def make_basic_text_annotation_from_dict(dikt,
x=0.50,
y=0.1,
fontpath='./fzpicalc/FiraMono-Medium.otf'):
names = dikt.keys()
values = dikt.values()
text = ''
for val,name in zip(values,names):
if isinstance(val, str) == True:
text += str(name)+'='+val+'<br>'
else:
text += str(name)+'='+str(basic_func.tidy(val,3))+'<br>'
an = dict(x=x,
y=y,
showarrow=False,
text=text,
bgcolor='rgb(255,255,255)',
opacity=0.8,
bordercolor='#000000',
borderwidth=1,
align='center',
xref='paper',
yref='paper',
font =dict(family=fontpath,
size=15,
color='#000000'))
return an
def check_input(row_dict, row_index):
check = True
for each_req in required_params:
if each_req not in row_dict.keys():
print('\n')
print('INPUT ERROR: ' + each_req +
' is missing in parameter set ' + str(row_index) +
' (row no. starting with 0)')
print('\n')
check = False
else:
if each_req == 'fz_thickness':
if row_dict[each_req] not in [
15, 20, 35, 50, 75, 100, 200, 300
]:
print('\n')
print(
'INPUT ERROR: ' + each_req + ' in line ' +
str(row_index) +
' invalid must be one of the following values: 15, 20, 35, 50, 75, 100, 200, 300 (row no. starting with 0)'
)
print('\n')
check = False
else:
if (basic_func.tidy(row_dict[each_req], 10) >
param_range_max[each_req] or basic_func.tidy(
row_dict[each_req], 10) < param_range_min[each_req]
or np.isnan(row_dict[each_req])):
print('\n')
print('INPUT ERROR: ' + each_req + ' in line ' +
str(row_index) +
' invalid must be in valid range: ' +
str(param_range_min[each_req]) + ' - ' +
str(param_range_max[each_req]) +
' (row no. starting with 0)')
print('\n')
check = False
return check
input_data_set.loc[ei, 'P_ref pick time [h]'] = Ref_Pi_matrix_picktime
input_data_set.loc[ei,
'Rel. fault zone PI influence [-]'] = Pi_change_rel
input_data_set.loc[ei, 'Pi change FZ [l/s/MPa]'] = Pi_change_si
input_data_set.loc[ei, 'dP change FZ [MPa]'] = delta_p_m_fz
input_data_set.loc[ei, 'Reference Matrix P[MPa]'] = P_refMatrix
input_data_set.loc[ei, 'Pi change pick time [h]'] = Pi_picktime
else: #if input check fails line will is not evaluated
input_data_set.loc[
ei,
'Rel. fault zone PI influence [-]'] = 'input not valid, skipped'
#update progressbar:
progressbar.print_progress(ei + 1,
l,
prefix='Progress:',
suffix='Complete',
bar_length=50)
t_consumed = time() - t
av_time_per_param = basic_func.tidy((t_consumed / l), 3)
print('\n')
print(
'calculations finished with an average calculation speed per parameter combination (= 2 simulations) of '
+ str(av_time_per_param) + ' seconds')
print('\n')
#saving results:
stor_name = 'calculated_' + input_filename
input_data_set.to_csv(os.path.join(dt_string, stor_name), index=False)
def plot_pressure(ipath_arr,
SIparams_dict,
fthickness,
compare_annotation=True,
itype=['golem'],
additionaltrace=[],
optional_annotation=[],
DERplot=[False],
indv_plotname = 'semilog_plot',
newfoldername = 'error_plots',
plotdir = os.getcwd(),
xtyp='log',
xtitle='time [h]',
ytyp='log',
ytitle='dp [MPa]',
auto_open=True,
pdf=True,
png=False,
html=False,
fontpath='./fzpicalc/FiraMono-Medium.otf',
identifier='identifier_str'):
'''
input documentation:
ipath_arr = list of path to csv or np.array
SIparams_dict = list of dictionary of parametersetting in SI units and names like this:
{'S_fault': 2e-12,
'S_matrix': 2e-12,
'k_fault': 1e-10,
'k_matrix': 1e-13,
'rate': 20,
'visocisty': 0.00028974}
fthickness = list of strings string containing fault zone thickness in meters eg. ['50']
itype = list of Datasources:
Dwarfelephant online stage output array = 'rb'
Dwarfelephant single FE csv = 'rb_fe'
GOLEM = 'golem'
tickcolor='rgb(0,0,0)'
additionaltrace = any kind of trace that should be plotted additionally to input csv can be provided here
DERplot = list of booleans, if True the DER curve will also be plotted
plotdir = set directory in which a plotting folder will be saved with the plots inside
example usage:
pls.plot_pressure([RB_outputs,
'/home/hombre/Desktop/VGL/1_base_FZ_out.csv',
'/home/hombre/Desktop/VGL/1_base_FZ_nolengthscale_out.csv'],
[parm_dict,parm_dict,parm_dict],
['100','50','50'],
itype=['rb','golem','golem'],
additionaltrace=[],
optional_annotation=[],
DERplot=[False,False,False],
indv_plotname = 'testing shit',
xtyp='log',
ytyp='linear',
plotdir = plotdir)
'''
#integrate input check for correct types here:
#generating plotlabel and checking for differences:
differences={}
if compare_annotation == True:
if len(SIparams_dict) > 1:
#check if dics are the same:
SIparams_dict_same = True
for eachdict in SIparams_dict:
if SIparams_dict[0] != eachdict:
SIparams_dict_same = False
#calculate differences in dictionaries
diff = set(SIparams_dict[0].items()) ^ set(eachdict.items())
differences.update(diff)
#check if fault thicknesses are the same:
SIparams_fz_same = True
for eachthickness in fthickness:
if fthickness[0] != eachthickness:
SIparams_fz_same = False
if len(SIparams_dict) == 1:
parametertext1 = '<b>'+str(fthickness[0])+'m fault thickness</b>'
p_names = SIparams_dict[0].keys()
parms_SI = SIparams_dict[0].values()
parms_scaled = scale_array(parms_SI,p_names,direction='normal')
parametertext2='<br><b>name=SI/scaled</b><br><br>'
for val,valscaled,name in zip(parms_SI,parms_scaled,p_names):
if isinstance(val, str) == True:
parametertext2 += str(name)+'='+val+'<br>'
else:
parametertext2 += str(name)+'='+str(basic_func.tidy(val,7))+'/'+str(basic_func.tidy(valscaled,7))+'<br>'
elif len(SIparams_dict) > 1:
parametertext1 = '<b>fault thicknesses [m]: </b><br>'
if SIparams_fz_same == False:
for each in fthickness:
parametertext1 += str(each)+', '
parametertext1=parametertext1[:-2]
else:
parametertext1 += str(fthickness[0])
if SIparams_dict_same == True:
p_names = SIparams_dict[0].keys()
parms_SI = SIparams_dict[0].values()
parms_scaled = scale_array(parms_SI,p_names,direction='normal')
parametertext2='<br><br><b>name=SI/scaled</b><br>'
for val,valscaled,name in zip(parms_SI,parms_scaled,p_names):
if isinstance(val, str) == True:
parametertext2 += str(name)+'='+val+'<br>'
else:
parametertext2 += str(name)+'='+str(basic_func.tidy(val,7))+'/'+str(basic_func.tidy(valscaled,7))+'<br>'
else:
parametertext2='<br><br>curve differences:<br>'
p_names = differences.keys()
parms_SI = differences.values()
parms_scaled = scale_array(parms_SI,p_names,direction='normal')
for val,valscaled,name in zip(parms_SI,parms_scaled,p_names):
parametertext2 += str(name)+', '
parametertext2=parametertext2[:-2]
if len(differences)>0:
diff_keys=differences.keys()
else:
parametertext1 = '<b>'+str(fthickness[0])+'m fault thickness</b>'
p_names = SIparams_dict[0].keys()
parms_SI = SIparams_dict[0].values()
parametertext2='<br><b>parameter=SI units</b><br>'
for val,name in zip(parms_SI,p_names):
if isinstance(val, str) == True:
parametertext2 += str(name)+'='+val+'<br>'
else:
parametertext2 += str(name)+'='+str(basic_func.tidy(val,7))+'<br>'
#initialze empty traces that will be plottet
traces = []
#add optional traces here already
for eachtrace in additionaltrace:
traces.append(eachtrace)
#print
#print('loading data:')
filenames=[]
ymaxvals=[]
yminvals=[]
#setting up categorical colors:
if len(ipath_arr) > 1:
n_min=0
n_max=len(ipath_arr)-1
cma = cm.rainbow
for index,eachinput in enumerate(ipath_arr):
if len(ipath_arr) > 1:
cn=basic_func.normalize(index,n_min,n_max)
c = cma(cn,1)
rgb = c[:3]
rgb = "rgb(%s, %s, %s)" % (int(rgb[0]*255),int(rgb[1]*255),int(rgb[2]*255))
#'rgba(255,255,255,0.8)'
else:
rgb = '#2B4369'
#checking and preparation for further well test analysis:
if itype[index] == 'rb_fe':
pta = True
#setting filename:
filename = ntpath.basename(eachinput)
df = pd.read_csv(eachinput)
if 'rb_dp' not in df.columns:
print('wrong input csv type set or input file broken')
return
#print('RB_FE input csv detected')
df.loc[:,'FoerderDruck'] = result_sclaing(df.loc[:,'rb_dp'])
elif itype[index] == 'golem':
pta = True
#setting filename:
filename = ntpath.basename(eachinput)
df = pd.read_csv(eachinput)
if 'FoerderDruck' not in df.columns:
print('wrong input csv type set or input file broken')
return
#print('GOLEM input csv detected')
elif itype[index] == 'rb':
pta = True
if type(eachinput) is np.ndarray and len(eachinput[0]) == 128:
#setting filename:
filename = 'rb online stage output'
#print('RB online input array detected')
df = pd.DataFrame()
df['time'] = t_list
df['t_hour'] = df['time']/3600
df['RB_out']= abs(pd.melt(pd.DataFrame(eachinput)).drop(['variable'],axis=1))
RB_backscaled = result_sclaing(eachinput)
df['FoerderDruck']= abs(pd.melt(pd.DataFrame(RB_backscaled)).drop(['variable'],axis=1))
#first row not needed, dublicate specific to RB online stage ouput, remove:
df.drop(df.index[0],inplace=True)
df.drop(df.tail(1).index,inplace=True) # drop last n rows
df.reset_index(drop=True,inplace=True)
else:
print('input array does not meet criteria needed, please check length=128 and type=np.ndarray')
return
elif itype[index] == 'preped_df':
df = eachinput
filename = 'preped df'
pta = False
else:
print('input type not recognised... aborting!')
return
#print('data loaded to df')
filenames.append(filename)
if pta == True:
#well testing data calculation and flow type evaluation:
if len(SIparams_dict) > 1:
df_calc,df_mft,faultzone_bd_detected = determine_main_flow_type_light(df,SIparams_dict[index],identifier)
else:
df_calc,df_mft,faultzone_bd_detected = determine_main_flow_type_light(df,SIparams_dict[0],identifier)
#set data for traces:
x=df.loc[1:,'t_hour']
y=df.loc[1:,'DruckAenderung']
y_extreme1 = abs(y).max()
ymaxvals.append(y_extreme1)
y_extreme2 = abs(y).min()
if np.allclose(y_extreme2,0.0) == True:
y_qunatile = y.quantile(0.20)
yminvals.append(y_qunatile)
else:
yminvals.append(y_extreme2)
#if differences exist in inputparams then put them into hover info:
if len(SIparams_dict) > 1:
current_dict = SIparams_dict[index]
diff_string = '<br>'+'FZ_th='+str(fthickness[index])+'m'
difftext = 'FZ_th='+str(fthickness[index])+'m'
else:
current_dict = SIparams_dict[0]
diff_string = '<br>'+'FZ_th='+str(fthickness[index])+'m'
difftext = 'FZ_th='+str(fthickness[index])+'m'
if len(differences)>0:
for eachkey in diff_keys:
if eachkey in current_dict.keys():
diff_string += '<br>'+str(eachkey)+'='+str(current_dict[eachkey])
difftext += '<br>'+str(eachkey)+'='+str(current_dict[eachkey])
#generate trace and add to traces
p_trace=go.Scatter(showlegend=True,
x=x,
y=y,
name='dP '+diff_string,
hoverinfo = 'y+name+text',
text=difftext,
mode = 'lines+markers',
line=dict(color=rgb,
width=2,
dash = 'solid'),
marker=dict(size=1,
opacity=0,
color=rgb,
),
)
traces.append(p_trace)
if DERplot[index] == True:
x=df.loc[2:124,'t_hour']
y=df.loc[2:124,'DER']
flowtype_list=df.loc[2:124,'flowtype']
flowtype_list=[letters[:3] for letters in flowtype_list]
flowtype_label= [None]*len(flowtype_list)
flowtype_label[::3]=flowtype_list[::3]
#generate trace and add to traces
p_trace=go.Scatter(showlegend=False,
x=x,
y=y,
name='DER '+filename+diff_string,
hoverinfo = 'y+name+text',
text=flowtype_label,
textposition='top center',
mode = 'markers+text',
textfont=dict(size=12,color=rgb),
line=dict(color=rgb,
width=2,
dash = 'dot'),
marker=dict(size=3,
opacity=1.0,
color=rgb,
),
)
traces.append(p_trace)
#adding minimum of DER to adjust scale of y axis
y_extreme3 = abs(y).min()
if np.allclose(y_extreme3,0.0) == True:
y_qunatile = y.quantile(0.20)
yminvals.append(y_qunatile)
else:
yminvals.append(y_extreme3)
#determin longest filename:
lname = max(filenames, key=len) + ' DER'
# setting x-axis:
if xtyp == 'log':
xmax=math.ceil(np.log10(df['t_hour'].max()))
xmin=basic_func.tidy(math.floor(np.log10(df['t_hour'][1])),1)
xaxis1=gen_xaxis_dict(title=xtitle,typ=xtyp,rang=[xmin,xmax])
elif xtyp == 'linear':
xmax=math.ceil(df['t_hour'].max())
xmin=math.floor(df['t_hour'].min())
dx = basic_func.tidy(xmax-xmin,1)/10
xaxis1=gen_xaxis_dict(title=xtitle,typ=xtyp,rang=[xmin,xmax],dtick=dx)
#setting y-axis:
if ytyp == 'linear':
#calculate y-axis limits and steps
ymax = max(ymaxvals)*1.1
dy=basic_func.tidy(ymax,1)/10
if True in DERplot:
yaxis1=gen_yaxis_dict(title=ytitle+' / DER',typ=ytyp,rang=[0,ymax],dtick=dy,tickformat="1.3f")
else:
yaxis1=gen_yaxis_dict(title=ytitle,typ=ytyp,rang=[0,ymax],dtick=dy,tickformat="1.3f")
elif ytyp == 'log':
logmax=np.log10(max(ymaxvals))
for i, eachy in enumerate(yminvals):
if np.allclose(eachy,0.0) == True:
del yminvals[i]
logmin=np.log10(min(yminvals))
ymax=math.ceil(logmax)
#if double log plot make show same amount of log cycles on each axis
if xtyp == 'log':
ymin=ymax-abs(xmin-xmax)
else:
ymin=math.floor(logmin)
if True in DERplot:
yaxis1=gen_yaxis_dict(title=ytitle+' / DER',typ=ytyp,rang=[ymin,ymax],tickformat=".1r")
else:
yaxis1=gen_yaxis_dict(title=ytitle,typ=ytyp,rang=[ymin,ymax],tickformat="1.3f")
annotations=[]
an = dict(x=0.50,
y=0.95,
showarrow=False,
text='Derivative Plot<br>Fault Zone vs Matrix',
bgcolor='rgb(255,255,255)',
opacity=0.8,
bordercolor='#000000',
borderwidth=1,
align='center',
xref='paper',
yref='paper',
font =dict(family=fontpath,
size=30,
color='#000000'))
annotations.append(an)
an = dict(x=0.50,
y=0.1,
showarrow=False,
text=parametertext2,
bgcolor='rgb(255,255,255)',
opacity=0.8,
bordercolor='#000000',
borderwidth=1,
align='center',
xref='paper',
yref='paper',
font =dict(family=fontpath,
size=15,
color='#000000'))
annotations.append(an)
#adding optional annotations here:
if len(optional_annotation) != 0:
for eachan in optional_annotation:
annotations.append(eachan)
#setting layout here:
layout = layout_f_plotly(xaxis1,yaxis1,llegendword=lname,annotations=annotations)
#plot construction and saving:
fig = dict(data=traces, layout=layout)
saveplot_routine(fig,indv_plotname,plotdir,newfoldername,auto_open,pdf,png,html)
path,
q_a,
q_f,
q_l,
q_m,
n_outputs,
n_timesteps,
dt,
euler_theta,
varying_timesteps=varying_timesteps,
growth_rate=growth_rate,
time_dependent_parameters=time_dependent_parameters,
ID_param=ID_param,
start_time=start_time,
end_time=end_time)
t_model_gen = basic_func.tidy(((time() - t)), 3)
print(
str(t_model_gen) + 's elapsed to add the ' + eachdir +
'm fault zone thickness model!')
models.append(model)
#add matrix model:
modelnames.append('matrix')
t = time()
matrix_model = rbm.model_from_offline_data(
os.path.join(RB_MODEL_FOLDER_NAME, 'matrix', 'offline_data'),
2,
2,
q_l,
2,
n_outputs,