from __future__ import division
# @modified 20180910 - Task #2588: Update dependencies
# matplotlib.use is now required before statsmodels.api
from matplotlib import use as matplotlib_use
matplotlib_use('Agg')
import pandas
import numpy as np
import scipy
import statsmodels.api as sm
# @modified 20160821 - Issue #23 Test dependency updates
# Use Agg for matplotlib==1.5.2 upgrade, backwards compatibile
# @modified 20180910 - Task #2588: Update dependencies
# import matplotlib
# matplotlib.use('Agg')
import matplotlib
import matplotlib.pyplot as plt
import traceback
import logging
import os
import time
from sys import version_info
from os.path import join
import sys
import os.path
sys.path.append(
os.path.join(os.path.dirname(os.path.realpath(__file__)), os.pardir))
sys.path.insert(0, os.path.dirname(__file__))
import shutil
import Queue
import threading
import random
import sys
import webbrowser
# [linux] sudo apt-get install python-numpy python-matplotlib
import matplotlib
# For fix os-x issue: http://stackoverflow.com/a/34109240/965686
# unrecognized selector sent to instance
from matplotlib import use as matplotlib_use
from sys import platform as sys_pf
if sys_pf == 'darwin':
matplotlib_use("TkAgg")
import matplotlib.pyplot as plt
# personal
from emoji import *
import analysis as ALS
import images
# 路径常量定义
VERSION = "v1.2"
DATA = "./WxConnData"
RESOURCE = DATA + "/resource"
RES_APP_TITLE = RESOURCE + "/app_title.png"
RES_APP_ICON = RESOURCE + "/app_icon.ico"
RES_MAIN_SHARE_TIP = RESOURCE + "/main_share_tip.png"
import __main__
s = __main__.main_session
proj = s.project
if len(s.selected_peaks()) != 1:
print('One peak should be selected.')
raise SystemExit
peak = s.selected_peaks()[0]
pos = peak.position
spec = peak.spectrum
import numpy as np
import matplotlib.pyplot as plt
from matplotlib import use as matplotlib_use
matplotlib_use('TkAgg')
for dim in range(len(pos)):
nucleus = spec.nuclei[dim]
region, npoint = spec.region, spec.data_size[dim]
ppm_per_pt = spec.spectrum_width[dim] / (npoint - 1)
xdata = np.array(
list(map(lambda x: region[1][dim] - ppm_per_pt * x, range(npoint))))
if len(pos) == 2:
if dim == 0:
ydata = np.array(
list(
map(lambda x: spec.data_height((xdata[x], pos[1])),
range(npoint))))
else:
ydata = np.array(
from os.path import join as os_path_join
from math import log10 as math_log10, sqrt as math_sqrt
from json import dump as json_dump
from logging import getLogger as logging_getLogger
from scipy.io import loadmat
# from numpy import meshgrid as np_meshgrid
# from numpy import sqrt as np_sqrt
from numpy import squeeze as np_squeeze, load as np_load
from matplotlib import use as matplotlib_use
matplotlib_use('Agg') # NOTE: Important: this prevents plt from blocking rest of code
from matplotlib.pyplot import subplots as plt_subplots, \
close as plt_close
# from lib.utils import load_single_value
DNN_IMAGE_FNAME = 'dnn_image.mat'
FONT_SIZE = 20
PROCESS_SCRIPTS_DIRNAME = 'process_scripts'
CIRCLE_RADIUS_FNAME = 'circle_radius.txt'
CIRCLE_COORDS_X_FNAME = 'circle_xc.txt'
CIRCLE_COORDS_Y_FNAME = 'circle_zc.txt'
BOX_XMIN_RIGHT_FNAME = 'box_right_min.txt'
BOX_XMAX_RIGHT_FNAME = 'box_right_max.txt'
BOX_XMIN_LEFT_FNAME = 'box_left_min.txt'
BOX_XMAX_LEFT_FNAME = 'box_left_max.txt'
SPECKLE_STATS_FNAME = 'speckle_stats_dnn.txt'
SPECKLE_STATS_DICT_FNAME = 'speckle_stats_dnn.json'
DNN_IMAGE_SAVE_FNAME = 'dnn.png'
MASKS_FNAME = 'masks.npz'
LOGGER = logging_getLogger('evaluate_keras')
from typing import Any, Iterator, List, Tuple, Union
from matplotlib import use as matplotlib_use
matplotlib_use("Agg") # noqa
from matplotlib.lines import Line2D # noqa: I202
import matplotlib.pyplot as plt
from numpy import arange
from torch import Tensor, tensor
from torch.nn.parameter import Parameter
from torch.nn.utils.rnn import PackedSequence
from torch.utils.tensorboard import SummaryWriter
def unpack_packed_sequence(packed_sequence: PackedSequence) -> List[Tensor]:
result: List[List[Any]] = [[] for _ in packed_sequence.batch_sizes]
batch_sizes = packed_sequence.batch_sizes.clone()
current = 0
while batch_sizes[0] > 0:
i = 0
while i < len(batch_sizes) and batch_sizes[i] > 0:
result[i].append(packed_sequence.data[current])
current += 1
batch_sizes[i] -= 1
i += 1
return [tensor(l, dtype=packed_sequence.data.dtype) for l in result]
def data_if_packed(input: Union[PackedSequence, Tensor]) -> Tensor:
if isinstance(input, PackedSequence):
return input.data
#!/usr/bin/env python
# Filename: get_sequence_function_data.py
import sys
from collections import deque
import time
from utils import get_gene_ontology
from matplotlib import (
pyplot as plt,
use as matplotlib_use)
matplotlib_use('Agg')
DATA_ROOT = 'data/'
FILES = (
'uniprot-swiss.txt',)
INVALID_ACIDS = set(['U', 'O', 'B', 'Z', 'J', 'X'])
go = get_gene_ontology('goslim_yeast.obo')
def get_go_set(go_id):
go_set = set()
q = deque()
q.append(go_id)
while len(q) > 0:
g_id = q.popleft()
go_set.add(g_id)
for ch_id in go[g_id]['children']:
q.append(ch_id)
return go_set
# -*- coding: utf-8 -*-
import math
import re
from PIL import Image, ImageFont, ImageDraw # images
from imageio import get_writer as imageio_get_writer, imread as imageio_imread # GIFs
from matplotlib import rc as matplotlib_rc # for regulating font
from matplotlib import use as matplotlib_use
matplotlib_use('Agg',force=True) # no display
from matplotlib.backends.backend_agg import FigureCanvasAgg as FigureCanvas
from matplotlib.figure import Figure
from matplotlib.gridspec import GridSpec
from scipy.interpolate import interp1d
from seaborn import heatmap as seaborn_heatmap # Heatmap
import numpy as np
import options
font_dict = {'size':22}
matplotlib_rc('font', **font_dict)
flags = options.get() # get command line args
def plot(logs, figure_file):
log_count = len(logs)
# Get plot types
stats = [None]*log_count
key_ids = {}
for i in range(log_count):
log = logs[i]
# Get statistics keys
# necessary in order to compile this script to .exe
from matplotlib import use as matplotlib_use
matplotlib_use("QT5Agg")
import os
import sys
import numpy
import decimal
from numpy import log10, sqrt, power, exp, log
from decimal import Decimal
from PyQt5.QtWidgets import *
from PyQt5.QtGui import QFont
from PyQt5 import QtCore
from matplotlib.backends.backend_qt5agg import FigureCanvasQTAgg
from matplotlib import pyplot as plt
from matplotlib.figure import Figure
decimal.getcontext().prec = 100
class InputField:
def __init__(self, text, coeffSI, left_bound=None, right_bound=None):
self.text = text
self.coeffSI = Decimal(coeffSI)
self.left_bound = left_bound
self.right_bound = right_bound
inputFields = {
from os import path
from optparse import OptionParser,OptionGroup
from datetime import date
import numpy as np
import sys
try:
from matplotlib import use as matplotlib_use, __version__ as matplotlib__version__
except ImportError:
sys.exit("\n\nERROR: can't find module matplotlib")
print " ( using MatPotLib version: " + matplotlib__version__ + " )\n\n"
print "Using Matplotlib v. " + matplotlib__version__
### Choose the matplotlib backend
matplotlib_use("WXAgg")
from matplotlib import pyplot as plt
from matplotlib import axes as axes
from matplotlib.backends.backend_pdf import PdfPages
### Debugging
import pdb
################################################################################
class cityemission:
import numpy as np
from matplotlib.figure import Figure
from PyQt5.QtWidgets import QSizePolicy, QWidget, QVBoxLayout
from matplotlib.backends.backend_qt5agg import FigureCanvasQTAgg as FigureCanvas
from matplotlib.backends.backend_qt5agg import NavigationToolbar2QT as NavigationToolbar
from matplotlib import use as matplotlib_use
from myGUIApplication_ver2.axes_1d import Axes1D
from myGUIApplication_ver2.axes_2d import Axes2D
matplotlib_use("Qt5Agg")
class H5Plot(QWidget):
def __init__(self, *args, **kwargs):
QWidget.__init__(self, *args, **kwargs)
self.setLayout(QVBoxLayout())
self.canvas = WidgetPlot(self)
self.toolbar = NavigationToolbar(self.canvas, self, coordinates=True)
self.layout().addWidget(self.toolbar)
self.layout().addWidget(self.canvas)
class WidgetPlot(FigureCanvas):
def __init__(self, parent=None):
self.status = 0
self.params_2d = {}
self.params_1d = {}
self.allowed_types = [np.float64, np.float32, np.ndarray, np.int32]
self.fig = Figure()
