def Save():
picBase = baseFileNames["PICBASE"]
picFile = picBase + "_" + datTime + ".png"
FigureCanvasTkAgg.print_png(canvas, picFile)
configBase = baseFileNames["CONFIGBASE"]
outFile = configBase + '_' + datTime + '.py'
xminStr = '# xmin = {0:8.1f}\n'.format(xmin)
xmaxStr = '# xmax = {0:8.1f}\n'.format(xmax)
yminStr = '# ymin = {0:8.1f}\n'.format(ymin)
ymaxStr = '# ymax = {0:8.1f}'.format(ymax)
minmaxStr = xminStr + xmaxStr + yminStr + ymaxStr
with open(outFile, "w") as outfile:
outfile.write(fullTxt)
outfile.write(minmaxStr)
outfile.write("\n")
# Conditionally save raw x-y data
flag = saveRaw.get()
if flag is True:
rawfileBase = baseFileNames["RAWBASE"]
rawFile = rawfileBase + '_' + datTime + '.txt'
np.savetxt(rawFile, np.array([xx, yy]).T, fmt='%+2.12e')
class PlotFrame(SubFrame):
"""
The visual design for the Plot frame.
"""
def _create_widgets(self):
self._canvas = FigureCanvasTkAgg(self._c.get_plot_figure(), self)
self._plot = self._canvas.get_tk_widget()
def _position_widgets(self):
self._plot.grid()
def redraw_canvas(self):
self._canvas.draw()
def export_png(self, file_name):
self._canvas.print_png(file_name)
def drawHTTPstring(fig):
canvas=CanvasBackend(fig)
cherrypy.response.headers['Content-Type']='image/png'
buf=StringIO()
canvas.print_png(buf)
return buf.getvalue()
def drawHTTPstring(fig):
canvas = CanvasBackend(fig)
cherrypy.response.headers['Content-Type'] = 'image/png'
buf = StringIO()
canvas.print_png(buf)
return buf.getvalue()
def OutputMonthlyCost(Algo, Term, AitaiM, AitaiN, Month):
fig_CostMonthly = plt.figure()
plt.grid(which='both')
ax_CostMonthly = fig_CostMonthly.add_subplot(111)
ax_CostMonthly.clear()
Input_CostMonthly = int(Month)
#Mの分データ取得
FileName_MC_pre = Term + "_" + Algo + "_Tokyo_"
FileName_MC_M = FileName_MC_pre + str(Input_CostMonthly) + "_" + str(
AitaiM[Input_CostMonthly - 1]) + "_M" + ".csv"
FileDir_MC_M = str(Term) + "_" + str(Algo) + "_Tokyo_M/"
FilePath_soutai_MC_M = 'scenarios/' + FileDir_MC_M + FileName_MC_M
base = os.path.dirname(os.path.abspath(__file__))
FilePath_MC_M = os.path.normpath(os.path.join(base, FilePath_soutai_MC_M))
data1 = pd.read_csv(FilePath_soutai_MC_M,
header=None,
encoding="shift-jis").values.tolist()
#Nの分データ
FileName_MC_N = FileName_MC_pre + str(Input_CostMonthly) + "_" + str(
AitaiN[Input_CostMonthly - 1]) + "_N" + ".csv"
FileDir_MC_N = str(Term) + "_" + str(Algo) + "_Tokyo_N/"
FilePath_soutai_MC_N = 'scenarios/' + FileDir_MC_N + FileName_MC_N
base = os.path.dirname(os.path.abspath(__file__))
FilePath_MC_N = os.path.normpath(os.path.join(base, FilePath_soutai_MC_N))
data2 = pd.read_csv(FilePath_soutai_MC_N,
header=None,
encoding="shift-jis").values.tolist()
SceMC = []
for i in range(4):
CurrentSceMC = []
days = int(len(data1[i]) / 25)
NumDays = [k + 1 for k in range(days)]
#NumDays = [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28]
for j in range(days):
day = []
StartM = j * 25
StartN = j * 23
MC_N1 = data2[i][StartN:(StartN + 15)]
day.extend(MC_N1)
MC_M = data1[i][StartM:(StartM + 25)]
day.extend(MC_M)
MC_N2 = data2[i][(StartN + 15):(StartN + 23)]
day.extend(MC_N2)
CurrentSceMC.append((sum(day) / 48))
SceMC.append(CurrentSceMC)
for i in range(4):
ax_CostMonthly.plot(NumDays, SceMC[i])
ax_CostMonthly.set_xlabel("Day")
ax_CostMonthly.set_ylabel("erectricity cost[yen/kwh]")
ax_CostMonthly.set_ylim(8, 16)
canvas_CostMonthly = FigureCanvasTkAgg(fig_CostMonthly)
png_output = BytesIO()
canvas_CostMonthly.print_png(png_output)
data = png_output.getvalue()
# HTML側に渡すレスポンスを生成する
response = make_response(data)
response.headers['Content-Type'] = 'image/png'
response.headers['Content-Length'] = len(data)
return response
