def make_plot(source, title): plot = Figure(plot_width=1000, plot_height=600, tools="", toolbar_location=None, y_range=[0,0.5]) plot.title = title # Each possible line must be created # In the current version of Bokeh, there is no way to hide or show lines interactively, which is # why the values had to be set to -1 in the get_dataset function plot.line(x='age_group', y='Asian', color="cornflowerblue", source=source, legend="Asian", line_width=2) plot.line(x='age_group', y='White', color="green", source=source, legend="White", line_width=2) plot.line(x='age_group', y='Black', color="darkviolet", source=source, legend="Black", line_width=2) plot.line(x='age_group', y='Hispanic', color="darkgoldenrod", source=source, legend="Hispanic", line_width=2) plot.line(x='age_group', y='Male', color="blue", source=source, legend="Male", line_width=2) plot.line(x='age_group', y='Female', color="crimson", source=source, legend="Female", line_width=2) plot.line(x='age_group', y='Totl', color="grey", source=source, legend="Total", line_width=2) # fixed attributes plot.xaxis.axis_label = 'Age' plot.yaxis.axis_label = 'Percent with Science Degrees' plot.xaxis.axis_label_text_font_size = "10pt" plot.yaxis.axis_label_text_font_size = "10pt" plot.legend.label_text_font_size = "7pt" return plot
def make_plot(cityData): hover = HoverTool(tooltips=[("GDD", "$y"), ("Date", "@dateStr")]) TOOLS = [BoxSelectTool(), hover] plot = Figure(x_axis_type="datetime", plot_width=1000, tools=TOOLS) plot.title = "Accumulated GDD of cities of Canada" colors = Spectral11[0:len(cityData)] index = 0 for src in cityData: plot.line(x='date', y='GDD', source=cityData[src], color=colors[index], line_width=4, legend=src) index = index + 1 # plot.quad(top='max', bottom='min', left='left', right='right', color=colors[2], source=src, legend="Record") # fixed attributes plot.border_fill_color = "whitesmoke" plot.xaxis.axis_label = None plot.yaxis.axis_label = "Accumulated GDD" plot.axis.major_label_text_font_size = "8pt" plot.axis.axis_label_text_font_size = "8pt" plot.axis.axis_label_text_font_style = "bold" plot.grid.grid_line_alpha = 0.3 plot.grid[0].ticker.desired_num_ticks = 12 return plot
def make_plot(cityData): # Hover option to make the plot interactive hover = HoverTool( tooltips=[ ("GDD", "$y"), ("Date", "@dateStr") ] ) TOOLS = [BoxSelectTool(), hover] plot = Figure(x_axis_type="datetime", plot_width=1000, title_text_font_size='12pt', tools=TOOLS) plot.title = "Accumulated GDD of cities of Canada" colors = Spectral11[0:len(cityData)] index = 0 for src in cityData: plot.line(x='date', y='GDD',source=cityData[src], color=colors[index], line_width=4, legend=src) index = index + 1 plot.border_fill_color = "whitesmoke" plot.xaxis.axis_label = "Months" plot.yaxis.axis_label = "Accumulated GDD" plot.axis.major_label_text_font_size = "10pt" plot.axis.axis_label_text_font_size = "12pt" plot.axis.axis_label_text_font_style = "bold" plot.grid.grid_line_alpha = 0.3 plot.grid[0].ticker.desired_num_ticks = 12 return plot
def make_precipitation_plot(source): plot = Figure(x_axis_type="datetime", plot_width=1000, plot_height=125, min_border_left=50, min_border_right=50, min_border_top=0, min_border_bottom=0, toolbar_location=None) plot.title = None plot.quad(top='actual_precipitation', bottom=0, left='left', right='right', color=Greens4[1], source=source) # fixed attributes plot.border_fill_color = "whitesmoke" plot.yaxis.axis_label = "Precipitation (in)" plot.axis.major_label_text_font_size = "8pt" plot.axis.axis_label_text_font_size = "8pt" plot.axis.axis_label_text_font_style = "bold" plot.x_range = DataRange1d(range_padding=0.0, bounds=None) plot.y_range = DataRange1d(range_padding=0.0, bounds=None) plot.grid.grid_line_alpha = 0.3 plot.grid[0].ticker.desired_num_ticks = 12 return plot
def hist_plot(ticker, alias, hsource, df, plot=None, selected_df=None): if selected_df is None: selected_df = df global_hist, global_bins = np.histogram(df[ticker + "_returns"], bins=50) hist, bins = np.histogram(selected_df[ticker + "_returns"], bins=50) top = hist.max() start = global_bins.min() end = global_bins.max() width = 0.7 * (bins[1] - bins[0]) hdata = dict( width = [width] * len(hist), center = (bins[:-1] + bins[1:]) / 2, hist2 = hist / 2.0, hist = hist ) hsource.data = hdata if plot is None: plot = Figure( plot_width=500, plot_height=200, tools="", title_text_font_size="10pt", x_range=[start, end], y_range=[0, top], ) plot.rect('center', 'hist2', 'width', 'hist', source=hsource) plot.x_range.start = start plot.x_range.end = end plot.y_range.start = 0 plot.y_range.end = top plot.title = "%s hist" % alias return plot
def make_plot(cityData): hover = HoverTool( tooltips=[ ("GDD", "$y"), ("Date", "@dateStr") ] ) TOOLS = [BoxSelectTool(), hover] plot = Figure(x_axis_type="datetime", plot_width=1000, tools=TOOLS) plot.title = "Accumulated GDD of cities of Canada" colors = Spectral11[0:len(cityData)] index = 0 for src in cityData: plot.line(x='date', y='GDD',source=cityData[src], color=colors[index], line_width=4, legend=src) index = index + 1 # plot.quad(top='max', bottom='min', left='left', right='right', color=colors[2], source=src, legend="Record") # fixed attributes plot.border_fill_color = "whitesmoke" plot.xaxis.axis_label = None plot.yaxis.axis_label = "Accumulated GDD" plot.axis.major_label_text_font_size = "8pt" plot.axis.axis_label_text_font_size = "8pt" plot.axis.axis_label_text_font_style = "bold" plot.grid.grid_line_alpha = 0.3 plot.grid[0].ticker.desired_num_ticks = 12 return plot
def performance(): plot = Figure(x_axis_type="datetime", tools="save", toolbar_location=None, plot_width=1000, plot_height=300) l1 = plot.line(x="time", y="power_out", source=source_perfomance, line_color="green", name="local") plot.add_tools(HoverTool(renderers=[l1])) plot.select(dict(type=HoverTool)).tooltips = [("Date", "@hover_time"), ("Performance", "@power_out")] l2 = plot.line(x="time", y="avg_perf", source=source_perfomance, line_color="red", name="global") plot.x_range = DataRange1d(range_padding=0.0, bounds=None) plot.title = "Plant Performance" return plot
def make_plot(source, title): print("make plot") plot = Figure(x_axis_type="datetime", plot_width=1000, tools="", toolbar_location=None) plot.title = title colors = Blues4[0:3] plot.quad(top='record_max_temp', bottom='record_min_temp', left='left', right='right', color=colors[2], source=source, legend="Record") plot.quad(top='average_max_temp', bottom='average_min_temp', left='left', right='right', color=colors[1], source=source, legend="Average") plot.quad(top='actual_max_temp', bottom='actual_min_temp', left='left', right='right', color=colors[0], alpha=0.5, line_color="black", source=source, legend="Actual") # fixed attributes plot.border_fill_color = "whitesmoke" plot.xaxis.axis_label = None plot.yaxis.axis_label = "Temperature (F)" plot.axis.major_label_text_font_size = "8pt" plot.axis.axis_label_text_font_size = "8pt" plot.axis.axis_label_text_font_style = "bold" plot.x_range = DataRange1d(range_padding=0.0, bounds=None) plot.grid.grid_line_alpha = 0.3 plot.grid[0].ticker.desired_num_ticks = 12 return plot
def make_plot(src, city): TOOLS = [BoxSelectTool()] global plot plot = Figure(x_axis_type="datetime", plot_width=1000, title_text_font_size='12pt', tools=TOOLS) plot.title = city colors = Blues4[0:3] plot.line(x='date', y='GDD',source=src, line_width=4) plot.border_fill_color = "whitesmoke" plot.xaxis.axis_label = "Months" plot.yaxis.axis_label = "Accumulated GDD" plot.axis.major_label_text_font_size = "10pt" plot.axis.axis_label_text_font_size = "12pt" plot.axis.axis_label_text_font_style = "bold" plot.grid.grid_line_alpha = 0.3 plot.grid[0].ticker.desired_num_ticks = 12 return plot
def make_plot(src, city): plot = Figure(x_axis_type="datetime", plot_width=1000, tools="", toolbar_location=None) plot.title = city colors = Blues4[0:3] plot.line(x='date', y='GDD',source=src) # plot.quad(top='max', bottom='min', left='left', right='right', color=colors[2], source=src, legend="Record") # fixed attributes plot.border_fill_color = "whitesmoke" plot.xaxis.axis_label = None plot.yaxis.axis_label = "Accumulated GDD" plot.axis.major_label_text_font_size = "8pt" plot.axis.axis_label_text_font_size = "8pt" plot.axis.axis_label_text_font_style = "bold" plot.grid.grid_line_alpha = 0.3 plot.grid[0].ticker.desired_num_ticks = 12 return plot
def create_arima_plot( df_history: pd.DataFrame, model_data: arima.MODEL_DATA ) -> Figure: """ Plot the fitting data for the specified model :param df_history: The historical data that was fitted by the ARIMA model :param model_data: The MODEL_DATA instance to plot """ results = model_data.results figure = Figure() figure.xaxis.axis_label = 'Year' figure.yaxis.axis_label = 'Temperature (Celsius)' df = df_history.sort_values(by='order') order = df['order'] add_to_arima_plot( figure, order, df['temperature'].values, 'Data', 'blue' ) add_to_arima_plot( figure, order, results.fittedvalues, 'Model', 'red' ) figure.title = '({p}, {d}, {q}) RMSE: {rmse:0.4f}'.format( **model_data._asdict() ) figure.legend.location = 'bottom_left' return figure
def make_plot(source, title): plot = Figure(x_axis_type="datetime", plot_width=1000, tools="", toolbar_location=None) plot.title = title colors = Blues4[0:3] plot.quad(top='record_max_temp', bottom='record_min_temp', left='left', right='right', color=colors[2], source=source, legend="Record") plot.quad(top='average_max_temp', bottom='average_min_temp', left='left', right='right', color=colors[1], source=source, legend="Average") plot.quad(top='actual_max_temp', bottom='actual_min_temp', left='left', right='right', color=colors[0], alpha=0.5, line_color="black", source=source, legend="Actual") # fixed attributes plot.border_fill_color = "whitesmoke" plot.xaxis.axis_label = None plot.yaxis.axis_label = "Temperature (F)" plot.axis.major_label_text_font_size = "8pt" plot.axis.axis_label_text_font_size = "8pt" plot.axis.axis_label_text_font_style = "bold" plot.x_range = DataRange1d(range_padding=0.0, bounds=None) plot.grid.grid_line_alpha = 0.3 plot.grid[0].ticker.desired_num_ticks = 12 return plot
def make_plot(source,AverageTemp,Parcentile_5_Min,Parcentile_5_Max,Parcentile_25_Min,Parcentile_25_Max,MinTemp,MaxTemp,plotDate, cityName): plot = Figure(x_axis_type="datetime", plot_width=1000, tools="", title_text_font_size='12pt', toolbar_location=None) plot.title = 'Optional Task # 1 : Growing Degree-day for '+cityName colors = Blues4[0:3] plot.circle(MaxTemp,MinTemp, alpha=0.9, color="#66ff33", fill_alpha=0.2, size=10,source=source,legend ='2015') plot.quad(top=Parcentile_5_Max, bottom=Parcentile_5_Min, left='left',right='right',source=source,color="#000000", legend="Percentile 5-95") plot.quad(top=Parcentile_25_Max, bottom=Parcentile_25_Min,left='left',right='right', source=source,color="#66ccff",legend="percentile 25-75") plot.line(plotDate,AverageTemp,source=source,line_color='Red', line_width=0.5, legend='AverageTemp') plot.border_fill_color = "whitesmoke" plot.xaxis.axis_label = "Months" plot.yaxis.axis_label = "Temperature (C)" plot.axis.major_label_text_font_size = "10pt" plot.axis.axis_label_text_font_size = "12pt" plot.axis.axis_label_text_font_style = "bold" plot.x_range = DataRange1d(range_padding=0.0, bounds=None) plot.grid.grid_line_alpha = 0.3 plot.grid[0].ticker.desired_num_ticks = 12 return plot
#For Red.............. #mean = 658nm #FWHM = 138nm #standard_dev = 138/2.355 r_mean = 658.0 r_start = 658.0-138.0 r_end = 658.0+138.0 r_dev = 138.0/2.335 r_x = np.linspace(r_start,r_end,1000) r_y = bb_filter_my(r_mean,r_dev,r_x) ''' p3 = Figure(plot_width = 500,plot_height = 500) p3.title = "Plot at 5500K" p3.yaxis.axis_label = "Flux" p3.xaxis.axis_label = "Wavelength (nm)" p3.patch(ux_pass,uz_pass,line_width = 2, line_alpha = 0.1, color = (62,6,148), legend = "Ultraviolet(U)") p3.patch(bx_pass,bz_pass,line_width = 2, line_alpha = 0.1, color = "blue", legend = "Blue(B)") p3.patch(vx_pass,vz_pass,line_width = 2, line_alpha = 0.1, color = "violet", legend = "Violet(V)") p3.patch(rx_pass,rz_pass,line_width = 2, line_alpha = 0.1, color = "red", legend = "Red(R)") #.....................................FluxVsWavelength Implementation......................................................... x = np.linspace(50,3000,500) #print x y = bb_flux(x,temp) #print y
def tab_learning(): config = tf.ConfigProto() config.gpu_options.allow_growth = True session = tf.Session(config=config) select_data = Select(title="Data:", value="", options=[]) select_model = Select(title="Model script:", value="", options=[]) data_descriptor = Paragraph(text=""" Data descriptor """, width=250, height=250) model_descriptor = Paragraph(text=""" Model descriptor """, width=250, height=250) select_grid = gridplot([[select_data, select_model], [data_descriptor, model_descriptor]]) problem_type = RadioGroup(labels=["Classification", "Regression"], active=0) def problem_handler(new): if(new == 0): select_data.options = glob.glob('./np/Classification/*.npy') select_model.options = list(filter(lambda x: 'model_' in x, dir(classification))) elif(new == 1): select_data.options = glob.glob('./np/Regression/*.npy') select_model.options = list(filter(lambda x: 'model_' in x, dir(regression))) problem_type.on_click(problem_handler) learning_rate = TextInput(value="0.01", title="Learning rate") epoch_size = Slider(start=2, end=200, value=5, step=1, title="Epoch") batch_size = Slider(start=16, end=256, value=64, step=1, title="Batch") model_insert = TextInput(value="model", title="Model name") opeimizer = Select(title="Optimizer:", value="", options=["SGD", "ADAM", "RMS"]) hyper_param = gridplot([[learning_rate], [epoch_size], [batch_size], [opeimizer], [model_insert]]) xs = [[1], [1]] ys = [[1], [1]] label = [['Train loss'], ['Validation loss']] color = [['blue'], ['green']] total_loss_src = ColumnDataSource(data=dict(xs=xs, ys=ys, label=label, color=color)) plot2 = Figure(plot_width=500, plot_height=300) plot2.multi_line('xs', 'ys', color='color', source=total_loss_src, line_width=3, line_alpha=0.6) TOOLTIPS = [("loss type", "@label"), ("loss value", "$y")] plot2.add_tools(HoverTool(tooltips=TOOLTIPS)) t = Title() t.text = 'Loss' plot2.title = t acc_src = ColumnDataSource(data=dict(x=[1], y=[1], label=['R^2 score'])) plot_acc = Figure(plot_width=500, plot_height=300, title="Accuracy") plot_acc.line('x', 'y', source=acc_src, line_width=3, line_alpha=0.7, color='red') TOOLTIPS = [("Type ", "@label"), ("Accuracy value", "$y")] plot_acc.add_tools(HoverTool(tooltips=TOOLTIPS)) acc_list = [] notifier = Paragraph(text=""" Notification """, width=200, height=100) def learning_handler(): print("Start learning") del acc_list[:] tf.reset_default_graph() K.clear_session() data = np.load(select_data.value) data = data.item() print("data load complete") time_window = data.get('x').shape[-2] model_name = model_insert.value model_name = '(' + str(time_window) + ')' + model_name if (problem_type.active == 0): sub_path = 'Classification/' elif (problem_type.active == 1): sub_path = 'Regression/' model_save_dir = './model/' + sub_path + model_name + '/' if not os.path.exists(model_save_dir): os.makedirs(model_save_dir) x_shape = list(data.get('x').shape) print("Optimizer: " + str(opeimizer.value)) print(select_model.value) if (problem_type.active == 0): target_model = getattr(classification, select_model.value) model = target_model(x_shape[-3], x_shape[-2], float(learning_rate.value), str(opeimizer.value), data.get('y').shape[-1]) elif (problem_type.active == 1): target_model = getattr(regression, select_model.value) model = target_model(x_shape[-3], x_shape[-2], float(learning_rate.value), str(opeimizer.value), data.get('y').shape[-1]) notifier.text = """ get model """ training_epochs = int(epoch_size.value) batch = int(batch_size.value) loss_train = [] loss_val = [] train_ratio = 0.8 train_x = data.get('x') train_y = data.get('y') length = train_x.shape[0] print(train_x.shape) data_descriptor.text = "Data shape: " + str(train_x.shape) # model_descriptor.text = "Model layer: " + str(model.model.summary()) val_x = train_x[int(length * train_ratio):] if(val_x.shape[-1] == 1 and not 'cnn' in select_model.value): val_x = np.squeeze(val_x, -1) val_y = train_y[int(length * train_ratio):] train_x = train_x[:int(length * train_ratio)] if (train_x.shape[-1] == 1 and not 'cnn' in select_model.value): train_x = np.squeeze(train_x, -1) train_y = train_y[:int(length * train_ratio)] print(train_x.shape) if('model_dl' in select_model.value): for epoch in range(training_epochs): notifier.text = """ learning -- epoch: """ + str(epoch) hist = model.fit(train_x, train_y, epochs=1, batch_size=batch, validation_data=(val_x, val_y), verbose=1) print("%d epoch's cost: %f" % (epoch, hist.history['loss'][0])) loss_train.append(hist.history['loss'][0]) loss_val.append(hist.history['val_loss'][0]) xs_temp = [] xs_temp.append([i for i in range(epoch + 1)]) xs_temp.append([i for i in range(epoch + 1)]) ys_temp = [] ys_temp.append(loss_train) ys_temp.append(loss_val) total_loss_src.data['xs'] = xs_temp total_loss_src.data['ys'] = ys_temp if (problem_type.active == 0): r2 = hist.history['val_acc'][0] label_str = 'Class accuracy' elif (problem_type.active == 1): pred_y = model.predict(val_x) r2 = r2_score(val_y, pred_y) label_str = 'R^2 score' print("%d epoch's acc: %f" % (epoch, r2)) acc_list.append(np.max([r2, 0])) acc_src.data['x'] = [i for i in range(epoch+1)] acc_src.data['y'] = acc_list acc_src.data['label'] = [label_str for _ in range(epoch + 1)] print(acc_src.data) notifier.text = """ learning complete """ model.save(model_save_dir + model_name + '.h5') notifier.text = """ model save complete """ K.clear_session() elif('model_ml' in select_model.value): notifier.text = """ Machine learning model """ if(train_x.shape[-2] != 1): notifier.text = """ Data include more then one time-frame. \n\n Data will automatically be flatten""" train_x = train_x.reshape([train_x.shape[0], -1]) val_x = val_x.reshape([val_x.shape[0], -1]) ##### shit if (problem_type.active == 0): train_y = np.argmax(train_y, axis=-1).astype(float) print(train_x.shape) print(train_y.shape) model.fit(train_x, train_y) notifier.text = """ Training done """ pred_y = model.predict(val_x) print(pred_y) pickle.dump(model, open(model_save_dir + model_name + '.sav', 'wb')) notifier.text = """ Machine learning model saved """ button_learning = Button(label="Run model") button_learning.on_click(learning_handler) learning_grid = gridplot( [[problem_type], [select_grid, hyper_param, button_learning, notifier], [plot2, plot_acc]]) tab = Panel(child=learning_grid, title='Learning') return tab
response_plus_noise = ColumnDataSource(data=dict(x=contrast,y=contrast**alpha + offset + (np.random.random(contrast.shape)-0.5)/CNR)) baseline = ColumnDataSource(data=dict(x=contrast,y=offset*np.ones(contrast.shape))) baseline_plus_noise = ColumnDataSource(data=dict(x=contrast,y=offset+(np.random.random(contrast.shape)-0.5)/CNR)) # sim some data data = np.zeros([nRep,3]) for iRep in range(nRep): stim = offset + np.array([0.08,0.16,0.32])**alpha + (np.random.random((1,3))-0.5)/CNR fonly = offset + (np.random.random()-0.5)/CNR data[iRep,:] = stim - fonly p1.line('x','y',source=baseline, line_dash=[8,8], color = 'green') p1.line('x','y',source=baseline_plus_noise, alpha=0.6, color='green') p1.line('x','y',source=response, alpha=0.6, color='black') p1.line('x','y',source=response_plus_noise, alpha=0.6, color='black') p1.title = 'CNR = %2.1f' %CNR p2.line([0,1], [0,0], line_dash=[8,8], color = 'green') data_all = [] data_eb = [] for iC,c in enumerate([0.08,0.16,0.32]): data_all.append(ColumnDataSource(data=dict(x=c*np.ones([nRep]),y=data[:,iC]))) p2.circle('x', 'y', source=data_all[iC], color='red', fill_alpha=0.3, line_alpha=0.3) data_means = ColumnDataSource(data=dict(x=np.array([0.08,0.16,0.32]),y=np.mean(data,axis=0))) p2.line('x', 'y', source=data_means, color='red') for iC,c in enumerate([0.08,0.16,0.32]): data_eb.append(ColumnDataSource(data=dict(x=[c,c],y=np.mean(data[:,iC]) + np.std(data[:,iC])/np.sqrt(nRep)*np.array([-1,1])))) p2.line('x', 'y', source=data_eb[iC], color='red') curdoc().add_root(hplot(vplot(CNRslider,offsetSlider,nREPslider,alphaSlider,redrawButton),p1,p2)) #vform, from bokeh.io also works in there
def temp(request): month = request.GET['month'] cnx = DB.connect(host='ec500-nasa.csmyiysxb7lc.us-east-1.rds.amazonaws.com',user='******',passwd='nasaenvironment',db='environment') cur = cnx.cursor() #cur.execute("SELECT VERSION()") #data = cur.fetchone() #print "Database Version:%s" % data sql = "SELECT * FROM environment.Temp_Deviation_Monthly WHERE Month=%s" %month year =[] month=[] USCRN =[] CLIMDIV =[] CMBUSHCN =[] try: cur.execute(sql) data=cur.fetchall() for row in data: year.append(row[0]) month.append(row[1]) USCRN.append(row[2]) CLIMDIV.append(row[3]) CMBUSHCN.append(row[4]) except: print "Error: unable to fecth data" plot = Figure(x_range=[1895,2016], y_range=[-10,8], plot_width=1000, tools="", toolbar_location=None) plot.title = "Plot of temprature of month %s from 1895-2015" % month[0] colors = Blues4[0:3] plot.border_fill_color = "whitesmoke" plot.xaxis.axis_label = "Year" plot.yaxis.axis_label = "Temperature (F)" plot.axis.major_label_text_font_size = "8pt" plot.axis.axis_label_text_font_size = "8pt" plot.axis.axis_label_text_font_style = "bold" plot.x_range = DataRange1d(range_padding=0.0, bounds=None) plot.grid.grid_line_alpha = 0.3 plot.grid[0].ticker.desired_num_ticks = 12 plot.line(year, USCRN, color='#A6CEE3', legend='AAPL') plot.line(year, CLIMDIV, color='#B2DF8A', legend='GOOG') plot.line(year, CMBUSHCN, color='#33A02C', legend='IBM') #fig=plt.figure(figsize=(16,12)) #plt.xlabel('Year') #plt.ylabel('Temprature') #plt.style.use('ggplot') #plt.plot(year,USCRN,'ro-') #plt.plot(year,CLIMDIV,'go-') #plt.plot(year,CMBUSHCN,'bo-') #plt.axis([1895,2016,-10,8]) #plt.title('Plot of temprature of month %s from 1895-2015' %month[0]) #os.remove(os.getcwd() + '\Temprature.png') #plt.savefig(os.path.join(BASE_DIR, 'static\img\Temprature.png'),dpi=80) #plt.savefig(os.path.join(BASE_DIR, 'static\img\Temprature2.png'),dpi=80) #fig.clf() #plt.close(fig) #img=open(os.getcwd() + '\Temprature.png',"rb") #response = django.http.HttpResponse(content_type="image/png") #plt.savefig(response, format="png") #img=open("Temprature.png", "rb") #img.save(response, "PNG") #img.close() #image_bytes = requests.get(os.path.join(BASE_DIR, 'static\img\Temprature.png').content #image_bytes.save(response,"PNG")#lambda x: x.startswith('Temprature') #f = open(os.path.join(BASE_DIR, 'static\img\Temprature.png'),"rb") #img = f.read() #f.close() cur.close() cnx.close() script,div=components(plot) return render(request, "temp.html",{"this_script": script, "this_div": div})
def temp(request): month = request.GET['month'] cnx = DB.connect( host='ec500-nasa.csmyiysxb7lc.us-east-1.rds.amazonaws.com', user='******', passwd='nasaenvironment', db='environment') cur = cnx.cursor() #cur.execute("SELECT VERSION()") #data = cur.fetchone() #print "Database Version:%s" % data sql = "SELECT * FROM environment.Temp_Deviation_Monthly WHERE Month=%s" % month year = [] month = [] USCRN = [] CLIMDIV = [] CMBUSHCN = [] try: cur.execute(sql) data = cur.fetchall() for row in data: year.append(row[0]) month.append(row[1]) USCRN.append(row[2]) CLIMDIV.append(row[3]) CMBUSHCN.append(row[4]) except: print "Error: unable to fecth data" plot = Figure(x_range=[1895, 2016], y_range=[-10, 8], plot_width=1000, tools="", toolbar_location=None) plot.title = "Plot of temprature of month %s from 1895-2015" % month[0] colors = Blues4[0:3] plot.border_fill_color = "whitesmoke" plot.xaxis.axis_label = "Year" plot.yaxis.axis_label = "Temperature (F)" plot.axis.major_label_text_font_size = "8pt" plot.axis.axis_label_text_font_size = "8pt" plot.axis.axis_label_text_font_style = "bold" plot.x_range = DataRange1d(range_padding=0.0, bounds=None) plot.grid.grid_line_alpha = 0.3 plot.grid[0].ticker.desired_num_ticks = 12 plot.line(year, USCRN, color='#A6CEE3', legend='AAPL') plot.line(year, CLIMDIV, color='#B2DF8A', legend='GOOG') plot.line(year, CMBUSHCN, color='#33A02C', legend='IBM') #fig=plt.figure(figsize=(16,12)) #plt.xlabel('Year') #plt.ylabel('Temprature') #plt.style.use('ggplot') #plt.plot(year,USCRN,'ro-') #plt.plot(year,CLIMDIV,'go-') #plt.plot(year,CMBUSHCN,'bo-') #plt.axis([1895,2016,-10,8]) #plt.title('Plot of temprature of month %s from 1895-2015' %month[0]) #os.remove(os.getcwd() + '\Temprature.png') #plt.savefig(os.path.join(BASE_DIR, 'static\img\Temprature.png'),dpi=80) #plt.savefig(os.path.join(BASE_DIR, 'static\img\Temprature2.png'),dpi=80) #fig.clf() #plt.close(fig) #img=open(os.getcwd() + '\Temprature.png',"rb") #response = django.http.HttpResponse(content_type="image/png") #plt.savefig(response, format="png") #img=open("Temprature.png", "rb") #img.save(response, "PNG") #img.close() #image_bytes = requests.get(os.path.join(BASE_DIR, 'static\img\Temprature.png').content #image_bytes.save(response,"PNG")#lambda x: x.startswith('Temprature') #f = open(os.path.join(BASE_DIR, 'static\img\Temprature.png'),"rb") #img = f.read() #f.close() cur.close() cnx.close() script, div = components(plot) return render(request, "temp.html", { "this_script": script, "this_div": div })
#For Red.............. #mean = 658nm #FWHM = 138nm #standard_dev = 138/2.355 r_mean = 658.0 r_start = 658.0-138.0 r_end = 658.0+138.0 r_dev = 138.0/2.335 r_x = np.linspace(r_start,r_end,1000) r_y = bb_filter_my(r_mean,r_dev,r_x) ''' p3 = Figure(plot_width=500, plot_height=500) p3.title = "Plot at 5500K" p3.yaxis.axis_label = "Flux" p3.xaxis.axis_label = "Wavelength (nm)" p3.patch(ux_pass, uz_pass, line_width=2, line_alpha=0.1, color=(62, 6, 148), legend="Ultraviolet(U)") p3.patch(bx_pass, bz_pass, line_width=2, line_alpha=0.1, color="blue", legend="Blue(B)") p3.patch(vx_pass,