA-B*35:117', 'HLA-B*35:118', 'HLA-B*35:119', 'HLA-B*35:12', 'HLA-B*35:120', 'HLA-B*35:121',
'HLA-B*35:122', 'HLA-B*35:123', 'HLA-B*35:124', 'HLA-B*35:125', 'HLA-B*35:126', 'HLA-B*35:127', 'HLA-B*35:128', 'HLA-B*35:13',
'HLA-B*35:131', 'HLA-B*35:132', 'HLA-B*35:133', 'HLA-B*35:135', 'HLA-B*35:136', 'HLA-B*35:137', 'HLA-B*35:138', 'HLA-B*35:139',
'HLA-B*35:14', 'HLA-B*35:140', 'HLA-B*35:141', 'HLA-B*35:142', 'HLA-B*35:143', 'HLA-B*35:144', 'HLA-B*35:15', 'HLA-B*35:16', 'HLA-B*35:17',
'HLA-B*35:18', 'HLA-B*35:19', 'HLA-B*35:20', 'HLA-B*35:21', 'HLA-B*35:22', 'HLA-B*35:23', 'HLA-B*35:24', 'HLA-B*35:25', 'HLA-B*35:26',
'HLA-B*35:27', 'HLA-B*35:28', 'HLA-B*35:29', 'HLA-B*35:30', 'HLA-B*35:31', 'HLA-B*35:32', 'HLA-B*35:33', 'HLA-B*35:34', 'HLA-B*35:35',
'HLA-B*35:36', 'HLA-B*35:37', 'HLA-B*35:38', 'HLA-B*35:39', 'HLA-B*35:41', 'HLA-B*35:42', 'HLA-B*35:43', 'HLA-B*35:44', 'HLA-B*35:45',
'HLA-B*35:46', 'HLA-B*35:47', 'HLA-B*35:48', 'HLA-B*35:49', 'HLA-B*35:50', 'HLA-B*35:51', 'HLA-B*35:52', 'HLA-B*35:54', 'HLA-B*35:55',
'HLA-B*35:56', 'HLA-B*35:57', 'HLA-B*35:58', 'HLA-B*35:59', 'HLA-B*35:60', 'HLA-B*35:61', 'HLA-B*35:62', 'HLA-B*35:63', 'HLA-B*35:64',
'HLA-B*35:66', 'HLA-B*35:67', 'HLA-B*35:68', 'HLA-B*35:69', 'HLA-B*35:70', 'HLA-B*35:71', 'HLA-B*35:72', 'HLA-B*35:74', 'HLA-B*35:75',
'HLA-B*35:76', 'HLA-B*35:77', 'HLA-B*35:78', 'HLA-B*35:79', 'HLA-B*35:80', 'HLA-B*35:81', 'HLA-B*35:82', 'HLA-B*35:83', 'HLA-B*35:84',
'HLA-B*35:85', 'HLA-B*35:86', 'HLA-B*35:87', 'HLA-B*35:88', 'HLA-B*35:89', 'HLA-B*35:90', 'HLA-B*35:91', 'HLA-B*35:92', 'HLA-B*35:93',
'HLA-B*35:94', 'HLA-B*35:95', 'HLA-B*35:96', 'HLA-B*35:97', 'HLA-B*35:98', 'HLA-B*35:99', 'HLA-B*37:01', 'HLA-B*37:02', 'HLA-B*37:04',
'HLA-B*37:05', 'HLA-B*37:06', 'HLA-B*37:07', 'HLA-B*37:08', 'HLA-B*37:09', 'HLA-B*37:10', 'HLA-B*37:11', 'HLA-B*37:12', 'HLA-B*37:13',
'HLA-B*37:14', 'HLA-B*37:15', 'HLA-B*37:17', 'HLA-B*37:18', 'HLA-B*37:19', 'HLA-B*37:20', 'HLA-B*37:21', 'HLA-B*37:22', 'HLA-B*37:23',
'HLA-B*38:01', 'HLA-B*38:02', 'HLA-B*38:03', 'HLA-B*38:04', 'HLA-B*38:05', 'HLA-B*38:06', 'HLA-B*38:07', 'HLA-B*38:08', 'HLA-B*38:09',
'HLA-B*38:10', 'HLA-B*38:11', 'HLA-B*38:12', 'HLA-B*38:13', 'HLA-B*38:14', 'HLA-B*38:15', 'HLA-B*38:16', 'HLA-B*38:17', 'HLA-B*38:18',
'HLA-B*38:19', 'HLA-B*38:20', 'HLA-B*38:21', 'HLA-B*38:22', 'HLA-B*38:23', 'HLA-B*39:01', 'HLA-B*39:02', 'HLA-B*39:03', 'HLA-B*39:04',
'HLA-B*39:05', 'HLA-B*39:06', 'HLA-B*39:07', 'HLA-B*39:08', 'HLA-B*39:09', 'HLA-B*39:10', 'HLA-B*39:11', 'HLA-B*39:12', 'HLA-B*39:13',
'HLA-B*39:14', 'HLA-B*39:15', 'HLA-B*39:16', 'HLA-B*39:17', 'HLA-B*39:18', 'HLA-B*39:19', 'HLA-B*39:20', 'HLA-B*39:22', 'HLA-B*39:23',
'HLA-B*39:24', 'HLA-B*39:26', 'HLA-B*39:27', 'HLA-B*39:28', 'HLA-B*39:29', 'HLA-B*39:30', 'HLA-B*39:31', 'HLA-B*39:32', 'HLA-B*39:33',
'HLA-B*39:34', 'HLA-B*39:35', 'HLA-B*39:36', 'HLA-B*39:37', 'HLA-B*39:39', 'HLA-B*39:41', 'HLA-B*39:42', 'HLA-B*39:43', 'HLA-B*39:44',
'HLA-B*39:45', 'HLA-B*39:46', 'HLA-B*39:47', 'HLA-B*39:48', 'HLA-B*39:49', 'HLA-B*39:50', 'HLA-B*39:51', 'HLA-B*39:52', 'HLA-B*39:53',
'HLA-B*39:54', 'HLA-B*39:55', 'HLA-B*39:56', 'HLA-B*39:57', 'HLA-B*39:58', 'HLA-B*39:59', 'HLA-B*39:60', 'HLA-B*40:01', 'HLA-B*40:02',
'HLA-B*40:03', 'HLA-B*40:04', 'HLA-B*40:05', 'HLA-B*40:06', 'HLA-B*40:07', 'HLA-B*40:08', 'HLA-B*40:09', 'HLA-B*40:10', 'HLA-B*40:100',
'HLA-B*40:101', 'HLA-B*40:102', 'HLA-B*40:103', 'HLA-B*40:104', 'HLA-B*40:105', 'HLA-B*40:106', 'HLA-B*40:107', 'HLA-B*40:108',
'HLA-B*40:109', 'HLA-B*40:11', 'HLA-B*40:110', 'HLA-B*40:111', 'HLA-B*40:112', 'HLA-B*40:113', 'HLA-B*40:114', 'HLA-B*40:115',
'HLA-B*40:116', 'HLA-B*40:117', 'HLA-B*40:119', 'HLA-B*40:12', 'HLA-B*40:120', 'HLA-B*40:121', 'HLA-B*40:122', 'HLA-B*40:123',
'HLA-B*40:124', 'HLA-B*40:125', 'HLA-B*40:126', 'HLA-B*40:127', 'HLA-B*40:128', 'HLA-B*40:129', 'HLA-B*40:13', 'HLA-B*40:130',
'HLA-B*40:131', 'HLA-B*40:132', 'HLA-B*40:134', 'HLA-B*40:135', 'HLA-B*40:136', 'HLA-B*40:137', 'HLA-B*40:138', 'HLA-B*40:139',
'HLA-B*40:14', 'HLA-B*40:140', 'HLA-B*40:141', 'HLA-B*40:143', 'HLA-B*40:145', 'HLA-B*40:146', 'HLA-B*40:147', 'HLA-B*40:15',
'HLA-B*40:16', 'HLA-B*40:18', 'HLA-B*40:19', 'HLA-B*40:20', 'HLA-B*40:21', 'HLA-B*40:23', 'HLA-B*40:24', 'HLA-B*40:25', 'HLA-B*40:26',
'HLA-B*40:27', 'HLA-B*40:28', 'HLA-B*40:29', 'HLA-B*40:30', 'HLA-B*40:31', 'HLA-B*40:32', 'HLA-B*40:33', 'HLA-B*40:34', 'HLA-B*40:35',
'HLA-B*40:36', 'HLA-B*40:37', 'HLA-B*40:38', 'HLA-B*40:39', 'HLA-B*40:40', 'HLA-B*40:42', 'HLA-B*40:43', 'HLA-B*40:44', 'HLA-B*40:45',
'HLA-B*40:46', 'HLA-B*40:47', 'HLA-B*40:48', 'HLA-B*40:49', 'HLA-B*40:50', 'HLA-B*40:51', 'HLA-B*40:52', 'HLA-B*40:53', 'HLA-B*40:54',
'HLA-B*40:55', 'HLA-B*40:56', 'HLA-B*40:57', 'HLA-B*40:58', 'HLA-B*40:59', 'HLA-B*40:60', 'HLA-B*40:61', 'HLA-B*40:62', 'HLA-B*40:63',
'HLA-B*40:64', 'HLA-B*40:65', 'HLA-B*40:66', 'HLA-B*40:67', 'HLA-B*40:68', 'HLA-B*40:69', 'HLA-B*40:70', 'HLA-B*40:71', 'HLA-B*40:72',
'HLA-B*40:73', 'HLA-B*40:74', 'HLA-B*40:75', 'HLA-B*40:76', 'HLA-B*40:77', 'HLA-B*40:78', 'HLA-B*40:79', 'HLA-B*40:80', 'HLA-B*40:81',
'HLA-B*40:82', 'HLA-B*40:83', 'HLA-B*40:84', 'HLA-B*40:85', 'HLA-B*40:86', 'HLA-B*40:87', 'HLA-B*40:88', 'HLA-B*40:89', 'HLA-B*40:90',
'HLA-B*40:91', 'HLA-B*40:92', 'HLA-B*40:93', 'HLA-B*40:94', 'HLA-B*40:95', 'HLA-B*40:96', 'HLA-B*40:97', 'HLA-B*40:98', 'HLA-B*40:99',
'HLA-B*41:01', 'HLA-B*41:02', 'HLA-B*41:03', 'HLA-B*41:04', 'HLA-B*41:05', 'HLA-B*41:06', 'HLA-B*41:07', 'HLA-B*41:08', 'HLA-B*41:09',
'HLA-B*41:10', 'HLA-B*41:11', 'HLA-B*41:12', 'HLA-B*42:01', 'HLA-B*42:02', 'HLA-B*42:04', 'HLA-B*42:05', 'HLA-B*42:06', 'HLA-B*42:07',
'HLA-B*42:08', 'HLA-B*42:09', 'HLA-B*42:10', 'HLA-B*42:11', 'HLA-B*42:12', 'HLA-B*42:13', 'HLA-B*42:14', 'HLA-B*44:02', 'HLA-B*44:03',
'HLA-B*44:04', 'HLA-B*44:05', 'HLA-B*44:06', 'HLA-B*44:07', 'HLA-B*44:08', 'HLA-B*44:09', 'HLA-B*44:10', 'HLA-B*44:100', 'HLA-B*44:101',
'HLA-B*44:102', 'HLA-B*44:103', 'HLA-B*44:104', 'HLA-B*44:105', 'HLA-B*44:106', 'HLA-B*44:107', 'HLA-B*44:109', 'HLA-B*44:11',
'HLA-B*44:110', 'HLA-B*44:12', 'HLA-B*44:13', 'HLA-B*44:14', 'HLA-B*44:15', 'HLA-B*44:16', 'HLA-B*44:17', 'HLA-B*44:18', 'HLA-B*44:20',
'HLA-B*44:21', 'HLA-B*44:22', 'HLA-B*44:24', 'HLA-B*44:25', 'HLA-B*44:26', 'HLA-B*44:27', 'HLA-B*44:28', 'HLA-B*44:29', 'HLA-B*44:30',
'HLA-B*44:31', 'HLA-B*44:32', 'HLA-B*44:33', 'HLA-B*44:34', 'HLA-B*44:35', 'HLA-B*44:36', 'HLA-B*44:37', 'HLA-B*44:38', 'HLA-B*44:39',
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'HLA-C*07:103', 'HLA-C*07:105', 'HLA-C*07:106', 'HLA-C*07:107', 'HLA-C*07:108', 'HLA-C*07:109', 'HLA-C*07:11', 'HLA-C*07:110',
'HLA-C*07:111', 'HLA-C*07:112', 'HLA-C*07:113', 'HLA-C*07:114', 'HLA-C*07:115', 'HLA-C*07:116', 'HLA-C*07:117', 'HLA-C*07:118',
'HLA-C*07:119', 'HLA-C*07:12', 'HLA-C*07:120', 'HLA-C*07:122', 'HLA-C*07:123', 'HLA-C*07:124', 'HLA-C*07:125', 'HLA-C*07:126',
'HLA-C*07:127', 'HLA-C*07:128', 'HLA-C*07:129', 'HLA-C*07:13', 'HLA-C*07:130', 'HLA-C*07:131', 'HLA-C*07:132', 'HLA-C*07:133',
'HLA-C*07:134', 'HLA-C*07:135', 'HLA-C*07:136', 'HLA-C*07:137', 'HLA-C*07:138', 'HLA-C*07:139', 'HLA-C*07:14', 'HLA-C*07:140',
'HLA-C*07:141', 'HLA-C*07:142', 'HLA-C*07:143', 'HLA-C*07:144', 'HLA-C*07:145', 'HLA-C*07:146', 'HLA-C*07:147', 'HLA-C*07:148',
'HLA-C*07:149', 'HLA-C*07:15', 'HLA-C*07:16', 'HLA-C*07:17', 'HLA-C*07:18', 'HLA-C*07:19', 'HLA-C*07:20', 'HLA-C*07:21', 'HLA-C*07:22',
'HLA-C*07:23', 'HLA-C*07:24', 'HLA-C*07:25', 'HLA-C*07:26', 'HLA-C*07:27', 'HLA-C*07:28', 'HLA-C*07:29', 'HLA-C*07:30', 'HLA-C*07:31',
'HLA-C*07:35', 'HLA-C*07:36', 'HLA-C*07:37', 'HLA-C*07:38', 'HLA-C*07:39', 'HLA-C*07:40', 'HLA-C*07:41', 'HLA-C*07:42', 'HLA-C*07:43',
'HLA-C*07:44', 'HLA-C*07:45', 'HLA-C*07:46', 'HLA-C*07:47', 'HLA-C*07:48', 'HLA-C*07:49', 'HLA-C*07:50', 'HLA-C*07:51', 'HLA-C*07:52',
'HLA-C*07:53', 'HLA-C*07:54', 'HLA-C*07:56', 'HLA-C*07:57', 'HLA-C*07:58', 'HLA-C*07:59', 'HLA-C*07:60', 'HLA-C*07:62', 'HLA-C*07:63',
'HLA-C*07:64', 'HLA-C*07:65', 'HLA-C*07:66', 'HLA-C*07:67', 'HLA-C*07:68', 'HLA-C*07:69', 'HLA-C*07:70', 'HLA-C*07:71', 'HLA-C*07:72',
'HLA-C*07:73', 'HLA-C*07:74', 'HLA-C*07:75', 'HLA-C*07:76', 'HLA-C*07:77', 'HLA-C*07:78', 'HLA-C*07:79', 'HLA-C*07:80', 'HLA-C*07:81',
'HLA-C*07:82', 'HLA-C*07:83', 'HLA-C*07:84', 'HLA-C*07:85', 'HLA-C*07:86', 'HLA-C*07:87', 'HLA-C*07:88', 'HLA-C*07:89', 'HLA-C*07:90',
'HLA-C*07:91', 'HLA-C*07:92', 'HLA-C*07:93', 'HLA-C*07:94', 'HLA-C*07:95', 'HLA-C*07:96', 'HLA-C*07:97', 'HLA-C*07:99', 'HLA-C*08:01',
'HLA-C*08:02', 'HLA-C*08:03', 'HLA-C*08:04', 'HLA-C*08:05', 'HLA-C*08:06', 'HLA-C*08:07', 'HLA-C*08:08', 'HLA-C*08:09', 'HLA-C*08:10',
'HLA-C*08:11', 'HLA-C*08:12', 'HLA-C*08:13', 'HLA-C*08:14', 'HLA-C*08:15', 'HLA-C*08:16', 'HLA-C*08:17', 'HLA-C*08:18', 'HLA-C*08:19',
'HLA-C*08:20', 'HLA-C*08:21', 'HLA-C*08:22', 'HLA-C*08:23', 'HLA-C*08:24', 'HLA-C*08:25', 'HLA-C*08:27', 'HLA-C*08:28', 'HLA-C*08:29',
'HLA-C*08:30', 'HLA-C*08:31', 'HLA-C*08:32', 'HLA-C*08:33', 'HLA-C*08:34', 'HLA-C*08:35', 'HLA-C*12:02', 'HLA-C*12:03', 'HLA-C*12:04',
'HLA-C*12:05', 'HLA-C*12:06', 'HLA-C*12:07', 'HLA-C*12:08', 'HLA-C*12:09', 'HLA-C*12:10', 'HLA-C*12:11', 'HLA-C*12:12', 'HLA-C*12:13',
'HLA-C*12:14', 'HLA-C*12:15', 'HLA-C*12:16', 'HLA-C*12:17', 'HLA-C*12:18', 'HLA-C*12:19', 'HLA-C*12:20', 'HLA-C*12:21', 'HLA-C*12:22',
'HLA-C*12:23', 'HLA-C*12:24', 'HLA-C*12:25', 'HLA-C*12:26', 'HLA-C*12:27', 'HLA-C*12:28', 'HLA-C*12:29', 'HLA-C*12:30', 'HLA-C*12:31',
'HLA-C*12:32', 'HLA-C*12:33', 'HLA-C*12:34', 'HLA-C*12:35', 'HLA-C*12:36', 'HLA-C*12:37', 'HLA-C*12:38', 'HLA-C*12:40', 'HLA-C*12:41',
'HLA-C*12:43', 'HLA-C*12:44', 'HLA-C*14:02', 'HLA-C*14:03', 'HLA-C*14:04', 'HLA-C*14:05', 'HLA-C*14:06', 'HLA-C*14:08', 'HLA-C*14:09',
'HLA-C*14:10', 'HLA-C*14:11', 'HLA-C*14:12', 'HLA-C*14:13', 'HLA-C*14:14', 'HLA-C*14:15', 'HLA-C*14:16', 'HLA-C*14:17', 'HLA-C*14:18',
'HLA-C*14:19', 'HLA-C*14:20', 'HLA-C*15:02', 'HLA-C*15:03', 'HLA-C*15:04', 'HLA-C*15:05', 'HLA-C*15:06', 'HLA-C*15:07', 'HLA-C*15:08',
'HLA-C*15:09', 'HLA-C*15:10', 'HLA-C*15:11', 'HLA-C*15:12', 'HLA-C*15:13', 'HLA-C*15:15', 'HLA-C*15:16', 'HLA-C*15:17', 'HLA-C*15:18',
'HLA-C*15:19', 'HLA-C*15:20', 'HLA-C*15:21', 'HLA-C*15:22', 'HLA-C*15:23', 'HLA-C*15:24', 'HLA-C*15:25', 'HLA-C*15:26', 'HLA-C*15:27',
'HLA-C*15:28', 'HLA-C*15:29', 'HLA-C*15:30', 'HLA-C*15:31', 'HLA-C*15:33', 'HLA-C*15:34', 'HLA-C*15:35', 'HLA-C*16:01', 'HLA-C*16:02',
'HLA-C*16:04', 'HLA-C*16:06', 'HLA-C*16:07', 'HLA-C*16:08', 'HLA-C*16:09', 'HLA-C*16:10', 'HLA-C*16:11', 'HLA-C*16:12', 'HLA-C*16:13',
'HLA-C*16:14', 'HLA-C*16:15', 'HLA-C*16:17', 'HLA-C*16:18', 'HLA-C*16:19', 'HLA-C*16:20', 'HLA-C*16:21', 'HLA-C*16:22', 'HLA-C*16:23',
'HLA-C*16:24', 'HLA-C*16:25', 'HLA-C*16:26', 'HLA-C*17:01', 'HLA-C*17:02', 'HLA-C*17:03', 'HLA-C*17:04', 'HLA-C*17:05', 'HLA-C*17:06',
'HLA-C*17:07', 'HLA-C*18:01', 'HLA-C*18:02', 'HLA-C*18:03', 'HLA-E*01:01', 'HLA-G*01:01', 'HLA-G*01:02', 'HLA-G*01:03', 'HLA-G*01:04',
'HLA-G*01:06', 'HLA-G*01:07', 'HLA-G*01:08', 'HLA-G*01:09',
'H2-Db', 'H2-Dd', 'H2-Kb', 'H2-Kd', 'H2-Kk', 'H2-Ld'])
__version = "1.1"
@property
def version(self):
"""The version of the predictor"""
return self.__version
def _represent(self, allele):
"""
Internal function transforming an allele object into its representative string
:param allele: The :class:`~epytope.Core.Allele.Allele` for which the internal predictor representation is
needed
:type alleles: :class:`~epytope.Core.Allele.Allele`
:return: str
"""
if isinstance(allele, MouseAllele):
return "H-2-%s%s%s" % (allele.locus, allele.supertype, allele.subtype)
else:
return "HLA-%s%s:%s" % (allele.locus, allele.supertype, allele.subtype)
def convert_alleles(self, alleles):
"""
Converts :class:`~epytope.Core.Allele.Allele` into the internal :class:`~epytope.Core.Allele.Allele` representation
of the predictor and returns a string representation
:param alleles: The :class:`~epytope.Core.Allele.Allele` for which the internal predictor representation is
needed
:type alleles: :class:`~epytope.Core.Allele.Allele`
:return: Returns a string representation of the input :class:`~epytope.Core.Allele.Allele`
:rtype: list(str)
"""
return [self._represent(a) for a in alleles]
@property
def supportedAlleles(self):
"""
A list of supported :class:`~epytope.Core.Allele.Allele`
"""
return self.__alleles
@property
def name(self):
"""The name of the predictor"""
return self.__name
@property
def command(self):
"""
Defines the commandline call for external tool
"""
return self.__command
@property
def supportedLength(self):
"""
A list of supported :class:`~epytope.Core.Peptide.Peptide` lengths
"""
return self.__supported_length
def parse_external_result(self, file):
"""
Parses external results and returns the result containing the predictors string representation
of alleles and peptides.
:param str file: The file path or the external prediction results
:return: A dictionary containing the prediction results
:rtype: dict
"""
scores = defaultdict(defaultdict)
alleles = []
with open(file, "r") as f:
for l in f:
if l.startswith("#") or l.startswith("-") or l.strip() == "":
continue
row = l.strip().split()
if not row[0].isdigit():
continue
epitope = row[PeptideIndex.NETCTLPAN_1_1]
# Allele input representation differs from output representation. Needs to be in input representation to parse the output properly
allele = row[HLAIndex.NETCTLPAN_1_1].replace('*','')
comb_score = float(row[ScoreIndex.NETCTLPAN_1_1])
if allele not in alleles:
alleles.append(allele)
scores[allele][epitope] = comb_score
result = {allele: {"Score": list(scores.values())[j]} for j, allele in enumerate(alleles)}
return result
def get_external_version(self, path=None):
"""
Returns the external version of the tool by executing
>{command} --version
might be dependent on the method and has to be overwritten
therefore it is declared abstract to enforce the user to
overwrite the method. The function in the base class can be called
with super()
:param str path: Optional specification of executable path if deviant from :attr:`self.__command`
:return: The external version of the tool or None if tool does not support versioning
:rtype: str
"""
return None
def prepare_input(self, input, file):
"""
Prepares input for external tools and writes them to file in the specific format
No return value!
:param: list(str) input: The :class:`~epytope.Core.Peptide.Peptide` sequences to write into file
:param File file: File-handler to input file for external tool
"""
file.write("\n".join(">pepe_%i\n%s" % (i, p) for i, p in enumerate(input)))
class PeptideIndex(IntEnum):
"""
Specifies the index of the peptide sequence from the parsed output format
"""
NETMHC_3_0 = 2
NETMHC_3_4 = 2
NETMHC_4_0 = 1
NETMHCPAN_2_4 = 1
NETMHCPAN_2_8 = 1
NETMHCPAN_3_0 = 1
NETMHCPAN_4_0 = 1
NETMHCSTABPAN_1_0 = 1
NETMHCII_2_2 = 2
NETMHCII_2_3 = 2
NETMHCIIPAN_3_0 = 1
NETMHCIIPAN_3_1 = 1
NETMHCIIPAN_4_0 = 1
PICKPOCKET_1_1 = 2
NETCTLPAN_1_1 = 3
class ScoreIndex(IntEnum):
"""
Specifies the score index from the parsed output format
"""
NETMHC_3_0 = 2
NETMHC_3_4 = 3
NETMHCPAN_2_4 = 3
NETMHCPAN_2_8 = 3
NETMHCPAN_3_0 = 4
NETMHCPAN_4_0 = 5
NETMHCSTABPAN_1_0 = 6
NETMHCII_2_2 = 4
NETMHCII_2_3 = 5
NETMHCIIPAN_3_0 = 3
NETMHCIIPAN_3_1 = 3
NETMHCIIPAN_4_0 = 3
PICKPOCKET_1_1 = 4
NETCTLPAN_1_1 = 7
class RankIndex(IntEnum):
"""
Specifies the rank index from the parsed output format if there is a rank score provided by the predictor
"""
NETMHCPAN_2_8 = 5
NETMHCPAN_3_0 = 6
NETMHCPAN_4_0 = 7
NETMHCSTABPAN_1_0 = 5
NETMHCII_2_3 = 7
NETMHCIIPAN_3_0 = 5
NETMHCIIPAN_3_1 = 5
NETMHCIIPAN_4_0 = 5
class Offset(IntEnum):
"""
Specifies the offset of columns for multiple predicted HLA-alleles in the given predictors in order to
correctly access score and rank per HLA-allele
"""
NETMHC_4_0 = 3
NETMHCPAN_2_8 = 3
NETMHCPAN_3_0 = 4
NETMHCPAN_4_0 = 5
NETMHCSTABPAN_1_0_W_SCORE = 8
NETMHCSTABPAN_1_0_WO_SCORE = 3
NETMHCIIPAN_3_0 = 3
NETMHCIIPAN_3_1 = 3
NETMHCIIPAN_4_0 = 1
class HLAIndex(IntEnum):
"""
Specifies the HLA-allele index in the parsed output of the predictor
"""
NETMHCII_2_2 = 0
NETMHCII_2_3 = 0
PICKPOCKET_1_1 = 1
NETCTLPAN_1_1 = 2
|
nilq/baby-python
|
python
|
from django.conf import settings
def init_permission(request, obj):
# 保存登录状态
request.session['is_login'] = True
# 查询出当前用户的权限
# 去除权限为空的权限
ret = obj.roles.filter(permissions__url__isnull=False).values(
'permissions__id',
'permissions__url',
'permissions__title',
'permissions__name',
'permissions__menu_id',
'permissions__menu__title',
'permissions__menu__icon',
'permissions__menu__weight',
'permissions__parent_id',
'permissions__parent__name',
).distinct()
# 权限字典
permission_dict = {}
# 菜单
menu_dict = {}
for i in ret:
# 权限
permission_dict[i['permissions__name']] = {
"url": i["permissions__url"],
"title": i["permissions__title"],
'id': i['permissions__id'],
'pid': i['permissions__parent_id'],
'pname': i['permissions__parent__name'],
}
menu_id = i.get('permissions__menu_id')
if not menu_id:
continue
if menu_id not in menu_dict:
menu_dict[menu_id] = {
'title': i['permissions__menu__title'],
'icon': i['permissions__menu__icon'],
'weight': i['permissions__menu__weight'],
'children': [
{
'url': i['permissions__url'],
'title': i['permissions__title'],
'id': i['permissions__id'],
}
]
}
else:
menu_dict[menu_id]['children'].append({'url': i['permissions__url'], 'title': i['permissions__title']})
# 保存权限信息 菜单信息
request.session[settings.PERMISSION_SESSION_KEY] = permission_dict
request.session[settings.MENU_SESSION_KEY] = menu_dict
|
nilq/baby-python
|
python
|
import sys
import yaml
import logging
import typing
from typing import Dict
import clingo
from PyQt5.QtSvg import QSvgRenderer
from . import actions
from .visualizeritem import *
from .visualizerabstract import VisualizerDemand, VisualizerGoods
from .spritecontainer import SpriteContainer
from .model import *
def parse_action(action: str, args: list, actioncfg: Dict[str, str], pickuplist):
ignore = ("dummy", "demand", "satisfy") # WIP functions
if actioncfg[action] in ignore:
return actions.dummy, ()
if actioncfg[action] == "move":
arglist = [x.number for x in args]
return actions.move, tuple(arglist)
if actioncfg[action] == "pick_up":
arglist = [(x.name, x.number) for x in args]
return actions.pick_up, tuple(arglist)
if actioncfg[action] == "pick_up_all":
# arglist = [x.number for x in args]
# arglist.append(pickuplist)
return actions.pick_up_all, [pickuplist]
if actioncfg[action] == "put_down":
arglist = [(x.name, x.number) for x in args]
return actions.put_down, tuple(arglist)
if actioncfg[action] == "put_down_all":
# arglist = [x.number for x in args]
# arglist.append(pickuplist)
return actions.put_down_all, [pickuplist]
#TODO: currently ignored
if actioncfg[action] == "demand":
arglist = [x.number for x in args]
return actions.demand, tuple(arglist)
#TODO: currently ignored
if actioncfg[action] == "satisfy":
arglist = [x.number for x in args]
return actions.satisfy, tuple(arglist)
def parse_item(name: str, number: int, initargs, itemcfg: Dict[str, str], sprites, zvalues):
coord = tuple(x.number for x in initargs[1].arguments)
item = VisualizerItem((name, number), coord, sprites, zvalues[name])
return item
#TODO: Rewrite for flexible goods/demand config
def parse_demand(name: str, number: int, initargs, itemcfg: Dict[str, str]):
#demand = VisualizerDemand((name, number))
return VisualizerDemand((name, number), None, "product", 0)
#TODO: Rewrite for flexible goods/demand config
def parse_goods(name: str, number: int, initargs, itemcfg: Dict[str, str]):
#goods = VisualizerGoods((name, number), 0, (0, 0))
return VisualizerGoods((name, number), 0, (0, 0))
def parse_init_atom(symbols, itemcfg, sprites, zvalues):
if len(symbols) != 2:
#TODO: Error
return
name = symbols[0].arguments[0].name
number = symbols[0].arguments[1].number
initargs = symbols[1].arguments
objtype = itemcfg[name]
obj_id = (name, number)
if objtype == "item":
obj = parse_item(
name, number, initargs, itemcfg, sprites, zvalues)
elif objtype == "demand":
obj = parse_demand(name, number, initargs, itemcfg)
elif objtype == "goods":
obj = parse_goods(name, number, initargs, itemcfg)
else:
pass # TODO: Error
return objtype, obj_id, obj
# TODO: Rewrite, Add configurability
def parse_occurs_atom(symbols, actioncfg, pickuplist):
if len(symbols) != 3:
#TODO: Error
return
index = symbols[2].number
obj_id = (symbols[0].arguments[0].name, symbols[0].arguments[1].number)
occargs = symbols[1].arguments
action = (parse_action(symbols[1].arguments[0].name,
occargs[1].arguments, actioncfg, pickuplist))
return index, (obj_id, action)
def parse_clingo_model(cl_handle, atomcfg):
"""
Parse a gringo model as returned by clingo and return an equivalent
visualizer model.
"""
print("Converting to VisualizerModel...")
objects = {"item": {}, "demand": {}, "goods": {}}
#init = []
occurs = {}
atoms = {}
# Dictionary of the form objname: objtype
itemcfg = {obj: att[0] for att in atomcfg["object"].items()
for obj in att[1]}
sprites = SpriteContainer(atomcfg["object"]["item"])
zvalues = {name: 2*atomcfg["layer"].index(name)
for name in atomcfg["layer"]}
for cl_model in cl_handle:
for symbol in cl_model.symbols(atoms=True):
if symbol.name == "occurs":
# Parse atom, append to states
index, occur = parse_occurs_atom(
symbol.arguments, atomcfg["action"], atomcfg.get("portable", []))
occurs.setdefault(index, []).append(occur)
# Keep string representation of atom in separate dict
atoms.setdefault(index, []).append(str(symbol))
elif symbol.name == "init":
# Parse atom, append to items/states
objtype, obj_id, obj = parse_init_atom(
symbol.arguments, itemcfg, sprites, zvalues)
objects[objtype][obj_id] = obj
# Keep string representation of atom in separate dict
atoms.setdefault(0, []).append(str(symbol))
else:
# TODO: Throw error "unknown atom type(occurs)"
pass
break
model = Model()
model.set_items(objects["item"])
model.set_demands(objects["demand"])
model.set_goods(objects["goods"])
#model.set_initial_state(init)
model.set_occurrences(occurs)
model.set_sprites(sprites)
for alist in atoms.values():
alist.sort()
model.set_atoms(atoms)
model.calculate_item_paths()
model.set_colorcoding(atomcfg.get("colorcode", []))
return model
def parse_config(yml):
"""
Attempts to read parameters from a YAML file and uses defaults when given None.
"""
if yml is None:
# Implement default case
sys.exit("no config file found")
else:
with yml as stream:
try:
cfg_dict = yaml.safe_load(stream)
except yaml.YAMLError as exception:
logging.error(exception)
sys.exit("Error while parsing config file")
return cfg_dict
|
nilq/baby-python
|
python
|
def check_close(input_string):
OPENS = ('(', '<', '[', '{')
CLOSES = (')', '>', ']', '}')
PAREN = ('(', ')')
ANGLE = ('<', '>')
SQUARE = ('[', ']')
CURLY = ('{', '}')
mem = []
for character in input_string:
try:
if character in OPENS:
mem.append(character)
elif character in CLOSES:
if (character in PAREN and
mem[-1] in PAREN):
del mem[-1]
elif (character in ANGLE and
mem[-1] in ANGLE):
del mem[-1]
elif (character in SQUARE and
mem[-1] in SQUARE):
del mem[-1]
elif (character in CURLY and
mem[-1] in CURLY):
del mem[-1]
else:
return False
except IndexError:
return False
if mem == []:
return True
else:
return False
if __name__ == '__main__':
while True:
print(check_close(input(' >>> ')))
|
nilq/baby-python
|
python
|
"""Password Policy tests"""
from django.test import TestCase
from guardian.shortcuts import get_anonymous_user
from authentik.lib.generators import generate_key
from authentik.policies.password.models import PasswordPolicy
from authentik.policies.types import PolicyRequest, PolicyResult
class TestPasswordPolicy(TestCase):
"""Test Password Policy"""
def setUp(self) -> None:
self.policy = PasswordPolicy.objects.create(
name="test_false",
amount_digits=1,
amount_uppercase=1,
amount_lowercase=2,
amount_symbols=3,
length_min=24,
error_message="test message",
)
def test_invalid(self):
"""Test without password"""
request = PolicyRequest(get_anonymous_user())
result: PolicyResult = self.policy.passes(request)
self.assertFalse(result.passing)
self.assertEqual(result.messages[0], "Password not set in context")
def test_failed_length(self):
"""Password too short"""
request = PolicyRequest(get_anonymous_user())
request.context["password"] = "test" # nosec
result: PolicyResult = self.policy.passes(request)
self.assertFalse(result.passing)
self.assertEqual(result.messages, ("test message",))
def test_failed_lowercase(self):
"""not enough lowercase"""
request = PolicyRequest(get_anonymous_user())
request.context["password"] = "1TTTTTTTTTTTTTTTTTTTTTTe" # nosec
result: PolicyResult = self.policy.passes(request)
self.assertFalse(result.passing)
self.assertEqual(result.messages, ("test message",))
def test_failed_uppercase(self):
"""not enough uppercase"""
request = PolicyRequest(get_anonymous_user())
request.context["password"] = "1tttttttttttttttttttttE" # nosec
result: PolicyResult = self.policy.passes(request)
self.assertFalse(result.passing)
self.assertEqual(result.messages, ("test message",))
def test_failed_symbols(self):
"""not enough symbols"""
request = PolicyRequest(get_anonymous_user())
request.context["password"] = "1ETETETETETETETETETETETETe!!!" # nosec
result: PolicyResult = self.policy.passes(request)
self.assertFalse(result.passing)
self.assertEqual(result.messages, ("test message",))
def test_failed_digits(self):
"""not enough digits"""
request = PolicyRequest(get_anonymous_user())
request.context["password"] = "TETETETETETETETETETETE1e!!!" # nosec
result: PolicyResult = self.policy.passes(request)
self.assertFalse(result.passing)
self.assertEqual(result.messages, ("test message",))
def test_true(self):
"""Positive password case"""
request = PolicyRequest(get_anonymous_user())
request.context["password"] = generate_key() + "1ee!!!" # nosec
result: PolicyResult = self.policy.passes(request)
self.assertTrue(result.passing)
self.assertEqual(result.messages, tuple())
|
nilq/baby-python
|
python
|
from .unet import *
from .metric import *
|
nilq/baby-python
|
python
|
# Simple and compound interest
P = float(input("Enter principal: "))
R = float(input("Enter annual RoI (%): ")) / 100
T = float(input("Enter time (years): "))
C = int(input("Compounds per annum: "))
# Calculate simple interest
SI = P*R*T
# Calculate compound interest
R = R/C
T = int(T*C)
CI = (P*(1+R)**T) - P
# Print results
print("Simple Interest:", SI)
print("Compound Interest:", CI)
|
nilq/baby-python
|
python
|
import torch
import torch.distributions
import numpy as np
import matplotlib.pyplot as plt
from pkg_resources import resource_filename
import activelo
def generated_example():
N = 20
truth = torch.randn(N)
n = torch.randint(1, 50, (N, N))
d = truth[:, None] - truth[None, :]
w = torch.distributions.Binomial(n, 1/(1 + np.exp(-d))).sample()
trace = activelo.solve(n, w)
activelo.plot(trace)
def example(filename):
raw = np.load(resource_filename(__package__, filename))
n = torch.as_tensor(raw['n'])
w = torch.as_tensor(raw['w'])
torch.set_rng_state(torch.as_tensor(raw['rng']))
return n, w
def saved_example(filename):
n, w = example(filename)
soln = activelo.solve(n, w)
activelo.plot(soln)
return soln
def saved_examples():
# Generated during development of my AlphaZero agent
saved_example('data/2020-12-07 21-59-18 az-test symmetric.npz')
# A 100-agent problem that seems prone to either line search failures or negdef Σ.
saved_example('data/line-search-failure.npz')
def reuse_example():
n, w = example('data/2021-02-06 12-16-42 wan-ticks.npz')
σs = []
contrast = 0
soln = activelo.solve(n, w)
for _ in range(20):
# Strip out this `soln=soln` to suppress soln reuse
soln = activelo.solve(n, w, soln=soln)
μ, Σ = soln.μ, soln.Σ
σ2 = np.diag(Σ) + Σ[contrast, contrast] - 2*Σ[contrast]
σs.append(σ2[-1]**.5)
σs = np.array(σs)
|
nilq/baby-python
|
python
|
def binary_search(l, value, low = 0, high = -1):
if not l: return -1
if(high == -1): high = len(l)-1
if low == high:
if l[low] == value: return low
else: return -1
mid = (low+high)//2
if l[mid] > value: return binary_search(l, value, low, mid-1)
elif l[mid] < value: return binary_search(l, value, mid+1, high)
else: return mid
|
nilq/baby-python
|
python
|
# from file_path import ClassName
# class Car:
# wheels = 4
#
# def __init__(self, make, model, year, color):
# self.make = make
# self.model = model
# self.year = year
# self.color = color
#
# def drive(self):
# return f"The {self.model} is driving"
#
# def stop(self):
# pass
#
#
# car = Car("foo", "bar", "1999", "blue")
# print(car.drive())
#
# print(f"the {car.model} has {car.wheels} wheels")
# car.wheels = 2
# print(f"now it only has {car.wheels} wheels")
# INHERITANCE
# parent class
class A:
pass
# child of class A
class B(A):
pass
# child of class B
class C(B):
pass
|
nilq/baby-python
|
python
|
from graphql_relay.node.node import from_global_id
from django_filters import FilterSet, CharFilter
from django_filters.filters import ModelMultipleChoiceFilter, ModelMultipleChoiceField
class BaseFilterSet(FilterSet):
"""
Base filter set for Movie and Person
"""
search = CharFilter(method='filter_by_search_term')
def filter_m2m(self, qs, name, value):
for instance in value:
qs = qs.filter(**{name: instance.id})
return qs
def filter_by_search_term(self, qs, _, value):
return qs.search(value)
class ModelGlobalIdMultipleChoiceField(ModelMultipleChoiceField):
"""
Multiple choice field with support of relay global_id
"""
def _check_values(self, value):
from cinemanio.api import schema
local_ids = []
for global_id in value:
node, local_id = from_global_id(global_id)
node = getattr(schema, node)
if node._meta.model != self.queryset.model:
raise ValueError(f"Wrong type of argument value {global_id}")
local_ids.append(int(local_id))
return super()._check_values(local_ids)
class ModelGlobalIdMultipleChoiceFilter(ModelMultipleChoiceFilter):
"""
Multiple choice filter with support of relay global_id
"""
field_class = ModelGlobalIdMultipleChoiceField
|
nilq/baby-python
|
python
|
#!/usr/bin/env python
# author: d.koch
# coding: utf-8
# naming: pep-0008
# typing: pep-0484
# docstring: pep-0257
# indentation: tabulation
""" canp_view_enaml.py
Enaml view
"""
# --- IMPORT ---
# Standard libraries (installed with python)
import os
import sys
sys.path.append(os.path.dirname(os.path.abspath(__file__)))
from typing import Any
from typing import Callable
from typing import Dict
from typing import List
from typing import Optional
from typing import Union
# . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
# External libraries (installed with pip, conda, setup.py, ...)
# python3 -m pip install --upgrade pyqtgraph
# python3 -m pip install --upgrade QScintilla
# python3 -m pip install --upgrade enamlx
#import enamlx
#enamlx.install()
# python3 -m pip install --upgrade rtree
# python3 -m pip install --upgrade intervaltree
# python3 -m pip install --upgrade enaml
import enaml
from enaml.qt.qt_application import QtApplication
# . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
# Included libraries (this module, local files)
from canp_enum import CANP_ENUM__HEAD_MAIN
from canp_enum import CANP_ENUM__IDX_X_CAN
from canp_enum import CANP_ENUM__IDX_X_REF
from canp_enum import CANP_ENUM__IDX_Y_CAN
from canp_enum import CANP_ENUM__IDX_Y_REF
from canp_enum import CANP_ENUM__IDX_Z_CAN
from canp_enum import CANP_ENUM__IDX_Z_REF
from canp_args import canp_args
from canp_logs import canp_logs
# --- GLOBAL ---
# Local settings (might be present in other files yet with different values)
# --- CLASS ---
class canp_view_enaml:
""" CAN view
CANNOT be used alone, but be fed some data
"""
@staticmethod
def display(
i_list_narr, # All the lists in one package
i_int_tmp, # Time index in each list (usually 0)
i_int_pos, # Pos index in each list (usually 1)
i_obj_can, # Can card object (chan, node, conf, ...)
) -> None:
# Display data in Enaml windows
with enaml.imports():
from canp_view_enaml_main import Main
app = QtApplication()
view = Main(
i_list_narr = i_list_narr,
i_int_tmp = i_int_tmp,
i_int_pos = i_int_pos,
i_obj_can = i_obj_can,
)
view.show()
app.start()
# --- MAIN ---
def __main__(i_list_args: List = []):
""" Basic self test (debugging)
"""
if True:
with enaml.imports():
from canp_view_enaml_main import Main
app = QtApplication()
view = Main()
view.show()
app.start()
else:
pass
if __name__ == CANP_ENUM__HEAD_MAIN:
""" Routine selector
"""
canp_args.dispatch(i_list_globals = globals())
|
nilq/baby-python
|
python
|
# -*- coding: utf-8 -*-
# File: base.py
# Author: Yuxin Wu <ppwwyyxxc@gmail.com>
from abc import ABCMeta, abstractmethod
import signal
import re
from six.moves import range
import tqdm
import tensorflow as tf
from tensorpack.utils.utils import get_tqdm_kwargs
from .config import TrainConfig
from ..utils import *
from ..utils.timer import *
from ..utils.concurrency import start_proc_mask_signal
from ..callbacks import StatHolder
from ..tfutils import *
from ..tfutils.summary import create_summary
from tensorflow.python.framework import ops
from time import sleep
__all__ = ['Trainer']
import neptune_mp_server
from threading import Thread
class Trainer(object):
"""
Base class for a trainer.
Available Attritbutes:
stat_holder: a `StatHolder` instance
summary_writer: a `tf.SummaryWriter`
config: a `TrainConfig`
model: a `ModelDesc`
global_step: a `int`
"""
__metaclass__ = ABCMeta
def __init__(self, config):
"""
:param config: a `TrainConfig` instance
"""
assert isinstance(config, TrainConfig), type(config)
self.config = config
self.model = config.model
self.model.get_input_vars() # ensure they are present
self._extra_threads_procs = config.extra_threads_procs
@abstractmethod
def train(self):
""" Start training"""
pass
@abstractmethod
def run_step(self):
""" run an iteration"""
pass
@abstractmethod
def get_predict_func(self, input_names, output_names):
""" return a online predictor"""
pass
def get_predict_funcs(self, input_names, output_names, n):
""" return n predictor functions.
Can be overwritten by subclasses to exploit more
parallelism among funcs.
"""
return [self.get_predict_func(input_names, output_names) for k in range(n)]
def trigger_epoch(self):
self._trigger_epoch()
self.config.callbacks.trigger_epoch()
self.summary_writer.flush()
@abstractmethod
def _trigger_epoch(self):
""" This is called right after all steps in an epoch are finished"""
pass
def _init_summary(self):
if not hasattr(logger, 'LOG_DIR'):
raise RuntimeError("Please use logger.set_logger_dir at the beginning of your script.")
self.summary_writer = tf.summary.FileWriter(
logger.LOG_DIR, graph=tf.get_default_graph())
self.summary_op = tf.summary.merge_all()
# create an empty StatHolder
self.stat_holder = StatHolder(logger.LOG_DIR)
# save global_step in stat.json, but don't print it
self.stat_holder.add_blacklist_tag(['global_step'])
def _process_summary(self, summary_str):
summary = tf.Summary.FromString(summary_str)
for val in summary.value:
if val.WhichOneof('value') == 'simple_value':
val.tag = re.sub('tower[p0-9]+/', '', val.tag) # TODO move to subclasses
self.stat_holder.add_stat(val.tag, val.simple_value)
self.summary_writer.add_summary(summary, self.global_step)
def write_scalar_summary(self, name, val):
self.summary_writer.add_summary(create_summary(name, val))
self.stat_holder.add_stat(name, val)
def main_loop(self):
# some final operations that might modify the graph
logger.info("[{}] Initializing graph variables ...".format(os.environ['SLURMD_NODENAME']))
#self.sess.run(tf.initialize_all_variables())
self.config.session_init.init(self.sess)
# tf.get_default_graph().finalize()
callbacks = self.config.callbacks
logger.info("[{}] Starting concurrency...".format(os.environ['SLURMD_NODENAME']))
self._start_concurrency()
#with self.sess.as_default():
logger.info("[{}] Setting default session".format(os.environ['SLURMD_NODENAME']))
with ops.default_session(self.sess):
try:
logger.info("[{}] Getting global step".format(os.environ['SLURMD_NODENAME']))
self.global_step = get_global_step()
logger.info("[{}] Start training with global_step={}".format(os.environ['SLURMD_NODENAME'], self.global_step))
if self.config.extra_arg['is_chief']:
server = neptune_mp_server.Server(
self.config.extra_arg['n_workers'],
port=self.config.extra_arg['port'],
debug_charts=self.config.extra_arg['debug_charts'],
adam_debug=self.config.extra_arg['adam_debug'],
schedule_hyper=self.config.extra_arg['schedule_hyper'],
experiment_dir=self.config.extra_arg['experiment_dir'])
server.main_loop()
callbacks.before_train()
for epoch in range(self.config.starting_epoch, self.config.max_epoch+1):
with timed_operation(
'Epoch {}, global_step={}'.format(
epoch, self.global_step + self.config.step_per_epoch)):
for step in tqdm.trange(
self.config.step_per_epoch,
**get_tqdm_kwargs(leave=True)):
if self.coord.should_stop():
return
self.run_step()
callbacks.trigger_step()
try:
self.global_step += 1
except:
self.global_step = -1
self.trigger_epoch()
print 'EPOCH ENDS HERE'
except (KeyboardInterrupt, Exception):
raise
finally:
# Do I need to run queue.close?
print('Handling finally block')
callbacks.after_train()
self.coord.request_stop()
self.summary_writer.close()
self.sess.close()
def init_session_and_coord(self):
worker_host = self.config.extra_arg['worker_host']
is_chief = self.config.extra_arg['is_chief']
use_sync_opt = self.config.extra_arg['use_sync_opt']
with tf.device('/cpu:0'):
with tf.variable_scope(tf.get_variable_scope(), reuse=None):
if False:
self.sess.set(tf.Session(target=worker_host,
config=self.config.session_config))
else:
logger.info("===============================================================")
if is_chief:
logger.info("CHIEF!")
logger.info("[{}] Creating the session".format(os.environ['SLURMD_NODENAME']))
logger.info("===============================================================")
if use_sync_opt == 0:
self.sess.set(tf.train.MonitoredTrainingSession(master=worker_host, is_chief=is_chief))
else: # use_sync_opt == 1
hooks = self.config.extra_arg['hooks']
self.sess.set(tf.train.MonitoredTrainingSession(master=worker_host, is_chief=is_chief, hooks=[hooks]))
logger.info("===============================================================")
logger.info("[{}] Session created".format(os.environ['SLURMD_NODENAME']))
logger.info("===============================================================")
self.coord.set(tf.train.Coordinator())
def _start_concurrency(self):
"""
Run all threads before starting training
"""
logger.info("Starting all threads & procs ...")
tf.train.start_queue_runners(sess=self.sess.get(), coord=self.coord, daemon=True, start=True)
#with self.sess.as_default():
with ops.default_session(self.sess):
# avoid sigint get handled by other processes
start_proc_mask_signal(self._extra_threads_procs)
def process_grads(self, grads):
g = []
for grad, var in grads:
if grad is None:
logger.warn("No Gradient w.r.t {}".format(var.op.name))
else:
g.append((grad, var))
procs = self.config.model.get_gradient_processor()
for proc in procs:
g = proc.process(g)
return g
|
nilq/baby-python
|
python
|
#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
:Description: Implementation of the Louvain algorithm using igraph framework with input/
output formats adapted to the NMIs evaluation.
:Authors: Artem Lutov <luart@ya.ru>
:Organizations: eXascale lab <http://exascale.info/>, ScienceWise <http://sciencewise.info/>,
Lumais <http://www.lumais.com/>
:Date: 2015-07
"""
from __future__ import print_function, division # Required for stderr output, must be the first import
import sys
import os # Pathes processing
import argparse
from igraph import Graph
try:
# ATTENTION: Python3 newer treats imports as realtive and results in error here unlike Python2
from utils.parser_nsl import asymnet, loadNsl #pylint: disable=E0611,E0401
except ImportError:
# Note: this case should be the second because explicit relative imports cause various errors
# under Python2 and Python3, which complicates thier handling
from .utils.parser_nsl import asymnet, loadNsl #pylint: disable=E0611,E0401
def louvain(args):
"""Execute Louvain algorithm on the specified network and output resulting communities to the specified file
args.network - input network
args.inpfmt - format of the input network
args.outpfile - output file name WITHOUT extension
args.outpext - extension of the output file
"""
#args.inpfmt = args.inpfmt.lower() # It should be already in the lower case
print('Starting Louvain (igraph) clustering:'
'\n\tnetwork: {}, format: {}'
'\n\tperlev output: {}, communities: {}'
.format(args.network, args.inpfmt, args.perlev, args.outpfile + args.outpext))
# Load Data from simple real-world networks
graph = None
if args.inpfmt == 'ncol': # Note: it's not clear whether .nce/.snap can be correctly readed as .ncol
# Weight are considered if present; .ncol format is always undirected
graph = Graph.Read_Ncol(args.network, directed=False)
elif args.inpfmt == 'pjk':
graph = Graph.Read_Pajek(args.network) #pylint: disable=E1101
elif args.inpfmt in ('nse', 'nsa'):
graph = loadNsl(args.network, asymnet(os.path.splitext(args.network)[1].lower(), args.inpfmt == 'nsa'))
else:
raise ValueError('Unknown network format: ' + args.inpfmt)
#community_multilevel(self, weights=None, return_levels=False)
#@param weights: edge attribute name or a list containing edge
# weights
#@param return_levels: if C{True}, the communities at each level are
# returned in a list. If C{False}, only the community structure with
# the best modularity is returned.
#@return: a list of L{VertexClustering} objects, one corresponding to
# each level (if C{return_levels} is C{True}), or a L{VertexClustering}
# corresponding to the best modularity.
#edges, weights = [], []
hier = graph.community_multilevel(weights='weight' if graph.is_weighted() else None, return_levels=True)
# Output levels
#fname = 'level'
communs = [] # All distinct communities of the hierarchy
descrs = set() # Communs descriptors for the fast comparison
props = 0 # Number of propagated (duplicated communities)
# Create output dir if not exists
outdir = os.path.split(args.outpfile)[0]
if outdir and not os.path.exists(outdir):
os.makedirs(outdir)
named = 'name' in graph.vertex_attributes()
for i, lev in enumerate(hier):
# Output statistics to the stderr
print('Q: {:.6f}, lev: {}. {}.'.format(hier[i].q, i, hier[i].summary()), file=sys.stderr)
if args.perlev:
with open('{}_{}{}'.format(args.outpfile, i, args.outpext), 'w') as fout:
for cl in lev:
if named:
fout.write(' '.join(graph.vs[nid]['name'] for nid in cl))
else:
fout.write(' '.join(str(nid) for nid in cl))
fout.write('\n')
else:
# Merge all hier levels excluding identical communities, use idNums comparison (len, sum, sum2)
for cl in lev:
clen = len(cl)
csum = 0
csum2 = 0
for nid in cl:
csum += nid
csum2 += nid * nid
dsr = (clen, csum, csum2)
if i == 0 or dsr not in descrs:
descrs.add(dsr)
communs.append(cl)
else:
props += 1
# Output communs
del descrs
if not args.perlev:
if props:
print('The number of propagated (duplicated) communities in the hieratchy: '
+ str(props), file=sys.stderr)
with open(args.outpfile + args.outpext, 'w') as fout:
for cl in communs:
if named:
fout.write(' '.join(graph.vs[nid]['name'] for nid in cl))
else:
fout.write(' '.join(str(nid) for nid in cl))
fout.write('\n')
print('The hierarchy has been successfully outputted')
def parseArgs(params=None):
"""Parse input parameters (arguments)
params - the list of arguments to be parsed (argstr.split()), sys.argv is used if args is None
return args - parsed arguments
"""
inpfmts = ('nse', 'nsa', 'pjk', 'ncol') # Note: louvain_igraph supports only undirected input graph
parser = argparse.ArgumentParser(description='Louvain Clustering of the undirected graph.')
ipars = parser.add_argument_group('Input Network (Graph)')
ipars.add_argument('network', help='input network (graph) filename.'
' The following formats are supported: {{{inpfmts}}}.'
' If the file has another extension then the format should be specified'
' explicitly.'.format(inpfmts=' '.join(inpfmts)))
ipars.add_argument('-i', '--inpfmt', dest='inpfmt', choices=inpfmts
, help='input network (graph) format, required only for the non-standard extension')
outpext = '.cnl' # Default extension of the output file
opars = parser.add_argument_group('Output Network (Graph)')
opars.add_argument('-o', '--outpfile', dest='outpfile'
, help='output all distinct resulting communities to the <outpfile>'
', default value is <network_name>{}'.format(outpext))
opars.add_argument('-l', '--perlev', dest='perlev', action='store_true'
, help='output communities of each hierarchy level to the separate file'
' <outpfile_name>/<outpfile_name>_<lev_num>{} starting from the bottom level of'
' the hierarchy (the most fine-grained level having large number of small clusters)'
' indexed with 0 up to the top level (root, the most coarse-grained level typically'
' having small number of large clusters) having the maximal index'.format(outpext))
args = parser.parse_args(params)
# Consider implicit default values
netname, netext = os.path.splitext(args.network)
if args.inpfmt is None and netext:
args.inpfmt = netext[1:] # Skip the leading point
args.inpfmt = args.inpfmt.lower()
if args.inpfmt not in inpfmts:
raise ValueError('Invalid format of the input network "{}" specified: {}'.format(args.network, args.inpfmt))
if args.outpfile is None:
args.outpfile = netname
args.outpext = outpext
else:
args.outpfile, args.outpext = os.path.splitext(args.outpfile)
return args
if __name__ == '__main__':
louvain(parseArgs())
|
nilq/baby-python
|
python
|
#!/usr/bin/env python3
# Custom imports
from .help import showHelp
from .cfg import (
MIN_WIDTH, MIN_HEIGHT,
MIN_BIG_WIDTH, MIN_BIG_HEIGHT,
CONF_FILEPATH,
MAX_COLORS,
startTime, stopSeconds,
arrNumBig, arrNumSmall,
helpMenu
)
from .utils import get_terminal_size
# External imports
import sys
import os
import curses
# import curses.ascii
import datetime
import locale
# Init global variables
arrNum = arrNumBig
isBig = True
isHelp = False
isDateAff = True
isSecsAff = False
colorClockNum = 1
colorDateNum = 6
def draw_number(num, posx, posy, stdscr):
for row in arrNum[num]:
stdscr.move(posy, posx)
stdscr.addstr(row)
posy = posy + 1
# try:
# for row in arrNum[num]:
# stdscr.move(posy, posx)
# stdscr.addstr(row)
# posy = posy + 1
# except:
# endcurse(stdscr, True)
# exit()
def init(stdscr):
# Clear and refresh the screen for a blank canvas
stdscr.clear()
# curses.init_color(0, 123, 0, 43)
# TODO bg color in conf file
stdscr.refresh()
# Turn of echo ok keypress / nodelay getch / no cursor etc.
curses.noecho()
curses.cbreak()
stdscr.nodelay(1)
stdscr.keypad(1)
curses.curs_set(False)
stdscr.border(2)
def intColors():
global MAX_COLORS
# Start colors in curses
curses.start_color()
curses.use_default_colors()
# -1 instead of curses.COLOR_BLACK for BG to use term default BG
curses.init_pair(1, curses.COLOR_WHITE, -1)
curses.init_pair(2, curses.COLOR_YELLOW, -1)
curses.init_pair(3, curses.COLOR_RED, -1)
curses.init_pair(4, curses.COLOR_GREEN, -1)
curses.init_pair(5, curses.COLOR_BLUE, -1)
curses.init_pair(6, curses.COLOR_MAGENTA, -1)
curses.init_pair(7, curses.COLOR_CYAN, -1)
MAX_COLORS = 7
# for black bg on box
# TODO laisser comme ça pour unix mais pour OSX et windows mettre COLOR_BLACK, tester avant
curses.init_pair(8, -1, -1)
# Clean ending of curses + handling of error message
def endcurse(stdscr, bError):
curses.nocbreak()
stdscr.keypad(False)
curses.echo()
curses.endwin()
if bError:
rows, columns = get_terminal_size()
sys.stdout.write("Need minimum of [" + str(MIN_HEIGHT) + "x" + str(MIN_WIDTH) + "] for ASCII render. Your terminal is [" + str(rows) + "x" + str(columns) + "]. ")
exit()
def draw_main(stdscr):
global isBig
global isHelp
global isDateAff
global isSecsAff
global colorClockNum
global colorDateNum
global lastKey
global arrNum
global startTime
global stopSeconds
init(stdscr)
intColors()
while True:
# Initialization
stdscr.clear()
height, width = stdscr.getmaxyx()
# Handling sizing depending on term size
if width < MIN_BIG_WIDTH or height < MIN_BIG_HEIGHT:
isBig = False
# Quitting if size would make ncurse crash
if height < MIN_HEIGHT or width < MIN_WIDTH:
endcurse(stdscr, True)
# Debug
# stdscr.addstr(0, 0, " " + str(width) + " x " + str(height))
# Get hours and minutes
locale.getlocale()
locale.setlocale(locale.LC_ALL, '')
now = datetime.datetime.now()
# datefull = now.strftime('%d / %m / %Y')
datefullSmall = now.strftime('%x')
datefull = " - ".join(datefullSmall.split("-")) # Add extra spacing on date to for 'neating'
datefull = " / ".join(datefullSmall.split("/"))
hoursmin = now.strftime('%H%M%S') # %X pour locale TIME format
seconds = hoursmin[-2:]
hoursmin = hoursmin[:4]
# TODO localisation du TIME T_FTM , see locale etc. if other char than ":", will there be AM/PM ?
# hoursmin = "".join([char for char in hoursmin if char in '1234567890'])
# Select size of block BIG or SMALL
arrNum = arrNumBig if isBig else arrNumSmall
# Calculate size of blocks with spacing
blockwidth = 0
for i in range(len(hoursmin)):
blockwidth += len(arrNum[int(hoursmin[i])][0])
blockwidth = blockwidth + 2 if isBig else blockwidth + 1
# Remove extra spacing and Add size of :
blockwidth = blockwidth - 2 + 5 if isBig else blockwidth - 1
# Centering calculations
start_x = int((width // 2) - (blockwidth // 2))
start_y = int((height // 2) - (len(arrNum[0]) // 2)) # - (1 if isBig else 0)
if isBig and (isDateAff or isSecsAff):
start_y = start_y - 1
# Draw TIME
# TODO : a refaire with la localisation AM/PM, other char than : ...
stdscr.attron(curses.color_pair(colorClockNum))
stdscr.attron(curses.A_BOLD)
for i in range(len(hoursmin)):
v = int(hoursmin[i])
draw_number(v, start_x, start_y, stdscr)
start_x += len(arrNum[v][0])
start_x = start_x + 2 if isBig else start_x + 1
if i == 1:
draw_number(10, start_x, start_y, stdscr)
start_x += len(arrNum[10][0])
start_x = start_x + 2 if isBig else start_x + 1
stdscr.attroff(curses.color_pair(colorClockNum))
stdscr.attroff(curses.A_BOLD)
# Draw SECONDS
if isSecsAff:
if isBig:
if isDateAff:
stdscr.addstr(start_y + 6, start_x - 5, " ".join(list(seconds)), curses.color_pair(colorClockNum))
else:
stdscr.addstr(start_y + 6, start_x - 5, " ".join(list(seconds)), curses.color_pair(colorClockNum))
else:
stdscr.addstr(start_y + 3, start_x - 3, seconds, curses.color_pair(colorClockNum))
# Draw DATE
stdscr.attroff(curses.A_BOLD)
if isDateAff:
if isBig:
if isSecsAff:
stdscr.addstr(start_y + 6, start_x - blockwidth - 2, "⠀⠀" + datefull + "⠀⠀", curses.A_REVERSE + curses.color_pair(colorDateNum))
else:
stdscr.addstr(start_y + 6, start_x - len(datefull) - 6, "⠀⠀" + datefull + "⠀⠀", curses.A_REVERSE + curses.color_pair(colorDateNum))
else:
stdscr.addstr(start_y + 4, start_x - len(datefullSmall) - 3, "⠀" + datefullSmall + "⠀", curses.A_REVERSE + curses.color_pair(colorDateNum))
# Draw Help
if isHelp:
showHelp(stdscr, helpMenu, -1, -1, "H E L P", "%C%@darokin ♥", (2 if width > (MIN_BIG_WIDTH - 4) else 1), colorClockNum)
# Test auto closing time
if startTime != -1:
if datetime.datetime.now().second - startTime > stopSeconds:
endcurse(stdscr, False)
# Refresh / Timeout
stdscr.refresh()
key = stdscr.getch()
stdscr.timeout(1000)
# Key inputs
if key == ord("z") or key == ord("Z"): # key == curses.KEY_UP:
colorClockNum -= 1
if colorClockNum == 0:
colorClockNum = MAX_COLORS
elif key == ord("x") or key == ord("X"): # curses.KEY_LEFT:
colorDateNum -= 1
if colorDateNum == 0:
colorDateNum = MAX_COLORS
elif key == ord('d') or key == ord("D"):
isDateAff = not isDateAff
elif key == ord('s') or key == ord("S"):
isSecsAff = not isSecsAff
elif key == ord('h'):
isHelp = not isHelp
# elif key == ord('e'):
# lastKey = "e"
# elif key == ord('r') and lastKey == "e":
# os.system("shutdown now")
elif key == ord('q') or key == ord("Q"):
break
else:
lastKey = ""
# Loop ended properly we save the conf
with open(CONF_FILEPATH, 'w') as writer:
writer.write(("1" if isDateAff else "0") + ";" + str(colorClockNum) + ";" + str(colorDateNum))
# Ending
endcurse(stdscr, False)
def readConfFile():
global isDateAff
global colorClockNum
global colorDateNum
# Read config file if present
if os.path.exists(CONF_FILEPATH):
with open(CONF_FILEPATH, 'r') as reader:
strconf = reader.read()
tabconf = strconf.split(";")
isDateAff = (int(tabconf[0]) == 1) # True
colorClockNum = int(tabconf[1])
colorDateNum = int(tabconf[2])
def start(_stopSeconds):
global startTime
global stopSeconds
# Init global variables for autoclosing
if _stopSeconds > 0:
startTime = datetime.datetime.now().second
stopSeconds = _stopSeconds
# Read configuration file
readConfFile()
# Test resolution si mini ok
# rows, columns = os.popen('stty size', 'r').read().split()
rows, columns = get_terminal_size()
if int(rows) < MIN_HEIGHT or int(columns) < MIN_WIDTH:
print("Need minimum of [" + str(MIN_HEIGHT) + "x" + str(MIN_WIDTH) + "] for ASCII render, your term indicates a [" + str(rows) + "x" + str(columns) + "] resolution")
df = datetime.datetime.now().strftime('%d/%m/%Y %H:%M')
print("Time : " + df)
exit()
curses.wrapper(draw_main)
|
nilq/baby-python
|
python
|
'''
@author: Aeolus
@url: x-fei.me
@time: 2019-04-08 21:26
'''
from os.path import join, dirname, abspath
from src import tool
# Basic project info
AUTHOR = "Felix"
PROGRAM = "Robust"
DESCRIPTION = "Defense against adversarial attacks. " \
"If you find any bug, please new issue. "
# Main CMDs. This decides what kind of cmd you will use.
cmd_list = ['temp', 'train', 'test']
log_name = 'Robust'
# add parsers to this procedure
globals().update(vars(tool.gen_parser()))
def init_path_config(main_file):
# global_variables
gv = globals()
project_dir = abspath(join(dirname(main_file), '..'))
gv['project_dir'] = project_dir
gv['data_dir'] = data_dir = join(project_dir, 'data')
gv['log_dir'] = join(data_dir, 'log')
gv['loss_dir'] = join(data_dir, 'loss')
gv['model_dir'] = join(data_dir, 'model')
gv['best_model_dir'] = join(data_dir, 'best_model')
# tensorboard dir
gv['tb_dir'] = join(data_dir, 'tb')
# local
# gv['ImageNet100_dir'] = '/data/DataSets/MyImagenet'
gv['CIFAR10_dir'] = '/data/DataSets/cifar10'
# 15
# gv['ImageNet100_dir'] = '/home/feifei/datasets/MyImagenet'
# 16
gv['ImageNet100_dir'] = '/data0/feifei/datasets/MyImagenet'
|
nilq/baby-python
|
python
|
# -*- coding: utf-8 -*-
# Copyright (c) 2019, Helio de Jesus and contributors
# For license information, please see license.txt
from __future__ import unicode_literals
import frappe
from frappe import _, msgprint, throw
from frappe.model.document import Document
from frappe.model.naming import make_autoname
from datetime import datetime, timedelta
from frappe.utils import cstr, get_datetime, getdate, cint, get_datetime_str
class Estacao(Document):
def autoname(self):
self.name = self.estacao_local
|
nilq/baby-python
|
python
|
from Source import ModelsIO as MIO
import numpy as np
from h5py import File
def E_fit(_cube: np.ndarray((10, 13, 21, 128, 128), '>f4'),
data: np.ndarray((128, 128), '>f4'),
seg: np.ndarray((128, 128), '>f4'),
noise: np.ndarray((128, 128), '>f4')) -> np.ndarray((10, 13, 21), '>f4'):
scaled_models: np.ndarray((10, 13, 21, 128, 128), '>f4')
flux_models: np.ndarray((10, 13, 21), '>f4')
flux_data: np.float('>f4')
X: np.ndarray((10, 13, 21), '>f4')
resta: np.ndarray((10, 13, 21, 128, 128), '>f4')
residuo: np.ndarray((10, 13, 21, 128, 128), '>f4')
chi: np.ndarray((10, 13, 21), '>f4')
area: int
flux_models = np.einsum("ijkxy,xy->ijk", _cube, seg)
flux_data = np.einsum("xy,xy", data, seg)
X = flux_data / flux_models
scaled_models = X[:, :, :, np.newaxis, np.newaxis] * _cube
resta = data - scaled_models
residuo = (resta ** 2) / (scaled_models + noise ** 2)
chi = np.einsum("ijkxy,xy->ijk", residuo, seg)
area = seg.sum()
chi = chi / area
return chi
def read_obj_h5(name):
# debe ser
try:
with File(name, 'r') as f:
data = f['obj'][:]
seg = f['seg'][:]
rms = f['rms'][:]
return data, seg, rms
except IOError:
print("{} not found".format(name))
return False, False, False
# se necesita esta funcion??
def read_obj(name):
try:
data = MIO.fits.open(name)[1].data
rms = MIO.fits.open(name.replace('objs', 'noise'))[1].data
seg = MIO.fits.open(name.replace('object', "segment").replace("objs", "segs"))[1].data
except IOError:
print("{} not found".format(name))
return False, False, False
noise = np.median(rms)
return data, seg, noise
def feed(name, cube):
"""
From a name and a models cube, run an object through the routine
Outputs the numpy array of the chi_cube
"""
a, b, s = read_obj_h5(name)
if a is not False:
chi = E_fit(cube, a, b, noise=s)
# outchi = MIO.fits.ImageHDU(data=chi)
# outchi.writeto(name.replace('cut_object',"chi_cube"),overwrite=True)
return chi
else:
return False
def save_chi(name, cube):
"""
Parameters
name : str of output file
cube : crunch.feed output
"""
outchi = MIO.fits.ImageHDU(data=cube)
outchi.writeto(name, overwrite=True)
return True
def get_cube(name):
cube = MIO.ModelsCube(name)
cube = cube.data.reshape((10, 13, 128, 21, 128))
cube = np.swapaxes(cube, 2, 3) # new shape (10, 13, 21, 128, 128)
return cube
def chi_index(chi_name):
"""
Parameters
----------
chi_name : chi_cube fits filename.
Returns
-------
tuple (i,j,k) of the index which minimize the residuals.
"""
chi_cube = MIO.fits.open(chi_name)
i, j, k = np.unravel_index(np.argmin(chi_cube[1].data), shape=(10, 13, 21))
return i, j, k
def pond_rad_like(chi_name, logh):
i, j, k = chi_index(chi_name)
chi_cubo = MIO.fits.open(chi_name)[1].data
weights = np.e ** (chi_cubo[i, j, :])
r_weight = 0
for r in range(21):
r_weight += (10 ** (logh[r])) / weights[r]
r_chi = np.log10(r_weight / np.sum(1. / weights))
r_var = 0
for r in range(21):
r_var += ((logh[r] - r_chi) ** 2) / (weights[r])
r_var = r_var / np.sum(1. / weights)
return r_chi, r_var
def pond_rad(chi_name, logh):
i, j, k = chi_index(chi_name)
chi_cubo = MIO.fits.open(chi_name)[1].data
weights = chi_cubo[i, j, :]
r_weight = 0
for r in range(21):
r_weight += (10 ** (logh[r])) / weights[r]
r_chi = np.log10(r_weight / np.sum(1. / weights))
r_var = 0
for r in range(21):
r_var += ((logh[r] - r_chi) ** 2) / (weights[r])
r_var = r_var / np.sum(1. / weights)
return r_chi, r_var
def pond_rad_3d(chi_name, logh):
chi_cubo = MIO.fits.open(chi_name)[1].data
sqrt_chi = np.sqrt(chi_cubo)
r_weight = 0
for e in range(10):
for t in range(13):
for r in range(21):
r_weight += (10 ** (logh[r])) / sqrt_chi[e, t, r]
r_chi = np.log10(r_weight / np.sum(1. / sqrt_chi))
r_var = 0
for e in range(10):
for t in range(13):
for r in range(21):
r_var += ((logh[r] - r_chi) ** 2) / (chi_cubo[e, t, r])
r_var = r_var / np.sum(1. / chi_cubo)
return r_chi, r_var
def make_mosaic(obj, chi, cube):
"""
Parameters
----------
obj : str
DESCRIPTION.
chi : str
DESCRIPTION.
cube : numpy array
DESCRIPTION.
Returns
-------
Bool
Builds a mosaic containing the data,segment,model and residual
"""
i, j, k = chi_index(chi)
model = cube[i, j, k]
gal, seg, noise = read_obj(obj)
output = chi.replace('chi_cube', 'mosaic').replace('cut_object', 'mosaic')
fg = np.sum(gal * seg)
fm1 = np.sum(model * seg)
aux = np.zeros((128, 128 * 4))
aux[:, 0:128] = gal
aux[:, 128:256] = seg * (fg / seg.sum())
aux[:, 256:384] = model * (fg / fm1)
aux[:, 384:] = gal - model * (fg / fm1)
gg = MIO.fits.ImageHDU(data=aux)
gg.writeto(output, overwrite=True)
return True
def make_mosaic_h5(obj, chi, cube):
"""
Parameters
----------
obj : str
DESCRIPTION.
chi : str
DESCRIPTION.
cube : numpy array
DESCRIPTION.
Returns
-------
Bool
Builds a mosaic containing the data,segment,model and residual
"""
i, j, k = chi_index(chi)
model = cube[i, j, k]
output = chi.replace('chi_cube', 'mosaic').replace('cut', 'mosaic')
with File(obj, 'r') as f:
gal = f['obj'][:]
seg = f['seg'][:]
fg = np.sum(gal * seg)
fm1 = np.sum(model * seg)
aux = np.zeros((128, 128 * 4))
aux[:, 0:128] = gal
aux[:, 128:256] = seg * (fg / seg.sum())
aux[:, 256:384] = model * (fg / fm1)
aux[:, 384:] = gal - model * (fg / fm1)
gg = MIO.fits.ImageHDU(data=aux)
gg.writeto(output, overwrite=True)
return True
|
nilq/baby-python
|
python
|
from kivymd.uix.carousel import MDCarousel
class StaticCarousel(MDCarousel):
def __init__(self, **kwargs):
super().__init__(**kwargs)
def on_touch_down(self, touch):
return False
|
nilq/baby-python
|
python
|
"""
Create a single inheritance class and use one method from a parent class.
"""
class Animal:
def __init__(self, breed):
self.breed = breed
self.living = True
def is_alive(self):
if self.living:
return f'{self.breed} is alive.'
return f'{self.breed} is dead.'
class Dog(Animal):
def __init__(self, breed, has_owner=False):
Animal.__init__(self, breed)
self.has_owner = has_owner
doggy = Dog('American Bulldog', has_owner=True)
print(doggy.is_alive())
|
nilq/baby-python
|
python
|
import torch
from torch import nn
from src.config import data
from src.utils import init_weights
class ClassifierModel(nn.Module):
def __init__(self):
super().__init__()
self.process = nn.Sequential(
nn.Linear(data.x_size, 64, bias=False),
nn.BatchNorm1d(64),
nn.LeakyReLU(),
nn.Linear(64, 32, bias=False),
nn.BatchNorm1d(32),
nn.LeakyReLU(),
nn.Linear(32, 16, bias=False),
nn.BatchNorm1d(16),
nn.LeakyReLU(),
nn.Linear(16, 8, bias=False),
nn.BatchNorm1d(8),
nn.LeakyReLU(),
nn.Linear(8, 4, bias=False),
nn.BatchNorm1d(4),
nn.LeakyReLU(),
nn.Linear(4, 2, bias=False),
nn.BatchNorm1d(2),
nn.LeakyReLU(),
nn.Linear(2, 1),
nn.Sigmoid(),
)
self.apply(init_weights)
def forward(self, x: torch.Tensor):
prob = self.process(x)
return prob
|
nilq/baby-python
|
python
|
import numbers
from bisect import bisect_right
from .. utils.cache import Cache
class NodeRefGroup:
"""
NodeRefGroup class
Members
-------
_ref_lists: dict
dictionary with keys as optimisation key and value as references to nodes sorted by that optimisation key
_temp_list: list
temporary node reference list for storing operations output (for function chaining)
"""
def __init__(self, optimisation_keys):
"""
Init method (constructor)
Parameters
----------
optimisation_keys: list
list of all optimisation keys
"""
self._ref_lists = {}
for key in optimisation_keys:
self._ref_lists[key] = []
self._temp_list = None
def add_optimisation_key(self, key):
"""
Add optimisation key to _ref_lists (for optimised search/sort on that key)
Parameters
----------
key: string
add a new optimisation key to self object
"""
self._ref_lists[key] = []
def remove_node_ref(self, node):
"""
Remove node reference from _ref_lists in all optimisation keys
Parameters
----------
node: Node object
Node class type object to remove from lists of every optimisation key
"""
optimisation_keys = list(self._ref_lists.keys())
for key in optimisation_keys:
if node.cache_key in self._ref_lists[key]:
self._ref_lists[key].remove(node.cache_key)
def add_node_ref(self, node):
"""
Add new node reference in _ref_lists in all optimisation keys
Parameters
----------
node: Node object
Node class type object to add to all lists of all optimisation key
"""
optimisation_keys = list(self._ref_lists.keys())
for key in optimisation_keys:
self.__add_node_at_appr_pos(key, node)
def sort_by(self, key):
"""
Sort by (any specified optimisation key)
Parameters
----------
key: string
one of the optimisation key
Returns
-------
NodeRefGroup object
self object with modified _temp_list, which stores the output
"""
if self._temp_list is None:
if key in self._ref_lists:
self._temp_list = self._ref_lists[key]
else:
self._temp_list = []
else:
# list of all filtered nodes
self._temp_list = [node_ref for node_ref in self._ref_lists[
key] if node_ref in self._temp_list]
return self
def filter_by(self, key, input1, operator="eq"):
"""
Filter nodes in the list
Returns nodes which has node.data[key] based on operator and respective values
Parameters
----------
key: string
any of the keys in node.data
input1: list
list of values (value supports numerical values only)
operator: string
defines what type of filter is being applied (optional)
example: "gt" defines greater than
Returns
-------
NodeRefGroup object
self object with modified _temp_list, which stores the output
"""
if self._temp_list is None:
optimisation_keys = list(self._ref_lists.keys())
self._temp_list = self._ref_lists[optimisation_keys[0]]
# less than
if operator == "lt":
assert isinstance(
input1, numbers.Real), ("Error: numerical value required, " + str(input1) + " given")
self._temp_list = [node.cache_key for node in self.get_all_nodes() if node.data[
key] < input1]
# less than or equal to
if operator == "le":
assert isinstance(
input1, numbers.Real), ("Error: numerical value required, " + str(input1) + " given")
self._temp_list = [node.cache_key for node in self.get_all_nodes() if node.data[
key] <= input1]
# greater than
elif operator == "gt":
assert isinstance(
input1, numbers.Real), ("Error: numerical value required, " + str(input1) + " given")
self._temp_list = [node.cache_key for node in self.get_all_nodes() if node.data[
key] > input1]
# greater than or equal to
elif operator == "ge":
assert isinstance(
input1, numbers.Real), ("Error: numerical value required, " + str(input1) + " given")
self._temp_list = [node.cache_key for node in self.get_all_nodes() if node.data[
key] >= input1]
# not equal to
elif operator == "ne":
assert isinstance(
input1, (list)), ("Error: value must be list of numbers, " + str(input1) + " given")
self._temp_list = [node.cache_key for node in self.get_all_nodes() if node.data[
key] not in input1]
# equal to
elif operator == "eq":
assert isinstance(
input1, (list)), ("Error: value must be list of numbers, " + str(input1) + " given")
self._temp_list = [node.cache_key for node in self.get_all_nodes() if node.data[
key] in input1]
# between range
elif operator == "range":
assert (isinstance(input1, (list)) and len(input1) == 2), (
"Error: input must be a list with two values defining the range, " + str(input1) + " given")
self._temp_list = [node.cache_key for node in self.get_all_nodes() if (
(node.data[key] >= input1[0]) and (node.data[key] <= input1[1]))]
# in array list
elif operator == "in":
assert (isinstance(input1, (list))), (
"Error: input must be a list, " + str(input1) + " given")
self._temp_list = [node.cache_key for node in self.get_all_nodes() if (
node.data[key] in input1)]
else:
raise Exception("Error: operator does not match, " +
str(operator) + " given")
return self
def get_all_nodes(self):
"""
Get all nodes (if method chaining is done, it will return nodes for previous operations)
Returns
-------
list
list of Node objects (if method chaining is done, it will return nodes for previous operations)
example:
node.get_outgoing().filter_by("bananas", [10]).get_all_nodes()
will give list of outgoing nodes with node.data['bananas'] equal to 10
"""
if self._temp_list is None:
optimisation_keys = list(self._ref_lists.keys())
self._temp_list = self._ref_lists[optimisation_keys[0]]
# expired node ref keys still exist in _ref_lists
nodes = filter(
None, map(lambda x: Cache.get(x, True), self._temp_list))
self._temp_list = None # reset _temp_list
return list(nodes)
def get_node_indexed_at(self, index):
"""
Get node at given index (if method chaining is done, it will return node at index in list from previous operations)
Parameters
----------
index: int
index of required node
Returns
-------
Node object
Node class type object at specified index
example:
node.get_outgoing().filter_by("bananas", [10]).get_node_indexed_at(3)
will give node at index 3 from list of outgoing nodes with node.data['bananas'] equal to 10
"""
if self._temp_list is None:
optimisation_keys = list(self._ref_lists.keys())
self._temp_list = self._ref_lists[optimisation_keys[0]]
if len(self._temp_list) > index:
node = Cache.get(self._temp_list[index])
self._temp_list = None # reset _temp_list
return node
else:
raise Exception("Index Error: " + index + " is not found")
def __add_node_at_appr_pos(self, key, node_to_add):
"""
Adds reference of node (ie node.cache_key) at appropriate index in sorted _ref_lists for given optimisation key
(private method)
Parameters
----------
key: string
optimisation key which specifies the list to add into
node_to_add: Node object
Node class type object to add in 'key' optimisation key's list of nodes
"""
nodes = self.sort_by(key).get_all_nodes()
if nodes:
# list of all node values of given key
list_of_values = list(
filter(None, ((yield node.data[key]) for node in nodes)))
# get appropriate position and insert
pos = bisect_right(list_of_values, node_to_add.data[key])
self._ref_lists[key].insert(pos, node_to_add.cache_key)
else:
self._ref_lists[key].append(node_to_add.cache_key)
|
nilq/baby-python
|
python
|
# Building a List
# Generate all possible combinations of a string
#
# https://www.hackerrank.com/challenges/building-a-list/problem
#
import itertools
def combo(s):
n = len(s)
def lex():
for i in range(1, 2 ** n):
j = i
w = ''
k = 0
while j != 0:
j, r = divmod(j, 2)
if r:
w += s[k]
k += 1
yield w
for w in sorted(lex()):
print(w)
for _ in range(int(input())):
input()
s = input()
combo(s)
|
nilq/baby-python
|
python
|
from utils import timer_decorator
@timer_decorator
def fact_1(n: int) -> int:
product = 1
for i in range(n):
product = product * (i+1)
return product
@timer_decorator
def fact_2(n: int) -> int:
if n == 0:
return 1
return n * fact_2(n-1)
|
nilq/baby-python
|
python
|
from phreedf import *
name = 'phreedf'
|
nilq/baby-python
|
python
|
import os
from fnmatch import fnmatch
import pandas as pd
class ReadFile:
def __init__(self, corpus_path, iters_bulk_size=1000000):
self.corpus_path = corpus_path
list_of_files_to_read = self.find_data_paths_in_corpus()
self.data_paths = [file_path for file_path in list_of_files_to_read if "ignore" not in file_path]
self.iters_bulk_size = iters_bulk_size
def read_file(self, file_name):
"""
This function is reading a parquet file contains several tweets
The file location is given as a string as an input to this function.
:param file_name: string - indicates the path to the file we wish to read.
:return: a dataframe contains tweets.
"""
# full_path = os.path.join(self.corpus_path, file_name)
full_path = self.corpus_path + "/" + os.path.join(file_name)
df = pd.read_parquet(full_path, engine="pyarrow")
return df.values.tolist()
def find_data_paths_in_corpus(self, file_type=".parquet"):
return self.find_data_paths_in_folder(self.corpus_path, file_type)
def find_data_paths_in_folder(self, folder_path, file_type):
# data_paths = []
# # for file_name in lstd(folder_path):
# for root, subdirs, files in os.walk(folder_path):
# for file_name in files:
# if file_name.endswith(file_type):
# actual_file_path = root + "\\" + file_name
# data_paths.append(actual_file_path)
# for subdir in subdirs:
# data_paths += self.find_data_paths_in_folder(root + "\\" + subdir, file_type)
# return data_paths
data_paths = []
root = folder_path
pattern = "*" + file_type
for path, subdirs, files in os.walk(root):
for name in files:
if fnmatch(name, pattern):
data_paths.append(os.path.join(path, name))
return data_paths
def __iter__(self):
return ReadFileIterator(self.data_paths, self.iters_bulk_size)
class ReadFileIterator:
def __init__(self, paths, bulk_size):
self.data_paths = list(paths)
self.bulk_size = bulk_size
self._file_index = 0
self._file_offset = 0
self.current_df = None
def __next__(self):
if self._file_index == len(self.data_paths):
raise StopIteration
if self.current_df is None:
self.current_df = pd.read_parquet(self.data_paths[self._file_index], engine="pyarrow")
from_offset = self._file_offset
until_offset = self._file_offset + self.bulk_size
return_results = self.current_df[from_offset:until_offset]
if len(self.current_df) <= until_offset:
self._file_index += 1
self._file_offset = 0
self.current_df = None
else:
self._file_offset = until_offset
return return_results.values.tolist()
|
nilq/baby-python
|
python
|
import os.path
import json
from prjxray import grid
from prjxray import tile
from prjxray import tile_segbits
from prjxray import site_type
from prjxray import connections
def get_available_databases(prjxray_root):
""" Return set of available directory to databases given the root directory
of prjxray-db
"""
db_types = set()
for d in os.listdir(prjxray_root):
if d.startswith("."):
continue
dpath = os.path.join(prjxray_root, d)
if os.path.exists(os.path.join(dpath, "settings.sh")):
db_types.add(dpath)
return db_types
class Database(object):
def __init__(self, db_root):
""" Create project x-ray Database at given db_root.
db_root: Path to directory containing settings.sh, *.db, tilegrid.json and
tileconn.json
"""
self.db_root = db_root
# tilegrid.json JSON object
self.tilegrid = None
self.tileconn = None
self.tile_types = None
self.tile_types = {}
self.tile_segbits = {}
self.site_types = {}
for f in os.listdir(self.db_root):
if f.endswith('.json') and f.startswith('tile_type_'):
tile_type = f[len('tile_type_'):-len('.json')].lower()
segbits = os.path.join(
self.db_root, 'segbits_{}.db'.format(tile_type))
if not os.path.isfile(segbits):
segbits = None
ppips = os.path.join(
self.db_root, 'ppips_{}.db'.format(tile_type))
if not os.path.isfile(ppips):
ppips = None
mask = os.path.join(
self.db_root, 'mask_{}.db'.format(tile_type))
if not os.path.isfile(mask):
mask = None
tile_type_file = os.path.join(
self.db_root, 'tile_type_{}.json'.format(
tile_type.upper()))
if not os.path.isfile(tile_type_file):
tile_type_file = None
self.tile_types[tile_type.upper()] = tile.TileDbs(
segbits=segbits,
ppips=ppips,
mask=mask,
tile_type=tile_type_file,
)
if f.endswith('.json') and f.startswith('site_type_'):
site_type_name = f[len('site_type_'):-len('.json')]
self.site_types[site_type_name] = os.path.join(self.db_root, f)
self.tile_types_obj = {}
def get_tile_types(self):
""" Return list of tile types """
return self.tile_types.keys()
def get_tile_type(self, tile_type):
""" Return Tile object for given tilename. """
if tile_type not in self.tile_types_obj:
self.tile_types_obj[tile_type] = tile.Tile(
tile_type, self.tile_types[tile_type])
return self.tile_types_obj[tile_type]
def _read_tilegrid(self):
""" Read tilegrid database if not already read. """
if not self.tilegrid:
with open(os.path.join(self.db_root, 'tilegrid.json')) as f:
self.tilegrid = json.load(f)
def _read_tileconn(self):
""" Read tileconn database if not already read. """
if not self.tileconn:
with open(os.path.join(self.db_root, 'tileconn.json')) as f:
self.tileconn = json.load(f)
def grid(self):
""" Return Grid object for database. """
self._read_tilegrid()
return grid.Grid(self.tilegrid)
def _read_tile_types(self):
for tile_type, db in self.tile_types.items():
with open(db.tile_type) as f:
self.tile_types[tile_type] = json.load(f)
def connections(self):
self._read_tilegrid()
self._read_tileconn()
self._read_tile_types()
tile_wires = dict(
(tile_type, db['wires'])
for tile_type, db in self.tile_types.items())
return connections.Connections(
self.tilegrid, self.tileconn, tile_wires)
def get_site_types(self):
return self.site_types.keys()
def get_site_type(self, site_type_name):
with open(self.site_types[site_type_name]) as f:
site_type_data = json.load(f)
return site_type.SiteType(site_type_data)
def get_tile_segbits(self, tile_type):
if tile_type not in self.tile_segbits:
self.tile_segbits[tile_type] = tile_segbits.TileSegbits(
self.tile_types[tile_type.upper()])
return self.tile_segbits[tile_type]
|
nilq/baby-python
|
python
|
"""Wordlists loaded from package data.
We can treat them as part of the code for the imperative mood check, and
therefore we load them at import time, rather than on-demand.
"""
import re
import pkgutil
import snowballstemmer
from typing import Iterator, Dict, Set
#: Regular expression for stripping comments from the wordlists
COMMENT_RE = re.compile(r'\s*#.*')
#: Stemmer function for stemming words in English
stem = snowballstemmer.stemmer('english').stemWord
def load_wordlist(name: str) -> Iterator[str]:
"""Iterate over lines of a wordlist data file.
`name` should be the name of a package data file within the data/
directory.
Whitespace and #-prefixed comments are stripped from each line.
"""
data = pkgutil.get_data('pydocstyle', 'data/' + name)
if data is not None:
text = data.decode('utf8')
for line in text.splitlines():
line = COMMENT_RE.sub('', line).strip()
if line:
yield line
#: A dict mapping stemmed verbs to the imperative form
def make_imperative_verbs_dict(wordlist: Iterator[str]) -> Dict[str, Set[str]]:
imperative_verbs = {} # type: Dict[str, Set[str]]
for word in wordlist:
imperative_verbs.setdefault(stem(word), set()).add(word)
return imperative_verbs
IMPERATIVE_VERBS = make_imperative_verbs_dict(load_wordlist('imperatives.txt'))
#: Words that are forbidden to appear as the first word in a docstring
IMPERATIVE_BLACKLIST = set(load_wordlist('imperatives_blacklist.txt'))
|
nilq/baby-python
|
python
|
# Copyright 2013-2018 Lawrence Livermore National Security, LLC and other
# Spack Project Developers. See the top-level COPYRIGHT file for details.
#
# SPDX-License-Identifier: (Apache-2.0 OR MIT)
from spack import *
class RBase64(RPackage):
"""Compatibility wrapper to replace the orphaned package by Romain
Francois. New applications should use the 'openssl' or 'base64enc'
package instead."""
homepage = "https://cran.r-project.org/package=base64"
url = "https://cran.rstudio.com/src/contrib/base64_2.0.tar.gz"
list_url = "https://cran.r-project.org/src/contrib/Archive/base64"
version('2.0', 'f5a653842f75ad717ef6a00969868ae5')
depends_on('r-openssl', type=('build', 'run'))
|
nilq/baby-python
|
python
|
from emanager.hr.worker import *
import emanager.utils.file_ops as fop
# fop.init_attendance_sheet(HR_DATA_DIR, ["WO2106242355"])
# test worker.py
worker_name = "Worker 1"
id_ = check_stakeholder_existance(WORKER_DATA_FILE, worker_name)
if id_ is None:
address = "address2"
age = 46
mobile_no = "7908795345"
join_date = "20/03/2017"
pay_r = 250.0
group = WORKER_GROUP["T"]
worker = AddWorker(
worker_name, age, address, mobile_no, join_date, pay_r, group=group
)
id_ = worker.id_
worker1 = Worker(id_)
# worker1.update_details(MOBILE_NO="756024861", ADDRESS="new address1")
# worker1.update_pay_rate(200)
update_attendance({id_: 1})
print("attendance check - ", worker1.check_attendance("2021-07-03", "2021-07-13"))
print("salary calc-",worker1.calc_salary())
worker1.credit_salary()
|
nilq/baby-python
|
python
|
# 使用__slots__限制类可定义的属性
class Student(object):
__slots__ = ("name","age")
#测试绑定其它属性
s = Student()
s.score = 90
|
nilq/baby-python
|
python
|
# !/usr/bin/env python
# -*- coding: utf-8 -*-
__author__ = "Bruce_H_Cottman"
__license__ = "MIT License"
import pandas as pd
from pydataset import data
from flask import Flask, request, render_template
from flask_bootstrap import Bootstrap
app = Flask(__name__)
app.debug = True
Bootstrap(app)
@app.route('/show', methods=['GET'])
def dataViewer():
dataset_name = request.args.get('dataset')
if dataset_name == None:
dataset_name = 'Aids2'
if type(data(dataset_name)) != pd.core.frame.DataFrame:
return('Bad dataset name:{}'.format(dataset_name))
df = data(dataset_name)
return render_template("df.html", name=dataset_name, data=df.to_html())
if __name__ == '__main__':
app.run(debug=True, host='0.0.0.0')
|
nilq/baby-python
|
python
|
class MegaCoolNumbersEasy:
def count(self, N):
def is_mega(s):
return len(set(ord(s[i]) - ord(s[i - 1]) for i in range(1, len(s)))) < 2
return sum([is_mega(str(e)) for e in xrange(1, N + 1)])
|
nilq/baby-python
|
python
|
#joel Lee
#3/21/2021
#creates a due date for a task
from driver import driver, Keys
import time
#connects to website
driver.get("http://localhost:8080")
#finds the create task button
findtask_button = driver.find_element_by_xpath('//*[@id="tasklist"]/div[1]/h2/span[1]/button').click()
time.sleep(2)
#finds the task input field
entertask = driver.find_element_by_xpath('/html/body/span[2]/div/div/span/input[1]')
time.sleep(1)
#asks the user for task name
task = input("Enter task name")
entertask.clear()
entertask.send_keys(task)
time.sleep(1)
dueDate = driver.find_element_by_xpath('/html/body/span[2]/div/div/span/input[3]')
MonthDayYear = input("input the Date using format Year-Month-Day Ex: 2021-03-22 :")
dueDate.clear()
dueDate.send_keys(MonthDayYear)
time.sleep(1)
dateTime = input("Do you want to set a specific time yes/no?")
#if statment to determine if you want to set a time for due date if not then it will set it automatically at midnight
if dateTime == 'yes':
dueDate.send_keys(Keys.TAB)
timeInput = input("Enter due date time following this format --:-- Ex: T05:30 :")
dueDate.send_keys(timeInput)
else:
print('skipping time')
time.sleep(1)
#clicks the create task button
click_create = driver.find_element_by_xpath('/html/body/span[2]/div/div/span/button').click()
test = input("press enter when done")
|
nilq/baby-python
|
python
|
import json
import requests
import base64
import Crypto
from Crypto.PublicKey import RSA
from Crypto.Cipher import PKCS1_OAEP
from Crypto.Hash import SHA
#from Crypto import Random
public_key = RSA.importKey(open('/tmp/duetpublickey.pem').read())
message = "1,27"
# default hash Algorithm is SHA1, mask generation function is MGF1, no label is specified
# https://pycryptodome.readthedocs.io/en/latest/src/cipher/oaep.html
cipher = PKCS1_OAEP.new(public_key)
encrypted_message = base64.encodestring(cipher.encrypt(message)).replace("\n","")
data = { "value" : encrypted_message }
headers = { 'Content-type': 'application/json', 'Accept': 'application/json' }
requests.post('http://localhost:5000/insert', data=json.dumps(data), headers=headers)
#print(encrypted_message)
|
nilq/baby-python
|
python
|
#!/bin/python
import sys
import math
def _pal2(n):
h = n / 1000 + 1
while True:
p = str(h)
p += p[2::-1]
yield int(p)
h -= 1
def ifact(i):
f1 = int(math.sqrt(float(i))) + 1
while f1 > 99:
f2,r = divmod(i,f1)
if f2 > 999:
return None
if r == 0:
return (f1,f2)
f1 -= 1
return None
T = int(raw_input().strip())
for t in range(T):
N = int(raw_input().strip())
for x in _pal2(N):
if x >= N:
continue
facts = ifact(x)
if facts:
print x
break
|
nilq/baby-python
|
python
|
#
# -*- coding: utf-8 -*-
#
# @Author: Arrack
# @Date: 2020-05-25 17:25:12
# @Last modified by: Arrack
# @Last Modified time: 2020-06-08 15:27:48
#
from wtforms import BooleanField
from wtforms import Form
from wtforms import PasswordField
from wtforms import StringField
from wtforms import SubmitField
from wtforms import TextAreaField
from wtforms import HiddenField
from wtforms.validators import DataRequired
from wtforms.validators import Length
class LoginForm(Form):
email = StringField(
label='Email',
validators=[DataRequired(), Length(1, 64)])
password = PasswordField(
label='Password',
validators=[DataRequired(), Length(1, 128)])
remember_me = BooleanField('Remember me.')
submit = SubmitField()
class TalkForm(Form):
content = TextAreaField(validators=[DataRequired()])
private = BooleanField()
submit = SubmitField()
class ArticleForm(Form):
title = StringField(validators=[DataRequired()])
content = TextAreaField(validators=[DataRequired()])
# time = StringField('datetime', validators=[DataRequired()])
tags = StringField()
newTags = StringField()
category = HiddenField()
tags = HiddenField()
# url_name = StringField('urlName', validators=[DataRequired()])
# save_draft = SubmitField('save')
submit = SubmitField()
|
nilq/baby-python
|
python
|
numero1 = float(input("primeiro numero: "))
numero2 = float(input("segundo numero: "))
numero3 = float(input("terceiro numero: "))
numero4 = float(input("quarto numero: "))
numero5 = float(input("quinto numero: "))
lista = [numero1,numero2,numero3,numero4,numero5]
soma = sum(lista)
print (soma)
|
nilq/baby-python
|
python
|
"""
Python definitions used to help with plotting routines.
*Methods Overview*
-> geo_scatter(): Geographical scatter plot.
"""
import matplotlib.pyplot as plt
from warnings import warn
from .logging_util import warn
import numpy as np
def r2_lin(x, y, fit):
"""For calculating r-squared of a linear fit. Fit should be a python polyfit object."""
y_estimate = fit(x)
difference = (y - y_estimate) ** 2
y_mean = np.nanmean(y)
mean_square_deviation = (y - y_mean) ** 2
total_deviation = np.nansum(mean_square_deviation)
residual = np.nansum(difference)
correlation_coefficient = 1 - residual / total_deviation
return correlation_coefficient
def scatter_with_fit(x, y, s=10, c="k", yex=True, dofit=True):
"""Does a scatter plot with a linear fit. Will also draw y=x for
comparison.
Parameters
----------
x : (array) Values for the x-axis
y : (array) Values for the y-axis
s : (float or array) Marker size(s)
c : (float or array) Marker colour(s)
yex : (bool) True to plot y=x
dofit : (bool) True to calculate and plot linear fit
Returns
-------
Figure and axis objects for further customisation
Example Useage
-------
x = np.arange(0,50)
y = np.arange(0,50)/1.5
f,a = scatter_with_fit(x,y)
a.set_title('Example scatter with fit')
a.set_xlabel('Example x axis')
a.set_ylabel('Example y axis')
"""
fig = plt.figure()
ax = fig.add_subplot(1, 1, 1)
x = np.ma.masked_invalid(x)
y = np.ma.masked_invalid(y)
combined_mask = np.ma.mask_or(x.mask, y.mask)
x.mask = combined_mask
y.mask = combined_mask
xmax = np.ma.max(x)
xmin = np.ma.min(x)
ymax = np.ma.max(y)
ymin = np.ma.min(y)
axmax0 = np.max([xmax, ymax])
axmin0 = np.min([xmin, ymin])
axmin = axmin0 - 0.1 * np.abs(axmax0 - axmin0)
axmax = axmax0 + 0.1 * np.abs(axmax0 - axmin0)
if yex:
line_x = [axmin, axmax]
fit_yx = np.poly1d([1, 0])
ax.plot(line_x, fit_yx(line_x), c=[0.5, 0.5, 0.5], linewidth=1)
ax.scatter(x, y, c=c, s=s)
if dofit:
line_x = [axmin, axmax]
# Calculate data fit and cast to poly1d object
fit_tmp = np.ma.polyfit(x, y, 1)
fit = np.poly1d(fit_tmp)
ax.plot(line_x, fit(line_x), c=[1, 128 / 255, 0], linewidth=1.5)
r2 = r2_lin(x, y, fit)
ax.set_xlim(axmin, axmax)
ax.set_ylim(axmin, axmax)
ax.set_aspect("equal", adjustable="box")
ax.grid()
if dofit:
ax.text(
0.4, 0.125, "{} {:03.2f} {} {:03.2f}".format("y =", fit_tmp[0], "x +", fit_tmp[1]), transform=ax.transAxes
)
ax.text(0.4, 0.05, "{} {:03.2f} ".format("$R^2$ =", r2), transform=ax.transAxes)
return fig, ax
def create_geo_subplots(lonbounds, latbounds, n_r=1, n_c=1, figsize=(7, 7)):
"""
A routine for creating an axis for any geographical plot. Within the
specified longitude and latitude bounds, a map will be drawn up using
cartopy. Any type of matplotlib plot can then be added to this figure.
For example:
Example Useage
#############
f,a = create_geo_axes(lonbounds, latbounds)
sca = a.scatter(stats.longitude, stats.latitude, c=stats.corr,
vmin=.75, vmax=1,
edgecolors='k', linewidths=.5, zorder=100)
f.colorbar(sca)
a.set_title('SSH correlations \n Monthly PSMSL tide gauge vs CO9_AMM15p0',
fontsize=9)
* Note: For scatter plots, it is useful to set zorder = 100 (or similar
positive number)
"""
import cartopy.crs as ccrs # mapping plots
from cartopy.feature import NaturalEarthFeature
# If no figure or ax is provided, create a new one
# fig = plt.figure()
# fig.clf()
fig, ax = plt.subplots(
n_r, n_c, subplot_kw={"projection": ccrs.PlateCarree()}, sharey=True, sharex=True, figsize=figsize
)
land_color = [0.9, 0.9, 0.9]
coast_color = [0, 0, 0]
coast_width = 0.25
if n_r * n_c > 1:
ax = ax.flatten()
for rr in range(n_r * n_c):
coast = NaturalEarthFeature(category="physical", facecolor=land_color, name="coastline", scale="50m")
ax[rr].add_feature(coast, edgecolor=coast_color, linewidth=coast_width)
# ax.coastlines(facecolor=[0.8,0.8,0.8])
gl = ax[rr].gridlines(crs=ccrs.PlateCarree(), draw_labels=True, linewidth=0.5, color="gray", linestyle="-")
gl.top_labels = False
gl.right_labels = False
if rr % n_c == 0:
gl.left_labels = True
else:
gl.left_labels = False
if np.abs(n_r * n_c - rr) <= n_c:
gl.bottom_labels = True
else:
gl.bottom_labels = False
ax[rr].set_xlim(lonbounds[0], lonbounds[1])
ax[rr].set_ylim(latbounds[0], latbounds[1])
ax[rr].set_aspect("auto")
ax = ax.reshape((n_r, n_c))
else:
coast = NaturalEarthFeature(category="physical", facecolor=land_color, name="coastline", scale="50m")
ax.add_feature(coast, edgecolor=coast_color, linewidth=coast_width)
# ax.coastlines(facecolor=[0.8,0.8,0.8])
gl = ax.gridlines(crs=ccrs.PlateCarree(), draw_labels=True, linewidth=0.5, color="gray", linestyle="-")
gl.top_labels = False
gl.right_labels = False
gl.left_labels = True
gl.bottom_labels = True
ax.set_xlim(lonbounds[0], lonbounds[1])
ax.set_ylim(latbounds[0], latbounds[1])
ax.set_aspect("auto")
plt.show()
return fig, ax
def create_geo_axes(lonbounds, latbounds):
"""
A routine for creating an axis for any geographical plot. Within the
specified longitude and latitude bounds, a map will be drawn up using
cartopy. Any type of matplotlib plot can then be added to this figure.
For example:
Example Useage
#############
f,a = create_geo_axes(lonbounds, latbounds)
sca = a.scatter(stats.longitude, stats.latitude, c=stats.corr,
vmin=.75, vmax=1,
edgecolors='k', linewidths=.5, zorder=100)
f.colorbar(sca)
a.set_title('SSH correlations \n Monthly PSMSL tide gauge vs CO9_AMM15p0',
fontsize=9)
* Note: For scatter plots, it is useful to set zorder = 100 (or similar
positive number)
"""
import cartopy.crs as ccrs # mapping plots
from cartopy.feature import NaturalEarthFeature
# If no figure or ax is provided, create a new one
fig = plt.figure(1)
fig.clf()
ax = fig.add_subplot(1, 1, 1, projection=ccrs.PlateCarree())
coast = NaturalEarthFeature(category="physical", facecolor=[0.9, 0.9, 0.9], name="coastline", scale="50m")
ax.add_feature(coast, edgecolor="gray")
# ax.coastlines(facecolor=[0.8,0.8,0.8])
gl = ax.gridlines(crs=ccrs.PlateCarree(), draw_labels=True, linewidth=0.5, color="gray", linestyle="-")
gl.top_labels = False
gl.bottom_labels = True
gl.right_labels = False
gl.left_labels = True
ax.set_xlim(lonbounds[0], lonbounds[1])
ax.set_ylim(latbounds[0], latbounds[1])
ax.set_aspect("auto")
plt.show()
return fig, ax
def ts_diagram(temperature, salinity, depth):
fig = plt.figure(figsize=(10, 7))
ax = plt.scatter(salinity, temperature, c=depth)
cbar = plt.colorbar()
cbar.set_label("Depth (m)")
plt.title("T-S Diagram")
plt.xlabel("Salinity")
plt.ylabel("Temperature")
return fig, ax
def geo_scatter(
longitude,
latitude,
c=None,
s=None,
scatter_kwargs=None,
coastline_kwargs=None,
gridline_kwargs=None,
figure_kwargs={},
title="",
figsize=None,
): # TODO Some unused parameters here
"""
Uses CartoPy to create a geographical scatter plot with land boundaries.
Parameters
----------
longitude : (array) Array of longitudes of marker locations
latitude : (array) Array of latitudes of marker locations
colors : (array) Array of values to use for colouring markers
title : (str) Plot title, to appear at top of figure
xlim : (tuple) Tuple of limits to apply to the x-axis (longitude axis)
ylim : (tuple) Limits to apply to the y-axis (latitude axis)
Returns
-------
Figure and axis objects for further customisation
"""
try:
import cartopy.crs as ccrs # mapping plots
from cartopy.feature import NaturalEarthFeature
except ImportError:
import sys
warn("No cartopy found - please run\nconda install -c conda-forge cartopy")
sys.exit(-1)
if coastline_kwargs is None:
coastline_kwargs = {"facecolor": [0.9, 0.9, 0.9], "name": "coastline", "scale": "50m"}
if scatter_kwargs is None:
scatter_kwargs = {}
fig = plt.figure(**figure_kwargs)
ax = plt.subplot(111, projection=ccrs.PlateCarree())
sca = ax.scatter(longitude, y=latitude, c=c, s=s, zorder=100, **scatter_kwargs)
coast = NaturalEarthFeature(category="physical", **coastline_kwargs)
ax.add_feature(coast, edgecolor="gray")
# ax.coastlines(facecolor=[0.8,0.8,0.8])
gl = ax.gridlines(crs=ccrs.PlateCarree(), draw_labels=True, linewidth=0.5, color="gray", linestyle="-")
gl.top_labels = False
gl.bottom_labels = True
gl.right_labels = False
gl.left_labels = True
plt.title(title)
if c is not None and "vmax" in scatter_kwargs.keys() and "vmin" in scatter_kwargs.keys():
extend_max = np.nanmax(c) > scatter_kwargs["vmax"]
extend_min = np.nanmin(c) < scatter_kwargs["vmin"]
extend = "neither"
if extend_max and extend_min:
extend = "both"
if extend_max and not extend_min:
extend = "max"
if not extend_max and extend_min:
extend = "min"
else:
extend = "neither"
plt.colorbar(sca, extend=extend)
ax.set_aspect("auto")
plt.show()
return fig, ax
def determine_colorbar_extension(color_data, vmin, vmax):
"""Can be used to automatically determine settings for colorbar
extension arrows. Color_data is the data used for the colormap, vmin
and vmax are the colorbar limits. Will output a string: "both", "max",
"min" or "neither", which can be inserted straight into a call to
matplotlib.pyplot.colorbar().
"""
extend_max = np.nanmax(color_data) > vmax
extend_min = np.nanmin(color_data) < vmin
if extend_max and extend_min:
return "both"
elif extend_max and not extend_min:
return "max"
elif not extend_max and extend_min:
return "min"
else:
return "neither"
def determine_clim_by_standard_deviation(color_data, n_std_dev=2.5):
"""Automatically determine color limits based on number of standard
deviations from the mean of the color data (color_data). Useful if there
are outliers in the data causing difficulties in distinguishing most of
the data. Outputs vmin and vmax which can be passed to plotting routine
or plt.clim().
"""
color_data_mean = np.nanmean(color_data)
color_data_std = np.nanstd(color_data)
vmin = color_data_mean - n_std_dev * color_data_std
vmax = color_data_mean + n_std_dev * color_data_std
return vmin, vmax
|
nilq/baby-python
|
python
|
from direct.directnotify import DirectNotifyGlobal
from src.connection.protocol import *
from direct.distributed.PyDatagram import PyDatagram
from direct.distributed.PyDatagramIterator import PyDatagramIterator
from src.messagedirector.ChannelWatcher import ChannelWatcher
class MDParticipant(ChannelWatcher):
notify = DirectNotifyGlobal.directNotify.newCategory("MessageDirectorParticipant")
notify.setInfo(True)
channelAllocator = None
canClearChannel = False
channelWatcher = ChannelWatcher()
def __init__(self, base_class):
self.base_class = base_class
def handleDatagram(self, dgi, connection):
messageType = dgi.getUint16()
if messageType == CONTROL_SET_CHANNEL:
self.registerChannel(dgi.getUint64(), connection)
elif messageType == CONTROL_REMOVE_CHANNEL:
self.unregisterChannel(dgi.getUint64(), connection)
elif messageType == CONTROL_MESSAGE:
self.base_class.routeMessageToChannel(dgi.getUint64(), dgi.getUint64(), dgi.getDatagram(), connection)
elif messageType == CONTROL_ADD_RANGE:
self.addChannelRange(dgi.getUint64())
elif messageType == CONTROL_REMOVE_RANGE:
self.removeChannelRange()
elif messageType == CONTROL_ADD_POST_REMOVE:
self.addPostRemove(dgi.getUint64())
elif messageType == CONTROL_CLEAR_POST_REMOVE:
if canClearChannel:
self.clearPostRemove(dgi.getUint64())
else:
self.notify.warning("Participant was not authorized to remove channel: %s" % str(dgi.getUint64()))
return
else:
self.notify.warning("Could not handle incoming datagram: %s" % str(messageType))
return
def registerChannel(self, channel, connection):
if channel not in self.base_class.channels:
if channel is None:
self.notify.warning("Someone tried to register a channel but the channel value was null!")
return
else:
self.base_class.channels[channel] = connection
self.channelWatcher.subscribed_channel(channel)
else:
self.notify.warning("Channel: %s is already registered!" % str(channel))
return
def unregisterChannel(self, channel, connection):
if channel in self.base_class.channels:
del self.base_class.channels[channel]
self.channelWatcher.unsubscribed_channel(channel)
else:
self.notify.warning("Channel: %s was never registered!" % str(channel))
return
def addChannelRange(self, channelRange):
pass
def removeChannelRange(self):
pass
def addPostRemove(self, channel):
pass
def clearPostRemove(self, channel):
pass
|
nilq/baby-python
|
python
|
# NOTE THAT 'data' IN THIS CODE IS TRANSPOSE, COMPARED WITH THE MANIFOLDER CODE
# data is shaped [13848,8] here ... ogm ...
# loads in data, dimlist, n_channels, npts, x
# file_location_in_data
# file_location_out_data
import numpy as np
# Coweb, if not installed, can 'pip install -U concept_formation'
# https://github.com/cmaclell/concept_formation
from concept_formation.cobweb3 import Cobweb3Tree
from concept_formation.cluster import cluster
import time
import os
from matplotlib.pyplot import cm
import matplotlib.pyplot as plt
dim_list = ['p_speed', 'p_density', 'xhelicity', 'O7to6', 'residualE', 'absB', 'Z_Fe', 'Fe_to_O']
file_location_in_data = '/Users/jonathan/Documents/repos/MANIFOLDER/whitened_short_set/whitened_short_set.csv'
file_location_out_data = '/Users/jonathan/Documents/repos/MANIFOLDER/whitened_short_set/whitened_short_set_2.csv'
# read data from csv file
def load_data():
""" load the data from csv, and do initial parsing """
x = np.genfromtxt(file_location_in_data, delimiter=',')
data = x[:, :].astype('float64') # data points: 13848 x 8 numpy array: 13848 points, 8 channels
print('data.shape = ', data.shape)
#npts = data.shape[0]
#n_channels = data.shape[1]
return data
def cluster_cobweb3(data):
""" cluster the data, using cobweb3"""
npts = data.shape[0]
n_channels = data.shape[1]
# convert data from np array to list of dictionariesw
data_new = []
for i in range(npts):
pt = data[i, :]
pt_dict = {dim_list[j]: pt[j] for j in range(n_channels)}
data_new.append(pt_dict)
# perform cobweb3 clustering and get labels
print('starting cobweb3')
print('note, this can take some time ...')
start_time = time.time()
tree = Cobweb3Tree()
clusters = cluster(tree, data_new[:])[0]
print('# points:', len(clusters))
clust_names = [c for c in set(clusters)]
print(' cluster names:', clust_names)
clust_dict = {c: idx for idx, c in enumerate(clust_names)}
print(clust_dict)
lbs = [clust_dict[c] for c in clusters]
print('length of lbs:', len(lbs))
clust_dict = {c: idx for idx, c in enumerate(clust_names)}
print(clust_dict)
lbs = [clust_dict[c] for c in clusters]
print('length of lbs:', len(lbs))
elapsed_time = time.time() - start_time
print('done, elapsed mins:', np.round(elapsed_time / 60, 2))
# append labels to csv file of data
lbs = np.asarray(lbs).reshape(len(lbs), 1)
print(lbs.shape)
new = np.concatenate((data, lbs), axis=1)
print(new.shape)
np.savetxt(file_location_out_data, new, delimiter=',')
print('done with cluster_cobweb3')
# main use of this function is to return the clusters, and the labels?
return clusters, lbs
def show_clusters_cobweb3(data, clusters, lbs, base_path='results/cobweb/'):
""" need clusters and lbs """
npts = data.shape[0]
n_channels = data.shape[1]
# get point indices for plotting
indices_lists = []
n_clusters = len(set(clusters))
for i in range(n_clusters):
indices = np.where(lbs == i)[0].tolist()
indices_lists.append(indices)
print('# clusters:', len(indices_lists))
npts_list = [len(i) for i in indices_lists]
print('# points in clusters: {}'.format(npts_list))
# sort indices_lists based on number of points in each sublist
npts_list_sorted = np.argsort(np.asarray(npts_list))
print('Sorted indices of lists of number of points:', npts_list_sorted)
indices_lists_sorted = [indices_lists[i] for i in npts_list_sorted]
indices_lists = indices_lists_sorted
print('Sorted # poitns in clusters: {}'.format([len(i) for i in indices_lists]))
npts_list = sorted(npts_list)
print('Sorted # poitns in clusters: {}'.format(npts_list))
# plot clusters - each figure shows two channels
colors = cm.rainbow(np.linspace(0, 1, n_clusters))
plt.figure(figsize=(20, 4))
for k in range(n_channels):
for i in range(n_channels):
plt.subplot(2, 4, i + 1)
for indices, c in zip(indices_lists, colors):
c = c.reshape((1, -1)) # NOTE - for the warning, make into a single row?
plt.scatter(data[indices, k], data[indices, i], c=c, s=1) # c causes issues
#plt.scatter(data[indices, k], data[indices,i], s=1)
#plt.title('x:c{} y:c{}'.format(k, i))
plt.tight_layout()
plt.savefig('results/cobweb3/c{}_vs_all.pdf'.format(k)) #note save pdfs instead (slower, but better images)
#savefig('c{}_vs_all.png'.format(k))
#plt.show() # NOTE - code did not originally show()
plt.clf()
print('Figure c{} done.'.format(k))
plt.close()
print('Figures saved successfully')
# plot original data (channel=speed) in time
target_var = data[:, 0]
# plot 10 intervals of 1000 time points
interval = 1000
plt.figure(figsize=(20, 4))
for i in range(10):
begin = i * interval
end = (i+1) * interval
# find indices of points in each cluster
lb_indices = []
for indices in indices_lists:
indices_interval = [idx for idx in indices if idx >= begin and idx < end]
lb_indices.append(indices_interval)
# plot different colors for clusters
plt.subplot(2, 5, i + 1)
for indices, c in zip(lb_indices, colors):
c = c.reshape((1, -1)) # NOTE - for the warning, make into a single row?
plt.scatter(indices, target_var[indices], c=c, s=1)
plt.title('x in [{}, {}]'.format(begin, end - 1))
print('Plot: x in [{}, {}]'.format(begin, end - 1))
plt.tight_layout()
# savefig('speed_10examples.png') # TODO, port the figure save
plt.savefig('results/cobweb3/speed_10examples.pdf')
plt.close()
print('Figure saved successfully')
###
### Portion for DBScan
###
from sklearn.cluster import DBSCAN
def scan(data):
# perform dbscan
db = DBSCAN(eps=0.5, min_samples=20).fit(data)
#print gmm.means_
lbs = db.labels_
print(lbs)
lbs = lbs.reshape(lbs.shape[0], 1)
print(lbs.shape)
new = np.concatenate((data, lbs), axis=1)
print(new.shape)
np.savetxt('results/dbscan/whitened_short_set_labels_dbscan.csv', new, delimiter=',')
# read data and labels
# NOTE, this file was already generated ... maybe gets regenerated, from above?
#file_location_in_data = '/Users/jonathan/Documents/repos/MANIFOLDER/whitened_short_set/whitened_short_set_labels.csv'
# NOTE - maybe there is a better place for the file?
### WHAT???
# originas is whitened_short_set_labels.csv, not sure ...
file_location_in_data = '/Users/jonathan/Documents/repos/MANIFOLDER/whitened_short_set/cobweb3/whitened_short_set_labels.csv'
# looks like this is loaded in from the wrong location ... ???
data = np.genfromtxt(file_location_in_data, delimiter=',')
lbs = data[:, -1]
n_channels = data.shape[1] - 1
print('Reading from file done.')
return lbs # clusters not needed?
def show_clusters_dbscan(data, lbs):
npts = data.shape[0]
n_channels = data.shape[1]
# get point indices for plotting
indices_lists = []
num_clusters = 0
num_points_used = 0
while True:
indices = np.where(lbs == num_clusters)[0].tolist()
if len(indices) == 0:
break
indices_lists.append(indices)
num_points_used += len(indices)
num_clusters += 1
n_cluster = len(indices_lists)
print('# clusters:', n_cluster)
print('# points used:', num_points_used)
print('# outliers:', npts - num_points_used)
npts_list = [len(i) for i in indices_lists]
print('# points in clusters: {}'.format(npts_list))
# sort indices_lists based on number of points in each sublist
npts_list_sorted = np.argsort(np.asarray(npts_list))
print('Sorted indices of lists of number of points:', npts_list_sorted)
indices_lists_sorted = [indices_lists[i] for i in npts_list_sorted]
indices_lists = indices_lists_sorted
print('Sorted # poitns in clusters: {}'.format([len(i) for i in indices_lists]))
npts_list = sorted(npts_list)
print('Sorted # poitns in clusters: {}'.format(npts_list))
# plot clusters - each figure shows two channels
colors = cm.rainbow(np.linspace(0, 1, n_cluster))
plt.figure(figsize=(20, 4))
for k in range(n_channels):
for i in range(n_channels):
plt.subplot(2, 4, i + 1)
for indices, c in zip(indices_lists, colors):
c = c.reshape((1, -1)) # NOTE - for the warning, make into a single row?
plt.scatter(data[indices, k], data[indices, i], c=c, s=1)
plt.title('x:c{} y:c{}'.format(k, i))
plt.tight_layout()
plt.savefig('results/dbscan/c{}_vs_all.pdf'.format(k))
plt.clf()
print('Figure c{} done.'.format(k))
print('Figures saved successfully')
# plot original data (channel=speed) in time
target_var = data[:, 0]
# plot 10 intervals of 1000 time points
interval = 1000
plt.figure(figsize=(20, 4))
for i in range(10):
begin = i * interval
end = (i+1) * interval
# find indices of points in each cluster
lb_indices = []
for indices in indices_lists:
indices_interval = [idx for idx in indices if idx >= begin and idx < end]
lb_indices.append(indices_interval)
# plot different colors for clusters
plt.subplot(2, 5, i + 1)
for indices, c in zip(lb_indices, colors):
c = c.reshape((1, -1)) # NOTE - for the warning, make into a single row?
plt.scatter(indices, target_var[indices], c=c, s=1)
plt.title('x in [{}, {}]'.format(begin, end - 1))
print('Plot: x in [{}, {}]'.format(begin, end - 1))
plt.tight_layout()
#savefig('speed_10examples.png')
plt.savefig('results/dbscan/speed_10examples.pdf')
plt.clf()
print('Figure saved successfully')
from matplotlib.legend_handler import HandlerLine2D
# plot colors
plt.figure(figsize=(4, 4))
colors = cm.rainbow(np.linspace(0, 1, n_cluster))
x = range(2)
for i, c in zip(range(n_cluster), colors):
y = [(1./n_cluster) * (i+1)] * 2
#c = c.reshape((1,-1)) # NOTE - for the warning, make into a single row?
plt.plot(x, y, c=c, label=str(npts_list[i]))
plt.legend()
plt.savefig('results/dbscan/colors and number of points.pdf')
plt.clf()
print('Plot colors and legend done.')
# if not installed, can 'pip install scikit-fuzzy'
#import skfuzzy_cluster as fcm
# looks like a newer version
import skfuzzy as fuzz
def fuzzy(data):
# jd - feel like this has been done?
# # read data from csv file
# x = np.genfromtxt('../whitened_short_set.csv', delimiter=',')
# data = x[:, :].astype('float64') # data points: 13848 x 8 numpy array: 13848 points, 8 channels
# dim_list = ['p_speed', 'p_density', 'xhelicity', 'O7to6', 'residualE', 'absB', 'Z_Fe', 'Fe_to_O']
# print('data.shape = ', data.shape)
# npts = data.shape[0]
# n_channels = data.shape[1]
n_channels = data.shape[1]
# perform fcm
n_clusters = 8
# cntr, u, u0, d, jm, p, fpc = fcm.cmeans(data.transpose(), n_clusters, 1.1, error=0.005, maxiter=1000)
cntr, u, u0, d, jm, p, fpc = fuzz.cmeans(data.transpose(), n_clusters, 1.1, error=0.005, maxiter=1000)
lbs = np.argmax(u, axis=0)
print('FCM done...')
print(lbs)
print(p)
# # append labels to csv file of data
# lbs = lbs.reshape(lbs.shape[0], 1)
# print lbs.shape
# new = np.concatenate((data, lbs), axis=1)
# print new.shape
# np.savetxt('whitened_short_set_labels.csv', new, delimiter=',')
# get point indices for plotting
indices_lists = []
for i in range(n_clusters):
indices = np.where(lbs == i)[0].tolist()
indices_lists.append(indices)
print('# clusters:', len(indices_lists))
npts_list = [len(i) for i in indices_lists]
print('# points in clusters: {}'.format(npts_list))
# sort indices_lists based on number of points in each sublist
npts_list_sorted = np.argsort(np.asarray(npts_list))
print('Sorted indices of lists of number of points:', npts_list_sorted)
indices_lists_sorted = [indices_lists[i] for i in npts_list_sorted]
indices_lists = indices_lists_sorted
print('Sorted # poitns in clusters: {}'.format([len(i) for i in indices_lists]))
npts_list = sorted(npts_list)
print('Sorted # poitns in clusters: {}'.format(npts_list))
# plot clusters - each figure shows two channels
colors = cm.rainbow(np.linspace(0, 1, n_clusters))
plt.figure(figsize=(20, 4))
for k in range(n_channels):
for i in range(n_channels):
plt.subplot(2, 4, i + 1)
for indices, c in zip(indices_lists, colors):
c = c.reshape((1, -1)) # NOTE - for the warning, make into a single row?
plt.scatter(data[indices, k], data[indices, i], c=c, s=1)
plt.title('x:c{} y:c{}'.format(k, i))
plt.tight_layout()
plt.savefig('results/fcm/c{}_vs_all.pdf'.format(k))
plt.clf()
print('Figure c{} done.'.format(k))
plt.close()
print('Figures saved successfully')
# plot original data (channel=speed) in time
target_var = data[:, 0]
# plot 10 intervals of 1000 time points
interval = 1000
plt.figure(figsize=(20, 4))
for i in range(10):
begin = i * interval
end = (i+1) * interval
# find indices of points in each cluster
lb_indices = []
for indices in indices_lists:
indices_interval = [idx for idx in indices if idx >= begin and idx < end]
lb_indices.append(indices_interval)
# plot different colors for clusters
plt.subplot(2, 5, i + 1)
for indices, c in zip(lb_indices, colors):
c = c.reshape((1, -1)) # NOTE - for the warning, make into a single row?
plt.scatter(indices, target_var[indices], c=c, s=1)
plt.title('x in [{}, {}]'.format(begin, end - 1))
print('Plot: x in [{}, {}]'.format(begin, end - 1))
plt.tight_layout()
plt.savefig('results/fcm/speed_10examples.pdf')
plt.close()
print('Figure saved successfully')
from matplotlib.legend_handler import HandlerLine2D
# plot colors
plt.figure(figsize=(4, 4))
colors = cm.rainbow(np.linspace(0, 1, n_clusters))
x = range(2)
for i, c in zip(range(n_clusters), colors):
y = [(1./n_clusters) * (i+1)] * 2
plt.plot(x, y, c=c, label=str(npts_list[i]))
plt.legend()
plt.savefig('results/fcm/colors and number of points.pdf')
plt.close()
print('Plot colors and legend done.')
|
nilq/baby-python
|
python
|
from django.conf import settings
from django.core.mail import send_mail
from chatbot.models import Notification
class NotificationProcessor:
def __init__(self, notification: Notification):
self.notification = notification
self.notification_map = {
'welcome': {
'subject': self.get_welcome_subject,
'message': self.get_welcome_message,
}
}
def get_welcome_subject(self) -> str:
"""Returns the subject for the welcome email"""
return 'Welcome {}'.format(self.notification.user.username)
def get_welcome_message(self) -> str:
"""Returns the message for the welcome email"""
return 'Lorem Ipsum is simply dummy text of the printing and typesetting industry. \
Lorem Ipsum has been the industry standard dummy text ever since the 1500s.'
def send(self) -> None:
"""Send the notification email"""
_type = self.notification.extra_data['type']
send_mail(
self.notification_map[_type]['subject'](),
self.notification_map[_type]['message'](),
settings.EMAIL_FROM,
[self.notification.user.email],
fail_silently=False,
)
self.notification.sent = True
self.notification.save()
|
nilq/baby-python
|
python
|
from django.shortcuts import render
from django.views.generic import (
ListView,
)
from .models import (
Event,
EventPhoto,
)
class AllEventsView(ListView):
model = Event
queryset = Event.objects.all().filter(status = True)
template_name = "event/all_events.html"
context_object_name = "context_allevents"
class EventDetailView(ListView):
model = Event
template_name = "event/event.html"
context_object_name = "context_eventdetail"
def get_queryset(self, **kwargs):
pk = self.kwargs.get('pk', None)
queryset = self.model.objects.all()
return queryset.filter(id = pk)
def get_context_data(self, **kwargs):
context = super(EventDetailView, self).get_context_data(**kwargs)
pk = self.kwargs.get('pk', None)
# EventPhoto context
query_eventphotos = EventPhoto.objects.filter(status = True, event__pk = pk).select_related()
context['context_eventphotos'] = query_eventphotos
return context
|
nilq/baby-python
|
python
|
from tests.utils.owtftest import OWTFCliTestCase
class OWTFCliExceptTest(OWTFCliTestCase):
categories = ['cli']
def test_except(self):
"""Run OWTF web plugins except one."""
self.run_owtf('-s', '-g', 'web', '-e', 'OWTF-WVS-006', "%s://%s:%s" % (self.PROTOCOL, self.IP, self.PORT))
self.assert_is_in_logs(
'All jobs have been done. Exiting.',
name='MainProcess',
msg='OWTF did not finish properly!')
self.assert_is_not_in_logs(
'Target: %s://%s:%s -> Plugin: Skipfish Unauthenticated' % (self.PROTOCOL, self.IP, self.PORT),
name='Worker',
msg='Skipfish plugin should not have been run!')
|
nilq/baby-python
|
python
|
import numpy as np
import matplotlib.pyplot as plt
import matplotlib as mpl
mpl.rc('font', **{'family': 'serif', 'serif': ['Computer Modern'], 'size': 14})
mpl.rc('text', usetex=True)
#V1 = np.genfromtxt('Heat.txt')[::-1]
V2 = np.genfromtxt('Heat_b25.txt')[::-1]
'''
N = len(V1)
plt.figure(figsize=(7,7))
plt.title(r'$\beta=0.1$')
#plt.title('Potential $V(x,y)$')
plt.imshow(V1, interpolation='kaiser') #, cmap=mpl.cm.get_cmap('jet')
#plt.plot(0, 1, 'bo')
#plt.plot(-np.sqrt(3)/2,-0.5,'bo', label='Magnet')
#plt.plot( np.sqrt(3)/2,-0.5,'bo')
plt.xlabel(r'$y$')
plt.ylabel(r'$x$')
plt.tight_layout()
plt.gca().set_xticks(np.arange(-1 , len(V1), len(V1)/4));
plt.gca().set_yticks(np.arange(len(V1)-1, -1, -len(V1)/4));
plt.gca().set_xticklabels(np.arange(-2, 3, 1));
plt.gca().set_yticklabels(np.arange(-2, 3, 1));
#plt.show()
plt.savefig('heat_map_b1.pdf')
plt.cla()
plt.clf()
'''
N = len(V2)
plt.figure(figsize=(7,7))
plt.title(r'$\beta=0.25$')
#plt.title('Potential $V(x,y)$')
plt.imshow(V2, interpolation='kaiser') #, cmap=mpl.cm.get_cmap('jet')
#plt.plot(0, 1, 'bo')
#plt.plot(-np.sqrt(3)/2,-0.5,'bo', label='Magnet')
#plt.plot( np.sqrt(3)/2,-0.5,'bo')
plt.xlabel(r'$y$')
plt.ylabel(r'$x$')
plt.tight_layout()
plt.gca().set_xticks(np.arange(-1 , len(V2), len(V2)/4));
plt.gca().set_yticks(np.arange(len(V2)-1, -1, -len(V2)/4));
plt.gca().set_xticklabels(np.arange(-2, 3, 1));
plt.gca().set_yticklabels(np.arange(-2, 3, 1));
plt.colorbar()
#plt.show()
plt.savefig('heat_map_b25.pdf')
|
nilq/baby-python
|
python
|
import time
import json
from random import choice, randrange, shuffle
class Citation:
"""
Notre objet Citation est composé de :
- Le contenu (un texte càd str)
- Un auteur (le nom de l'auteur càd str)
- Une origine (titre de l'oeuvre dont elle est extraitre càd str)
- Année de publication càd int
"""
def __init__ (self, dict): #constructeur de notre citation
self.__dict__.update(dict)
def version_texte(self):
if self.date == -1 and not self.origine:
return "« {0} » par {1}".format(self.contenu, self.auteur)
return "« {0} » par {1} en {2}, extrait de {3}".format(self.contenu, self.auteur, self.date, self.origine)
recueil = []
#lit le fichier data.json et convertit son contenu en Citations
for json_citation in json.load(open('data.json')):
recueil.append(Citation(json_citation))
#supprime les doublons (un set ne peut pas avoir d'élément en double)
set_auteurs = set()
for citation in recueil:
set_auteurs.add(citation.auteur)
score = 0
#game loop
for i in range (20):
#tire au hasard une citation et la supprime du recueil
index = randrange(len(recueil))
citation_a_trouver = recueil[index]
del recueil[index]
#génère une liste d'auteurs et un texte stylisé
six_authors = {citation_a_trouver.auteur}
while len(six_authors) < 6:
fake_author = choice(list(set_auteurs))
six_authors.add(fake_author)
result = list(six_authors)
shuffle(result)
right_author_index = result.index(citation_a_trouver.auteur)
designed_authors = "\n"
for author_index in range (len(result)):
designed_authors += "\n{0} - {1}".format(str(author_index+1), result[author_index])
#énoncé
print("---[Question {0}/20]---".format(i+1))
print ("\n> Citation : « {0} »".format(citation_a_trouver.contenu))
print ("\n> Liste d'auteurs possibles :", designed_authors)
proposition = input("\n> Donne le numéro correspondant : ")
if not proposition:
print("Tu as sauté la question")
elif int(proposition)-1 == right_author_index:
print("Tu as trouvé, tu gagnes 1 point")
score += 1
else:
print("Tu t'es trompé, tu perds 2 points")
score -= 2
input("\nVoici la citation et ses informations complémentaires : {0}, pour passer à la question suivante appuie sur entrer !\n\n".format(citation_a_trouver.version_texte()))
#affiche le message final
print("Ton score est de:", str(score))
|
nilq/baby-python
|
python
|
# Copyright (c) 2015, Frappe Technologies Pvt. Ltd. and Contributors
# See license.txt
from __future__ import unicode_literals
import frappe, json
no_cache = True
def get_context(context):
token = frappe.local.form_dict.token
if token:
paypal_express_payment = frappe.get_doc("Paypal Express Payment", token)
paypal_express_payment.status = "Verified"
paypal_express_payment.save(ignore_permissions=True)
frappe.db.commit()
context.token = token
context.data = json.loads(paypal_express_payment.data or "{}")
|
nilq/baby-python
|
python
|
from tensorflow.keras import layers
from tensorflow.keras.layers import TimeDistributed, LayerNormalization
from tensorflow.keras.models import Model
from tensorflow.keras.regularizers import l2
import kapre
from kapre.composed import get_melspectrogram_layer
import tensorflow as tf
import os
def Conv1D(N_CLASSES=10, SR=16000, DT=1.0):
input_shape = (int(SR*DT), 1)
i = get_melspectrogram_layer(input_shape=input_shape,
n_mels=128,
pad_end=True,
n_fft=512,
win_length=400,
hop_length=160,
sample_rate=SR,
return_decibel=True,
input_data_format='channels_last',
output_data_format='channels_last')
x = LayerNormalization(axis=2, name='batch_norm')(i.output)
x = TimeDistributed(layers.Conv1D(8, kernel_size=(4), activation='tanh'), name='td_conv_1d_tanh')(x)
x = layers.MaxPooling2D(pool_size=(2,2), name='max_pool_2d_1')(x)
x = TimeDistributed(layers.Conv1D(16, kernel_size=(4), activation='relu'), name='td_conv_1d_relu_1')(x)
x = layers.MaxPooling2D(pool_size=(2,2), name='max_pool_2d_2')(x)
x = TimeDistributed(layers.Conv1D(32, kernel_size=(4), activation='relu'), name='td_conv_1d_relu_2')(x)
x = layers.MaxPooling2D(pool_size=(2,2), name='max_pool_2d_3')(x)
x = TimeDistributed(layers.Conv1D(64, kernel_size=(4), activation='relu'), name='td_conv_1d_relu_3')(x)
x = layers.MaxPooling2D(pool_size=(2,2), name='max_pool_2d_4')(x)
x = TimeDistributed(layers.Conv1D(128, kernel_size=(4), activation='relu'), name='td_conv_1d_relu_4')(x)
x = layers.GlobalMaxPooling2D(name='global_max_pooling_2d')(x)
x = layers.Dropout(rate=0.1, name='dropout')(x)
x = layers.Dense(64, activation='relu', activity_regularizer=l2(0.001), name='dense')(x)
o = layers.Dense(N_CLASSES, activation='softmax', name='softmax')(x)
model = Model(inputs=i.input, outputs=o, name='1d_convolution')
model.compile(optimizer='adam',
loss='categorical_crossentropy',
metrics=['accuracy'])
return model
def Conv2D(N_CLASSES=10, SR=16000, DT=1.0):
input_shape = (int(SR*DT), 1)
i = get_melspectrogram_layer(input_shape=input_shape,
n_mels=128,
pad_end=True,
n_fft=512,
win_length=400,
hop_length=160,
sample_rate=SR,
return_decibel=True,
input_data_format='channels_last',
output_data_format='channels_last')
x = LayerNormalization(axis=2, name='batch_norm')(i.output)
x = layers.Conv2D(8, kernel_size=(7,7), activation='tanh', padding='same', name='conv2d_tanh')(x)
x = layers.MaxPooling2D(pool_size=(2,2), padding='same', name='max_pool_2d_1')(x)
x = layers.Conv2D(16, kernel_size=(5,5), activation='relu', padding='same', name='conv2d_relu_1')(x)
x = layers.MaxPooling2D(pool_size=(2,2), padding='same', name='max_pool_2d_2')(x)
x = layers.Conv2D(16, kernel_size=(3,3), activation='relu', padding='same', name='conv2d_relu_2')(x)
x = layers.MaxPooling2D(pool_size=(2,2), padding='same', name='max_pool_2d_3')(x)
x = layers.Conv2D(32, kernel_size=(3,3), activation='relu', padding='same', name='conv2d_relu_3')(x)
x = layers.MaxPooling2D(pool_size=(2,2), padding='same', name='max_pool_2d_4')(x)
x = layers.Conv2D(32, kernel_size=(3,3), activation='relu', padding='same', name='conv2d_relu_4')(x)
x = layers.Flatten(name='flatten')(x)
x = layers.Dropout(rate=0.2, name='dropout')(x)
x = layers.Dense(64, activation='relu', activity_regularizer=l2(0.001), name='dense')(x)
o = layers.Dense(N_CLASSES, activation='softmax', name='softmax')(x)
model = Model(inputs=i.input, outputs=o, name='2d_convolution')
model.compile(optimizer='adam',
loss='categorical_crossentropy',
metrics=['accuracy'])
return model
def LSTM(N_CLASSES=10, SR=16000, DT=1.0):
input_shape = (int(SR*DT), 1)
i = get_melspectrogram_layer(input_shape=input_shape,
n_mels=128,
pad_end=True,
n_fft=512,
win_length=400,
hop_length=160,
sample_rate=SR,
return_decibel=True,
input_data_format='channels_last',
output_data_format='channels_last',
name='2d_convolution')
x = LayerNormalization(axis=2, name='batch_norm')(i.output)
x = TimeDistributed(layers.Reshape((-1,)), name='reshape')(x)
s = TimeDistributed(layers.Dense(64, activation='tanh'),
name='td_dense_tanh')(x)
x = layers.Bidirectional(layers.LSTM(32, return_sequences=True),
name='bidirectional_lstm')(s)
x = layers.concatenate([s, x], axis=2, name='skip_connection')
x = layers.Dense(64, activation='relu', name='dense_1_relu')(x)
x = layers.MaxPooling1D(name='max_pool_1d')(x)
x = layers.Dense(32, activation='relu', name='dense_2_relu')(x)
x = layers.Flatten(name='flatten')(x)
x = layers.Dropout(rate=0.2, name='dropout')(x)
x = layers.Dense(32, activation='relu',
activity_regularizer=l2(0.001),
name='dense_3_relu')(x)
o = layers.Dense(N_CLASSES, activation='softmax', name='softmax')(x)
model = Model(inputs=i.input, outputs=o, name='long_short_term_memory')
model.compile(optimizer='adam',
loss='categorical_crossentropy',
metrics=['accuracy'])
return model
|
nilq/baby-python
|
python
|
"""Stack Analysis Load test."""
import os
import datetime
import time
from requests_futures.sessions import FuturesSession
start_time = datetime.datetime.utcnow().strftime("%a %b %d %H:%M:%S %Z %Y")
print("TEST START TIME: {}".format(start_time))
three_scale_token = os.getenv('THREE_SCALE_PREVIEW_USER_KEY', '')
api_url = os.getenv('F8A_API_V2_URL')
api_suffix = ''
def close_fps(fp_arr):
"""Close all the file pointers."""
for file_p in fp_arr:
file_p.close()
fp_array = []
futures = []
ecosystem_file_mapping = {'npm': 'npmlist.json', 'pypi': 'pylist.json', 'maven': 'dependencies.txt'}
params = {'x-3scale-account-secret': three_scale_token}
session = FuturesSession(max_workers=3)
try:
for i in range(0, 15):
for ecosystem in ['npm', 'maven', 'pypi']:
file_name = ecosystem_file_mapping[ecosystem]
fp = open('data/{}'.format(file_name), 'rb')
fp_array.append(fp)
file_path = os.path.abspath(os.path.dirname('data/{}'.format(file_name)))
future = session.post('{}/api/v2/stack-analyses{}'.format(api_url, api_suffix),
files={'manifest': (file_name, fp)},
data={'file_path': file_path, 'ecosystem': ecosystem},
headers=params)
futures.append(future)
time.sleep(4)
i += 1
except Exception as e:
print(e)
pass
for future in futures:
print('The response details are {}'.format(future.result().text))
close_fps(fp_array)
|
nilq/baby-python
|
python
|
from flask import render_template, flash
from flask_login import login_required
from mcadmin.config import CONFIG, _F_USE_JAR
from mcadmin.forms.config.version_form import SetVersionForm
from mcadmin.io.files.server_list import SERVER_LIST
from mcadmin.io.server.server import SERVER
from mcadmin.main import app
@app.route('/panel/configuration/versions', methods=['GET', 'POST'])
@login_required
def server_versions():
version_form = SetVersionForm()
versions = SERVER_LIST.versions()
if version_form.is_submitted() and version_form.validate():
# Update configuration with the new jar name
CONFIG.set_use_jar(version_form.jar_name.data)
flash('Server executable _set to be %s. It will be used next time the server boots.' % SERVER.jar)
return render_template('panel/config/server_versions.html',
current_jar=SERVER.jar,
version_form=version_form,
versions=versions)
|
nilq/baby-python
|
python
|
import os
import math
import torch
import pickle
import argparse
# Data
from data.data import add_data_args
# Model
from model.model import get_model, get_model_id
from model.baseline import get_baseline
from survae.distributions import DataParallelDistribution
# Optim
from optim import get_optim, get_optim_id, add_optim_args
# Experiment
from experiment.student_experiment import StudentExperiment
from experiment.dropout_experiment import DropoutExperiment
from experiment.gp_experiment import GaussianProcessExperiment
###########
## Setup ##
###########
parser = argparse.ArgumentParser()
parser.add_argument('--model', type=str, default=None)
parser.add_argument('--model_type', type=str, default=None, choices=['flow', 'gp', 'dropout'])
parser.add_argument('--seed', type=int, default=0)
eval_args = parser.parse_args()
path_args = '{}/args.pickle'.format(eval_args.model)
path_check = '{}/check/checkpoint.pt'.format(eval_args.model)
torch.manual_seed(eval_args.seed)
###############
## Load args ##
###############
with open(path_args, 'rb') as f:
args = pickle.load(f)
################
## Experiment ##
################
if eval_args.model_type == "flow":
student, teacher, data_id = get_model(args)
model_id = get_model_id(args)
args.dataset = data_id
optimizer, scheduler_iter, scheduler_epoch = get_optim(args, student.parameters())
optim_id = get_optim_id(args)
exp = StudentExperiment(args=args, data_id=data_id, model_id=model_id, optim_id=optim_id,
model=student,
teacher=teacher,
optimizer=optimizer,
scheduler_iter=scheduler_iter,
scheduler_epoch=scheduler_epoch)
else:
student, teacher, data_id = get_baseline(args)
model_id = get_model_id(args)
args.dataset = data_id
if args.baseline == "gp":
exp = GaussianProcessExperiment(args=args, data_id=data_id,model_id=model_id,
model=student,
teacher=teacher)
elif args.baseline == "dropout":
optimizer, scheduler_iter, scheduler_epoch = get_optim(args, student.parameters())
optim_id = get_optim_id(args)
exp = DropoutExperiment(args=args, data_id=data_id, model_id=model_id, optim_id=optim_id,
model=student,
teacher=teacher,
optimizer=optimizer,
scheduler_iter=scheduler_iter,
scheduler_epoch=scheduler_epoch)
# Load checkpoint
exp.checkpoint_load('{}/check/'.format(more_args.model), device=more_args.new_device)
##############
## Evaluate ##
##############
exp.eval_fn()
|
nilq/baby-python
|
python
|
"""
Copyright 2020 Hype3808
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
"""
from typing import Mapping
__all__ = ['SearchError', 'QuotaExceededError']
class SearchError(Exception):
"""Raised when the API returns an error."""
def __init__(self, data: Mapping[str, str]) -> None:
super().__init__("[{code}: {status}] {message}".format(**data))
class QuotaExceededError(SearchError):
"""Raised when the active API key has run out of uses."""
def __init__(self) -> None:
Exception.__init__(self, "100 queries/day quota has been exceeded for this API key")
|
nilq/baby-python
|
python
|
import os
from setuptools import setup
this_dir = os.path.abspath(os.path.dirname(__file__))
with open(os.path.join(this_dir, "README.rst"), "r") as f:
long_description = f.read()
setup(
name="flake8parser",
description=(
"A public python API for flake8 created by parsing the command line output."
),
long_description=long_description,
version="0.1.1",
author="Alex M.",
author_email="7845120+newAM@users.noreply.github.com",
url="https://github.com/newAM/flake8parser",
license="MIT",
python_requires=">=3.6",
install_requires=["flake8>=3.8.2"],
classifiers=[
"Programming Language :: Python :: 3.6",
"Programming Language :: Python :: 3.7",
"Programming Language :: Python :: 3.8",
],
packages=["flake8parser"],
)
|
nilq/baby-python
|
python
|
import typing
import unittest
from m3c import mwb
from m3c import prefill
List = typing.List
class TestPrefill(unittest.TestCase):
def setUp(self):
self.olddb = [
prefill.db.add_organization,
prefill.db.find_organizations,
prefill.db.get_organization,
prefill.db.add_person,
prefill.get_person,
]
prefill.db.add_organization = add_organization
prefill.db.find_organizations = find_organizations
prefill.db.get_organization = get_organization
prefill.db.add_person = add_person
prefill.get_person = get_person
def tearDown(self):
prefill.db.add_organization,
prefill.db.find_organizations,
prefill.db.get_organization,
prefill.db.add_person,
prefill.get_person = self.olddb
del self.olddb
organizations.clear()
def test_monkeypatch(self):
expected = "The Corporation"
cursor = MockDatabaseConnection().cursor()
rec = make_record(institute=expected)
prefill.add_organizations(cursor, rec)
self.assertEqual(len(organizations), 1)
self.assertEqual(organizations[0], expected)
def test_strip_names(self):
expected = ["The Corporation", "College University"]
cursor = MockDatabaseConnection().cursor()
rec = make_record(institute="The Corporation; College University")
prefill.add_organizations(cursor, rec)
self.assertListEqual(organizations, expected)
def test_multiname_single_institute_department_lab(self):
expected = ["The Institute", "The Department", "The Lab"]
cursor = MockDatabaseConnection().cursor()
rec = make_record(institute=expected[0], department=expected[1],
laboratory=expected[2])
prefill.add_organizations(cursor, rec)
self.assertListEqual(organizations, expected)
def test_multiname_same_number_of_institutes_departments_and_labs(self):
cursor = MockDatabaseConnection().cursor()
rec = make_record(institute="UF ; FSU",
department="Chem; Chem",
laboratory="Smith;Jones")
prefill.add_organizations(cursor, rec)
expected = ["UF", "FSU", "Chem", "Chem", "Smith", "Jones"]
self.assertListEqual(organizations, expected)
def test_multiname_single_institute_and_dept_many_labs(self):
cursor = MockDatabaseConnection().cursor()
rec = make_record(institute="UF",
department="Computers",
laboratory="Teabeau; Clueknee")
prefill.add_organizations(cursor, rec)
expected = ["UF", "Computers", "Teabeau", "Clueknee"]
self.assertListEqual(organizations, expected)
def test_multiname_single_institute_same_number_of_depts_and_labs(self):
cursor = MockDatabaseConnection().cursor()
rec = make_record(institute="UF",
department="Chemistry; Taste and Smell",
laboratory="Smith;Akkbar")
prefill.add_organizations(cursor, rec)
expected = ["UF", "Chemistry", "Taste and Smell", "Smith", "Akkbar"]
self.assertListEqual(organizations, expected)
def test_multiname_fewer_depts_than_institutes_errors(self):
# Ambiguity: which institute does the department belong to?
rec = make_record(institute="UF;FSU",
department="Biology",
laboratory="Bobby")
cursor = MockDatabaseConnection().cursor()
with self.assertRaises(prefill.AmbiguityError):
prefill.add_organizations(cursor, rec)
def test_multiname_fewer_labs_than_departments_errors(self):
# Ambiguity: which department does the lab belong to?
rec = make_record(institute="UF",
department="Biology;Chem",
laboratory="Bobby")
cursor = MockDatabaseConnection().cursor()
with self.assertRaises(prefill.AmbiguityError):
prefill.add_organizations(cursor, rec)
def test_multiname_too_many_labs(self):
# Ambiguity: which department does the last lab belong to?
rec = make_record(institute="UF;FSU",
department="Biology;Chem",
laboratory="Bobby;Jones;Davis")
cursor = MockDatabaseConnection().cursor()
with self.assertRaises(prefill.AmbiguityError):
prefill.add_organizations(cursor, rec)
def test_multiname_too_many_departments(self):
# Ambiguity: which institute does the last department belong to?
rec = make_record(institute="UF;FSU",
department="Biology;Chem;Yo",
laboratory="Bobby;Jones;Davis")
cursor = MockDatabaseConnection().cursor()
with self.assertRaises(prefill.AmbiguityError):
prefill.add_organizations(cursor, rec)
def test_multiname_single_person(self):
rec = make_record(last_name="Bond", first_name="James")
cursor = MockDatabaseConnection().cursor()
actual = prefill.add_people(cursor, rec)
self.assertEqual(len(actual), 1)
self.assertEqual(people[0], "James Bond")
def test_multiname_too_few_surnames(self):
rec = make_record(last_name="Bond", first_name="James;Michael")
cursor = MockDatabaseConnection().cursor()
with self.assertRaises(prefill.AmbiguousNamesError):
prefill.add_people(cursor, rec)
def test_multiname_too_many_emails(self):
rec = make_record(last_name="Bond", first_name="James",
email="007@secret.gov.uk and foo@example.com")
cursor = MockDatabaseConnection().cursor()
prefill.add_people(cursor, rec)
self.assertEqual(emails[0], "")
organizations: List[str] = []
people: List[str] = []
emails: List[str] = []
def add_organization(cursor, type, name, parent_id=None):
organizations.append(name)
return len(organizations)
def add_person(cursor, first_name, last_name, email, phone):
people.append(f"{first_name} {last_name}")
emails.append(email)
return len(people)
def find_organizations(cursor):
return []
def get_organization(cursor, type, name, parent_id=None):
if not parent_id:
parent_id = 0
try:
return organizations.index((name, parent_id))+1
except ValueError:
return 0
def get_person(cursor, first_name, last_name, exclude_withheld=True):
return []
class MockDatabaseConnection:
def cursor(self):
return self
def __enter__(self):
pass
def __exit__(self, a, b, c):
pass
def make_record(psid="PR123", pstype=mwb.PROJECT, first_name="", last_name="",
institute="", department="", laboratory="", email="", phone=""
) -> mwb.NameRecord:
return mwb.NameRecord(psid, pstype, first_name, last_name, institute,
department, laboratory, email, phone)
if __name__ == "__main__":
unittest.main()
|
nilq/baby-python
|
python
|
from abc import ABC, abstractmethod
from stateful_simulator.datatypes.DataTypes import FeatureVector
from typing import List
class StatelessModel(ABC):
@abstractmethod
def predict(self, fv: FeatureVector) -> float:
pass
@abstractmethod
def train(self, fvs: List[FeatureVector]):
pass
|
nilq/baby-python
|
python
|
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals
from turtle import *
import os
import shutil
class Recorder(object):
def __init__(self, func, fps=30):
self.func = func
self.fps = fps
def __enter__(self):
self.record()
return self
def __exit__(self, type, value, traceback):
self.remove_temp()
def draw(self):
self.func()
ontimer(self.stop, 500)
def stop(self):
self.running = False
def save_eps(self, counter=[1]):
if not os.path.exists('tmp'):
os.mkdir('tmp')
getcanvas().postscript(file='./tmp/{0:03d}.eps'.format(counter[0]))
counter[0] += 1
if self.running:
ontimer(self.save_eps, int(1000 / self.fps))
def save_animation(self):
print('Capturing animation: Please close the window when the animation ends.')
self.running = True
self.save_eps()
ontimer(self.draw, 500)
done()
self.frames = len(os.listdir('./tmp'))
print('Captured frames: {}'.format(self.frames))
def load_animation(self):
import matplotlib.pyplot as plt
from matplotlib import animation
fig = plt.figure()
plt.axis('off')
plt.subplots_adjust(left=0, bottom=0, right=1, top=1)
img = plt.imread('./tmp/001.eps')
plt.imshow(img)
# plt.show()
def init():
img = plt.imread('./tmp/001.eps')
return img
def animate(i):
filename = './tmp/{0:03d}.eps'.format(i + 1)
plt.clf()
plt.axis('off')
plt.subplots_adjust(left=0, bottom=0, right=1, top=1)
img = plt.imread(filename)
plt.imshow(img)
return img
self.animation = animation.FuncAnimation(fig, animate, init_func=init, frames=self.frames, interval=1000 / self.fps)
print('Animation loaded. Prepare to generate video/gif...')
def remove_temp(self):
shutil.rmtree('./tmp')
def record(self):
self.save_animation()
self.load_animation()
# self.remove_temp()
def to_video(self, output):
print('Generating video...')
self.animation.save(output, fps=self.fps, extra_args=['-vcodec', 'libx264'])
# self.animation.save(output, fps=self.fps)
print('Video generated successfully.')
def to_gif(self, output):
print('Generating gif...')
self.animation.save(output, writer='imagemagick')
print('Gif generated successfully.')
|
nilq/baby-python
|
python
|
import unittest
from unittest import mock
from flumine.events import events
class BaseEventTest(unittest.TestCase):
def setUp(self) -> None:
self.mock_event = mock.Mock()
self.base_event = events.BaseEvent(self.mock_event)
def test_init(self):
mock_event = mock.Mock()
base_event = events.BaseEvent(mock_event)
self.assertIsNone(base_event.EVENT_TYPE)
self.assertIsNone(base_event.QUEUE_TYPE)
self.assertEqual(base_event.event, mock_event)
self.assertIsNotNone(base_event._time_created)
def test_elapsed_seconds(self):
self.assertGreaterEqual(self.base_event.elapsed_seconds, 0)
def test_str(self):
self.base_event = events.MarketBookEvent(None)
self.assertEqual(str(self.base_event), "<MARKET_BOOK [HANDLER]>")
|
nilq/baby-python
|
python
|
import json
import requests
class Gen3FileError(Exception):
pass
class Gen3File:
"""For interacting with Gen3 file management features.
A class for interacting with the Gen3 file download services.
Supports getting presigned urls right now.
Args:
endpoint (str): The URL of the data commons.
auth_provider (Gen3Auth): A Gen3Auth class instance.
Examples:
This generates the Gen3File class pointed at the sandbox commons while
using the credentials.json downloaded from the commons profile page.
>>> endpoint = "https://nci-crdc-demo.datacommons.io"
... auth = Gen3Auth(endpoint, refresh_file="credentials.json")
... sub = Gen3File(endpoint, auth)
"""
def __init__(self, endpoint, auth_provider):
self._auth_provider = auth_provider
self._endpoint = endpoint
def get_presigned_url(self, guid, protocol="http"):
"""Generates a presigned URL for a file.
Retrieves a presigned url for a file giving access to a file for a limited time.
Args:
guid (str): The GUID for the object to retrieve.
protocol (:obj:`str`, optional): The protocol to use for picking the available URL for generating the presigned URL.
Examples:
>>> Gen3File.get_presigned_url(query)
"""
api_url = "{}/user/data/download/{}?protocol={}".format(
self._endpoint, guid, protocol
)
output = requests.get(api_url, auth=self._auth_provider).text
try:
data = json.loads(output)
except:
return output
return data
|
nilq/baby-python
|
python
|
"""
setup.py: Install IsCAn
"""
import os
import sys
import re
import subprocess
from os.path import join as pjoin
from glob import glob
import setuptools
from distutils.extension import Extension
from distutils.core import setup
from Cython.Distutils import build_ext
import numpy
# ------------------------------------------------------------------------------
# HEADER
#
VERSION = "0.0.1"
ISRELEASED = False
DISABLE_CUDA = True
__author__ = "Frederic Poitevin"
__version__ = VERSION
metadata = {
'name': 'IsCAn',
'version': VERSION,
'author': __author__,
'author_email': 'frederic.poitevin@stanford.edu',
'license': 'MIT',
'url': 'https://github.com/fredericpoitevin/IsCAn',
'download_url': 'https://github.com/fredericpoitevin/IsCAn',
'platforms': ['Linux', 'OSX'],
'description': "Component analysis and clustering of structural datasets",
'long_description': """IsCAn offers analysis tools for structural datasets."""}
# ------------------------------------------------------------------------------
# HELPER FUNCTIONS -- path finding, git, python version, readthedocs
#
class bcolors:
HEADER = '\033[95m'
OKBLUE = '\033[94m'
OKGREEN = '\033[92m'
WARNING = '\033[93m'
FAIL = '\033[91m'
ENDC = '\033[0m'
def print_warning(string):
print(bcolors.WARNING + string + bcolors.ENDC)
def find_in_path(name, path):
"Find a file in a search path"
#adapted fom http://code.activestate.com/recipes/52224-find-a-file-given-a-search-path/
for dir in path.split(os.pathsep):
binpath = pjoin(dir, name)
if os.path.exists(binpath):
return os.path.abspath(binpath)
return None
def get_numpy_include():
"""
Obtain the numpy include directory. This logic works across numpy versions.
"""
try:
numpy_include = numpy.get_include()
except AttributeError:
numpy_include = numpy.get_numpy_include()
return numpy_include
def git_version():
"""
Return the git revision as a string.
Copied from numpy setup.py
"""
def _minimal_ext_cmd(cmd):
# construct minimal environment
env = {}
for k in ['SYSTEMROOT', 'PATH']:
v = os.environ.get(k)
if v is not None:
env[k] = v
# LANGUAGE is used on win32
env['LANGUAGE'] = 'C'
env['LANG'] = 'C'
env['LC_ALL'] = 'C'
out = subprocess.Popen(cmd, stdout = subprocess.PIPE, env=env).communicate()[0]
return out
try:
out = _minimal_ext_cmd(['git', 'rev-parse', 'HEAD'])
GIT_REVISION = out.strip().decode('ascii')
except OSError:
GIT_REVISION = "Unknown"
return GIT_REVISION
# -----------------------------------------------------------------------------
# INSTALL
metadata['packages'] = ['IsCAn']
metadata['package_dir'] = {'IsCAn' : 'src'}
metadata['ext_modules'] = []
metadata['scripts'] = [s for s in glob('scripts/*') if not s.endswith('__.py')]
#metadata['data_files'] = [('reference', glob('./reference/*'))]
#metadata['cmdclass'] = {'build_ext': custom_build_ext}
# ------------------------------------------------------------------------------
#
# Finally, print a warning at the *end* of the build if something fails
#
def print_warnings():
print("\n")
if __name__ == '__main__':
setup(**metadata) # ** will unpack dictionary 'metadata' providing the values as arguments
print_warnings()
|
nilq/baby-python
|
python
|
from setuptools import find_packages, setup
setup(
name="typer", packages=find_packages(),
)
|
nilq/baby-python
|
python
|
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