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_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 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,
