def hi_res(): try: # Try vector graphic first from IPython.display import set_matplotlib_formats set_matplotlib_formats( 'svg') except: # if that fails, at least raise the resolution of the plots. import matplotlib as mpl mpl.rcParams['savefig.dpi'] = 200
def compute_output(self, output_module, configuration=None): from IPython.display import set_matplotlib_formats from IPython.core.display import display set_matplotlib_formats('png') # TODO: use size from configuration fig = output_module.get_input('value') display(fig.figInstance)
def semilogy(x_vals, y_vals, x_label, y_label, x2_vals=None, y2_vals=None, legend=None, figsize=(3.5, 2.5)): """Plot x and log(y).""" plt.rcParams['figure.figsize'] = figsize set_matplotlib_formats('retina') plt.xlabel(x_label) plt.ylabel(y_label) plt.semilogy(x_vals, y_vals) if x2_vals and y2_vals: plt.semilogy(x2_vals, y2_vals) plt.legend(legend) plt.show()
def init_pyplot(): from IPython.display import set_matplotlib_formats set_matplotlib_formats('pdf', 'png') plt.rcParams['savefig.dpi'] = 75 plt.rcParams['figure.autolayout'] = False plt.rcParams['figure.figsize'] = 10, 6 plt.rcParams['axes.labelsize'] = 18 plt.rcParams['axes.titlesize'] = 20 plt.rcParams['font.size'] = 12 plt.rcParams['lines.linewidth'] = 2.0 plt.rcParams['lines.markersize'] = 8 plt.rcParams['legend.fontsize'] = 10 plt.rcParams['text.usetex'] = False plt.rcParams['font.family'] = "sans serif" plt.rcParams['font.serif'] = "cm" plt.rcParams['text.latex.preamble'] = r"\usepackage{subdepth}, \usepackage{type1cm}"
def metagPlotspdf(SRAList, Names, Params): # # # ---------- Output graphics quality setings ------------- # # modify according your needs and system setup # OSX users safest is to uncomment all # # from IPython.display import set_matplotlib_formats set_matplotlib_formats('pdf', 'svg') # Using laTeX to set Helvetica as default font # from matplotlib import rc # rc('font', **{'family': 'sans-serif', 'sans-serif': ['Helvetica']}) # rc('text', usetex=True) # ------------------------------------------------------- # # using pandas, matplotlib, seaborn, numpy makeDirectory("8-MetagPlot") sns.set_style("white") # seaborn_aesthetic sns.set_context("paper") # seaborn_aesthetic Span = int(Params["MetagSpan"]) Mapping = Params["Mapping"] dataNorm = Params["Normalised"] # Mapping 5 or 3 prime end rlrange = Params["ReadLenMiN"] + "-" + Params[ "ReadLenMaX"] # readlength range -> filename readLen_l = [ str(i) for i in range(int(Params["ReadLenMiN"]), int(Params["ReadLenMaX"]) + 1) ] + ["sum"] # colors for plot colors = { '25': 'fuchsia', '26': 'blueviolet', '27': 'darkblue', '28': 'b', '29': 'r', '30': 'salmon', '31': 'orange', '32': 'olive', '33': 'g', '34': 'tan', '35': 'y', 'sum': 'brown' } for iN in Names: for iX in ["Start", "Stop"]: infile = "7-MetagTbl/" + iN + "_" + iX + ".txt" outfig = "8-MetagPlot/" + iN + "_" + iX + ".pdf" outfig_title = "{} {} {}' mapping".format(iN.replace('_', '-'), iX, Mapping) legend_location = 'upper right' if iX == 'Stop' else 'upper left' if os.path.isfile(infile): # infile exits w = 8 # figure width h = 1.2 * len(readLen_l) # figure height fig, axes = plt.subplots(nrows=len(readLen_l), figsize=(w, h)) fig.suptitle(outfig_title, y=0.9, fontsize=12) df = pd.read_csv(infile, index_col=0, sep='\t') df.set_index("rel_Pos", inplace=True) # Adjust plot for mapping and Start/Stop if (Mapping == '5') & (iX == "Start"): df = dfTrimmiX5(df, Span, iX, inside_gene=39, outside_gene=21) elif (Mapping == '5') & (iX == "Stop"): df = dfTrimmiX5(df, Span, iX, inside_gene=60, outside_gene=3) elif (Mapping == '3') & (iX == "Start"): df = dfTrimmiX5(df, Span, iX, inside_gene=60, outside_gene=3) elif (Mapping == '3') & (iX == "Stop"): df = dfTrimmiX5(df, Span, iX, inside_gene=39, outside_gene=30) else: pass for i, readLen in enumerate(readLen_l): a = 0.6 colors = colorsCheck(colors, readLen) x = df.index y = list(df.loc[:, readLen]) axes[i].bar(x, y, color=colors[readLen], alpha=a) axes[i].legend([readLen], loc=legend_location) # colors for guide lines; adjust for beg and end for 5pr b, e = (df.index.min(), df.index.max()) if Mapping == '5': for k in list(range(b, e + 1, 3)): color = 'gray' if k == -12: color = 'g' a = 0.5 elif k == 0: color = 'r' a = 0.4 elif k < 0: color = 'gray' a = 0.2 else: color = 'gray' a = 0.2 # add line after each 3 nt axes[i].axvline(x=k, linewidth=1, alpha=a, color=color) elif Mapping == '3': for k in list(range(b, e + 1, 3)): color = 'gray' if k == 12: color = 'g' a = 0.5 elif k == 0: color = 'r' a = 0.4 elif k < 0: color = 'gray' a = 0.2 else: color = 'gray' a = 0.2 # add line after each 3 nt axes[i].axvline(x=k, linewidth=1, alpha=a, color=color) else: # any other type of mapping pass axes[i].set_ylabel(Params["Normalised"]) sns.despine() # seaborn_aesthetic plt.tight_layout() fig.savefig(outfig, format='pdf', dpi=300, bbox_inches='tight') print("{}".format(outfig)) else: print("Missing InFile -> {}".format(infile))
def set_figsize(figsize=(3.5, 2.5)): """Set matplotlib figure size.""" set_matplotlib_formats('retina') plt.rcParams['figure.figsize'] = figsize
def use_svg_display(): display.set_matplotlib_formats("svg")
import matplotlib.pyplot as plt import numpy as np import pandas as pd from cycler import cycler from IPython.display import display, set_matplotlib_formats, HTML display( HTML(data=""" <style> div#notebook-container { width: 95%; } div#menubar-container { width: 65%; } div#maintoolbar-container { width: 99%; } </style> """)) set_matplotlib_formats('pdf', 'png') plt.rcParams['savefig.dpi'] = 300 plt.rcParams['image.cmap'] = "viridis" plt.rcParams['image.interpolation'] = "none" plt.rcParams['savefig.bbox'] = "tight" plt.rcParams['lines.linewidth'] = 2 plt.rcParams['legend.numpoints'] = 1 np.set_printoptions(precision=3, suppress=True) pd.set_option("display.max_columns", 8) pd.set_option('precision', 2) __all__ = ['np', 'display', 'plt', 'pd']
def use_svg_display(): """Use svg format to display plot in jupyter""" display.set_matplotlib_formats('svg')
import numpy as np import cv2 import matplotlib.pyplot as plt from IPython.display import set_matplotlib_formats from collections import Counter set_matplotlib_formats('svg') def show_video(video): array = [] while video.isOpened(): ret, frame = video.read() try: gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) except cv2.error: print("End of the video") break blur = cv2.medianBlur(gray, 5) cimg = cv2.cvtColor(blur, cv2.COLOR_GRAY2BGR) roi, gray_roi = crop_video(cimg, gray) if ret: circles = find_circles(gray_roi) if circles is None:
def use_svg_display(): # Display in vector graphics display.set_matplotlib_formats('svg')
import mxnet as mx from mxnet import nd import numpy as np from matplotlib import pyplot as plt from IPython import display import random import math display.set_matplotlib_formats('svg') probabilities = nd.ones(6) / 6 rand = nd.random.multinomial(probabilities, shape=(5, 10)) print(probabilities) print(rand) total = 1000 rolls = nd.random.multinomial(probabilities, shape=(total)) counts = nd.zeros((6, total)) totals = nd.zeros(6) for i, roll in enumerate(rolls): totals[int(roll.asscalar())] += 1 counts[:, i] = totals print(totals / total) print(counts) x = nd.arange(total).reshape((1, total)) + 1 estimates = counts / x print(estimates[:, 0]) print(estimates[:, 1]) print(estimates[:, 2])
import coco_preprocess import matplotlib.pyplot as plt import numpy as np from IPython.display import set_matplotlib_formats set_matplotlib_formats('jpg') from PIL import Image, ImageDraw #%% visualizing queries and retrievals def show_image(imgID, dataSet, coco): img_pil = get_img_pil(imgID, dataSet, coco) plt.axis('off') plt.imshow(img_pil) plt.show() def get_img_pil(imgID, dataSet, coco): img = coco.loadImgs([imgID])[0] # make sure image ID exists in the dataset given to you. return Image.open('%s/%s/%s'%(coco_preprocess.dataDir, dataSet, img['file_name'])) # make sure data dir is correct def plot_dev_box(pt_idx): imgID = box_img_ids_dev[pt_idx] img_pil = get_img_pil(imgID, 'val2014_2', coco_dev) x, y, w, h = bboxes_dev[pt_idx] draw = ImageDraw.Draw(img_pil) draw.rectangle(((x, y, x+w, y+h)), fill=None, outline=(255, 255, 51)) #RGB plt.imshow(img_pil) plt.show def plot_train_box(pt_idx): imgID = box_img_ids[pt_idx] img_pil = get_img_pil(imgID, 'train2014_2', coco_train)
def use_jpg_display(): display.set_matplotlib_formats('jpg')
def user_svg_display(): display.set_matplotlib_formats('svg')
# load packages import pandas as pd import numpy as np # import pandas_datareader as pdr import seaborn as sns from matplotlib import pyplot as plt # not needed, only to prettify the plots. import matplotlib from IPython.display import set_matplotlib_formats %matplotlib inline # ploting setup plt.style.use(['seaborn-white', 'seaborn-paper']) matplotlib.rc('font', family='Times New Roman', size=15) set_matplotlib_formats('png', 'png', quality=90) plt.rcParams['savefig.dpi'] = 150 plt.rcParams['figure.autolayout'] = False plt.rcParams['figure.figsize'] = 8, 5 plt.rcParams['axes.labelsize'] = 10 plt.rcParams['axes.titlesize'] = 15 plt.rcParams['font.size'] = 12 plt.rcParams['lines.linewidth'] = 1.0 plt.rcParams['lines.markersize'] = 8 plt.rcParams['legend.fontsize'] = 12 plt.rcParams['ytick.labelsize'] = 11 plt.rcParams['xtick.labelsize'] = 11 plt.rcParams['font.family'] = 'Times New Roman' plt.rcParams['font.serif'] = 'cm' plt.rcParams['axes.grid'] = True kw_save = dict(bbox_iches='tight', transparent=True)
def set_cloud(self): matplotlib.rc('font',family = 'Malgun Gothic') set_matplotlib_formats('retina') matplotlib.rc('axes',unicode_minus = False)
"""Prepare the inputs to the PRESC report.""" import pandas as pd from presc.dataset import Dataset from presc.model import ClassificationModel from sklearn.pipeline import Pipeline from sklearn.preprocessing import StandardScaler from sklearn.svm import SVC # Better quality plots from IPython.display import set_matplotlib_formats set_matplotlib_formats("svg") # Load the dataset. df = pd.read_csv("../../datasets/winequality.csv") df = df.drop(columns=["quality"]) dataset = Dataset(df, label="recommend") dataset.split_test_train(0.3) # Set up the model model = Pipeline([("scaler", StandardScaler()), ("clf", SVC(class_weight="balanced"))]) cm = ClassificationModel(model, dataset, should_train=True) # Config options (TODO: read from file) config = {"misclass_rate": {"num_bins": 20}}
from IPython.display import set_matplotlib_formats set_matplotlib_formats('png', 'pdf') import cobra.test from cobra.flux_analysis import calculate_phenotype_phase_plane model = cobra.test.create_test_model("textbook") data = calculate_phenotype_phase_plane( model, "EX_glc__D_e", "EX_o2_e") data.plot_matplotlib();
def latexify(column_width_pt=243.91125, text_width_pt=505.89, scale=2, fontsize_pt=11, usetex=True): import matplotlib.pyplot as plt from IPython.display import set_matplotlib_formats set_matplotlib_formats('pdf', 'png') # sorted([f.name for f in mpl.matplotlib.font_manager.fontManager.ttflist]) fig_width_pt = column_width_pt inches_per_pt = 1.0 / 72.27 # to convert pt to inches golden_mean = 0.61803398875 # (math.sqrt(5) - 1.0) / 2.0 # Aesthetic ratio fig_proportion = golden_mean fig_width = fig_width_pt * inches_per_pt # width in inches fig_height = fig_width * fig_proportion # height in inches fig_size = [scale * fig_width, scale * fig_height] # Legend plt.rcParams['legend.fontsize'] = 14 # in pts (e.g. "x-small") # Lines plt.rcParams['lines.markersize'] = 8 plt.rcParams['lines.linewidth'] = 2.0 # Ticks # plt.rcParams['xtick.labelsize'] = 'x-small' # plt.rcParams['ytick.labelsize'] = 'x-small' # plt.rcParams['xtick.major.pad'] = 1 # plt.rcParams['ytick.major.pad'] = 1 # Axes plt.rcParams['axes.titlesize'] = 20 plt.rcParams['axes.labelsize'] = 18 # plt.rcParams['axes.labelpad'] = 0 # LaTeX plt.rcParams['text.usetex'] = usetex plt.rcParams['text.latex.unicode'] = True plt.rcParams['text.latex.preview'] = False # use utf8 fonts becasue your computer can handle it :) # plots will be generated using this preamble plt.rcParams['text.latex.preamble'] = [ r'\usepackage[utf8x]{inputenc}', r'\usepackage[T1]{fontenc}', r'\usepackage{amssymb}', r'\usepackage{amsmath}', r'\usepackage{wasysym}', r'\usepackage{stmaryrd}', r'\usepackage{subdepth}', r'\usepackage{type1cm}' ] # Fonts plt.rcParams['font.size'] = fontsize_pt # font size in pts (good size 16) plt.rcParams[ 'font.family'] = 'sans-serif' # , 'Merriweather Sans' # ['DejaVu Sans Display', "serif"] plt.rcParams['font.serif'] = [ 'Merriweather', 'cm' ] # blank entries should cause plots to inherit fonts from the document plt.rcParams['font.sans-serif'] = 'Merriweather Sans' plt.rcParams['font.monospace'] = 'Operator Mono' # Figure plt.rcParams['savefig.dpi'] = 300 plt.rcParams['savefig.dpi'] = 75 plt.rcParams['savefig.pad_inches'] = 0.01 plt.rcParams['savefig.bbox'] = 'tight' plt.rcParams['figure.autolayout'] = False plt.rcParams['figure.figsize'] = fig_size plt.rcParams['image.interpolation'] = 'none' plt.rcParams['pdf.fonttype'] = 42 plt.rcParams['ps.fonttype'] = 42
def use_svg_display(): """[summary] use svg format to display plot in juypter """ display.set_matplotlib_formats('svg')
matplotlib.use('nbAgg') except: pass # ip.enable # default to inline in kernel environments # if hasattr(ip, 'kernel'): # print('enabling inline matplotlib') # ip.enable_matplotlib('inline') # else: # print('enabling matplotlib') # ip.enable_matplotlib() # Set format for inline plots from IPython.display import set_matplotlib_formats # %config InlineBackend.figure_formats = ['png'] set_matplotlib_formats('png', 'svg', 'pdf', 'jpeg', quality=90) # Load pyoti plugins from .plugins import plugin_loader plugin_loader.load_modules() from . import experiment as ep from . import evaluate as ev def info(): print("PyOTI - the investigator package of the PyOTIC software") print("Version: %s" % version()) print() nb_path = os.path.abspath(".")
def use_svg_display(): # @save """使用svg格式在Jupyter中显示绘图。""" display.set_matplotlib_formats('svg')
def set_figsize(figsize=(3.5, 2.5)): set_matplotlib_formats('retina') # 打印高清图。 plt.rcParams['figure.figsize'] = figsize # 设置图的尺寸。
from ipywidgets import ( interact, interactive, IntSlider, widget, FloatText, FloatSlider, fixed, ) import matplotlib.pyplot as plt import matplotlib.gridspec as gridspec from ..base import wiggle set_matplotlib_formats("png") matplotlib.rcParams["savefig.dpi"] = 70 # Change this to adjust figure size def ViewWiggle(syndat, obsdat): syndata = np.load(syndat) obsdata = np.load(obsdat) dx = 20 _, ax = plt.subplots(1, 2, figsize=(14, 8)) kwargs = { "skipt": 1, "scale": 0.05, "lwidth": 1.0, "dx": dx, "sampr": 0.004, "clip": dx * 10.0,
# -*- coding: utf-8 -*- import csv import locale import time import matplotlib as mpl import matplotlib.pyplot as plt from cycler import cycler from collections import defaultdict import numpy as np import math locale.setlocale(locale.LC_ALL, 'en_US.UTF8') from IPython.display import set_matplotlib_formats set_matplotlib_formats('pdf') onecolsize = (4, 1.5) # Tweak based on figure's appearance in the paper seaborn_colorblind = cycler( 'color', ['#0072B2', '#D55E00', '#009E73', '#CC79A7', '#F0E442', '#56B4E9']) seaborn_muted = cycler( 'color', ['#4878CF', '#6ACC65', '#D65F5F', '#B47CC7', '#C4AD66', '#77BEDB']) def setrcparams(): # setup matplotlib rcparams plt.style.use(['seaborn-paper', 'seaborn-colorblind']) # color cyclers seaborn_colorblind = cycler( 'color', ['#0072B2', '#D55E00', '#009E73', '#CC79A7', '#F0E442', '#56B4E9'])
def use_svg_display(): #@save """Use the svg format to display a plot in Jupyter.""" display.set_matplotlib_formats('svg')
accuracy += torch.mean(equals.type(torch.FloatTensor)) model.train() train_losses.append(running_loss / len(trainloader)) test_losses.append(test_loss / len(testloader)) print("Epoch: {}/{}.. ".format(e + 1, epochs), "Training Loss: {:.3f}.. ".format(train_losses[-1]), "Test Loss: {:.3f}.. ".format(test_losses[-1]), "Test Accuracy: {:.3f}".format(accuracy / len(testloader))) from IPython import get_ipython get_ipython().run_line_magic('matplotlib', 'inline') from IPython.display import set_matplotlib_formats set_matplotlib_formats('retina') import matplotlib.pyplot as plt plt.plot(train_losses, label='Training loss') plt.plot(test_losses, label='Validation loss') plt.legend(frameon=False) import helper # Test out your network! model.eval() dataiter = iter(testloader) images, labels = dataiter.next()
from __future__ import division import os, sys, time, pickle import itertools get_ipython().run_line_magic('matplotlib', 'inline') from pycocotools.coco import COCO import numpy as np import matplotlib.pyplot as plt import matplotlib as mpl mpl.rcParams['figure.figsize'] = (8.0, 10.0) import cv2 from IPython.display import set_matplotlib_formats set_matplotlib_formats('jpg') from PIL import Image, ImageDraw import torch import torch.nn as nn from math import ceil, floor # ### Load Data # In[17]: dataType = 'train2014' dataDir=''
def set_fig_display_format(pic_format="svg"): """set figure format, such as svg(default), jpg, png, retina, pdf""" display.set_matplotlib_formats(pic_format)
def set_figsize(figsize=(3.5, 2.5)): set_matplotlib_formats('retina') plt.reParams['figure.figsize'] = figsize
def med_res(): # Try vector graphic first from IPython.display import set_matplotlib_formats import matplotlib as mpl set_matplotlib_formats( 'png') mpl.rcParams['savefig.dpi'] = 144
def setup_matplotlib( output=('pdf', 'svg'), rcparams=None, usetex=True, print_errors=False): """ import and setup matplotlib in the jupyter notebook Parameters ---------- output: tuple[str] the output formats to save to the notebook rcparams: None or dict update default parameters set for matplotlib usetex: bool if True, and the 'latex' command is available, create figures with LaTeX print_errors: bool print errors for unavailable rcparams """ from IPython import get_ipython from IPython.display import set_matplotlib_formats import matplotlib as mpl try: from shutil import which except ImportError: from shutilwhich import which ipython = get_ipython() latex_available = which('latex') is not None # if not latex_available: # output = [o for o in output if o != "pdf"] set_matplotlib_formats(*output) ipython.magic('matplotlib inline') if 'svg' in output: ipython.magic("config InlineBackend.figure_format = 'svg'") final_params = dict(MPL_OPTIONS) if rcparams is not None: final_params.update(rcparams) if usetex and latex_available: final_params.update({'text.usetex': True}) else: final_params.update({'text.usetex': False}) keyerrors = [] valerrors = {} for key, val in final_params.items(): try: mpl.rcParams[key] = val except KeyError: keyerrors.append(key) except ValueError: valerrors[key] = val if print_errors: if keyerrors: print('KeyErrors:') for key in keyerrors: print('- key') if valerrors: print('ValueError:') print(json.dumps(valerrors, indent=2)) return mpl.pyplot
from IPython.display import set_matplotlib_formats import numpy as np import matplotlib.pyplot as plt import mglearn set_matplotlib_formats("pdf", "png") plt.rcParams["savefig.dpi"] = 300 plt.rcParams["image.interpolation"] = "none" np.set_printoptions(precision=3) np, mglearn
def use_svg(): display.set_matplotlib_formats('svg')
import random from time import time from IPython.display import set_matplotlib_formats from matplotlib import pyplot as plt import mxnet as mx from mxnet import autograd, gluon, image, nd from mxnet.gluon import nn, data as gdata, loss as gloss, utils as gutils import numpy as np # set default figure size set_matplotlib_formats('retina') plt.rcParams['figure.figsize'] = (3.5, 2.5) class DataLoader(object): """similiar to gluon.data.DataLoader, but might be faster. The main difference this data loader tries to read more exmaples each time. But the limits are 1) all examples in dataset have the same shape, 2) data transfomer needs to process multiple examples at each time """ def __init__(self, dataset, batch_size, shuffle, transform=None): self.dataset = dataset self.batch_size = batch_size self.shuffle = shuffle self.transform = transform def __iter__(self): data = self.dataset[:] X = data[0] y = nd.array(data[1])
def use_svg_display(): # ⽤⽮量图显⽰。 display.set_matplotlib_formats('svg')
def use_svg_display(): # 用矢量图显示 display.set_matplotlib_formats('svg')
# IPYTHON # ======= try: from IPython import get_ipython from IPython.display import Image, Latex from IPython.display import set_matplotlib_formats _ipy_present = True except ImportError: _ipy_present = False if _ipy_present: ipython = get_ipython() if ipython is not None: ipython.magic("config InlineBackend.figure_format = 'svg'") ipython.magic("matplotlib inline") set_matplotlib_formats("pdf", "svg") # NUMPY # ===== try: import numpy as np except ImportError: pass # MATPLOTLIB # =========== try: import matplotlib as mpl _mpl_present = True except ImportError:
from IPython.display import HTML from IPython.display import set_matplotlib_formats import matplotlib set_matplotlib_formats('png') matplotlib.rcParams['savefig.dpi'] = 100 # Change this to adjust figure size from IPython.html.widgets import * import sys sys.path.append('./FEM3loop') from FEM3loop import fem3loop, interactfem3loop
import numpy as np import plotly.graph_objects as go from chart_studio.plotly import plot, iplot import matplotlib import matplotlib.pyplot as plt # 파이플롯 사용 from IPython.display import set_matplotlib_formats import seaborn as sns sns.set_style('whitegrid') set_matplotlib_formats('retina') # 한글코드를 더 선명하게 해주는 조치, 레티나 설정 matplotlib.rc('font', family='AppleGothic') # 폰트 설정 matplotlib.rc('axes', unicode_minus=False) # import chart_studio chart_studio.tools.set_credentials_file(username="******", api_key="32K3mAOdfE2DboHNbGmK") # def draw_map(list_dic_geo ,list_centroid, user_district, size, dic_result_gather): # # # 구 코드 정리 # # gu_code_dict = {} # # for num in range(25): # # gu_code_dict[geo_json['features'][num]['properties']['name']] = num # # # # gu_code = gu_code_dict[gu_name] # # # 경도 (좌 - 우) # # left_lon = sorted(np.array(geo_json['features'][gu_code]['geometry']['coordinates'][0])[:, 0].tolist())[ # # 0] # 가장 작은 경도값 - 제일 왼쪽 # # right_lon = sorted(np.array(geo_json['features'][gu_code]['geometry']['coordinates'][0])[:, 0].tolist())[ # # -1] # 가장 큰 경도값 - 제일 오른쪽 # # gap_lon = ((right_lon - left_lon) / size)