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()