def main(): plt.plotfile('prices.txt', delimiter=' ', cols=(0, 1, 2), names=('t', 'untraded', 'correlated'), marker='o') plt.show()
def plotter(fname, fdelimiter, foutput, fargument, fdir): """ This function parse the data in csv file and then calls different plotting functions" """ try: fopen = open(fname,'r') csvreader = csv.reader(fopen, delimiter = ",") fields = csvreader.next() except: print "Can not open input csv file",fname fopen.close data = np.loadtxt(fname, delimiter = fdelimiter, unpack = True, skiprows = 1) mpl.plotfile(fname, cols = range(len(fields)/2), delimiter = fdelimiter, subplots = True, newfig = True) print "creating a example figure with ", len(fields)/2-1, "subplots" allfig = fdir + os.path.sep + "All_fig" + "." + foutput mpl.savefig(allfig) mpl.clf() i = 0; while i < len(fields): if fargument != i: mpl.plot(data[fargument], data[i]) fieldname=fdir +os.path.sep + fields[fargument] +"_" + fields[i] + "." + foutput mpl.xlabel(fields[fargument]) mpl.ylabel(fields[i]) mpl.title(fields[fargument] + " vs " + fields[i]) #Issues with transparent option when png is opted need fine tuning. #Check Howto section of matplotlib website #mpl.savefig(fieldname, transparent = True) mpl.grid(True) mpl.savefig(fieldname) print "Saving", fieldname mpl.clf() i=i+1
def delta_od_L(): pyplot.plotfile(dane_dir + 'delta_od_L.txt', delimiter=' ', names=['a', 'b'], cols=(0, 1), marker='.', color=niebieski, ls='') pyplot.plot((1, 5), (0.4, 0.4), 'k-') pyplot.text(1, 0.5, 'wartosc bulk') pyplot.xlabel('L [nm]') pyplot.ylabel(u'\u0394 [eV]') pyplot.savefig(wykresy_dir + 'delta_od_L.png')
def Tc_od_L(): pyplot.plotfile(dane_dir + 'Tc_od_L.txt', delimiter=' ', names=['a', 'b'], cols=(0, 1), marker='.', color=niebieski, ls='') pyplot.plot((1, 5), (4, 4), 'k-') pyplot.text(1, 4, 'wartosc bulk') pyplot.xlabel('L [nm]') pyplot.ylabel(r'$T_c$ [K]') pyplot.savefig(wykresy_dir + 'Tc_od_L.png')
def delta_od_T(): pliki = glob(dane_dir + 'delta_od_T*.txt') for nazwa in pliki: pyplot.plotfile(nazwa, delimiter=' ', names=['a', 'b'], cols=(0, 1), marker='.', color=niebieski, ls='') pyplot.xlabel('T [K]') pyplot.ylabel(u'\u03bc [eV]') # czy mev? pyplot.savefig(nazwa.replace('dane', 'wykresy').replace('txt', 'png'))
def queue_length(): plt.clf() plt.plotfile(fname=DATA_ROOT_DIR + "queue_length.data", cols=(0, 1), skiprows=0, delimiter=" ", newfig=False, label="Items") plt.title("Queue Length") plt.xlabel("Time (milliseconds)") plt.ylabel("Queue Length") plt.legend() plt.grid() plt.savefig(PLOT_SAVE_LOCATION + "queue_length")
def mem(serviceName): plt.clf() plt.plotfile(fname=DATA_ROOT_DIR + serviceName + "/" + "mem.data", cols=(0, 1), skiprows=0, delimiter=" ", newfig=False, label="Memory") plt.title("Memory") plt.xlabel("Time (seconds)") plt.ylabel("Memory utilization (%)") plt.legend() plt.grid() plt.savefig(PLOT_SAVE_LOCATION + serviceName + "/" + "mem")
def cpu(serviceName): plt.clf() plt.plotfile(fname=DATA_ROOT_DIR + serviceName + "/" + "cpu0.data", cols=(0, 1), skiprows=0, delimiter=" ", newfig=False, label="CPU 0") plt.plotfile(fname=DATA_ROOT_DIR + serviceName + "/" + "cpu1.data", cols=(0, 1), skiprows=0, delimiter=" ", newfig=False, label="CPU 1") plt.plotfile(fname=DATA_ROOT_DIR + serviceName + "/" + "cpu2.data", cols=(0, 1), skiprows=0, delimiter=" ", newfig=False, label="CPU 2") plt.plotfile(fname=DATA_ROOT_DIR + serviceName + "/" + "cpu3.data", cols=(0, 1), skiprows=0, delimiter=" ", newfig=False, label="CPU 3") plt.title("CPU") plt.xlabel("Time (seconds)") plt.ylabel("CPU utilization (%)") plt.legend() plt.grid() plt.savefig(PLOT_SAVE_LOCATION + serviceName + "/" + "cpu")
def point_in_time_distribution(): plt.clf() plt.plotfile(fname=DATA_ROOT_DIR + "rt_pit.data", cols=(0, 1), skiprows=0, delimiter=" ", newfig=False, label="PIT Response Time") plt.title("Point in Time Distribution") plt.xlabel("Time (milliseconds)") plt.ylabel("PIT Response Time (milliseconds)") plt.legend() plt.grid() plt.savefig(PLOT_SAVE_LOCATION + "point_in_time_distribution")
def requests_per_sec(): plt.clf() plt.plotfile(fname=DATA_ROOT_DIR + "requests_per_sec.data", cols=(0, 1), skiprows=0, delimiter=" ", newfig=False, label="Requests") plt.title("Requests per Second") plt.xlabel("Time (seconds)") plt.ylabel("# Requests") plt.legend() plt.grid() plt.savefig(PLOT_SAVE_LOCATION + "reqs_per_sec")
def stockstats_figures(self): for line in stockstats_files: print line try: faname = cbook.get_sample_data(line, asfileobj=False) faname = faname[71:] plt.style.use("Solarize_Light2") plt.plotfile(faname, ('date', '30', '70', 'rsi_12'), subplots=False) plt.savefig(line + '_rsi.png') # plt.plotfile(faname, ('date','macdh','macd','macds'), plotfuncs={'macdh': 'bar'}, subplots=False) # plt.savefig(line+'_macd.png') except: pass
def plot_by_cpu(serviceName, i): plt.clf() plt.plotfile(fname=DATA_ROOT_DIR + serviceName + "/" + "cpu" + str(i) + ".data", cols=(0, 1), skiprows=0, delimiter=" ", newfig=False, label=str("CPU " + str(i))) plt.title("CPU " + str(i) + " Plot") plt.xlabel("Time (seconds)") plt.ylabel("CPU utilization (%)") plt.legend() plt.grid() plt.savefig(PLOT_SAVE_LOCATION + serviceName + "/" + "cpu-" + str(i))
def response_time_distribution(): plt.clf() plt.plotfile(fname=DATA_ROOT_DIR + "rt_dist.data", cols=(0, 1), skiprows=0, delimiter=" ", newfig=False, label="# Requests") plt.title("Response Time Distribution") plt.xlabel("Response Time (milliseconds)") plt.ylabel("# Requests") plt.xlim((-30, 3000)) plt.yscale(value='log') plt.legend() plt.grid() plt.savefig(PLOT_SAVE_LOCATION + "response_time_distribution")
def plotgraph(filepath, imagefolder): p1 = filepath.split('/') p2 = p1[-1] iname = p2.strip('.txt') plt.plotfile(filepath, delimiter=',', cols=(0, 1), names=('YYYY-MM', 'Total Hits'), marker='o') new_graph_name = iname + str(time.time()) + ".png" for filename in os.listdir(imagefolder): if filename.startswith(iname): # not to remove other images os.remove(imagefolder + filename) plt.savefig(imagefolder + new_graph_name) imagename = imagefolder + new_graph_name return imagename
def plot(self, fromCsvFile, col1, col1Name, col2, col2Name): """ Plots out the csvFile. Takes 2 columns and their names. """ plt.plotfile(fromCsvFile, delimiter=self.delimiter, cols=(col1, col2), skiprows=1, names=(col1Name, col2Name), linestyle='None', marker='.') y = [] x = [] with open(fromCsvFile, 'rb') as csvFile: dat = csv.reader(csvFile, delimiter=self.delimiter) for row in dat: y.append(int(row[col2])) x.append(int(row[col1])) m, b = np.polyfit(x, y, 1) plt.plot(x, np.array(x) * m + b, color='red')
def OnPlot(self, event): cursor= self.conn.execute("SELECT FILE_NAME FROM MOLECULE where MOL_NUMBER==?", (self.plot_list[0],)) files = cursor.fetchall() #print files[0][0] tf = open(files[0][0],'r+') d = tf.readlines() tf.seek(0) for line in d: s=re.search(r'[a-zA-Z]',line) if s: tf.write('#'+line) else: tf.write(line) tf.truncate() tf.close() plt.plotfile(str(files[0][0]), delimiter=' ',comments = '#', cols=(0, 1), names=('Raman Shift ($\mathregular{Cm^{-1}}$)', 'Intensity (arb. units)'), ) plt.title('Raman Spectra of {}'.format(files[0][0])) plt.show()
def main(argv): inputfile = '' outputfile = '' try: opts, args = getopt.getopt(argv,"hi:o:",["ifile=","ofile="]) except getopt.GetoptError: print ('test.py -i <inputfile> -o <outputfile>') sys.exit(2) for opt, arg in opts: if opt == '-h': print ('test.py -i <inputfile> -o <outputfile>') sys.exit() elif opt in ("-i", "--ifile"): inputfile = arg elif opt in ("-o", "--ofile"): outputfile = arg plt.plotfile('reinas-tiempo.txt', delimiter=' ', cols=(0, 1), names=('reinas', 'tiempo'), marker='o') plt.savefig(outputfile)
def plot(): #reading data from two recent files list_of_files = glob.glob('/Users/anuja/Desktop/testing/signals/*') recent_data = sorted(list_of_files, key=os.path.getmtime) #signal 1 try: #reading data from file and ploting plt.plotfile(recent_data[-1],('time','dis','vel','acc'), delimiter=' ', marker='None',newfig=True, subplots=True) except IOError: print("Error reading file.") sys.exit(1) actual_time = strftime("%Y-%m-%d %H-%M-%S", gmtime()) save_path = '/Users/anuja/Desktop/testing/plots' file_name = os.path.join(save_path, 'signal_1' + str(actual_time)+".png") pl.savefig(file_name, bbox_inches='tight') plt.show()
def disk(serviceName): plt.clf() plt.plotfile(fname=DATA_ROOT_DIR + serviceName + "/" + "diskread.data", cols=(0, 1), skiprows=0, delimiter=" ", newfig=False, label="Read") plt.plotfile(fname=DATA_ROOT_DIR + serviceName + "/" + "diskwrite.data", cols=(0, 1), skiprows=0, delimiter=" ", newfig=False, label="Write") plt.title("Disk") plt.xlabel("Time (seconds)") plt.ylabel("Disk I/O (in kB)") plt.legend() plt.grid() plt.savefig(PLOT_SAVE_LOCATION + serviceName + "/" + "disk")
def main(argv): inputfile = '' outputfile = '' try: opts, args = getopt.getopt(argv, "hi:o:", ["ifile=", "ofile="]) except getopt.GetoptError: print('test.py -i <inputfile> -o <outputfile>') sys.exit(2) for opt, arg in opts: if opt == '-h': print('test.py -i <inputfile> -o <outputfile>') sys.exit() elif opt in ("-i", "--ifile"): inputfile = arg elif opt in ("-o", "--ofile"): outputfile = arg plt.plotfile('reinas-tiempo.txt', delimiter=' ', cols=(0, 1), names=('reinas', 'tiempo'), marker='o') plt.savefig(outputfile)
def main(): """ dispatcher """ xdata = [] ydata = [] with open (incvs, 'rb') as fin, open (tcsv, 'wb') as touf: print >>touf, "%s,%s,%s,%s" % ("dt", "cont", "cpu", "virtmem") for row in fin: r = row.split() print >>touf, "%s,%s,%s,%s" % (r[0] + ' ' + r[1], r[2][4:], r[3], r[4]) print "%s,%s,%s,%s" % (r[0] + ' ' + r[1], r[2][4:], r[3], r[4]) plt.plotfile (tcsv, cols = ("cont", "cpu")) plt.xlabel(u"число контейнеров") # plt.xlabel("number of containers") plt.ylabel(u"использование ЦПУ") # plt.ylabel("CPU usage") plt.ylim(0, 100) plt.grid() plt.xticks(rotation=45) # plt.xticks(rotation='vertical') plt.savefig ("docker-cpu.png", bbox_inches='tight') plt.plotfile (tcsv, cols = ("cont", "virtmem")) # plt.xlabel("number of containers") plt.xlabel(u"число контейнеров") # plt.ylabel("virtual memory") plt.ylabel(u"виртуальная память") plt.ylim(0, 100) plt.grid() plt.xticks(rotation=45) # plt.xticks(rotation='vertical') plt.savefig ("docker-virtmem.png", bbox_inches='tight') #~ plt.show() return 0
import glob import os import numpy as np import array import datetime import time import pandas as pd import sklearn.cluster import numpy as np import matplotlib.pyplot as plt import Node import re import csv import matplotlib.cbook as cbook file = os.getcwd() + 'topics.hist.csv' fname = cbook.get_sample_data(file) plt.plotfile(fname, cols=(0, 1), delimiter=',')
def updatecanvas(self): incsv='test.csv' utcsv='create.csv' ################################################## print "starting" rownr = 5 head = False headlist = False columns = False with open('test.csv') as inf: for line in inf: parts = line.split(",") partlen = len(parts) #print "partlen: ",partlen, " parts: ", parts if not headlist: headlist = parts elif not columns: columns = parts print "headlist: ", headlist print "columns : ", partlen new_cvs_file_list = [] #new_cvs_file_list.append("empty") colum_with_values = [] colum_with_no_values = [] replace_append_data = '' for colum in range(partlen): print "COLUM NAME: ", headlist[colum] check_empty_data = True countlines = 0 with open(incsv) as inf: for line in inf: parts = line.split(",") # split line into parts if len(parts) > 1: # if at least 2 parts/columns #print "NEW_CVS_FILE_LIST: ", new_cvs_file_list, " fethcning value: ", colum if not head: head = parts[colum] else: print parts[colum] # print column 2 if parts[colum] == headlist[colum]: print "header TOP" elif parts[colum] != '"0"': print "parts have value: ", parts[colum] check_empty_data = False countlines +=1 if not check_empty_data: print "DATA in: ", headlist[colum] colum_with_values.append(headlist[colum]) else: print "EMPTY IN: ", headlist[colum] colum_with_no_values.append(headlist[colum]) print "These columes have legit valuse: ", colum_with_values for colum in range(partlen): print "COLUM NAME: ", headlist[colum] check_empty_data = True countlines = 0 with open(incsv) as inf: for line in inf: parts = line.split(",") # split line into parts if len(parts) > 1: # if at least 2 parts/columns if colum == 0: new_cvs_file_list.append(parts[colum]) #print "NEW_CVS_FILE_LIST: ", new_cvs_file_list, " fethcning value: ", colum #elif headlist[colum] != parts[colum] and headlist[colum] in colum_with_values: elif headlist[colum] in colum_with_values: if colum != 0: # reading new file structure read_copy_of_row = new_cvs_file_list[countlines] new_cvs_file_list[countlines] = read_copy_of_row + ',' + parts[colum] countlines +=1 print headlist[colum]," -> ", parts[colum] myfile = open(utcsv,'w') for row in new_cvs_file_list: print row myfile.write(row + '\n') myfile.close() fname = open(utcsv) plt.plotfile(fname, (colum_with_values), subplots=False) plt.xlabel(r'$date$') plt.ylabel(r'$levels$') # #plt.show() plt.savefig('data/test.png') # # print "column with missing valuse: ", colum_with_no_values ################################################## self.image.source = "data/test.png"
def simple_plot(csv_file, columns, headers): plt.clf() plt.close() plt.plotfile(csv_file, columns, names=headers, newfig=True) plt.show()
# When working with dates, additionally call # `pandas.plotting.register_matplotlib_converters` and use the ``parse_dates`` # argument of `pandas.read_csv`:: pd.plotting.register_matplotlib_converters() with cbook.get_sample_data('msft.csv') as file: msft = pd.read_csv(file, parse_dates=['Date']) ############################################################################### # Use indices # ----------- # Deprecated: plt.plotfile(fname, (0, 5, 6)) # Use instead: msft.plot(0, [5, 6], subplots=True) ############################################################################### # Use names # --------- # Deprecated: plt.plotfile(fname, ('date', 'volume', 'adj_close')) # Use instead: msft.plot("Date", ["Volume", "Adj. Close*"], subplots=True) ###############################################################################
import numpy as np import matplotlib.pyplot as plt import sys import os args = sys.argv[1:] filenames = [f for f in os.listdir('./widths') if f.endswith(".out")] for filename in filenames: try: plt.plotfile('./widths/' + filename, delimiter=' ', cols=(0, 1), names=('y from resonance', 'winding number'), marker='o') """plt.show()""" plt.savefig("./plots/" + filename.replace(".out", ".png")) print("Making plots for", filename) plt.close() except ValueError: print("There was nothing in", filename)
import matplotlib.pyplot as plt import matplotlib.dates as mdates import numpy as np #workUnits=pd.read_csv('C:\Users\lokesh_chandra\PycharmProjects\Streaming\work_units.csv',header=None,names=['date_trunc','count'],index_col=0) # #print(workUnits) # #plot1=workUnits[1:3] # # print (workUnits) #workUnits.set_index(workUnits['date_trunc'],inplace=True) #workUnits.plot() #plt.show() #plt.plot(workUnits) # read_csv = pd.read_csv('C:/Users/lokesh_chandra/PycharmProjects/Streaming/work_units.csv') # read_csv['date_trunc'] = pd.to_datetime(read_csv['date_trunc']) # #print(read_csv['count']) # plt.plot(read_csv["date_trunc"], read_csv["count"]) # # plt.show() # #plt.clf() #workUnits=pd.read_csv('C:\Users\lokesh_chandra\PycharmProjects\Streaming\work_units.csv',parse_dates=[1],infer_datetime_format=True,keep_date_col=True) #workUnits= pd.DatetimeIndex(workUnits['Date'],format='%Y-%m-%d') #plt.plot_date(x='Date', y='count', fmt="r-") plt.plotfile('work_units.csv', (0, 1)) plt.title("Workunits completed in a day") plt.xlabel("Dates") plt.ylabel("No. of Workunits") #plt.grid(True) plt.show()
if colum != 0: # reading new file structure read_copy_of_row = new_cvs_file_list[countlines] new_cvs_file_list[countlines] = read_copy_of_row + ',' + parts[colum] countlines +=1 print headlist[colum]," -> ", parts[colum] myfile = open(utcsv,'w') for row in new_cvs_file_list: print row myfile.write(row + '\n') myfile.close() fname = open(utcsv) plt.plotfile(fname, (colum_with_values), subplots=False) plt.xlabel(r'$date$') plt.ylabel(r'$levels$') plt.show() plt.savefig('data_graph.png') print "column with missing valuse: ", colum_with_no_values plotdata()
import sys import matplotlib.pyplot as plt for argument in range(1,len(sys.argv),2): plt.plotfile(sys.argv[argument],(0,int(sys.argv[argument+1]))) plt.show()
import sys from matplotlib.ticker import FuncFormatter import matplotlib.pyplot as plt import numpy as np import matplotlib.cbook as cbook filename = sys.argv[1] data = cbook.get_sample_data(filename, asfileobj=False) plt.plotfile(data, ('time', 'rss', 'storage_used', 'je_allocated', 'je_resident', 'reclaim_pending', 'queue_size'), subplots=False, delimiter=',') plt.xlabel(r'seconds') plt.ylabel(r'memory_used') yaxis = plt.twiny() yaxis.yaxis.set_major_formatter(FuncFormatter(lambda y,_: '%1.f' % (y/(1024*1024)))) plt.plotfile(data, ('time', 'mainrecord', 'docrecords'), subplots=False, delimiter=',') plt.show()
#!/usr/bin/env python #import numpy as np import matplotlib.pyplot as plot plot.plotfile('sw_open.txt',delimiter=' ', cols=(0, 1), names=('col1', 'col2'), marker='o') plot.show()
import matplotlib.pyplot as plt import numpy as np import matplotlib.cbook as cbook fname = cbook.get_sample_data( '/home/akshaymshet/processed_data/events/bishop.csv', asfileobj=False) # test 1; use ints plt.plotfile(fname, (10, 12)) # test 2; use names plt.plotfile(fname, ('weekday', 'pixel')) # test 3; use semilogy for volume #plt.plotfile(fname, ('weekday', 'pixel'),plotfuncs={'volume': 'semilogy'}) # test 4; use semilogy for volume #plt.plotfile(fname, (10, 12), plotfuncs={5: 'semilogy'}) # test 5; single subplot #plt.plotfile(fname, ('weekday', 'pixel'), subplots=False) # test 6; labeling, if no names in csv-file #plt.plotfile(fname2, cols=(0, 1, 2), delimiter=' ', names=['$x$', '$f(x)=x^2$', '$f(x)=x^3$']) # test 7; more than one file per figure--illustrated here with a single file #plt.plotfile(fname2, cols=(0, 1), delimiter=' ') #plt.plotfile(fname2, cols=(0, 2), newfig=False,delimiter=' ') # use current figure #plt.xlabel(r'$x$') #plt.ylabel(r'$f(x) = x^2, x^3$')
print analysis if (x - 1) % 50000 == 0: chainsSSR = np.load(os.getcwd() + '/data/chainsSSRDual_' + str(x) + y + '.npy') else: chainsSSR = np.load(os.getcwd() + '/data/chainsSSRDual_' + str(x - x % 50000 + 1) + y + '.npy') fig = plt.figure() ax = fig.add_subplot(111, projection='3d') for chain in chains: chain = chain[chain[:, 0] >= 0] ax.plot(chain[:, 0], chain[:, 1], chain[:, 2]) plt.savefig(os.getcwd() + '/data/3dplot_' + str(x) + y + ".pdf") plt.plotfile(os.getcwd() + '/data/energyDual_' + str(x) + y) plt.savefig(os.getcwd() + '/data/energyDual_' + str(x) + y + ".pdf") plt.close() binomial = [] for k in xrange(0, 6): m = 5 binomial += [an.choose(m, k) * (0.5**k) * (0.5**(m - k))] saved = np.save(os.getcwd() + '/data/savedSSRDual_' + str(x) + y, analysis) spectra = open(os.getcwd() + '/data/savedSpectraDual_' + str(x), 'a') spectra.write(str(analysis) + "\n") spectra.flush() spectra.close()
for saw in chains: # if saw[len(saw)-1, 0] == -1 or saw[len(saw)-1, 1] == -1: # saw[::2, 0] += 0.5 # plt.plot(saw[0:int(len(saw)*.333)+1, 0], saw[0:int(len(saw)*.333)+1, 1]) # else: saw = saw[saw[:,0] >= 0] #saw[::2, 0] += 0.5 plt.plot(saw[:, 0], saw[:, 1]) plt.show() profile = lattice.sum(axis=0) plt.plot(profile[0:400]) plt.show() plt.scatter(points[:,0],points[:,1]) # plt.show() plt.plotfile('EnergiesHex42b') plt.show() binomial = [] for k in xrange(0,4): n =3 binomial += [choose(n,k)*(0.6**k)*(0.4**(n-k))] spectra = open('Saved_spectraHex40b','a') spectra.write(str(analysis)+"\n") ssr = SSR(analysis, binomial) SSR = open('Saved_SSRhex40b', 'a') SSR.write('+1\t' + str(ssr) + '\t' + '250000\n') SSR.close()
import sys import matplotlib.pyplot as plt for argument in range(1, len(sys.argv), 2): print(0, int(sys.argv[argument + 1]) * 2 + 1, int(sys.argv[argument + 1]) * 2 + 2) plt.plotfile(sys.argv[argument], (0, int(sys.argv[argument + 1]) * 2 + 1, int(sys.argv[argument + 1]) * 2 + 2)) plt.show()
import matplotlib.pyplot as plt plt.plotfile('datos1.csv', cols=(0,2), delimiter=',') plt.plotfile('datos1.csv', cols=(0,3), delimiter=',', newfig=False) plt.xlabel('x') plt.ylabel('Derivada') plt.savefig('datos1') plt.show()
import matplotlib.pyplot as plt import numpy as np import matplotlib.cbook as cbook fname = cbook.get_sample_data('/home/akshaymshet/processed_data/events/bishop.csv', asfileobj=False) # test 1; use ints plt.plotfile(fname, (10, 12)) # test 2; use names plt.plotfile(fname, ('weekday', 'pixel')) # test 3; use semilogy for volume #plt.plotfile(fname, ('weekday', 'pixel'),plotfuncs={'volume': 'semilogy'}) # test 4; use semilogy for volume #plt.plotfile(fname, (10, 12), plotfuncs={5: 'semilogy'}) # test 5; single subplot #plt.plotfile(fname, ('weekday', 'pixel'), subplots=False) # test 6; labeling, if no names in csv-file #plt.plotfile(fname2, cols=(0, 1, 2), delimiter=' ', names=['$x$', '$f(x)=x^2$', '$f(x)=x^3$']) # test 7; more than one file per figure--illustrated here with a single file #plt.plotfile(fname2, cols=(0, 1), delimiter=' ') #plt.plotfile(fname2, cols=(0, 2), newfig=False,delimiter=' ') # use current figure #plt.xlabel(r'$x$') #plt.ylabel(r'$f(x) = x^2, x^3$')
parser.add_argument('-o', dest='output', type=str, default='', help='output file name') args = parser.parse_args() names = tuple(args.columns.split(',')) cols = tuple([int(x) for x in names]) for infile in args.datfile: label = "%s%s" % (os.path.basename(infile), cols) plt.plotfile(infile, cols=cols, names=names, delimiter=args.delimiter, marker=args.marker, linestyle=args.linestyle, label=label) if args.xlabel: plt.xlabel(args.xlabel) if args.ylabel: plt.ylabel(args.ylabel) if args.xlog: plt.xscale('log') if args.ylog: plt.yscale('log') if args.legend: plt.legend(loc='best') if args.output: plt.savefig(args.output)
parser.add_argument('--delm', dest='delimiter', type=str, default=' ', help='delimiter') parser.add_argument('-l', '--legend', dest='legend', action='store_true', help='legend') parser.add_argument('-m', '--marker', dest='marker', type=str, default='o', help='delimiter') parser.add_argument('--ls', dest='linestyle', type=str, default='-', help='linestyle') parser.add_argument('-c', '--columns', dest='columns', type=str, default='0,1', help='column index') parser.add_argument('-f', '--format', dest='format', type=str, default='', help='output file format') parser.add_argument('-o', dest='output', type=str, default='', help='output file name') args = parser.parse_args() names = tuple(args.columns.split(',')) cols = tuple([int(x) for x in names]) for infile in args.datfile: label = "%s%s" % (os.path.basename(infile), cols) plt.plotfile(infile, cols=cols, names=names, delimiter=args.delimiter, marker=args.marker, linestyle=args.linestyle, label=label) if args.xlabel: plt.xlabel(args.xlabel) if args.ylabel: plt.ylabel(args.ylabel) if args.xlog: plt.xscale('log') if args.ylog: plt.yscale('log') if args.legend: plt.legend(loc='best') if args.output: plt.savefig(args.output) print("file %s was written" % args.output) else:
#!/usr/bin/python import numpy as np import matplotlib.pyplot as plt plt.rcParams.update({'font.size': 14, 'font.family': 'sans'}) fig, ax = plt.subplots() ax = plt.plotfile('./time.dat', cols=(0, 1), delimiter=' ') #plt.plotfile(*np.loadtxt('time.dat', unpack=True)) #ax.plot(t, s, lw=2, color="red", ls = "-", alpha = 0.5, # marker = "o", markersize = 8.0, # ) # #animated = True, aa = True, clip_on = True) fig.savefig('fig.pdf')
#!/usr/bin/env python import os import sys import matplotlib.pyplot as plt print "Enter the number of grid points" n=raw_input() print "Enter the name of the Figure" Fig=raw_input() #plt.plotfile('Solar_VV.dat', cols=(1,2), label='Mercury', delimiter=' ') #plt.plotfile('Solar_VV.dat', cols=(5,6),label='Venus', newfig=False, delimiter=' ') #plt.plotfile('Solar_VV.dat', cols=(9,10),label='Earth', newfig=False, delimiter=' ') #plt.plotfile('Solar_VV.dat', cols=(13,14),label='Mars', newfig=False, delimiter=' ') plt.plotfile('Solar_VV.dat', cols=(17,18),label='Jupiter', newfig=False, delimiter=' ') plt.plotfile('Solar_VV.dat', cols=(21,22),label='Saturn', newfig=False, delimiter=' ') plt.plotfile('Solar_VV.dat', cols=(25,26),label='Uranus', newfig=False, delimiter=' ') plt.plotfile('Solar_VV.dat', cols=(29,30),label='Neptune', newfig=False, delimiter=' ') plt.plotfile('Solar_VV.dat', cols=(33,34),label='Pluto', newfig=False, delimiter=' ') plt.title('y vs x '+n) plt.xlabel('x(au)') plt.ylabel('y(au)') plt.legend() plt.grid() plt.savefig(Fig) plt.show()
import matplotlib.pyplot as plt import numpy as np import matplotlib.cbook as cbook fname = cbook.get_sample_data('msft.csv', asfileobj=False) fname2 = cbook.get_sample_data('data_x_x2_x3.csv', asfileobj=False) # test 1; use ints plt.plotfile(fname, (0, 5, 6)) # test 2; use names plt.plotfile(fname, ('date', 'volume', 'adj_close')) # test 3; use semilogy for volume plt.plotfile(fname, ('date', 'volume', 'adj_close'), plotfuncs={'volume': 'semilogy'}) # test 4; use semilogy for volume plt.plotfile(fname, (0, 5, 6), plotfuncs={5: 'semilogy'}) # test 5; single subplot plt.plotfile(fname, ('date', 'open', 'high', 'low', 'close'), subplots=False) # test 6; labeling, if no names in csv-file plt.plotfile(fname2, cols=(0, 1, 2), delimiter=' ', names=['$x$', '$f(x)=x^2$', '$f(x)=x^3$']) # test 7; more than one file per figure--illustrated here with a single file plt.plotfile(fname2, cols=(0, 1), delimiter=' ') plt.plotfile(fname2, cols=(0, 2), newfig=False,
import matplotlib.pyplot as plt import numpy as np import csv import sys file = 'Data2.csv' fname = open(file, 'rt') plt.plotfile(fname, ('angle', 'raw', 'median', 'harmonic', 'arithmetic'), subplots=False) plt.show()
import matplotlib.pyplot as plt import numpy as np from csv import reader data = np.genfromtxt('sample_data.csv',delimiter=',',names=['t','a','b','c','d']) # test 1; use ints plt.plot(data['t']) plt.show() ''' # test 2; use names plt.plotfile(fname, ('date', 'volume', 'adj_close')) # test 3; use semilogy for volume plt.plotfile(fname, ('date', 'volume', 'adj_close'), plotfuncs={'volume': 'semilogy'}) # test 4; use semilogy for volume plt.plotfile(fname, (0, 5, 6), plotfuncs={5: 'semilogy'}) # test 5; single subplot plt.plotfile(fname, ('date', 'open', 'high', 'low', 'close'), subplots=False) # test 6; labeling, if no names in csv-file plt.plotfile(fname2, cols=(0, 1, 2), delimiter=' ', names=['$x$', '$f(x)=x^2$', '$f(x)=x^3$']) # test 7; more than one file per figure--illustrated here with a single file
def plotfile(*args, **kwargs): r"""starkplot wrapper for plotfile""" return _pyplot.plotfile(*args, **kwargs)
def testPlot(): plt.plotfile(fname, ('date', 'volume', 'adj_close')) plt.plot([1, 0, 0, 4]) plt.ylabel('some numbers') plt.show()
import matplotlib.pyplot as plt plt.plotfile('600_1%_532e_Mo F1_80sec.asc', delimiter=' ', cols=(0, 1), names=('col1', 'col2'), ) plt.show()
#!/usr/bin/env python import os import sys import matplotlib.pyplot as plt with open(sys.argv[1],'r') as File, open(sys.argv[2],'r') as File2: print "Enter the number of grid points" n=raw_input() print "Enter the name of the Figure" Fig=raw_input() plt.plotfile(File, cols=(0,3), label='RK4', delimiter=' ') plt.plotfile(File2, cols=(0,3),label='Verlet', newfig=False, delimiter=' ') plt.title('Energy vs time '+n) plt.xlabel('time(yr)') plt.ylabel(r'$ E_{tot} (au/yr)^2$') plt.legend() plt.grid() plt.savefig(Fig) plt.show()
import matplotlib.pyplot as plt import numpy as np debug_file = open("../debug.csv","r") #headers = debug_file.readline() #labels = headers.split() #print labels #for label in labels: plt.plotfile(debug_file, (0, 1, 2),subplots=False,newfig=False) plt.show()
# add some text for labels, title and axes ticks ax.set_ylabel('---------------------------Scores----------------------------') ax.set_title('Scores by group and gender') ax.set_xticks(ind + width) ax.set_yticklabels(('Ahh', '--G1--', 'G2', 'G3', 'G4', 'G5', 'G5', 'G5', 'G5'), rotation=90) ax.legend((rects1[0], rects2[0]), ('Men', 'Women')) def autolabel(rects): # attach some text labels for rect in rects: height = rect.get_height() ax.text(rect.get_x() + rect.get_width() / 2., 1.05 * height, '%d' % int(height), ha='center', va='bottom') autolabel(rects1) autolabel(rects2) fname = cbook.get_sample_data('msft.csv', asfileobj=False) plt.plotfile(fname, ('date', 'open', 'high', 'low', 'close'), subplots=False) #plt.xlabel(r'$x$') #plt.ylabel(r'$f(x) = x^2, x^3$') plt.draw() #fig1.set_size_inches(18.5, 10.5, forward = True) plt.savefig("test.png") plt.show()
#!/usr/bin/env python from argparse import ArgumentParser import matplotlib.pyplot as plt if __name__ == '__main__' : parser = ArgumentParser(description="Plot a CSV file") parser.add_argument("datafile", help="The CSV file") # Require at least one column name parser.add_argument("columns", nargs="+", help="Names of columns to plot") parser.add_argument("--save", help="Save the plot as ...") parser.add_argument("--no-show", action="store_true", help="Don't show the plot") args = parser.parse_args() plt.plotfile(args.datafile, args.columns) if args.save: plt.savefig(args.save) if not args.no_show: plt.show()
import matplotlib.pyplot as plt import numpy as np #fname = open('msft.csv.org') #fname = open('test.csv') fname = open('create.csv') #plt.plotfile(fname, ('date','emotion','headache','hipleft','hipright','shoulderleft','shoulderright','spinallow','spinalmiddle','spinalneck'), subplots=False) plt.plotfile(fname, ('date','emotion','headache','hipleft','hipright','shoulderleft','spinallow','spinalneck'), subplots=False) #Date,emotion,headache,hipleft,hipright,shoulderleft,spinallow,spinalneck #plt.figtext("hello") plt.xlabel(r'$date$') plt.ylabel(r'$levels$') plt.show() plt.savefig('data_graph.png')
#!/usr/bin/env python import os import sys import matplotlib.pyplot as plt print "Enter the number of grid points" n=raw_input() print "Enter the name of the Figure" Fig=raw_input() plt.plotfile('Ea_Ju_VVE3.dat', cols=(1,2), label='Earth', delimiter=' ') plt.plotfile('Ea_Ju_VVE3.dat', cols=(5,6),label='Jupiter', newfig=False, delimiter=' ') plt.title('y vs x '+n) plt.xlabel('x(au)') plt.ylabel('y(au)') plt.legend() plt.axis([-10,10,-10,10]) plt.grid() plt.savefig(Fig) plt.show()
import numpy as np import matplotlib.pyplot as plt import seaborn as sns sns.set_style('white') import warnings warnings.filterwarnings("ignore") num_eq = 3 it_max = 100 # set to it_max run fname = "coordinates.dat" # data file name # plot each coordinate (angular momentum) vs time for i in range(num_eq): # note that i,j correspond to q_i, and not the column number in .dat file plt.plotfile(fname, delimiter=' ', cols=(0, i + 1), c='black') plt.title( '$q_{%d}$ as a function of time for torque-free motion (it_max = %d)' % (i, it_max)) plt.xlabel('Time') plt.ylabel('$q_{%d}$' % i) plt.savefig('q_%d_vs_t_%d.pdf' % (i, it_max)) # plots are saved under q_i_vs_t.pdf plt.show(block=True) # plot kinetic energy vs time plt.plotfile(fname, delimiter=' ', cols=(0, 4), c='black') plt.title( 'Kinetic Energy as a function of time for torque-free motion (it_max = %d)' % it_max) plt.xlabel("Time")
# test 1; use ints #plt.plotfile(fname, (0, 5, 6)) # test 2; use names #plt.plotfile(fname, ('date', 'volume', 'adj_close')) # test 3; use semilogy for volume #plt.plotfile(fname, ('date', 'volume', 'adj_close'), # plotfuncs={'volume': 'semilogy'}) # test 4; use semilogy for volume #plt.plotfile(fname, (0, 5, 6), plotfuncs={5: 'semilogy'}) # test 5; single subplot plt.plotfile(fname, ('date', 'pain', 'high', 'low', 'close'), subplots=True) # test 6; labeling, if no names in csv-file #plt.plotfile(fname2, cols=(0, 1, 2), delimiter=' ', # names=['$x$', '$f(x)=x^2$', '$f(x)=x^3$']) # test 7; more than one file per figure--illustrated here with a single file #plt.plotfile(fname2, cols=(0, 1), delimiter=' ') #plt.plotfile(fname2, cols=(0, 2), newfig=False, # delimiter=' ') # use current figure plt.xlabel(r'$x$') plt.ylabel(r'$f(x) = x^2, x^3$') # test 8; use bar for volume #plt.plotfile(fname, (0, 5, 6), plotfuncs={5: 'bar'})
#!/usr/bin/python import numpy as np import matplotlib.pyplot as plt plt.rcParams.update({'font.size': 14, 'font.family': 'sans'}) fig, ax = plt.subplots() ax = plt.plotfile('./time.dat', cols=(0, 1), delimiter = ' ') #plt.plotfile(*np.loadtxt('time.dat', unpack=True)) #ax.plot(t, s, lw=2, color="red", ls = "-", alpha = 0.5, # marker = "o", markersize = 8.0, # ) # #animated = True, aa = True, clip_on = True) fig.savefig('fig.pdf')
an.acceptance_rate(i+1,count) an.chains_to_xyz(chains, 'ShortDual_'+str(n)+alph, lattice) analysis = an.sep_analysis(chains) analysis = tuple(x/float(sum(analysis)) for x in analysis) print analysis fig = plt.figure() ax = fig.add_subplot(111, projection='3d') for chain in chains: chain = chain[chain[:,0] >= 0] ax.plot(chain[:,0],chain[:,1],chain[:,2]) #plt.show() plt.plotfile('EnergiesDual_'+str(n)+alph) plt.savefig("energyDual_" +str(n)+alph+".pdf") plt.close() binomial = [] for k in xrange(0,6): m =5 binomial += [an.choose(m,k)*(0.5**k)*(0.5**(m-k))] saved = np.save('Safe_SSRDual'+str(n)+alph, analysis) #spectra = open(r'C:\Users\Maggie\Documents\GitHub\scf-mc\Saved_spectra3D_'+str(n), 'w') #with spectra: spectra = open('Saved_spectraDual_'+str(n), 'a') spectra.write(str(analysis) +"\n") spectra.flush()
__author__ = 'Brian' import csv import matplotlib.pyplot as plt import numpy as np plt.plotfile('C:\Users\Brian\Desktop\Brian\Universitetet\Kandidat\Master Thesis\WeLoveGREEN-ENERGY\DATASET_FOR_GREEN_ENERGY_PLOTTING\wind_vs_prices.csv', delimiter=';', cols=(0, 1), names=('Wind Speed', 'Electricity Price'), linestyle='None', marker='.') y=[] x=[] with open('C:\Users\Brian\Desktop\Brian\Universitetet\Kandidat\Master Thesis\WeLoveGREEN-ENERGY\DATASET_FOR_GREEN_ENERGY_PLOTTING\wind_vs_prices.csv', 'rb') as csvfile: dat = csv.reader(csvfile, delimiter=';') for row in dat: y.append(int(row[1])) x.append(int(row[0])) m,b = np.polyfit(x,y,1) plt.plot(x, np.array(x) * m +b, color='red') plt.show()
import matplotlib.pyplot as plt plt.plotfile("graph_ten_topics.txt", ("features", "accuracy")) plt.title("Simple Plot") plt.show()