repo
stringlengths 2
99
| file
stringlengths 13
225
| code
stringlengths 0
18.3M
| file_length
int64 0
18.3M
| avg_line_length
float64 0
1.36M
| max_line_length
int64 0
4.26M
| extension_type
stringclasses 1
value |
|---|---|---|---|---|---|---|
synfeal | synfeal-main/models/depthnet.py | import torch
import torch.nn as nn
import torch.nn.parallel
import torch.utils.data
import numpy as np
import torch.nn.functional as F
class CNNDepth(nn.Module): #https://towardsdatascience.com/covolutional-neural-network-cb0883dd6529
def __init__(self):
super(CNNDepth, self).__init__() # call the init constructor of the nn.Module. This way, we are only adding attributes.
self.conv1 = nn.Conv2d(in_channels=1, out_channels=64, kernel_size=5, stride=2, padding=2)
self.conv2 = nn.Conv2d(in_channels=64, out_channels=128, kernel_size=5, stride=2, padding=2)
self.conv3 = nn.Conv2d(in_channels=128, out_channels=256, kernel_size=5, stride=2, padding=2)
self.conv4 = nn.Conv2d(in_channels=256, out_channels=512, kernel_size=5, stride=2, padding=2)
self.conv5 = nn.Conv2d(in_channels=512, out_channels=512, kernel_size=5, stride=2, padding=2)
self.fc1 = nn.Linear(25088, 4096)
self.fc2 = nn.Linear(4096, 1024)
self.fc3 = nn.Linear(1024, 512)
self.fc_out_translation = nn.Linear(512, 3)
self.fc_out_rotation = nn.Linear(512, 4)
# instead of treating the relu as modules, we can treat them as functions. We can access them via torch funtional
def forward(self, x, verbose=False): # this is where we pass the input into the module
if verbose: print('shape ' + str(x.shape))
x = F.relu(self.conv1(x))
if verbose: print('layer1 shape ' + str(x.shape))
x = F.relu(self.conv2(x))
if verbose: print('layer2 shape ' + str(x.shape))
x = F.relu(self.conv3(x))
if verbose: print('layer3 shape ' + str(x.shape))
x = F.relu(self.conv4(x))
if verbose: print('layer4 shape ' + str(x.shape))
x = F.relu(self.conv5(x))
if verbose: print('layer5 shape ' + str(x.shape))
x = x.view(x.size(0), -1)
if verbose: print('x shape ' + str(x.shape))
# x = F.dropout(x, p=0.5)
# x = F.relu(self.fc1(x))
# if verbose: print('fc1 shape ' + str(x.shape))
#
# x = F.relu(self.fc2(x))
# if verbose: print('fc2 shape ' + str(x.shape))
#
# x = F.relu(self.fc3(x))
# if verbose: print('fc3 shape ' + str(x.shape))
x = F.relu(self.fc1(x))
if verbose: print('fc1 shape ' + str(x.shape))
x = F.relu(self.fc2(x))
if verbose: print('fc2 shape ' + str(x.shape))
x = F.relu(self.fc3(x))
if verbose: print('fc3 shape ' + str(x.shape))
x_translation = self.fc_out_translation(x)
if verbose: print('x_translation shape ' + str(x_translation.shape))
x_rotation = self.fc_out_rotation(x)
if verbose: print('x_rotation shape ' + str(x_rotation.shape))
x_pose = torch.cat((x_translation, x_rotation), dim=1)
return x_pose
class CNNDepthLow(nn.Module): #https://towardsdatascience.com/covolutional-neural-network-cb0883dd6529
def __init__(self):
super(CNNDepthLow, self).__init__() # call the init constructor of the nn.Module. This way, we are only adding attributes.
self.conv1 = nn.Conv2d(in_channels=1, out_channels=64, kernel_size=3, stride=2)
self.conv2 = nn.Conv2d(in_channels=64, out_channels=128, kernel_size=3, stride=2)
self.conv3 = nn.Conv2d(in_channels=128, out_channels=256, kernel_size=3, stride=2)
self.conv4 = nn.Conv2d(in_channels=256, out_channels=512, kernel_size=3, stride=2)
self.conv5 = nn.Conv2d(in_channels=512, out_channels=512, kernel_size=3, stride=2)
self.fc1 = nn.Linear(18432, 4096)
self.fc2 = nn.Linear(4096, 1024)
#self.fc3 = nn.Linear(1024, 512)
self.fc_out_translation = nn.Linear(1024, 3)
self.fc_out_rotation = nn.Linear(1024, 4)
# instead of treating the relu as modules, we can treat them as functions. We can access them via torch funtional
def forward(self, x, verbose=False): # this is where we pass the input into the module
if verbose: print('shape ' + str(x.shape))
x = F.relu(self.conv1(x))
if verbose: print('layer1 shape ' + str(x.shape))
x = F.relu(self.conv2(x))
if verbose: print('layer2 shape ' + str(x.shape))
x = F.relu(self.conv3(x))
if verbose: print('layer3 shape ' + str(x.shape))
x = F.relu(self.conv4(x))
if verbose: print('layer4 shape ' + str(x.shape))
x = F.relu(self.conv5(x))
if verbose: print('layer5 shape ' + str(x.shape))
x = x.view(x.size(0), -1)
if verbose: print('x shape ' + str(x.shape))
# x = F.dropout(x, p=0.5)
# x = F.relu(self.fc1(x))
# if verbose: print('fc1 shape ' + str(x.shape))
#
# x = F.relu(self.fc2(x))
# if verbose: print('fc2 shape ' + str(x.shape))
#
# x = F.relu(self.fc3(x))
# if verbose: print('fc3 shape ' + str(x.shape))
x = F.relu(self.fc1(x))
if verbose: print('fc1 shape ' + str(x.shape))
x = F.relu(self.fc2(x))
if verbose: print('fc2 shape ' + str(x.shape))
# x = F.relu(self.fc3(x))
# if verbose: print('fc3 shape ' + str(x.shape))
x_translation = self.fc_out_translation(x)
if verbose: print('x_translation shape ' + str(x_translation.shape))
x_rotation = self.fc_out_rotation(x)
if verbose: print('x_rotation shape ' + str(x_rotation.shape))
x_pose = torch.cat((x_translation, x_rotation), dim=1)
return x_pose
class CNNDepthDropout(nn.Module): #https://towardsdatascience.com/covolutional-neural-network-cb0883dd6529
def __init__(self):
super(CNNDepthDropout, self).__init__() # call the init constructor of the nn.Module. This way, we are only adding attributes.
self.conv1 = nn.Conv2d(in_channels=1, out_channels=64, kernel_size=5, stride=2, padding=2)
self.conv2 = nn.Conv2d(in_channels=64, out_channels=128, kernel_size=5, stride=2, padding=2)
self.conv3 = nn.Conv2d(in_channels=128, out_channels=256, kernel_size=5, stride=2, padding=2)
self.conv4 = nn.Conv2d(in_channels=256, out_channels=512, kernel_size=5, stride=2, padding=2)
self.conv5 = nn.Conv2d(in_channels=512, out_channels=512, kernel_size=5, stride=2, padding=2)
self.dropout1 = nn.Dropout(p=0.5)
self.dropout2 = nn.Dropout(p=0.3)
self.dropout3 = nn.Dropout(p=0.2)
self.fc1 = nn.Linear(25088, 4096)
self.fc2 = nn.Linear(4096, 1024)
self.fc3 = nn.Linear(1024, 512)
self.fc_out_translation = nn.Linear(512, 3)
self.fc_out_rotation = nn.Linear(512, 4)
# instead of treating the relu as modules, we can treat them as functions. We can access them via torch funtional
def forward(self, x, verbose=False): # this is where we pass the input into the module
if verbose: print('shape ' + str(x.shape))
x = F.relu(self.droupout3(self.conv1(x)))
if verbose: print('layer1 shape ' + str(x.shape))
x = F.relu(self.dropout3(self.conv2(x)))
if verbose: print('layer2 shape ' + str(x.shape))
x = F.relu(self.dropout3(self.conv3(x)))
if verbose: print('layer3 shape ' + str(x.shape))
x = F.relu(self.dropout3(self.conv4(x)))
if verbose: print('layer4 shape ' + str(x.shape))
x = F.relu(self.dropout3(self.conv5(x)))
if verbose: print('layer5 shape ' + str(x.shape))
x = x.view(x.size(0), -1)
if verbose: print('x shape ' + str(x.shape))
# x = F.dropout(x, p=0.5)
# x = F.relu(self.fc1(x))
# if verbose: print('fc1 shape ' + str(x.shape))
#
# x = F.relu(self.fc2(x))
# if verbose: print('fc2 shape ' + str(x.shape))
#
# x = F.relu(self.fc3(x))
# if verbose: print('fc3 shape ' + str(x.shape))
x = F.relu(self.dropout1(self.fc1(x)))
if verbose: print('fc1 shape ' + str(x.shape))
x = F.relu(self.dropout2(self.fc2(x)))
if verbose: print('fc2 shape ' + str(x.shape))
x = F.relu(self.dropout3(self.fc3(x)))
if verbose: print('fc3 shape ' + str(x.shape))
x_translation = self.fc_out_translation(x)
if verbose: print('x_translation shape ' + str(x_translation.shape))
x_rotation = self.fc_out_rotation(x)
if verbose: print('x_rotation shape ' + str(x_rotation.shape))
x_pose = torch.cat((x_translation, x_rotation), dim=1)
return x_pose
class CNNDepthBatch(nn.Module): #https://towardsdatascience.com/covolutional-neural-network-cb0883dd6529
def __init__(self):
super(CNNDepthBatch, self).__init__() # call the init constructor of the nn.Module. This way, we are only adding attributes.
self.conv1 = nn.Conv2d(in_channels=1, out_channels=64, kernel_size=5, stride=2, padding=2)
self.conv2 = nn.Conv2d(in_channels=64, out_channels=128, kernel_size=5, stride=2, padding=2)
self.conv3 = nn.Conv2d(in_channels=128, out_channels=256, kernel_size=5, stride=2, padding=2)
self.conv4 = nn.Conv2d(in_channels=256, out_channels=512, kernel_size=5, stride=2, padding=2)
self.conv5 = nn.Conv2d(in_channels=512, out_channels=512, kernel_size=5, stride=2, padding=2)
# Batch norm should be before relu
self.bn1 = nn.BatchNorm2d(64)
self.bn2 = nn.BatchNorm2d(128)
self.bn3 = nn.BatchNorm2d(256)
self.bn4 = nn.BatchNorm2d(512)
self.bn5 = nn.BatchNorm2d(512)
self.bn6 = nn.BatchNorm1d(4096)
self.bn7 = nn.BatchNorm1d(1024)
self.bn8 = nn.BatchNorm1d(512)
self.dropout = nn.Dropout(p=0.4)
self.fc1 = nn.Linear(25088, 4096)
self.fc2 = nn.Linear(4096, 1024)
self.fc3 = nn.Linear(1024, 512)
self.fc_out_translation = nn.Linear(512, 3)
self.fc_out_rotation = nn.Linear(512, 4)
# instead of treating the relu as modules, we can treat them as functions. We can access them via torch funtional
def forward(self, x, verbose=False): # this is where we pass the input into the module
if verbose: print('shape ' + str(x.shape))
x = F.relu(self.bn1(self.conv1(x)))
if verbose: print('layer1 shape ' + str(x.shape))
x = F.relu(self.bn2(self.conv2(x)))
if verbose: print('layer2 shape ' + str(x.shape))
x = F.relu(self.bn3(self.conv3(x)))
if verbose: print('layer3 shape ' + str(x.shape))
x = F.relu(self.bn4(self.conv4(x)))
if verbose: print('layer4 shape ' + str(x.shape))
x = F.relu(self.bn5(self.conv5(x)))
if verbose: print('layer5 shape ' + str(x.shape))
x = x.view(x.size(0), -1)
if verbose: print('x shape ' + str(x.shape))
# x = F.dropout(x, p=0.5)
# x = F.relu(self.fc1(x))
# if verbose: print('fc1 shape ' + str(x.shape))
#
# x = F.relu(self.fc2(x))
# if verbose: print('fc2 shape ' + str(x.shape))
#
# x = F.relu(self.fc3(x))
# if verbose: print('fc3 shape ' + str(x.shape))
x = F.relu(self.dropout(self.bn6(self.fc1(x))))
if verbose: print('fc1 shape ' + str(x.shape))
x = F.relu(self.bn7(self.fc2(x)))
if verbose: print('fc2 shape ' + str(x.shape))
x = F.relu(self.bn8(self.fc3(x)))
if verbose: print('fc3 shape ' + str(x.shape))
x_translation = self.fc_out_translation(x)
if verbose: print('x_translation shape ' + str(x_translation.shape))
x_rotation = self.fc_out_rotation(x)
if verbose: print('x_rotation shape ' + str(x_rotation.shape))
x_pose = torch.cat((x_translation, x_rotation), dim=1)
return x_pose
class CNNDepthBatchK3(nn.Module): #https://towardsdatascience.com/covolutional-neural-network-cb0883dd6529
def __init__(self):
super(CNNDepthBatchK3, self).__init__() # call the init constructor of the nn.Module. This way, we are only adding attributes.
self.conv1 = nn.Conv2d(in_channels=1, out_channels=64, kernel_size=3, stride=2, padding=1)
self.conv2 = nn.Conv2d(in_channels=64, out_channels=128, kernel_size=3, stride=2, padding=1)
self.conv3 = nn.Conv2d(in_channels=128, out_channels=256, kernel_size=3, stride=2, padding=1)
self.conv4 = nn.Conv2d(in_channels=256, out_channels=512, kernel_size=3, stride=2, padding=1)
self.conv5 = nn.Conv2d(in_channels=512, out_channels=512, kernel_size=3, stride=2, padding=1)
# Batch norm should be before relu
self.bn1 = nn.BatchNorm2d(64)
self.bn2 = nn.BatchNorm2d(128)
self.bn3 = nn.BatchNorm2d(256)
self.bn4 = nn.BatchNorm2d(512)
self.bn5 = nn.BatchNorm2d(512)
self.bn6 = nn.BatchNorm1d(4096)
self.bn7 = nn.BatchNorm1d(1024)
self.bn8 = nn.BatchNorm1d(512)
self.dropout = nn.Dropout(p=0.4)
self.fc1 = nn.Linear(25088, 4096)
self.fc2 = nn.Linear(4096, 1024)
self.fc3 = nn.Linear(1024, 512)
self.fc_out_translation = nn.Linear(512, 3)
self.fc_out_rotation = nn.Linear(512, 4)
# instead of treating the relu as modules, we can treat them as functions. We can access them via torch funtional
def forward(self, x, verbose=True): # this is where we pass the input into the module
if verbose: print('shape ' + str(x.shape))
x = F.relu(self.bn1(self.conv1(x)))
if verbose: print('layer1 shape ' + str(x.shape))
x = F.relu(self.bn2(self.conv2(x)))
if verbose: print('layer2 shape ' + str(x.shape))
x = F.relu(self.bn3(self.conv3(x)))
if verbose: print('layer3 shape ' + str(x.shape))
x = F.relu(self.bn4(self.conv4(x)))
if verbose: print('layer4 shape ' + str(x.shape))
x = F.relu(self.bn5(self.conv5(x)))
if verbose: print('layer5 shape ' + str(x.shape))
x = x.view(x.size(0), -1)
if verbose: print('x shape ' + str(x.shape))
# x = F.dropout(x, p=0.5)
# x = F.relu(self.fc1(x))
# if verbose: print('fc1 shape ' + str(x.shape))
#
# x = F.relu(self.fc2(x))
# if verbose: print('fc2 shape ' + str(x.shape))
#
# x = F.relu(self.fc3(x))
# if verbose: print('fc3 shape ' + str(x.shape))
x = F.relu(self.dropout(self.bn6(self.fc1(x))))
if verbose: print('fc1 shape ' + str(x.shape))
x = F.relu(self.bn7(self.fc2(x)))
if verbose: print('fc2 shape ' + str(x.shape))
x = F.relu(self.bn8(self.fc3(x)))
if verbose: print('fc3 shape ' + str(x.shape))
x_translation = self.fc_out_translation(x)
if verbose: print('x_translation shape ' + str(x_translation.shape))
x_rotation = self.fc_out_rotation(x)
if verbose: print('x_rotation shape ' + str(x_rotation.shape))
x_pose = torch.cat((x_translation, x_rotation), dim=1)
return x_pose
class CNNDepthBatchLeaky(nn.Module): #https://towardsdatascience.com/covolutional-neural-network-cb0883dd6529
def __init__(self):
super(CNNDepthBatchLeaky, self).__init__() # call the init constructor of the nn.Module. This way, we are only adding attributes.
self.conv1 = nn.Conv2d(in_channels=1, out_channels=64, kernel_size=5, stride=2, padding=2)
self.conv2 = nn.Conv2d(in_channels=64, out_channels=128, kernel_size=5, stride=2, padding=2)
self.conv3 = nn.Conv2d(in_channels=128, out_channels=256, kernel_size=5, stride=2, padding=2)
self.conv4 = nn.Conv2d(in_channels=256, out_channels=512, kernel_size=5, stride=2, padding=2)
self.conv5 = nn.Conv2d(in_channels=512, out_channels=512, kernel_size=5, stride=2, padding=2)
# Batch norm should be before relu
self.bn1 = nn.BatchNorm2d(64)
self.bn2 = nn.BatchNorm2d(128)
self.bn3 = nn.BatchNorm2d(256)
self.bn4 = nn.BatchNorm2d(512)
self.bn5 = nn.BatchNorm2d(512)
self.bn6 = nn.BatchNorm1d(4096)
self.bn7 = nn.BatchNorm1d(1024)
self.bn8 = nn.BatchNorm1d(512)
self.lrelu = nn.LeakyReLU(0.1)
self.fc1 = nn.Linear(25088, 4096)
self.fc2 = nn.Linear(4096, 1024)
self.fc3 = nn.Linear(1024, 512)
self.fc_out_translation = nn.Linear(512, 3)
self.fc_out_rotation = nn.Linear(512, 4)
# instead of treating the relu as modules, we can treat them as functions. We can access them via torch funtional
def forward(self, x, verbose=False): # this is where we pass the input into the module
if verbose: print('shape ' + str(x.shape))
x = self.lrelu(self.bn1(self.conv1(x)))
if verbose: print('layer1 shape ' + str(x.shape))
x = self.lrelu(self.bn2(self.conv2(x)))
if verbose: print('layer2 shape ' + str(x.shape))
x = self.lrelu(self.bn3(self.conv3(x)))
if verbose: print('layer3 shape ' + str(x.shape))
x = self.lrelu(self.bn4(self.conv4(x)))
if verbose: print('layer4 shape ' + str(x.shape))
x = self.lrelu(self.bn5(self.conv5(x)))
if verbose: print('layer5 shape ' + str(x.shape))
x = x.view(x.size(0), -1)
if verbose: print('x shape ' + str(x.shape))
# x = F.dropout(x, p=0.5)
# x = F.relu(self.fc1(x))
# if verbose: print('fc1 shape ' + str(x.shape))
#
# x = F.relu(self.fc2(x))
# if verbose: print('fc2 shape ' + str(x.shape))
#
# x = F.relu(self.fc3(x))
# if verbose: print('fc3 shape ' + str(x.shape))
x = self.lrelu(self.dropout(self.bn6(self.fc1(x))))
if verbose: print('fc1 shape ' + str(x.shape))
x = self.lrelu(self.bn7(self.fc2(x)))
if verbose: print('fc2 shape ' + str(x.shape))
x = self.lrelu(self.bn8(self.fc3(x)))
if verbose: print('fc3 shape ' + str(x.shape))
x_translation = self.fc_out_translation(x)
if verbose: print('x_translation shape ' + str(x_translation.shape))
x_rotation = self.fc_out_rotation(x)
if verbose: print('x_rotation shape ' + str(x_rotation.shape))
x_pose = torch.cat((x_translation, x_rotation), dim=1)
return x_pose
class CNNDepthBatchLow(nn.Module): #https://towardsdatascience.com/covolutional-neural-network-cb0883dd6529
def __init__(self):
super(CNNDepthBatchLow, self).__init__() # call the init constructor of the nn.Module. This way, we are only adding attributes.
self.conv1 = nn.Conv2d(in_channels=1, out_channels=32, kernel_size=3, stride=2, padding=1)
self.conv2 = nn.Conv2d(in_channels=32, out_channels=64, kernel_size=3, stride=2, padding=1)
self.conv3 = nn.Conv2d(in_channels=64, out_channels=128, kernel_size=3, stride=2, padding=1)
self.conv4 = nn.Conv2d(in_channels=128, out_channels=256, kernel_size=3, stride=2, padding=1)
self.conv5 = nn.Conv2d(in_channels=256, out_channels=512, kernel_size=3, stride=2, padding=1)
# Batch norm should be before relu
self.bn1 = nn.BatchNorm2d(32)
self.bn2 = nn.BatchNorm2d(64)
self.bn3 = nn.BatchNorm2d(128)
self.bn4 = nn.BatchNorm2d(256)
self.bn5 = nn.BatchNorm2d(512)
self.bn6 = nn.BatchNorm1d(4096)
self.bn7 = nn.BatchNorm1d(1024)
#self.bn8 = nn.BatchNorm1d(512)
#self.lrelu = nn.LeakyReLU(0.2)
self.dropout = nn.Dropout(p=0.5)
self.fc1 = nn.Linear(25088, 4096)
self.fc2 = nn.Linear(4096, 1024)
#self.fc3 = nn.Linear(1024, 512)
self.fc_out_translation = nn.Linear(1024, 3)
self.fc_out_rotation = nn.Linear(1024, 4)
# instead of treating the relu as modules, we can treat them as functions. We can access them via torch funtional
def forward(self, x, verbose=True): # this is where we pass the input into the module
if verbose: print('shape ' + str(x.shape))
x = F.relu(self.bn1(self.conv1(x)))
if verbose: print('layer1 shape ' + str(x.shape))
x = F.relu(self.bn2(self.conv2(x)))
if verbose: print('layer2 shape ' + str(x.shape))
x = F.relu(self.bn3(self.conv3(x)))
if verbose: print('layer3 shape ' + str(x.shape))
x = F.relu(self.bn4(self.conv4(x)))
if verbose: print('layer4 shape ' + str(x.shape))
x = F.relu(self.bn5(self.conv5(x)))
if verbose: print('layer5 shape ' + str(x.shape))
x = x.view(x.size(0), -1)
if verbose: print('x shape ' + str(x.shape))
# x = F.dropout(x, p=0.5)
# x = self.lrelu(self.fc1(x))
# if verbose: print('fc1 shape ' + str(x.shape))
#
# x = self.lrelu(self.fc2(x))
# if verbose: print('fc2 shape ' + str(x.shape))
#
# x = self.lrelu(self.fc3(x))
# if verbose: print('fc3 shape ' + str(x.shape))
x = F.relu(self.dropout(self.bn6(self.fc1(x))))
if verbose: print('fc1 shape ' + str(x.shape))
x = F.relu(self.bn7(self.fc2(x)))
if verbose: print('fc2 shape ' + str(x.shape))
# x = self.lrelu(self.bn8(self.fc3(x)))
# if verbose: print('fc3 shape ' + str(x.shape))
x_translation = self.fc_out_translation(x)
if verbose: print('x_translation shape ' + str(x_translation.shape))
x_rotation = self.fc_out_rotation(x)
if verbose: print('x_rotation shape ' + str(x_rotation.shape))
x_pose = torch.cat((x_translation, x_rotation), dim=1)
return x_pose
class CNNDepthBatchLowL2RegLeaky(nn.Module): #https://towardsdatascience.com/covolutional-neural-network-cb0883dd6529
def __init__(self):
super(CNNDepthBatchLowL2RegLeaky, self).__init__() # call the init constructor of the nn.Module. This way, we are only adding attributes.
self.conv1 = nn.Conv2d(in_channels=1, out_channels=64, kernel_size=3, stride=3, padding=1)
self.conv2 = nn.Conv2d(in_channels=64, out_channels=128, kernel_size=3, stride=3, padding=1)
self.conv3 = nn.Conv2d(in_channels=128, out_channels=256, kernel_size=3, stride=3, padding=1)
#self.conv4 = nn.Conv2d(in_channels=128, out_channels=256, kernel_size=3, stride=3, padding=1)
#self.conv5 = nn.Conv2d(in_channels=256, out_channels=512, kernel_size=3, stride=3, padding=1)
# Batch norm should be before relu
self.bn1 = nn.BatchNorm2d(64)
self.bn2 = nn.BatchNorm2d(128)
self.bn3 = nn.BatchNorm2d(256)
#self.bn4 = nn.BatchNorm2d(256)
#self.bn5 = nn.BatchNorm2d(512)
self.bn6 = nn.BatchNorm1d(4096)
self.bn7 = nn.BatchNorm1d(1024)
self.bn8 = nn.BatchNorm1d(512)
self.lrelu = nn.LeakyReLU(0.2)
#self.dropout = nn.Dropout(p=0.5)
self.fc1 = nn.Linear(20736, 4096)
self.fc2 = nn.Linear(4096, 1024)
self.fc3 = nn.Linear(1024, 512)
self.fc_out_translation = nn.Linear(512, 3)
self.fc_out_rotation = nn.Linear(512, 4)
# instead of treating the relu as modules, we can treat them as functions. We can access them via torch funtional
def forward(self, x, verbose=False): # this is where we pass the input into the module
if verbose: print('shape ' + str(x.shape))
x = self.lrelu(self.bn1(self.conv1(x)))
if verbose: print('layer1 shape ' + str(x.shape))
x = self.lrelu(self.bn2(self.conv2(x)))
if verbose: print('layer2 shape ' + str(x.shape))
x = self.lrelu(self.bn3(self.conv3(x)))
if verbose: print('layer3 shape ' + str(x.shape))
# x = self.lrelu(self.bn4(self.conv4(x)))
# if verbose: print('layer4 shape ' + str(x.shape))
# x = self.lrelu(self.bn5(self.conv5(x)))
# if verbose: print('layer5 shape ' + str(x.shape))
x = x.view(x.size(0), -1)
if verbose: print('x shape ' + str(x.shape))
# x = F.dropout(x, p=0.5)
# x = self.lrelu(self.fc1(x))
# if verbose: print('fc1 shape ' + str(x.shape))
#
# x = self.lrelu(self.fc2(x))
# if verbose: print('fc2 shape ' + str(x.shape))
#
# x = self.lrelu(self.fc3(x))
# if verbose: print('fc3 shape ' + str(x.shape))
x = self.lrelu(self.bn6(self.fc1(x)))
if verbose: print('fc1 shape ' + str(x.shape))
x = self.lrelu(self.bn7(self.fc2(x)))
if verbose: print('fc2 shape ' + str(x.shape))
x = self.lrelu(self.bn8(self.fc3(x)))
if verbose: print('fc3 shape ' + str(x.shape))
x_translation = self.fc_out_translation(x)
if verbose: print('x_translation shape ' + str(x_translation.shape))
x_rotation = self.fc_out_rotation(x)
if verbose: print('x_rotation shape ' + str(x_rotation.shape))
x_pose = torch.cat((x_translation, x_rotation), dim=1)
return x_pose
class CNNDepthBatchLowL2Reg2(nn.Module): #https://towardsdatascience.com/covolutional-neural-network-cb0883dd6529
def __init__(self):
super(CNNDepthBatchLowL2Reg2, self).__init__() # call the init constructor of the nn.Module. This way, we are only adding attributes.
self.conv1 = nn.Conv2d(in_channels=1, out_channels=64, kernel_size=5, stride=2, padding=1)
self.conv2 = nn.Conv2d(in_channels=64, out_channels=128, kernel_size=5, stride=2, padding=1)
self.conv3 = nn.Conv2d(in_channels=128, out_channels=256, kernel_size=5, stride=2, padding=1)
self.conv4 = nn.Conv2d(in_channels=256, out_channels=512, kernel_size=5, stride=2, padding=1)
self.conv5 = nn.Conv2d(in_channels=512, out_channels=512, kernel_size=5, stride=2, padding=1)
#self.conv6 = nn.Conv2d(in_channels=512, out_channels=1024, kernel_size=5, stride=2, padding=1)
# Batch norm should be before relu
self.bn1 = nn.BatchNorm2d(64)
self.bn2 = nn.BatchNorm2d(128)
self.bn3 = nn.BatchNorm2d(256)
self.bn4 = nn.BatchNorm2d(512)
self.bn5 = nn.BatchNorm2d(512)
#self.bn6 = nn.BatchNorm2d(1024)
self.bn6 = nn.BatchNorm1d(4096)
self.bn7 = nn.BatchNorm1d(1024)
self.bn8 = nn.BatchNorm1d(512)
#self.lrelu = nn.LeakyReLU(0.2)
#self.dropout = nn.Dropout(p=0.5)
self.fc1 = nn.Linear(18432, 4096)
self.fc2 = nn.Linear(4096, 1024)
self.fc3 = nn.Linear(1024, 512)
self.fc_out_translation = nn.Linear(512, 3)
self.fc_out_rotation = nn.Linear(512, 4)
# instead of treating the relu as modules, we can treat them as functions. We can access them via torch funtional
def forward(self, x, verbose=False): # this is where we pass the input into the module
if verbose: print('shape ' + str(x.shape))
x = F.relu(self.bn1(self.conv1(x)))
if verbose: print('layer1 shape ' + str(x.shape))
x = F.relu(self.bn2(self.conv2(x)))
if verbose: print('layer2 shape ' + str(x.shape))
x = F.relu(self.bn3(self.conv3(x)))
if verbose: print('layer3 shape ' + str(x.shape))
x = F.relu(self.bn4(self.conv4(x)))
if verbose: print('layer4 shape ' + str(x.shape))
x = F.relu(self.bn5(self.conv5(x)))
if verbose: print('layer5 shape ' + str(x.shape))
# x = self.lrelu(self.bn6(self.conv6(x)))
# if verbose: print('layer6 shape ' + str(x.shape))
x = x.view(x.size(0), -1)
if verbose: print('x shape ' + str(x.shape))
# x = F.dropout(x, p=0.5)
# x = self.lrelu(self.fc1(x))
# if verbose: print('fc1 shape ' + str(x.shape))
#
# x = self.lrelu(self.fc2(x))
# if verbose: print('fc2 shape ' + str(x.shape))
#
# x = self.lrelu(self.fc3(x))
# if verbose: print('fc3 shape ' + str(x.shape))
x = F.relu(self.bn6(self.fc1(x)))
if verbose: print('fc1 shape ' + str(x.shape))
x = F.relu(self.bn7(self.fc2(x)))
if verbose: print('fc2 shape ' + str(x.shape))
x = F.relu(self.bn8(self.fc3(x)))
if verbose: print('fc3 shape ' + str(x.shape))
x_translation = self.fc_out_translation(x)
if verbose: print('x_translation shape ' + str(x_translation.shape))
x_rotation = self.fc_out_rotation(x)
if verbose: print('x_rotation shape ' + str(x_rotation.shape))
x_pose = torch.cat((x_translation, x_rotation), dim=1)
return x_pose
class CNNDepthBatchDropout8(nn.Module): #https://towardsdatascience.com/covolutional-neural-network-cb0883dd6529
def __init__(self):
super(CNNDepthBatchDropout8, self).__init__() # call the init constructor of the nn.Module. This way, we are only adding attributes.
self.conv1 = nn.Conv2d(in_channels=1, out_channels=64, kernel_size=5, stride=2, padding=2)
self.conv2 = nn.Conv2d(in_channels=64, out_channels=128, kernel_size=5, stride=2, padding=2)
self.conv3 = nn.Conv2d(in_channels=128, out_channels=256, kernel_size=5, stride=2, padding=2)
self.conv4 = nn.Conv2d(in_channels=256, out_channels=512, kernel_size=5, stride=2, padding=2)
self.conv5 = nn.Conv2d(in_channels=512, out_channels=512, kernel_size=5, stride=2, padding=2)
# Batch norm should be before relu
self.bn1 = nn.BatchNorm2d(64)
self.bn2 = nn.BatchNorm2d(128)
self.bn3 = nn.BatchNorm2d(256)
self.bn4 = nn.BatchNorm2d(512)
self.bn5 = nn.BatchNorm2d(512)
self.bn6 = nn.BatchNorm1d(4096)
self.bn7 = nn.BatchNorm1d(1024)
self.bn8 = nn.BatchNorm1d(512)
self.dropout = nn.Dropout(p=0.8)
self.fc1 = nn.Linear(25088, 4096)
self.fc2 = nn.Linear(4096, 1024)
self.fc3 = nn.Linear(1024, 512)
self.fc_out_translation = nn.Linear(512, 3)
self.fc_out_rotation = nn.Linear(512, 4)
# instead of treating the relu as modules, we can treat them as functions. We can access them via torch funtional
def forward(self, x, verbose=False): # this is where we pass the input into the module
if verbose: print('shape ' + str(x.shape))
x = F.relu(self.bn1(self.conv1(x)))
if verbose: print('layer1 shape ' + str(x.shape))
x = F.relu(self.bn2(self.conv2(x)))
if verbose: print('layer2 shape ' + str(x.shape))
x = F.relu(self.bn3(self.conv3(x)))
if verbose: print('layer3 shape ' + str(x.shape))
x = F.relu(self.bn4(self.conv4(x)))
if verbose: print('layer4 shape ' + str(x.shape))
x = F.relu(self.bn5(self.conv5(x)))
if verbose: print('layer5 shape ' + str(x.shape))
x = x.view(x.size(0), -1)
if verbose: print('x shape ' + str(x.shape))
# x = F.dropout(x, p=0.5)
# x = F.relu(self.fc1(x))
# if verbose: print('fc1 shape ' + str(x.shape))
#
# x = F.relu(self.fc2(x))
# if verbose: print('fc2 shape ' + str(x.shape))
#
# x = F.relu(self.fc3(x))
# if verbose: print('fc3 shape ' + str(x.shape))
x = F.relu(self.dropout(self.bn6(self.fc1(x))))
if verbose: print('fc1 shape ' + str(x.shape))
x = F.relu(self.bn7(self.fc2(x)))
if verbose: print('fc2 shape ' + str(x.shape))
x = F.relu(self.bn8(self.fc3(x)))
if verbose: print('fc3 shape ' + str(x.shape))
x_translation = self.fc_out_translation(x)
if verbose: print('x_translation shape ' + str(x_translation.shape))
x_rotation = self.fc_out_rotation(x)
if verbose: print('x_rotation shape ' + str(x_rotation.shape))
x_pose = torch.cat((x_translation, x_rotation), dim=1)
return x_pose
class CNNDepthBatchDropoutVar(nn.Module): #https://towardsdatascience.com/covolutional-neural-network-cb0883dd6529
def __init__(self):
super(CNNDepthBatchDropoutVar, self).__init__() # call the init constructor of the nn.Module. This way, we are only adding attributes.
self.conv1 = nn.Conv2d(in_channels=1, out_channels=64, kernel_size=5, stride=2, padding=2)
self.conv2 = nn.Conv2d(in_channels=64, out_channels=128, kernel_size=5, stride=2, padding=2)
self.conv3 = nn.Conv2d(in_channels=128, out_channels=256, kernel_size=5, stride=2, padding=2)
self.conv4 = nn.Conv2d(in_channels=256, out_channels=512, kernel_size=5, stride=2, padding=2)
self.conv5 = nn.Conv2d(in_channels=512, out_channels=512, kernel_size=5, stride=2, padding=2)
# Batch norm should be before relu
self.bn1 = nn.BatchNorm2d(64)
self.bn2 = nn.BatchNorm2d(128)
self.bn3 = nn.BatchNorm2d(256)
self.bn4 = nn.BatchNorm2d(512)
self.bn5 = nn.BatchNorm2d(512)
self.bn6 = nn.BatchNorm1d(4096)
self.bn7 = nn.BatchNorm1d(1024)
self.bn8 = nn.BatchNorm1d(512)
self.dropout1 = nn.Dropout(p=0.5)
self.dropout2 = nn.Dropout(p=0.3)
self.dropout3 = nn.Dropout(p=0.2)
self.fc1 = nn.Linear(25088, 4096)
self.fc2 = nn.Linear(4096, 1024)
self.fc3 = nn.Linear(1024, 512)
self.fc_out_translation = nn.Linear(512, 3)
self.fc_out_rotation = nn.Linear(512, 4)
# instead of treating the relu as modules, we can treat them as functions. We can access them via torch funtional
def forward(self, x, verbose=False): # this is where we pass the input into the module
if verbose: print('shape ' + str(x.shape))
x = F.relu(self.bn1(self.conv1(x)))
if verbose: print('layer1 shape ' + str(x.shape))
x = F.relu(self.bn2(self.conv2(x)))
if verbose: print('layer2 shape ' + str(x.shape))
x = F.relu(self.bn3(self.conv3(x)))
if verbose: print('layer3 shape ' + str(x.shape))
x = F.relu(self.bn4(self.conv4(x)))
if verbose: print('layer4 shape ' + str(x.shape))
x = F.relu(self.bn5(self.conv5(x)))
if verbose: print('layer5 shape ' + str(x.shape))
x = x.view(x.size(0), -1)
if verbose: print('x shape ' + str(x.shape))
# x = F.dropout(x, p=0.5)
# x = F.relu(self.fc1(x))
# if verbose: print('fc1 shape ' + str(x.shape))
#
# x = F.relu(self.fc2(x))
# if verbose: print('fc2 shape ' + str(x.shape))
#
# x = F.relu(self.fc3(x))
# if verbose: print('fc3 shape ' + str(x.shape))
x = F.relu(self.dropout1(self.bn6(self.fc1(x))))
if verbose: print('fc1 shape ' + str(x.shape))
x = F.relu(self.dropout2(self.bn7(self.fc2(x))))
if verbose: print('fc2 shape ' + str(x.shape))
x = F.relu(self.dropout3(self.bn8(self.fc3(x))))
if verbose: print('fc3 shape ' + str(x.shape))
x_translation = self.fc_out_translation(x)
if verbose: print('x_translation shape ' + str(x_translation.shape))
x_rotation = self.fc_out_rotation(x)
if verbose: print('x_rotation shape ' + str(x_rotation.shape))
x_pose = torch.cat((x_translation, x_rotation), dim=1)
return x_pose
class CNNDepthBatchDropout8Cont(nn.Module): #https://towardsdatascience.com/covolutional-neural-network-cb0883dd6529
def __init__(self):
super(CNNDepthBatchDropout8Cont, self).__init__() # call the init constructor of the nn.Module. This way, we are only adding attributes.
self.conv1 = nn.Conv2d(in_channels=1, out_channels=64, kernel_size=5, stride=2, padding=2)
self.conv2 = nn.Conv2d(in_channels=64, out_channels=128, kernel_size=5, stride=2, padding=2)
self.conv3 = nn.Conv2d(in_channels=128, out_channels=256, kernel_size=5, stride=2, padding=2)
self.conv4 = nn.Conv2d(in_channels=256, out_channels=512, kernel_size=5, stride=2, padding=2)
self.conv5 = nn.Conv2d(in_channels=512, out_channels=512, kernel_size=5, stride=2, padding=2)
# Batch norm should be before relu
self.bn1 = nn.BatchNorm2d(64)
self.bn2 = nn.BatchNorm2d(128)
self.bn3 = nn.BatchNorm2d(256)
self.bn4 = nn.BatchNorm2d(512)
self.bn5 = nn.BatchNorm2d(512)
self.bn6 = nn.BatchNorm1d(4096)
self.bn7 = nn.BatchNorm1d(1024)
self.bn8 = nn.BatchNorm1d(512)
self.dropout1 = nn.Dropout(p=0.8)
self.dropout2 = nn.Dropout(p=0.5)
self.dropout3 = nn.Dropout(p=0.3)
self.fc1 = nn.Linear(25088, 4096)
self.fc2 = nn.Linear(4096, 1024)
#self.fc3 = nn.Linear(1024, 512)
self.fc_out_translation = nn.Linear(1024, 3)
self.fc_out_rotation = nn.Linear(1024, 4)
# instead of treating the relu as modules, we can treat them as functions. We can access them via torch funtional
def forward(self, x, verbose=False): # this is where we pass the input into the module
if verbose: print('shape ' + str(x.shape))
x = F.relu(self.bn1(self.conv1(x)))
if verbose: print('layer1 shape ' + str(x.shape))
x = F.relu(self.bn2(self.conv2(x)))
if verbose: print('layer2 shape ' + str(x.shape))
x = F.relu(self.bn3(self.conv3(x)))
if verbose: print('layer3 shape ' + str(x.shape))
x = F.relu(self.bn4(self.conv4(x)))
if verbose: print('layer4 shape ' + str(x.shape))
x = F.relu(self.bn5(self.conv5(x)))
if verbose: print('layer5 shape ' + str(x.shape))
x = x.view(x.size(0), -1)
if verbose: print('x shape ' + str(x.shape))
# x = F.dropout(x, p=0.5)
# x = F.relu(self.fc1(x))
# if verbose: print('fc1 shape ' + str(x.shape))
#
# x = F.relu(self.fc2(x))
# if verbose: print('fc2 shape ' + str(x.shape))
#
# x = F.relu(self.fc3(x))
# if verbose: print('fc3 shape ' + str(x.shape))
x = F.relu(self.dropout1(self.bn6(self.fc1(x))))
if verbose: print('fc1 shape ' + str(x.shape))
x = F.relu(self.dropout2(self.bn7(self.fc2(x))))
if verbose: print('fc2 shape ' + str(x.shape))
#x = F.relu(self.dropout3(self.bn8(self.fc3(x))))
#if verbose: print('fc3 shape ' + str(x.shape))
x_translation = self.fc_out_translation(x)
if verbose: print('x_translation shape ' + str(x_translation.shape))
x_rotation = self.fc_out_rotation(x)
if verbose: print('x_rotation shape ' + str(x_rotation.shape))
x_pose = torch.cat((x_translation, x_rotation), dim=1)
return x_pose
class CNNDepthBatchDropout8Kernel7(nn.Module): #https://towardsdatascience.com/covolutional-neural-network-cb0883dd6529
def __init__(self):
super(CNNDepthBatchDropout8Kernel7, self).__init__() # call the init constructor of the nn.Module. This way, we are only adding attributes.
self.conv1 = nn.Conv2d(in_channels=1, out_channels=64, kernel_size=7, stride=2, padding=1)
self.conv2 = nn.Conv2d(in_channels=64, out_channels=128, kernel_size=7, stride=2, padding=1)
self.conv3 = nn.Conv2d(in_channels=128, out_channels=256, kernel_size=5, stride=2, padding=1)
self.conv4 = nn.Conv2d(in_channels=256, out_channels=512, kernel_size=5, stride=2, padding=1)
self.conv5 = nn.Conv2d(in_channels=512, out_channels=512, kernel_size=3, stride=2, padding=1)
# Batch norm should be before relu
self.bn1 = nn.BatchNorm2d(64)
self.bn2 = nn.BatchNorm2d(128)
self.bn3 = nn.BatchNorm2d(256)
self.bn4 = nn.BatchNorm2d(512)
self.bn5 = nn.BatchNorm2d(512)
self.bn6 = nn.BatchNorm1d(4096)
self.bn7 = nn.BatchNorm1d(1024)
self.bn8 = nn.BatchNorm1d(512)
self.dropout = nn.Dropout(p=0.8)
self.fc1 = nn.Linear(18432, 4096)
self.fc2 = nn.Linear(4096, 1024)
self.fc3 = nn.Linear(1024, 512)
self.fc_out_translation = nn.Linear(512, 3)
self.fc_out_rotation = nn.Linear(512, 4)
# instead of treating the relu as modules, we can treat them as functions. We can access them via torch funtional
def forward(self, x, verbose=False): # this is where we pass the input into the module
if verbose: print('shape ' + str(x.shape))
x = F.relu(self.bn1(self.conv1(x)))
if verbose: print('layer1 shape ' + str(x.shape))
x = F.relu(self.bn2(self.conv2(x)))
if verbose: print('layer2 shape ' + str(x.shape))
x = F.relu(self.bn3(self.conv3(x)))
if verbose: print('layer3 shape ' + str(x.shape))
x = F.relu(self.bn4(self.conv4(x)))
if verbose: print('layer4 shape ' + str(x.shape))
x = F.relu(self.bn5(self.conv5(x)))
if verbose: print('layer5 shape ' + str(x.shape))
x = x.view(x.size(0), -1)
if verbose: print('x shape ' + str(x.shape))
# x = F.dropout(x, p=0.5)
# x = F.relu(self.fc1(x))
# if verbose: print('fc1 shape ' + str(x.shape))
#
# x = F.relu(self.fc2(x))
# if verbose: print('fc2 shape ' + str(x.shape))
#
# x = F.relu(self.fc3(x))
# if verbose: print('fc3 shape ' + str(x.shape))
x = F.relu(self.dropout(self.bn6(self.fc1(x))))
if verbose: print('fc1 shape ' + str(x.shape))
x = F.relu(self.bn7(self.fc2(x)))
if verbose: print('fc2 shape ' + str(x.shape))
x = F.relu(self.bn8(self.fc3(x)))
if verbose: print('fc3 shape ' + str(x.shape))
x_translation = self.fc_out_translation(x)
if verbose: print('x_translation shape ' + str(x_translation.shape))
x_rotation = self.fc_out_rotation(x)
if verbose: print('x_rotation shape ' + str(x_rotation.shape))
x_pose = torch.cat((x_translation, x_rotation), dim=1)
return x_pose | 45,692 | 44.784569 | 152 | py |
synfeal | synfeal-main/models/hourglass.py |
import torch
import torch.nn as nn
import torch.nn.parallel
import torch.utils.data
import torch.nn.functional as F
from torchvision import transforms, models
# paper: https://arxiv.org/abs/1703.07971
# github: https://github.com/youngguncho/HourglassPose-Pytorch/blob/master/model.py
class HourglassBatch(nn.Module):
def __init__(self, pretrained, sum_mode=False, dropout_rate=0.5, aux_logits=False):
super(HourglassBatch, self).__init__()
self.sum_mode = sum_mode
self.dropout_rate = dropout_rate
self.aux_logits = aux_logits
if pretrained:
base_model = models.resnet34('ResNet34_Weights.DEFAULT')
else:
base_model = models.resnet34()
# encoding blocks!
self.init_block = nn.Sequential(*list(base_model.children())[:4])
self.res_block1 = base_model.layer1
self.res_block2 = base_model.layer2
self.res_block3 = base_model.layer3
self.res_block4 = base_model.layer4
# decoding blocks
if sum_mode:
self.deconv_block1 = nn.ConvTranspose2d(512, 256, kernel_size=(
3, 3), stride=(2, 2), padding=(1, 1), bias=False, output_padding=1)
self.deconv_block2 = nn.ConvTranspose2d(256, 128, kernel_size=(
3, 3), stride=(2, 2), padding=(1, 1), bias=False, output_padding=1)
self.deconv_block3 = nn.ConvTranspose2d(128, 64, kernel_size=(
3, 3), stride=(2, 2), padding=(1, 1), bias=False, output_padding=1)
self.conv_block = nn.Conv2d(64, 32, kernel_size=(
3, 3), stride=(1, 1), padding=(1, 1), bias=False)
else:
# concatenation with the encoder feature vectors
self.deconv_block1 = nn.ConvTranspose2d(512, 256, kernel_size=(
3, 3), stride=(2, 2), padding=(1, 1), bias=False, output_padding=1)
self.deconv_block2 = nn.ConvTranspose2d(512, 128, kernel_size=(
3, 3), stride=(2, 2), padding=(1, 1), bias=False, output_padding=1)
self.deconv_block3 = nn.ConvTranspose2d(256, 64, kernel_size=(
3, 3), stride=(2, 2), padding=(1, 1), bias=False, output_padding=1)
self.conv_block = nn.Conv2d(128, 32, kernel_size=(
3, 3), stride=(1, 1), padding=(1, 1), bias=False)
self.bn1 = nn.BatchNorm2d(256)
self.bn2 = nn.BatchNorm2d(128)
self.bn3 = nn.BatchNorm2d(64)
self.bn4 = nn.BatchNorm2d(32)
self.bn5 = nn.BatchNorm1d(1024)
# Regressor
self.fc_dim_reduce = nn.Linear(56 * 56 * 32, 1024)
self.fc_trans = nn.Linear(1024, 3)
self.fc_rot = nn.Linear(1024, 4)
# Initialize Weights
init_modules = [self.deconv_block1, self.deconv_block2, self.deconv_block3, self.conv_block,
self.fc_dim_reduce, self.fc_trans, self.fc_rot]
for module in init_modules:
if isinstance(module, nn.ConvTranspose2d) or isinstance(module, nn.Linear) or isinstance(module, nn.Conv3d):
nn.init.kaiming_normal_(module.weight)
if module.bias is not None:
nn.init.constant_(module.bias, 0)
def forward(self, x):
# conv
x = self.init_block(x)
x_res1 = self.res_block1(x)
x_res2 = self.res_block2(x_res1)
x_res3 = self.res_block3(x_res2)
x_res4 = self.res_block4(x_res3)
# Deconv
x_deconv1 = self.bn1(F.relu(self.deconv_block1(x_res4)))
if self.sum_mode:
x_deconv1 = x_res3 + x_deconv1
else:
x_deconv1 = torch.cat((x_res3, x_deconv1), dim=1)
x_deconv2 = self.bn2(F.relu(self.deconv_block2(x_deconv1)))
if self.sum_mode:
x_deconv2 = x_res2 + x_deconv2
else:
x_deconv2 = torch.cat((x_res2, x_deconv2), dim=1)
x_deconv3 = self.bn3(F.relu(self.deconv_block3(x_deconv2)))
if self.sum_mode:
x_deconv3 = x_res1 + x_deconv3
else:
x_deconv3 = torch.cat((x_res1, x_deconv3), dim=1)
x_conv = self.bn4(F.relu(self.conv_block(x_deconv3)))
x_linear = x_conv.view(x_conv.size(0), -1)
x_linear = self.bn5(F.relu(self.fc_dim_reduce(x_linear)))
x_linear = F.dropout(x_linear, p=self.dropout_rate,
training=self.training)
position = self.fc_trans(x_linear)
rotation = self.fc_rot(x_linear)
x_pose = torch.cat((position, rotation), dim=1)
return x_pose
| 4,639 | 36.723577 | 120 | py |
synfeal | synfeal-main/process_dataset/src/validate_dataset.py | from utils import projectToCamera
import numpy as np
import os
import shutil
from dataset import Dataset
from sensor_msgs.msg import PointField
import sensor_msgs.point_cloud2 as pc2
# from utils import
from utils_ros import read_pcd, write_pcd
import random
from os.path import exists
import yaml
from colorama import Fore
import math
import copy
import cv2
class ValidateDataset():
def __init__(self):
self.files = ['.pcd', '.rgb.png', '.pose.txt']
def resetConfig(self, config={}):
self.config = config
def duplicateDataset(self, dataset, suffix):
# copy folder and create dataset object, return object
path_dataset = dataset.path_seq
path_validated_dataset = f'{dataset.path_seq}{suffix}'
shutil.copytree(path_dataset, path_validated_dataset)
return Dataset(path_seq=f'{dataset.seq}{suffix}')
def numberOfPoints(self, dataset, frame = None):
dct = {}
if frame == None: # calculate number of points for all pointclouds
for index in range(len(dataset)):
n_points = read_pcd(f'{dataset.path_seq}/frame-{index:05d}.pcd').points
dct[index] = n_points
else:
n_points = read_pcd(f'{dataset.path_seq}/frame-{frame:05d}.pcd').points
dct[frame] = n_points
return dct
def numberOfNans(self, dataset, frame = None):
dct = {}
if frame == None:
for index in range(len(dataset)):
pc_raw = read_pcd(f'{dataset.path_seq}/frame-{index:05d}.pcd')
pts = np.vstack([pc_raw.pc_data['x'], pc_raw.pc_data['y'], pc_raw.pc_data['z']]).T # stays NX3
dct[index] = np.sum(np.isnan(pts).any(axis=1))
else:
pc_raw = read_pcd(f'{dataset.path_seq}/frame-{frame:05d}.pcd')
pts = np.vstack([pc_raw.pc_data['x'], pc_raw.pc_data['y'], pc_raw.pc_data['z']]).T # stays NX3
dct[frame] = np.sum(np.isnan(pts).any(axis=1))
return dct
def removeNansPointCloud(self, dataset, frame = None):
fields = [PointField('x', 0, PointField.FLOAT32, 1),
PointField('y', 4, PointField.FLOAT32, 1),
PointField('z', 8, PointField.FLOAT32, 1),
PointField('intensity', 12, PointField.FLOAT32, 1)]
if frame == None:
for index in range(len(dataset)):
pc_raw = read_pcd(f'{dataset.path_seq}/frame-{index:05d}.pcd')
pts = np.vstack([pc_raw.pc_data['x'], pc_raw.pc_data['y'], pc_raw.pc_data['z'], pc_raw.pc_data['intensity']]).T # stays NX4
pts = pts[~np.isnan(pts).any(axis=1)]
# del
os.remove(f'{dataset.path_seq}/frame-{index:05d}.pcd')
# save new point clouds
pc_msg =pc2.create_cloud(None, fields, pts)
write_pcd(f'{dataset.path_seq}/frame-{index:05d}.pcd', pc_msg)
else:
pc_raw = read_pcd(f'{dataset.path_seq}/frame-{frame:05d}.pcd')
pts = np.vstack([pc_raw.pc_data['x'], pc_raw.pc_data['y'], pc_raw.pc_data['z'], pc_raw.pc_data['intensity']]).T # stays NX4
pts = pts[~np.isnan(pts).any(axis=1)]
# del
os.remove(f'{dataset.path_seq}/frame-{frame:05d}.pcd')
# save new point clouds
pc_msg =pc2.create_cloud(None, fields, pts)
write_pcd(f'{dataset.path_seq}/frame-{frame:05d}.pcd', pc_msg)
def downsamplePointCloud(self, dataset, npoints):
fields = [PointField('x', 0, PointField.FLOAT32, 1),
PointField('y', 4, PointField.FLOAT32, 1),
PointField('z', 8, PointField.FLOAT32, 1),
PointField('intensity', 12, PointField.FLOAT32, 1)]
for index in range(len(dataset)):
pc_raw = read_pcd(f'{dataset.path_seq}/frame-{index:05d}.pcd')
pts = np.vstack([pc_raw.pc_data['x'], pc_raw.pc_data['y'], pc_raw.pc_data['z'], pc_raw.pc_data['intensity']]).T # stays NX4
initial_npoints = pc_raw.points
step = initial_npoints // npoints
idxs = list(range(0,initial_npoints, step))
for i in range(len(idxs) - npoints):
idxs.pop(random.randrange(len(idxs)))
pts = pts[idxs,:]
# del
os.remove(f'{dataset.path_seq}/frame-{index:05d}.pcd')
# save new point clouds
pc_msg =pc2.create_cloud(None, fields, pts)
write_pcd(f'{dataset.path_seq}/frame-{index:05d}.pcd', pc_msg)
config = dataset.getConfig()
config['npoints'] = npoints
with open(f'{dataset.path_seq}/config.yaml', 'w') as file:
yaml.dump(config, file)
def scalePointCloud(self, dataset):
fields = [PointField('x', 0, PointField.FLOAT32, 1),
PointField('y', 4, PointField.FLOAT32, 1),
PointField('z', 8, PointField.FLOAT32, 1),
PointField('intensity', 12, PointField.FLOAT32, 1)]
for index in range(len(dataset)):
pc_raw = read_pcd(f'{dataset.path_seq}/frame-{index:05d}.pcd')
pts = np.vstack([pc_raw.pc_data['x'], pc_raw.pc_data['y'], pc_raw.pc_data['z'], pc_raw.pc_data['intensity']]).T # stays NX4
pts = pts - np.expand_dims(np.mean(pts, axis=0), 0) # center
dist = np.max(np.sqrt(np.sum(pts ** 2, axis=1)), 0)
pts = pts / dist
os.remove(f'{dataset.path_seq}/frame-{index:05d}.pcd')
# save new point clouds
pc_msg =pc2.create_cloud(None, fields, pts)
write_pcd(f'{dataset.path_seq}/frame-{index:05d}.pcd', pc_msg)
config = dataset.getConfig()
config['scaled'] = True
with open(f'{dataset.path_seq}/config.yaml', 'w') as file:
yaml.dump(config, file)
def invalidFrames(self, dataset):
# return a list with invalid frames
idxs = []
files = copy.deepcopy(self.files)
config = dataset.getConfig()
if config['fast']:
files = ['.rgb.png','.pose.txt']
else:
files = copy.deepcopy(self.files)
files.append('.depth.png')
for index in range(len(dataset)):
for file in files:
if not exists(f'{dataset.path_seq}/frame-{index:05d}{file}'):
idxs.append(index)
break
return idxs
def removeFrames(self, dataset, idxs):
for idx in idxs:
for file in os.listdir(f'{dataset.path_seq}'):
if file.startswith(f'frame-{idx:05d}'):
os.remove(f'{dataset.path_seq}/{file}')
def reorganizeDataset(self, dataset):
# here I assume the invalidFrames and removeFrames were called before.
# last_pose_idx is the idx of the last frame. We cannot use len(dataset) because the dataset might be missing some frames!
last_pose_idx = int(sorted([f for f in os.listdir(dataset.path_seq) if f.endswith('pose.txt')])[-1][6:11])
for idx in range(last_pose_idx+1):
print(idx)
if not exists(f'{dataset.path_seq}/frame-{idx:05d}.pose.txt'):
# idx does not exists, so we have to rename the close one.
print(f'{idx} is missing!!!')
new_idx = None
for idx2 in range(idx+1, last_pose_idx+1):
print(f'trying {idx2}')
if exists(f'{dataset.path_seq}/frame-{idx2:05d}.pose.txt'):
new_idx = idx2
break
if not new_idx==None:
print(f'renaming idx {new_idx} to idx {idx}')
for file in self.files:
os.rename(f'{dataset.path_seq}/frame-{new_idx:05d}{file}', f'{dataset.path_seq}/frame-{idx:05d}{file}')
else:
print(f'No candidate to replace {idx}')
def validateDataset(self, dataset):
# update config, check if all point clouds have the same size, if any has nans
# check for invalid frames
idxs = self.invalidFrames(dataset)
if idxs != []:
print(f'{Fore.RED} There are invalid frames in the dataset! {Fore.RESET}')
return False
# # check for missing data
# dct = self.numberOfNans(dataset)
# n_nans = 0
# for count in dct.values():
# n_nans += count
# if n_nans != 0:
# print(f'{Fore.RED} There are Nans in the dataset! {Fore.RESET}')
# return False
# #check for point clouds of different size
# dct = self.numberOfPoints(dataset)
# number_of_points = list(dct.values())
# result = all(element == number_of_points[0] for element in number_of_points)
# if not result:
# print(f'{Fore.RED} Not all pointclouds have the same number of points! {Fore.RESET}')
# return False
config = dataset.getConfig()
config['is_valid'] = True
with open(f'{dataset.path_seq}/config.yaml', 'w') as file:
yaml.dump(config, file)
return True
def mergeDatasets(self, dataset1, dataset2, dataset3_name):
# both datasets must be valids
# they should share the same number of points
# if not (dataset1.getConfig()['is_valid'] and dataset2.getConfig()['is_valid']):
# print(f'{Fore.RED} The datasets are not valid! Validate before merge. {Fore.RESET}')
# return False
# if not (dataset1.getConfig()['npoints'] == dataset2.getConfig()['npoints']):
# print(f'{Fore.RED} The datasets dont have the same number of points! {Fore.RESET}')
# return False
# if not (dataset1.getConfig()['scaled'] == dataset2.getConfig()['scaled']):
# print(f'{Fore.RED} Property scaled is different! {Fore.RESET}')
# return False
config = dataset1.getConfig()
if config['fast']:
files = ['.rgb.png','.pose.txt']
else:
files = self.files
size_dataset1 = len(dataset1)
shutil.copytree(dataset1.path_seq, f'{dataset1.root}/{dataset3_name}')
shutil.copytree(dataset2.path_seq, f'{dataset2.path_seq}_tmp')
dataset3 = Dataset(path_seq=f'{dataset3_name}')
dataset2_tmp = Dataset(path_seq=f'{dataset2.seq}_tmp')
for idx in range(len(dataset2_tmp)):
for file in files:
os.rename(f'{dataset2_tmp.path_seq}/frame-{idx:05d}{file}', f'{dataset3.path_seq}/frame-{idx+size_dataset1:05d}{file}')
shutil.rmtree(dataset2_tmp.path_seq)
def createDepthImages(self, dataset, rescale):
# loop through all point clouds
config = dataset.getConfig()
intrinsic = np.loadtxt(f'{dataset.path_seq}/depth_intrinsic.txt', delimiter=',')
width = config['depth']['width']
height = config['depth']['height']
for idx in range(len(dataset)):
pc_raw = read_pcd(f'{dataset.path_seq}/frame-{idx:05d}.pcd')
pts = np.vstack([pc_raw.pc_data['x'], pc_raw.pc_data['y'], pc_raw.pc_data['z'], pc_raw.pc_data['intensity']]) # stays 4xN
pixels, valid_pixels, dist = projectToCamera(intrinsic, [0, 0, 0, 0, 0], width, height, pts)
range_sparse = np.zeros((height, width), dtype=np.float32)
mask = 255 * np.ones((range_sparse.shape[0], range_sparse.shape[1]), dtype=np.uint8)
for idx_point in range(0, pts.shape[1]):
if valid_pixels[idx_point]:
x0 = math.floor(pixels[0, idx_point])
y0 = math.floor(pixels[1, idx_point])
mask[y0, x0] = 0
range_sparse[y0, x0] = dist[idx_point]
range_sparse = cv2.resize(range_sparse, (0, 0), fx=rescale, fy=rescale, interpolation=cv2.INTER_NEAREST)
mask = cv2.resize(mask, (0, 0), fx=rescale, fy=rescale, interpolation=cv2.INTER_NEAREST)
# Computing the dense depth map
print('Computing inpaint ...')
range_dense = cv2.inpaint(range_sparse, mask, 3, cv2.INPAINT_NS)
print('Inpaint done')
range_dense = cv2.resize(range_dense, (0, 0), fx=1 / rescale, fy=1 / rescale, interpolation=cv2.INTER_NEAREST)
tmp = copy.deepcopy(range_dense)
tmp = tmp * 1000.0 # to milimeters
tmp = tmp.astype(np.uint16)
cv2.imwrite(f'{dataset.path_seq}/frame-{idx:05d}.depth.png', tmp)
print(f'Saved depth image {dataset.path_seq}/frame-{idx:05d}.depth.png')
def createStatistics(self, dataset):
# loop through all point clouds
config = dataset.getConfig()
config['statistics'] = {'B' : {'max' : np.empty((len(dataset))),
'min' : np.empty((len(dataset))),
'mean' : np.empty((len(dataset))),
'std' : np.empty((len(dataset)))},
'G' : {'max' : np.empty((len(dataset))),
'min' : np.empty((len(dataset))),
'mean' : np.empty((len(dataset))),
'std' : np.empty((len(dataset)))},
'R' : {'max' : np.empty((len(dataset))),
'min' : np.empty((len(dataset))),
'mean' : np.empty((len(dataset))),
'std' : np.empty((len(dataset)))},
'D' : {'max' : np.empty((len(dataset))),
'min' : np.empty((len(dataset))),
'mean' : np.empty((len(dataset))),
'std' : np.empty((len(dataset)))}}
for idx in range(len(dataset)):
print(f'creating stats of frame {idx}')
# Load RGB image
cv_image = cv2.imread(f'{dataset.path_seq}/frame-{idx:05d}.rgb.png', cv2.IMREAD_UNCHANGED)
#cv2.imshow('fig', cv_image)
#cv2.waitKey(0)
#print(cv_image.shape)
blue_image = cv_image[:,:,0]/255
green_image = cv_image[:,:,1]/255
red_image = cv_image[:,:,2]/255
# cv2.imshow('fig', green_image)
# cv2.waitKey(0)
## B channel
config['statistics']['B']['max'][idx] = np.max(blue_image)
config['statistics']['B']['min'][idx] = np.min(blue_image)
config['statistics']['B']['mean'][idx] = np.mean(blue_image)
config['statistics']['B']['std'][idx] = np.std(blue_image)
## G channel
config['statistics']['G']['max'][idx] = np.max(green_image)
config['statistics']['G']['min'][idx] = np.min(green_image)
config['statistics']['G']['mean'][idx] = np.mean(green_image)
config['statistics']['G']['std'][idx] = np.std(green_image)
## R channel
config['statistics']['R']['max'][idx] = np.max(red_image)
config['statistics']['R']['min'][idx] = np.min(red_image)
config['statistics']['R']['mean'][idx] = np.mean(red_image)
config['statistics']['R']['std'][idx] = np.std(red_image)
# Load Depth image
if not config['fast']:
depth_image = cv2.imread(f'{dataset.path_seq}/frame-{idx:05d}.depth.png', cv2.IMREAD_UNCHANGED)
depth_image = depth_image.astype(np.float32) / 1000.0 # to meters
else:
depth_image = -1
## D channel
config['statistics']['D']['max'][idx] = np.max(depth_image)
config['statistics']['D']['min'][idx] = np.min(depth_image)
config['statistics']['D']['mean'][idx] = np.mean(depth_image)
config['statistics']['D']['std'][idx] = np.std(depth_image)
config['statistics']['B']['max'] = round(float(np.mean(config['statistics']['B']['max'])),5)
config['statistics']['B']['min'] = round(float(np.mean(config['statistics']['B']['min'])),5)
config['statistics']['B']['mean'] = round(float(np.mean(config['statistics']['B']['mean'])),5)
config['statistics']['B']['std'] = round(float(np.mean(config['statistics']['B']['std'])),5)
config['statistics']['G']['max'] = round(float(np.mean(config['statistics']['G']['max'])),5)
config['statistics']['G']['min'] = round(float(np.mean(config['statistics']['G']['min'])),5)
config['statistics']['G']['mean'] = round(float(np.mean(config['statistics']['G']['mean'])),5)
config['statistics']['G']['std'] = round(float(np.mean(config['statistics']['G']['std'])),5)
config['statistics']['R']['max'] = round(float(np.mean(config['statistics']['R']['max'])),5)
config['statistics']['R']['min'] = round(float(np.mean(config['statistics']['R']['min'])),5)
config['statistics']['R']['mean'] = round(float(np.mean(config['statistics']['R']['mean'])),5)
config['statistics']['R']['std'] = round(float(np.mean(config['statistics']['R']['std'])),5)
config['statistics']['D']['max'] = round(float(np.mean(config['statistics']['D']['max'])),5)
config['statistics']['D']['min'] = round(float(np.mean(config['statistics']['D']['min'])),5)
config['statistics']['D']['mean'] = round(float(np.mean(config['statistics']['D']['mean'])),5)
config['statistics']['D']['std'] = round(float(np.mean(config['statistics']['D']['std'])),5)
dataset.setConfig(config)
def createStatisticsRGB01(self, dataset):
# loop through all point clouds
config = dataset.getConfig()
config['statistics'] = {'B' : {'max' : np.empty((len(dataset))),
'min' : np.empty((len(dataset))),
'mean' : np.empty((len(dataset))),
'std' : np.empty((len(dataset)))},
'G' : {'max' : np.empty((len(dataset))),
'min' : np.empty((len(dataset))),
'mean' : np.empty((len(dataset))),
'std' : np.empty((len(dataset)))},
'R' : {'max' : np.empty((len(dataset))),
'min' : np.empty((len(dataset))),
'mean' : np.empty((len(dataset))),
'std' : np.empty((len(dataset)))}}
for idx in range(len(dataset)):
print(f'creating stats of frame {idx}')
# Load RGB image
cv_image = cv2.imread(f'{dataset.path_seq}/frame-{idx:05d}.rgb.png', cv2.IMREAD_UNCHANGED)
#cv2.imshow('fig', cv_image)
#cv2.waitKey(0)
#print(cv_image.shape)
blue_image = cv_image[:,:,0]/255
green_image = cv_image[:,:,1]/255
red_image = cv_image[:,:,2]/255
# cv2.imshow('fig', green_image)
# cv2.waitKey(0)
## B channel
config['statistics']['B']['max'][idx] = np.max(blue_image)
config['statistics']['B']['min'][idx] = np.min(blue_image)
config['statistics']['B']['mean'][idx] = np.mean(blue_image)
config['statistics']['B']['std'][idx] = np.std(blue_image)
## G channel
config['statistics']['G']['max'][idx] = np.max(green_image)
config['statistics']['G']['min'][idx] = np.min(green_image)
config['statistics']['G']['mean'][idx] = np.mean(green_image)
config['statistics']['G']['std'][idx] = np.std(green_image)
## R channel
config['statistics']['R']['max'][idx] = np.max(red_image)
config['statistics']['R']['min'][idx] = np.min(red_image)
config['statistics']['R']['mean'][idx] = np.mean(red_image)
config['statistics']['R']['std'][idx] = np.std(red_image)
config['statistics']['B']['max'] = round(float(np.mean(config['statistics']['B']['max'])),5)
config['statistics']['B']['min'] = round(float(np.mean(config['statistics']['B']['min'])),5)
config['statistics']['B']['mean'] = round(float(np.mean(config['statistics']['B']['mean'])),5)
config['statistics']['B']['std'] = round(float(np.mean(config['statistics']['B']['std'])),5)
config['statistics']['G']['max'] = round(float(np.mean(config['statistics']['G']['max'])),5)
config['statistics']['G']['min'] = round(float(np.mean(config['statistics']['G']['min'])),5)
config['statistics']['G']['mean'] = round(float(np.mean(config['statistics']['G']['mean'])),5)
config['statistics']['G']['std'] = round(float(np.mean(config['statistics']['G']['std'])),5)
config['statistics']['R']['max'] = round(float(np.mean(config['statistics']['R']['max'])),5)
config['statistics']['R']['min'] = round(float(np.mean(config['statistics']['R']['min'])),5)
config['statistics']['R']['mean'] = round(float(np.mean(config['statistics']['R']['mean'])),5)
config['statistics']['R']['std'] = round(float(np.mean(config['statistics']['R']['std'])),5)
dataset.setConfig(config)
def processImages(self, dataset, technique, global_dataset):
config = dataset.getConfig()
config['statistics'] = {'B' : {'max' : np.empty((len(dataset))),
'min' : np.empty((len(dataset))),
'mean' : np.empty((len(dataset))),
'std' : np.empty((len(dataset)))},
'G' : {'max' : np.empty((len(dataset))),
'min' : np.empty((len(dataset))),
'mean' : np.empty((len(dataset))),
'std' : np.empty((len(dataset)))},
'R' : {'max' : np.empty((len(dataset))),
'min' : np.empty((len(dataset))),
'mean' : np.empty((len(dataset))),
'std' : np.empty((len(dataset)))},
'D' : {'max' : np.empty((len(dataset))),
'min' : np.empty((len(dataset))),
'mean' : np.empty((len(dataset))),
'std' : np.empty((len(dataset)))}}
# Local Processing
if global_dataset == None:
for idx in range(len(dataset)):
# RGB image
bgr_image = cv2.imread(f'{dataset.path_seq}/frame-{idx:05d}.rgb.png', cv2.IMREAD_UNCHANGED)
blue_image = bgr_image[:,:,0]
green_image = bgr_image[:,:,1]
red_image = bgr_image[:,:,2]
depth_image = cv2.imread(f'{dataset.path_seq}/frame-{idx:05d}.depth.png', cv2.IMREAD_UNCHANGED)
depth_image = depth_image.astype(np.float32) / 1000.0 # to meters
if technique =='standardization':
# B channel
mean = np.mean(blue_image)
std = np.std(blue_image)
blue_image = (blue_image - mean) / std
# G channel
mean = np.mean(green_image)
std = np.std(green_image)
green_image = (green_image - mean) / std
# R channel
mean = np.mean(red_image)
std = np.std(red_image)
red_image = (red_image - mean) / std
# D channel
mean = np.mean(depth_image)
std = np.std(depth_image)
depth_image = (depth_image - mean) / std
elif technique == 'normalization':
# B channel
min_v = np.min(blue_image)
max_v = np.max(blue_image)
blue_image = (blue_image - min_v) / (max_v - min_v)
# G channel
min_v = np.min(green_image)
max_v = np.max(green_image)
green_image = (green_image - min_v) / (max_v - min_v)
# R channel
min_v = np.min(red_image)
max_v = np.max(red_image)
red_image = (red_image - min_v) / (max_v - min_v)
# D channel
min_v = np.min(depth_image)
max_v = np.max(depth_image)
depth_image = (depth_image - min_v) / (max_v - min_v)
else:
print('Tehcnique not implemented. Available techniques are: normalization and standardization')
exit(0)
blue_image = blue_image.astype(np.float32)
green_image = green_image.astype(np.float32)
red_image = red_image.astype(np.float32)
depth_image = depth_image.astype(np.float32)
## B channel
config['statistics']['B']['max'][idx] = np.max(blue_image)
config['statistics']['B']['min'][idx] = np.min(blue_image)
config['statistics']['B']['mean'][idx] = np.mean(blue_image)
config['statistics']['B']['std'][idx] = np.std(blue_image)
## G channel
config['statistics']['G']['max'][idx] = np.max(green_image)
config['statistics']['G']['min'][idx] = np.min(green_image)
config['statistics']['G']['mean'][idx] = np.mean(green_image)
config['statistics']['G']['std'][idx] = np.std(green_image)
## R channel
config['statistics']['R']['max'][idx] = np.max(red_image)
config['statistics']['R']['min'][idx] = np.min(red_image)
config['statistics']['R']['mean'][idx] = np.mean(red_image)
config['statistics']['R']['std'][idx] = np.std(red_image)
## D channel
config['statistics']['D']['max'][idx] = np.max(depth_image)
config['statistics']['D']['min'][idx] = np.min(depth_image)
config['statistics']['D']['mean'][idx] = np.mean(depth_image)
config['statistics']['D']['std'][idx] = np.std(depth_image)
# joint BGR images as nparray
bgr_image = cv2.merge([blue_image, green_image, red_image])
os.remove(f'{dataset.path_seq}/frame-{idx:05d}.depth.png')
os.remove(f'{dataset.path_seq}/frame-{idx:05d}.rgb.png')
np.save(f'{dataset.path_seq}/frame-{idx:05d}.depth.npy', depth_image)
np.save(f'{dataset.path_seq}/frame-{idx:05d}.rgb.npy', bgr_image)
#cv2.imwrite(f'{dataset.path_seq}/frame-{idx:05d}.depth.png', tmp)
config['processing'] = {'global' : None,
'technique' : technique}
config['statistics']['B']['max'] = round(float(np.mean(config['statistics']['B']['max'])),5)
config['statistics']['B']['min'] = round(float(np.mean(config['statistics']['B']['min'])),5)
config['statistics']['B']['mean'] = round(float(np.mean(config['statistics']['B']['mean'])),5)
config['statistics']['B']['std'] = round(float(np.mean(config['statistics']['B']['std'])),5)
config['statistics']['G']['max'] = round(float(np.mean(config['statistics']['G']['max'])),5)
config['statistics']['G']['min'] = round(float(np.mean(config['statistics']['G']['min'])),5)
config['statistics']['G']['mean'] = round(float(np.mean(config['statistics']['G']['mean'])),5)
config['statistics']['G']['std'] = round(float(np.mean(config['statistics']['G']['std'])),5)
config['statistics']['R']['max'] = round(float(np.mean(config['statistics']['R']['max'])),5)
config['statistics']['R']['min'] = round(float(np.mean(config['statistics']['R']['min'])),5)
config['statistics']['R']['mean'] = round(float(np.mean(config['statistics']['R']['mean'])),5)
config['statistics']['R']['std'] = round(float(np.mean(config['statistics']['R']['std'])),5)
config['statistics']['D']['max'] = round(float(np.mean(config['statistics']['D']['max'])),5)
config['statistics']['D']['min'] = round(float(np.mean(config['statistics']['D']['min'])),5)
config['statistics']['D']['mean'] = round(float(np.mean(config['statistics']['D']['mean'])),5)
config['statistics']['D']['std'] = round(float(np.mean(config['statistics']['D']['std'])),5)
dataset.setConfig(config)
# Global Processing
else:
global_config = global_dataset.getConfig()
global_stats = global_config['statistics']
for idx in range(len(dataset)):
bgr_image = cv2.imread(f'{dataset.path_seq}/frame-{idx:05d}.rgb.png', cv2.IMREAD_UNCHANGED)
blue_image = bgr_image[:,:,0]
green_image = bgr_image[:,:,1]
red_image = bgr_image[:,:,2]
depth_image = cv2.imread(f'{dataset.path_seq}/frame-{idx:05d}.depth.png', cv2.IMREAD_UNCHANGED)
depth_image = depth_image.astype(np.float32) / 1000.0 # to meters
if technique =='standardization':
blue_image = (blue_image - global_stats['B']['mean']) / global_stats['B']['std']
green_image = (green_image - global_stats['G']['mean']) / global_stats['G']['std']
red_image = (red_image - global_stats['R']['mean']) / global_stats['R']['std']
depth_image = (depth_image - global_stats['D']['mean']) / global_stats['D']['std']
elif technique == 'normalization':
blue_image = (blue_image - global_stats['B']['min']) / (global_stats['B']['max'] - global_stats['B']['min'])
green_image = (green_image - global_stats['G']['min']) / (global_stats['G']['max'] - global_stats['G']['min'])
red_image = (red_image - global_stats['R']['min']) / (global_stats['R']['max'] - global_stats['R']['min'])
depth_image = (depth_image - global_stats['D']['min']) / (global_stats['D']['max'] - global_stats['D']['min'])
else:
print('Tehcnique not implemented. Available techniques are: normalization and standardization')
exit(0)
blue_image = blue_image.astype(np.float32)
green_image = green_image.astype(np.float32)
red_image = red_image.astype(np.float32)
depth_image = depth_image.astype(np.float32)
## B channel
config['statistics']['B']['max'][idx] = np.max(blue_image)
config['statistics']['B']['min'][idx] = np.min(blue_image)
config['statistics']['B']['mean'][idx] = np.mean(blue_image)
config['statistics']['B']['std'][idx] = np.std(blue_image)
## G channel
config['statistics']['G']['max'][idx] = np.max(green_image)
config['statistics']['G']['min'][idx] = np.min(green_image)
config['statistics']['G']['mean'][idx] = np.mean(green_image)
config['statistics']['G']['std'][idx] = np.std(green_image)
## R channel
config['statistics']['R']['max'][idx] = np.max(red_image)
config['statistics']['R']['min'][idx] = np.min(red_image)
config['statistics']['R']['mean'][idx] = np.mean(red_image)
config['statistics']['R']['std'][idx] = np.std(red_image)
## D channel
config['statistics']['D']['max'][idx] = np.max(depth_image)
config['statistics']['D']['min'][idx] = np.min(depth_image)
config['statistics']['D']['mean'][idx] = np.mean(depth_image)
config['statistics']['D']['std'][idx] = np.std(depth_image)
# joint BGR images as nparray
bgr_image = cv2.merge([blue_image, green_image, red_image])
os.remove(f'{dataset.path_seq}/frame-{idx:05d}.depth.png')
os.remove(f'{dataset.path_seq}/frame-{idx:05d}.rgb.png')
np.save(f'{dataset.path_seq}/frame-{idx:05d}.depth.npy', depth_image)
np.save(f'{dataset.path_seq}/frame-{idx:05d}.rgb.npy', bgr_image)
config['processing'] = {'global' : global_dataset.path_seq,
'technique' : technique}
config['statistics']['B']['max'] = round(float(np.mean(config['statistics']['B']['max'])),5)
config['statistics']['B']['min'] = round(float(np.mean(config['statistics']['B']['min'])),5)
config['statistics']['B']['mean'] = round(float(np.mean(config['statistics']['B']['mean'])),5)
config['statistics']['B']['std'] = round(float(np.mean(config['statistics']['B']['std'])),5)
config['statistics']['G']['max'] = round(float(np.mean(config['statistics']['G']['max'])),5)
config['statistics']['G']['min'] = round(float(np.mean(config['statistics']['G']['min'])),5)
config['statistics']['G']['mean'] = round(float(np.mean(config['statistics']['G']['mean'])),5)
config['statistics']['G']['std'] = round(float(np.mean(config['statistics']['G']['std'])),5)
config['statistics']['R']['max'] = round(float(np.mean(config['statistics']['R']['max'])),5)
config['statistics']['R']['min'] = round(float(np.mean(config['statistics']['R']['min'])),5)
config['statistics']['R']['mean'] = round(float(np.mean(config['statistics']['R']['mean'])),5)
config['statistics']['R']['std'] = round(float(np.mean(config['statistics']['R']['std'])),5)
config['statistics']['D']['max'] = round(float(np.mean(config['statistics']['D']['max'])),5)
config['statistics']['D']['min'] = round(float(np.mean(config['statistics']['D']['min'])),5)
config['statistics']['D']['mean'] = round(float(np.mean(config['statistics']['D']['mean'])),5)
config['statistics']['D']['std'] = round(float(np.mean(config['statistics']['D']['std'])),5)
dataset.setConfig(config)
| 37,230 | 49.312162 | 140 | py |
synfeal | synfeal-main/process_dataset/src/__init__.py | 0 | 0 | 0 | py |
|
synfeal | synfeal-main/process_dataset/scripts/reduce_dataset.py | #!/usr/bin/env python3
# stdlib
import sys
import argparse
import copy
# 3rd-party
from dataset import Dataset
import os
import shutil
import yaml
def main():
parser = argparse.ArgumentParser(description='Validate dataset')
parser.add_argument('-d', '--dataset', type=str, required=True, help='Name of the dataset')
parser.add_argument('-dr', '--dataset_reduced', type=str, required=True, help='Suffix to append to the name of the dataset')
parser.add_argument('-s', '--size', type=int, required=True, help='Sample size')
arglist = [x for x in sys.argv[1:] if not x.startswith('__')]
args = vars(parser.parse_args(args=arglist))
dataset = Dataset(path_seq=args['dataset'])
path_root = dataset.root
dataset_reduced_path = f'{path_root}/{args["dataset_reduced"]}'
if os.path.exists(dataset_reduced_path):
print(f'{dataset_reduced_path} already exits. Aborting reducing')
exit(0)
else:
os.makedirs(dataset_reduced_path) # Create the new folder
# get config
config = dataset.getConfig()
if 'statistics' in config:
config.pop('statistics')
if not config['fast']:
files_to_copy = ['.pcd', '.rgb.png', '.depth.png','.pose.txt']
else:
files_to_copy = ['.rgb.png','.pose.txt']
# copy intrinsics to both datasets
for idx in range(len(dataset)):
print(f'original idx: {idx}')
if idx <= args['size']:
print(f'copying {idx} to {idx} in {dataset_reduced_path}')
for file in files_to_copy:
shutil.copy2(f'{dataset.path_seq}/frame-{idx:05d}{file}', f'{dataset_reduced_path}/frame-{idx:05d}{file}')
# copy intrinsics to both datasets
shutil.copy2(f'{dataset.path_seq}/depth_intrinsic.txt', f'{dataset_reduced_path}/depth_intrinsic.txt')
shutil.copy2(f'{dataset.path_seq}/rgb_intrinsic.txt', f'{dataset_reduced_path}/rgb_intrinsic.txt')
config['raw'] = args['dataset_reduced']
with open(f'{dataset_reduced_path}/config.yaml', 'w') as f:
yaml.dump(config, f)
if __name__ == "__main__":
main()
| 2,174 | 30.071429 | 128 | py |
synfeal | synfeal-main/synfeal_bringup/scripts/model_states_to_tf.py | #!/usr/bin/env python3
# --------------------------------------------------
# Adapted from http://wiki.ros.org/tf2/Tutorials/Writing%20a%20tf2%20broadcaster%20%28Python%29
# -------------------------------------------------
from functools import partial
import rospy
import tf2_ros
import geometry_msgs.msg
from gazebo_msgs.msg import ModelStates
def callbackModelStatesReceived(msg, tf_broadcaster):
childs = msg.name
pose = msg.pose
world = 'world'
now = rospy.Time.now()
# the gazebo has several models, so we have to pick the one we want
if 'localbot' in childs:
idx = childs.index('localbot')
transform = geometry_msgs.msg.TransformStamped()
transform.header.frame_id = world
transform.child_frame_id = '/base_footprint'
transform.header.stamp = now
transform.transform.translation.x = pose[idx].position.x
transform.transform.translation.y = pose[idx].position.y
transform.transform.translation.z = pose[idx].position.z
transform.transform.rotation.x = pose[idx].orientation.x
transform.transform.rotation.y = pose[idx].orientation.y
transform.transform.rotation.z = pose[idx].orientation.z
transform.transform.rotation.w = pose[idx].orientation.w
tf_broadcaster.sendTransform(transform)
def main():
rospy.init_node('model_states_to_tf')
rospy.Subscriber("/gazebo/model_states_throttle", ModelStates,
partial(callbackModelStatesReceived, tf_broadcaster=tf2_ros.TransformBroadcaster()))
rospy.spin()
if __name__ == '__main__':
main()
| 1,631 | 33.723404 | 105 | py |
synfeal | synfeal-main/synfeal_visualization/src/generate_real_vs_predicted.py | #!/usr/bin/env python3
import rospy
import os
from gazebo_msgs.srv import SetModelState, GetModelState, GetModelStateRequest, SetModelStateRequest
from colorama import Fore
from sensor_msgs.msg import Image
from cv_bridge import CvBridge
from utils import write_img
class GenerateRealPredicted():
def __init__(self, model_name, results):
self.set_state_service = rospy.ServiceProxy('/gazebo/set_model_state', SetModelState)
self.model_name = model_name # model_name = 'localbot'
self.bridge = CvBridge()
self.folder = f'{results.path}/images'
if not os.path.exists(self.folder):
print(f'Creating folder {self.folder}')
os.makedirs(self.folder) # Create the new folder
else:
print(f'{Fore.RED} {self.folder} already exists... Aborting GenerateRealPredicted initialization! {Fore.RESET}')
exit(0)
rospy.wait_for_service('/gazebo/get_model_state')
self.get_model_state_service = rospy.ServiceProxy('/gazebo/get_model_state', GetModelState)
def getPose(self):
return self.get_model_state_service(self.model_name, 'world')
def setPose(self, pose):
req = SetModelStateRequest() # Create an object of type SetModelStateRequest
req.model_state.model_name = self.model_name
req.model_state.pose.position.x = pose.position.x
req.model_state.pose.position.y = pose.position.y
req.model_state.pose.position.z = pose.position.z
req.model_state.pose.orientation.x = pose.orientation.x
req.model_state.pose.orientation.y = pose.orientation.y
req.model_state.pose.orientation.z = pose.orientation.z
req.model_state.pose.orientation.w = pose.orientation.w
req.model_state.reference_frame = 'world'
self.set_state_service(req.model_state)
def getImage(self):
rgb_msg = rospy.wait_for_message('/kinect/rgb/image_raw', Image)
return self.bridge.imgmsg_to_cv2(rgb_msg, "bgr8") # convert to opencv image
def saveImage(self, filename, image):
filename = f'{self.folder}/{filename}'
write_img(filename, image)
| 2,221 | 39.4 | 124 | py |
synfeal | synfeal-main/produce_results/src/results.py | import numpy as np
import pandas as pd
import os
import yaml
from yaml.loader import SafeLoader
from utils import matrixToXYZ, matrixToQuaternion, normalize_quat
class Results():
def __init__(self, results_path):
path=os.environ.get("SYNFEAL_DATASET")
self.path = f'{path}/results/localbot/{results_path}'
self.nframes = int(sum(f.endswith('.txt') for f in os.listdir(self.path))/2)
self.csv = pd.read_csv(f'{self.path}/errors.csv')
def __getitem__(self, index):
# load pose
matrix_predicted = np.loadtxt(f'{self.path}/frame-{index:05d}.predicted.pose.txt', delimiter=',')
matrix_real = np.loadtxt(f'{self.path}/frame-{index:05d}.real.pose.txt', delimiter=',')
quaternion_real = matrixToQuaternion(matrix_real)
quaternion_real = normalize_quat(quaternion_real)
xyz_real = matrixToXYZ(matrix_real)
pose_real = np.append(xyz_real, quaternion_real)
quaternion_predicted = matrixToQuaternion(matrix_predicted)
quaternion_predicted = normalize_quat(quaternion_predicted)
xyz_predicted = matrixToXYZ(matrix_predicted)
pose_predicted = np.append(xyz_predicted, quaternion_predicted)
return pose_real, pose_predicted
def __len__(self):
return self.nframes
def getErrorsArrays(self):
pos_error_array = self.csv.iloc[:-1]['position_error (m)'].to_numpy()
rot_error_array = self.csv.iloc[:-1]['rotation_error (rads)'].to_numpy()
return pos_error_array, rot_error_array
def updateCSV(self):
self.csv.to_csv(f'{self.path}/errors.csv', index=False, float_format='%.5f')
def getConfig(self):
with open(f'{self.path}/config.yaml') as f:
config = yaml.load(f, Loader=SafeLoader)
return config
| 1,911 | 32.54386 | 105 | py |
synfeal | synfeal-main/produce_results/src/save_results.py | #!/usr/bin/env python3
import os
import yaml
import pandas as pd
import shutil
import matplotlib.pyplot as plt
from utils import write_transformation
from colorama import Fore
from datetime import datetime
class SaveResults():
"""
class to save results
"""
def __init__(self, output, model_path, seq_path, overwrite):
# attribute initializer
path=os.environ.get("SYNFEAL_DATASET")
self.output_folder = f'{path}/results/localbot/{output}'
self.model_path = model_path
self.seq_path = seq_path
if not os.path.exists(self.output_folder):
print(f'Creating folder {self.output_folder}')
os.makedirs(self.output_folder) # Create the new folder
elif overwrite:
print(f'Overwriting folder {self.output_folder}')
shutil.rmtree(self.output_folder)
os.makedirs(self.output_folder) # Create the new folder
else:
print(f'{Fore.RED} {self.output_folder} already exists... Aborting SaveResults initialization! {Fore.RESET}')
exit(0)
dt_now = datetime.now() # current date and time
config = {'user' : os.environ["USER"],
'date' : dt_now.strftime("%d/%m/%Y, %H:%M:%S"),
'model_path' : self.model_path,
'seq_path' : self.seq_path}
with open(f'{self.output_folder}/config.yaml', 'w') as file:
yaml.dump(config, file)
self.frame_idx = 0 # make sure to save as 00000
self.csv = pd.DataFrame(columns=('frame', 'position_error (m)', 'rotation_error (rads)'))
print('SaveResults initialized properly')
def saveTXT(self, real_transformation, predicted_transformation):
filename = f'frame-{self.frame_idx:05d}'
write_transformation(f'{self.output_folder}/{filename}.real.pose.txt', real_transformation)
write_transformation(f'{self.output_folder}/{filename}.predicted.pose.txt', predicted_transformation)
def updateCSV(self, position_error, rotation_error):
row = {'frame' : f'{self.frame_idx:05d}',
'position_error (m)' : position_error,
'rotation_error (rads)' : rotation_error}
self.csv = self.csv.append(row, ignore_index=True)
def saveCSV(self):
# save averages values in the last row
mean_row = {'frame' : 'mean_values',
'position_error (m)' : self.csv.mean(axis=0).loc["position_error (m)"],
'rotation_error (rads)' : self.csv.mean(axis=0).loc["rotation_error (rads)"]}
median_row = {'frame' : 'median_values',
'position_error (m)' : self.csv.median(axis=0).loc["position_error (m)"],
'rotation_error (rads)' : self.csv.median(axis=0).loc["rotation_error (rads)"]}
self.csv = self.csv.append(mean_row, ignore_index=True)
self.csv = self.csv.append(median_row, ignore_index=True)
print(self.csv)
self.csv.to_csv(f'{self.output_folder}/errors.csv', index=False, float_format='%.5f')
def saveErrorsFig(self):
frames_array = self.csv.iloc[:-2]['frame'].to_numpy().astype(int)
pos_error_array = self.csv.iloc[:-2]['position_error (m)'].to_numpy()
rot_error_array = self.csv.iloc[:-2]['rotation_error (rads)'].to_numpy()
fig, (ax1, ax2) = plt.subplots(2, sharex=True)
fig.suptitle('position and rotation errors')
ax1.plot(frames_array, pos_error_array, 'cyan', label='position error')
ax2.plot(frames_array, rot_error_array, 'navy', label='rotation error')
ax2.set_xlabel('frame idx')
ax2.set_ylabel('[rads]')
ax1.set_ylabel('[m]')
ax1.legend()
ax2.legend()
plt.savefig(f'{self.output_folder}/errors.png')
def step(self):
self.frame_idx+=1
| 4,142 | 37.719626 | 121 | py |
ngmm_tools | ngmm_tools-master/Analyses/Python_lib/__init__.py | 0 | 0 | 0 | py |
|
ngmm_tools | ngmm_tools-master/Analyses/Python_lib/catalog/pylib_catalog.py | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Tue Jul 20 10:39:12 2021
@author: glavrent
"""
#load libraries
#arithmetic libraries
import numpy as np
def IndexAvgColumns(df_data, col_idx, col2avg):
'''
Average columns based on index column
Parameters
----------
df_data : pd.dataframe
Data data-frame.
col_idx : str
Name of index column.
col2avg : list
List of column names to be averaged.
Returns
-------
df_data : pd.dataframe
Data data-frame.
'''
#unique ids
idx_array, inv_array = np.unique(df_data[col_idx], return_inverse=True)
#iterate over columns
for col in col2avg:
#compute average values for all unique indices
avg_vals = np.array([np.nanmean(df_data.loc[df_data[col_idx] == idx,col]) for idx in idx_array])
df_data.loc[:,col] = avg_vals[inv_array]
return df_data
def ColocatePt(df_flatfile, col_idx, col_coor, thres_dist=0.01, return_df_pt=False):
'''
Colocate points (assign same ID) based on threshold distance.
Parameters
----------
df_flatfile : pd.DataFrame
Catalog flatfile.
col_idx : str
Name of index column.
col_coor : list of str
List of coordinate name columns.
thres_dist : real, optional
Value of threshold distance. The default is 0.01.
return_df_pt : bool, optional
Option for returning point data frame. The default is False.
Returns
-------
df_flatfile : pd.DataFrame
Catalog flatfile with updated index column.
df_pt: pd.DataFrame
Point data frame with updated index column.
'''
#dataframe with unique points
_, pt_idx, pt_inv = np.unique(df_flatfile[col_idx], axis=0, return_index=True, return_inverse=True)
df_pt = df_flatfile.loc[:,[col_idx] + col_coor].iloc[pt_idx,:]
#find and merge collocated points
for _, pt in df_pt.iterrows():
#distance between points
dist2pt = np.linalg.norm((df_pt[col_coor] - pt[col_coor]).astype(float), axis=1)
#indices of collocated points
i_pt_coll = dist2pt < thres_dist
#assign new id for collocated points
df_pt.loc[i_pt_coll,col_idx] = pt[col_idx].astype(int)
#update pt info to main catalog
df_flatfile.loc[:,col_idx] = df_pt[col_idx].values[pt_inv]
if not return_df_pt:
return df_flatfile
else:
return df_flatfile, df_pt
def UsableSta(mag_array, dist_array, df_coeffs):
'''
Find records that meet the mag-distance limits
Parameters
----------
mag_array : np.array
Magnitude array.
dist_array : np.array
Distance array.
df_coeffs : pd.DataFrame
Coefficients dataframe.
Returns
-------
rec_lim : np.array
logical array with True for records that meet M/R limits.
'''
#rrup limit
rrup_lim = dist_array <= df_coeffs.loc['max_rrup','coefficients']
#mag limit
mag_min = (df_coeffs.loc['b1','coefficients'] +
df_coeffs.loc['b1','coefficients'] * dist_array +
df_coeffs.loc['b2','coefficients'] * dist_array**2)
mag_lim = mag_array >= mag_min
#find records that meet both conditions
rec_lim = np.logical_and(rrup_lim, mag_lim)
return rec_lim
| 3,361 | 26.557377 | 104 | py |
ngmm_tools | ngmm_tools-master/Analyses/Python_lib/catalog/__init__.py | 0 | 0 | 0 | py |
|
ngmm_tools | ngmm_tools-master/Analyses/Python_lib/plotting/pylib_contour_plots.py | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Sat Nov 9 13:12:38 2019
@author: glavrent
"""
## load libraries
#arithmetic
import numpy as np
from scipy.interpolate import griddata
from scipy.ndimage import gaussian_filter
#plotting
import matplotlib
import matplotlib.pyplot as plt
from mpl_toolkits.axes_grid1 import make_axes_locatable
#from matplotlib.ticker import FormatStrFormatter
from matplotlib import ticker
#base map
from cartopy import config
import cartopy.crs as ccrs
import cartopy.feature as cfeature
class FormatScalarFormatter(matplotlib.ticker.ScalarFormatter):
def __init__(self, fformat="%1.1f", offset=True, mathText=True):
self.fformat = fformat
matplotlib.ticker.ScalarFormatter.__init__(self,useOffset=offset,
useMathText=mathText)
def _set_format(self, vmin, vmax):
self.format = self.fformat
if self._useMathText:
self.format = '$%s$' % matplotlib.ticker._mathdefault(self.format)
## Main functions
##---------------------------------------
def PlotContourMapObs(cont_latlondata, cmin=None, cmax=None, flag_grid=False, title=None, cbar_label=None, log_cbar = False, frmt_clb = '%.2f',
prj_map = False):
'''
PlotContourMapObs:
Input Arguments:
cont_latlondata (np.array [n1,3]): contains the latitude, logitude and contour values
cont_latlondata = [lat, long, data]
cmin (double-opt): lower limit for color levels for contour plot
cmax (double-opt): upper limit for color levels for contour plot
title (str-opt): figure title
cbar_label (str-opt): contour plot color bar label
ptlevs (np.array-opt): color levels for points
pt_label (str-opt): points color bar label
log_cbar (bool-opt): if true use log-scale for contour plots
frmt_clb string format color bar ticks
Output Arguments:
'''
plt_res = '50m'
plt_scale = '50m'
#number of interpolation points, x & y direction
#ngridx = 5000
#ngridy = 5000
#ngridx = 500
#ngridy = 500
ngridx = 100
ngridy = 100
#create figure
fig = plt.figure(figsize=(10, 10))
#fig = plt.figure(figsize=(15, 15))
#create basemap
if prj_map == True:
data_crs = ccrs.PlateCarree()
ax = fig.add_subplot(1, 1, 1, projection=data_crs)
else:
data_crs = None
ax = fig.add_subplot(1, 1, 1)
#project contour data
x_cont = cont_latlondata[:,1]
y_cont = cont_latlondata[:,0]
#interpolation grid
x_int = np.linspace(x_cont.min(), x_cont.max(), ngridx)
y_int = np.linspace(y_cont.min(), y_cont.max(), ngridy)
X_grid, Y_grid = np.meshgrid(x_int, y_int)
#interpolate contour data on grid
if log_cbar:
data_cont = np.log(cont_latlondata[:,2])
else:
data_cont = cont_latlondata[:,2]
data_grid = griddata((x_cont, y_cont) , data_cont, (X_grid, Y_grid), method='linear')
#data colorbar
cbmin = data_cont.min() if cmin is None else cmin
cbmax = data_cont.max() if cmax is None else cmax
clevs = np.linspace(cbmin, cbmax, 41).tolist()
#plot interpolated data
if prj_map == True:
cs = ax.contourf(X_grid, Y_grid, data_grid, transform = data_crs, vmin=cmin, vmax=cmax, levels = clevs, zorder=3, alpha = 0.75)
else:
cs = ax.contourf(X_grid, Y_grid, data_grid, vmin=cmin, vmax=cmax, levels = clevs, zorder=3, alpha = 0.75)
#color bar
fmt_clb = ticker.FormatStrFormatter(frmt_clb)
cbar_ticks = clevs[0:41:8]
cbar = fig.colorbar(cs, boundaries=clevs, ticks=cbar_ticks, pad=0.05, orientation="horizontal", format=fmt_clb) # add colorbar
if log_cbar:
cbar_labels = [frmt_clb%np.exp(c_t) for c_t in cbar_ticks]
cbar.set_ticklabels(cbar_labels)
#add tick labs
cbar.ax.tick_params(labelsize=18)
if (not cbar_label is None): cbar.set_label(cbar_label, size=20)
if prj_map == True:
#add costal lines
ax.coastlines(resolution=plt_res, edgecolor='black', zorder=5);
#add state boundaries
states = cfeature.NaturalEarthFeature(category='cultural', name='admin_1_states_provinces_lines',
scale=plt_scale, facecolor='none')
ax.add_feature(states, edgecolor='black', zorder=3)
borders = cfeature.NaturalEarthFeature(category='cultural', name='admin_0_countries',
scale=plt_scale, facecolor='none')
ax.add_feature(borders, edgecolor='black', zorder=4)
#add water bodies
oceans = cfeature.NaturalEarthFeature(category='physical', name='ocean', facecolor='lightblue',
scale=plt_scale)
ax.add_feature(oceans, zorder=6)
#add figure title
if (not title is None): plt.title(title, fontsize=25)
plt.xlabel('Latitude (deg)', fontsize=20)
plt.ylabel('Longitude (deg)', fontsize=20)
#grid lines
if flag_grid:
# gl = ax.gridlines(crs=ccrs.PlateCarree(), draw_labels=True)
gl = ax.gridlines(crs=ccrs.PlateCarree(), draw_labels=True,
linewidth=1, color='gray', alpha=0.5, linestyle='--')
gl.xlabels_top = False
gl.ylabels_right = False
else:
gl = None
# fig.show()
# fig.draw()
fig.tight_layout()
return fig, ax, cbar, data_crs, gl
# Original PlotContourCAMap function
#---- ---- ---- ---- ---- ---- ----
def PlotContourCAMapAdv(cont_latlondata, line_latlon=None, pt_latlondata=None, clevs=None, flag_grid=False, title=None, cbar_label=None,
ptlevs = None, pt_label = None, log_cbar = False, frmt_clb = '%.2f', **kwargs):
'''
PlotContourCAMapAdv:
create a contour plot of the data in cont_latlondata
Input Arguments:
cont_latlondata (np.array [n1,3]): contains the latitude, logitude and contour values
cont_latlondata = [lat, long, data]
line_latlon (np.array-opt [n2,2]): contains the latitde and logitude coordinates of any lines
pt_latlondata (np.array-opt [n3,(2,3)]): contains the latitude, logitude and values of disp points
pt_latlondata = [lat, long, data-optional]
clevs (np.array-opt): color levels for contour plot
title (str-opt): figure title
cbar_label (str-opt): contour plot color bar label
ptlevs (np.array-opt): color levels for points
pt_label (str-opt): points color bar label
log_cbar (bool-opt): if true use log-scale for contour plots
frmt_clb string format color bar ticks
Output Arguments:
'''
#additional input arguments
flag_smooth = kwargs['flag_smooth'] if 'flag_smooth' in kwargs else False
sig_smooth = kwargs['smooth_sig'] if 'smooth_sig' in kwargs else 0.1
plt_res = '10m'
plt_scale = '10m'
#number of interpolation points, x & y direction
#ngridx = 5000
#ngridy = 5000
#ngridx = 500
#ngridy = 500
ngridx = 100
ngridy = 100
#create figure
fig = plt.figure(figsize=(10, 10))
#fig = plt.figure(figsize=(15, 15))
#create basemap
data_crs = ccrs.PlateCarree()
ax = fig.add_subplot(1, 1, 1, projection=data_crs)
#project contour data
x_cont = cont_latlondata[:,1]
y_cont = cont_latlondata[:,0]
#interpolation grid
x_int = np.linspace(x_cont.min(), x_cont.max(), ngridx)
y_int = np.linspace(y_cont.min(), y_cont.max(), ngridy)
X_grid, Y_grid = np.meshgrid(x_int, y_int)
#interpolate contour data on grid
data_cont = cont_latlondata[:,2]
data_grid = griddata((x_cont, y_cont) , data_cont, (X_grid, Y_grid), method='linear')
#smooth
if flag_smooth:
data_grid = gaussian_filter(data_grid, sigma=sig_smooth)
#data colorbar
if clevs is None:
if not log_cbar:
clevs = np.linspace(data_cont.min(),data_cont.max(),11).tolist()
else:
clevs = np.logspace(np.log10(data_cont.min()),np.log10(data_cont.max()),11).tolist()
#plot interpolated data
if not log_cbar:
cs = ax.contourf(X_grid, Y_grid, data_grid, transform = data_crs, levels = clevs, zorder=3, alpha = 0.75)
else:
cs = ax.contourf(X_grid, Y_grid, data_grid, transform = data_crs, levels = clevs, zorder=3, alpha = 0.75,
locator=ticker.LogLocator())
#color bar
fmt_clb = ticker.FormatStrFormatter(frmt_clb)
if not log_cbar:
cbar = fig.colorbar(cs, boundaries = clevs, pad=0.05, orientation="horizontal", format=fmt_clb) # add colorbar
else:
cbar = fig.colorbar(cs, boundaries = clevs, pad=0.05, orientation="horizontal", format=fmt_clb) # add colorbar
cbar.ax.tick_params(labelsize=18)
if (not cbar_label is None): cbar.set_label(cbar_label, size=20)
#plot line
if not line_latlon is None:
ax.plot(line_latlon[:,1], line_latlon[:,0], latlon = True, linewidth=3, color='k', zorder= 5 )
#plot points
if not pt_latlondata is None:
if np.size(pt_latlondata,1) == 2:
ax.plot(pt_latlondata[:,1], pt_latlondata[:,0], 'o', latlon=True, color = 'k', markersize = 4, zorder = 8)
elif np.size(pt_latlondata,1) == 2:
raise ValueError('Unimplemented plotting option')
#add costal lines
ax.coastlines(resolution=plt_res, edgecolor='black', zorder=5);
#add state boundaries
states = cfeature.NaturalEarthFeature(category='cultural', name='admin_1_states_provinces_lines',
scale=plt_scale, facecolor='none')
ax.add_feature(states, edgecolor='black', zorder=3)
ax.add_feature(cfeature.BORDERS, zorder=4)
#add oceans
oceans = cfeature.NaturalEarthFeature(category='physical', name='ocean', facecolor='lightblue',
scale=plt_scale)
ax.add_feature(oceans, zorder=6)
#add figure title
if (not title is None): plt.title(title, fontsize=25)
plt.xlabel('Latitude (deg)', fontsize=20)
plt.ylabel('Longitude (deg)', fontsize=20)
#grid lines
if flag_grid:
# gl = ax.gridlines(crs=ccrs.PlateCarree(), draw_labels=True)
gl = ax.gridlines(crs=ccrs.PlateCarree(), draw_labels=True,
linewidth=1, color='gray', alpha=0.5, linestyle='--')
gl.xlabels_top = False
gl.ylabels_right = False
else:
gl = None
fig.tight_layout()
return fig, ax, cbar, data_crs, gl
# Updated PlotContourCAMap function
#---- ---- ---- ---- ---- ---- ----
def PlotContourCAMap(cont_latlondata, cmin=None, cmax=None, flag_grid=False, title=None, cbar_label=None, log_cbar = False,
frmt_clb = '%.2f', cmap = 'viridis', **kwargs):
'''
PlotContourCAMap:
simplifed function to create a contour plot of the data in cont_latlondata
Input Arguments:
cont_latlondata (np.array [n1,3]): contains the latitude, logitude and contour values
cont_latlondata = [lat, long, data]
cmin (double-opt): lower limit for color levels for contour plot
cmax (double-opt): upper limit for color levels for contour plot
title (str-opt): figure title
cbar_label (str-opt): contour plot color bar label
ptlevs (np.array-opt): color levels for points
pt_label (str-opt): points color bar label
log_cbar (bool-opt): if true use log-scale for contour plots
frmt_clb string format color bar ticks
Output Arguments:
'''
#additional input arguments
flag_smooth = kwargs['flag_smooth'] if 'flag_smooth' in kwargs else False
sig_smooth = kwargs['smooth_sig'] if 'smooth_sig' in kwargs else 0.1
intrp_method = kwargs['intrp_method'] if 'intrp_method' in kwargs else 'linear'
plt_res = '50m'
plt_scale = '50m'
#number of interpolation points, x & y direction
#ngridx = 5000
#ngridy = 5000
ngridx = 500
ngridy = 500
#ngridx = 100
#ngridy = 100
#create figure
fig = plt.figure(figsize=(10, 10))
#fig = plt.figure(figsize=(15, 15))
#create basemap
data_crs = ccrs.PlateCarree()
ax = fig.add_subplot(1, 1, 1, projection=data_crs)
#project contour data
x_cont = cont_latlondata[:,1]
y_cont = cont_latlondata[:,0]
#interpolation grid
x_int = np.linspace(x_cont.min(), x_cont.max(), ngridx)
y_int = np.linspace(y_cont.min(), y_cont.max(), ngridy)
X_grid, Y_grid = np.meshgrid(x_int, y_int)
#interpolate contour data on grid
if log_cbar:
data_cont = np.log(cont_latlondata[:,2])
else:
data_cont = cont_latlondata[:,2]
data_grid = griddata((x_cont, y_cont) , data_cont, (X_grid, Y_grid), method=intrp_method )
#smooth
if flag_smooth:
data_grid = gaussian_filter(data_grid, sigma=sig_smooth)
#data colorbar
cbmin = data_cont.min() if cmin is None else cmin
cbmax = data_cont.max() if cmax is None else cmax
clevs = np.linspace(cbmin, cbmax, 41).tolist()
#plot interpolated data
cs = ax.contourf(X_grid, Y_grid, data_grid, transform = data_crs, vmin=cmin, vmax=cmax,
levels = clevs, zorder=3, alpha = 0.75, cmap=cmap)
#color bar
#import pdb; pdb.set_trace()
fmt_clb = ticker.FormatStrFormatter(frmt_clb)
cbar_ticks = clevs[0:41:10]
cbar = fig.colorbar(cs, boundaries=clevs, ticks=cbar_ticks, pad=0.05, orientation="horizontal", format=fmt_clb) # add colorbar
if log_cbar:
cbar_labels = [frmt_clb%np.exp(c_t) for c_t in cbar_ticks]
cbar.set_ticklabels(cbar_labels)
cbar.ax.tick_params(labelsize=18)
if (not cbar_label is None): cbar.set_label(cbar_label, size=20)
#add costal lines
ax.coastlines(resolution=plt_res, edgecolor='black', zorder=5);
#add state boundaries
states = cfeature.NaturalEarthFeature(category='cultural', name='admin_1_states_provinces_lines',
scale=plt_scale, facecolor='none')
ax.add_feature(states, edgecolor='black', zorder=3)
borders = cfeature.NaturalEarthFeature(category='cultural', name='admin_0_countries',
scale=plt_scale, facecolor='none')
ax.add_feature(borders, edgecolor='black', zorder=4)
#add oceans
oceans = cfeature.NaturalEarthFeature(category='physical', name='ocean', scale=plt_scale)
ax.add_feature(oceans, zorder=6)
#add figure title
if (not title is None): plt.title(title, fontsize=25)
plt.xlabel('Latitude (deg)', fontsize=20)
plt.ylabel('Longitude (deg)', fontsize=20)
#grid lines
if flag_grid:
# gl = ax.gridlines(crs=ccrs.PlateCarree(), draw_labels=True)
gl = ax.gridlines(crs=ccrs.PlateCarree(), draw_labels=True,
linewidth=1, color='gray', alpha=0.5, linestyle='--')
gl.xlabels_top = False
gl.ylabels_right = False
else:
gl = None
# fig.show()
# fig.draw()
fig.tight_layout()
return fig, ax, cbar, data_crs, gl
# PlotContourSloveniaMap function
#---- ---- ---- ---- ---- ---- ----
def PlotContourSloveniaMap(cont_latlondata, cmin=None, cmax=None, flag_grid=False, title=None, cbar_label=None, log_cbar = False,
frmt_clb = '%.2f', **kwargs):
'''
PlotContourCAMap:
simplifed create a contour plot of the data in cont_latlondata
Input Arguments:
cont_latlondata (np.array [n1,3]): contains the latitude, logitude and contour values
cont_latlondata = [lat, long, data]
cmin (double-opt): lower limit for color levels for contour plot
cmax (double-opt): upper limit for color levels for contour plot
title (str-opt): figure title
cbar_label (str-opt): contour plot color bar label
ptlevs (np.array-opt): color levels for points
pt_label (str-opt): points color bar label
log_cbar (bool-opt): if true use log-scale for contour plots
frmt_clb string format color bar ticks
Output Arguments:
'''
plt_res = '50m'
plt_scale = '50m'
#number of interpolation points, x & y direction
#ngridx = 5000
#ngridy = 5000
#ngridx = 500
#ngridy = 500
ngridx = 100
ngridy = 100
#create figure
fig = plt.figure(figsize=(10, 10))
#fig = plt.figure(figsize=(15, 15))
#create basemap
data_crs = ccrs.PlateCarree()
ax = fig.add_subplot(1, 1, 1, projection=data_crs)
#project contour data
x_cont = cont_latlondata[:,1]
y_cont = cont_latlondata[:,0]
#interpolation grid
x_int = np.linspace(x_cont.min(), x_cont.max(), ngridx)
y_int = np.linspace(y_cont.min(), y_cont.max(), ngridy)
X_grid, Y_grid = np.meshgrid(x_int, y_int)
#interpolate contour data on grid
if log_cbar:
data_cont = np.log(cont_latlondata[:,2])
else:
data_cont = cont_latlondata[:,2]
data_grid = griddata((x_cont, y_cont) , data_cont, (X_grid, Y_grid), method='linear')
#smooth
if (kwargs['flag_smooth'] if 'flag_smooth' in kwargs else False):
sig_smooth = kwargs['smooth_sig'] if 'smooth_sig' in kwargs else 0.1
data_grid = gaussian_filter(data_grid, sigma=sig_smooth)
#data colorbar
cbmin = data_cont.min() if cmin is None else cmin
cbmax = data_cont.max() if cmax is None else cmax
clevs = np.linspace(cbmin, cbmax, 41).tolist()
#plot interpolated data
cs = ax.contourf(X_grid, Y_grid, data_grid, transform = data_crs, vmin=cmin, vmax=cmax, levels = clevs, zorder=3, alpha = 0.75)
#color bar
fmt_clb = ticker.FormatStrFormatter(frmt_clb)
cbar_ticks = clevs[0:41:8]
cbar = fig.colorbar(cs, boundaries=clevs, ticks=cbar_ticks, pad=0.05, orientation="horizontal", format=fmt_clb) # add colorbar
if log_cbar:
cbar_labels = [frmt_clb%np.exp(c_t) for c_t in cbar_ticks]
cbar.set_ticklabels(cbar_labels)
cbar.ax.tick_params(labelsize=18)
if (not cbar_label is None): cbar.set_label(cbar_label, size=20)
#add costal lines
ax.coastlines(resolution=plt_res, edgecolor='black', zorder=5);
#add state boundaries
#states = cfeature.NaturalEarthFeature(category='cultural', name='admin_1_states_provinces_lines',
# scale=plt_scale, facecolor='none')
#ax.add_feature(states, edgecolor='black', zorder=3)
borders = cfeature.NaturalEarthFeature(category='cultural', name='admin_0_countries',
scale=plt_scale, facecolor='none')
ax.add_feature(borders, edgecolor='black', zorder=4)
#ax.add_feature(cfeature.BORDERS, zorder=4)
#add oceans
oceans = cfeature.NaturalEarthFeature(category='physical', name='ocean', facecolor='lightblue',
scale=plt_scale)
ax.add_feature(oceans, zorder=6)
#add figure title
if (not title is None): plt.title(title, fontsize=25)
plt.xlabel('Latitude (deg)', fontsize=20)
plt.ylabel('Longitude (deg)', fontsize=20)
#grid lines
if flag_grid:
# gl = ax.gridlines(crs=ccrs.PlateCarree(), draw_labels=True)
gl = ax.gridlines(crs=ccrs.PlateCarree(), draw_labels=True,
linewidth=1, color='gray', alpha=0.5, linestyle='--')
gl.xlabels_top = False
gl.ylabels_right = False
else:
gl = None
# fig.show()
# fig.draw()
fig.tight_layout()
return fig, ax, cbar, data_crs, gl
# Scatter plot function
#---- ---- ---- ---- ---- ---- ----
def PlotScatterCAMap(scat_latlondata, cmin=None, cmax=None, flag_grid=False, title=None, cbar_label=None, log_cbar = False,
frmt_clb = '%.2f', alpha_v = 0.7, cmap='seismic', marker_size=10.):
'''
PlotContourCAMap:
create a contour plot of the data in cont_latlondata
Input Arguments:
scat_latlondata (np.array [n1,(3,4)]): contains the latitude, logitude, contour values, and size values (optional)
scat_latlondata = [lat, long, data_color, data_size]
cmin (double-opt): lower limit for color levels for contour plot
cmax (double-opt): upper limit for color levels for contour plot
title (str-opt): figure title
cbar_label (str-opt): contour plot color bar label
ptlevs (np.array-opt): color levels for points
pt_label (str-opt): points color bar label
log_cbar (bool-opt): if true use log-scale for contour plots
frmt_clb: string format color bar ticks
alpha_v: opacity value
cmap: color palette
marker_size: marker size, if scat_latlondata dimensions is [n1, 3]
Output Arguments:
'''
#import pdb; pdb.set_trace()
plt_res = '10m'
plt_scale = '10m'
#create figure
fig = plt.figure(figsize=(10, 10))
#fig = plt.figure(figsize=(15, 15))
#create basemap
data_crs = ccrs.PlateCarree()
ax = fig.add_subplot(1, 1, 1, projection=data_crs)
#project contour data
x_scat = scat_latlondata[:,1]
y_scat = scat_latlondata[:,0]
#color scale
if log_cbar:
data_scat_c = np.log(scat_latlondata[:,2])
else:
data_scat_c = scat_latlondata[:,2]
#size scale
if scat_latlondata.shape[1] > 3:
data_scat_s = scat_latlondata[:,3]
else:
data_scat_s = marker_size * np.ones(data_scat_c.shape)
#data colorbar
cbmin = data_scat_c.min() if cmin is None else cmin
cbmax = data_scat_c.max() if cmax is None else cmax
clevs = np.linspace(cbmin, cbmax, 41).tolist()
#plot scatter bubble plot data
cs = ax.scatter(x_scat, y_scat, s = data_scat_s, c = data_scat_c,
transform = data_crs, vmin=cmin, vmax=cmax, zorder=3, alpha=alpha_v, cmap=cmap)
#color bar
#import pdb; pdb.set_trace()
fmt_clb = ticker.FormatStrFormatter(frmt_clb)
cbar_ticks = clevs[0:41:8]
cbar = fig.colorbar(cs, boundaries=clevs, ticks=cbar_ticks, pad=0.05, orientation="horizontal", format=fmt_clb) # add colorbar
if log_cbar:
cbar_labels = [frmt_clb%np.exp(c_t) for c_t in cbar_ticks]
cbar.set_ticklabels(cbar_labels)
cbar.ax.tick_params(labelsize=18)
if (not cbar_label is None): cbar.set_label(cbar_label, size=20)
#add costal lines
ax.coastlines(resolution=plt_res, edgecolor='black', zorder=5);
#add state boundaries
states = cfeature.NaturalEarthFeature(category='cultural', name='admin_1_states_provinces_lines',
scale=plt_scale, facecolor='none')
ax.add_feature(states, edgecolor='black', zorder=3)
ax.add_feature(cfeature.BORDERS, zorder=4)
#oceans
oceans = cfeature.NaturalEarthFeature(category='physical', name='ocean', facecolor='lightblue',
scale=plt_scale)
ax.add_feature(oceans, zorder=2)
#add figure title
if (not title is None): plt.title(title, fontsize=25)
plt.xlabel('Latitude (deg)', fontsize=20)
plt.ylabel('Longitude (deg)', fontsize=20)
#grid lines
if flag_grid:
# gl = ax.gridlines(crs=ccrs.PlateCarree(), draw_labels=True)
gl = ax.gridlines(crs=ccrs.PlateCarree(), draw_labels=True,
linewidth=1, color='gray', alpha=0.5, linestyle='--')
gl.xlabels_top = False
gl.ylabels_right = False
else:
gl = None
# fig.show()
# fig.draw()
fig.tight_layout()
return fig, ax, cbar, data_crs, gl
# Updated PlotContourCAMap function
#---- ---- ---- ---- ---- ---- ----
def PlotCellsCAMap(cell_latlondata, cmin=None, cmax=None, flag_grid=False, title=None, cbar_label=None, log_cbar = False, frmt_clb = '%.2f',
alpha_v = .8, cell_size = 50, cmap='seismic'):
'''
PlotCellsCAMap:
PlotCellsCAMap function to create a contour plot of the data in cont_latlondata
Input Arguments:
cell_latlondata (np.array [n1,3]): contains the latitude, logitude and color values
cell_latlondata = [lat, long, data]
cmin (double-opt): lower limit for color levels for contour plot
cmax (double-opt): upper limit for color levels for contour plot
title (str-opt): figure title
cbar_label (str-opt): contour plot color bar label
ptlevs (np.array-opt): color levels for points
pt_label (str-opt): points color bar label
log_cbar (bool-opt): if true use log-scale for contour plots
frmt_clb string format color bar ticks
Output Arguments:
'''
plt_res = '50m'
plt_scale = '50m'
#create figure
fig = plt.figure(figsize=(10, 10))
#create basemap
data_crs = ccrs.PlateCarree()
ax = fig.add_subplot(1, 1, 1, projection=data_crs)
#project contour data
x_cell = cell_latlondata[:,1]
y_cell = cell_latlondata[:,0]
#contour transfomration
if log_cbar:
data_cell = np.log(cell_latlondata[:,2])
else:
data_cell = cell_latlondata[:,2]
#data colorbar
cbmin = data_cell.min() if cmin is None else cmin
cbmax = data_cell.max() if cmax is None else cmax
clevs = np.linspace(cbmin, cbmax, 41).tolist()
#plot interpolated data
cs = ax.scatter(x_cell, y_cell, s = cell_size, c = data_cell, transform = data_crs, vmin=cmin, vmax=cmax, zorder=3,
alpha = alpha_v, cmap=cmap)
#cs = ax.contourf(X_grid, Y_grid, data_grid, transform = data_crs, vmin=cmin, vmax=cmax, levels = clevs, zorder=3, alpha = 0.75)
#color bar
#import pdb; pdb.set_trace()
fmt_clb = ticker.FormatStrFormatter(frmt_clb)
cbar_ticks = clevs[0:41:8]
cbar = fig.colorbar(cs, boundaries=clevs, ticks=cbar_ticks, pad=0.05, orientation="horizontal", format=fmt_clb) # add colorbar
if log_cbar:
cbar_labels = [frmt_clb%np.exp(c_t) for c_t in cbar_ticks]
cbar.set_ticklabels(cbar_labels)
cbar.ax.tick_params(labelsize=18)
if (not cbar_label is None): cbar.set_label(cbar_label, size=20)
#add costal lines
ax.coastlines(resolution=plt_res, edgecolor='black', zorder=5);
#add state boundaries
states = cfeature.NaturalEarthFeature(category='cultural', name='admin_1_states_provinces_lines',
scale=plt_scale, facecolor='none')
ax.add_feature(states, edgecolor='black', zorder=3)
borders = cfeature.NaturalEarthFeature(category='cultural', name='admin_0_countries',
scale=plt_scale, facecolor='none')
ax.add_feature(borders, edgecolor='black', zorder=4)
#add oceans
#ax.stock_img()
oceans = cfeature.NaturalEarthFeature(category='physical', name='ocean', facecolor='lightblue',
scale=plt_scale)
ax.add_feature(oceans, zorder=2)
#add figure title
if (not title is None): ax.set_title(title, fontsize=25)
ax.set_xlabel('Latitude (deg)', fontsize=20)
ax.set_ylabel('Longitude (deg)', fontsize=20)
#grid lines
if flag_grid:
# gl = ax.gridlines(crs=ccrs.PlateCarree(), draw_labels=True)
gl = ax.gridlines(crs=ccrs.PlateCarree(), draw_labels=True,
linewidth=1, color='gray', alpha=0.5, linestyle='--')
gl.xlabels_top = False
gl.ylabels_right = False
else:
gl = None
# fig.show()
# fig.draw()
fig.tight_layout()
return fig, ax, cbar, data_crs, gl
# Plotting coefficient function
#---- ---- ---- ---- ---- ---- ----
#plotting of median values coefficients
def PlotCoeffCAMapMed(cont_latlondata, cmin=None, cmax=None, flag_grid=False, title=None, cbar_label=None, log_cbar = False, frmt_clb = '%.2f', **kwargs):
cmap = 'seismic'
fig, ax, cbar, data_crs, gl = PlotContourCAMap(cont_latlondata, cmin=cmin, cmax=cmax, flag_grid=flag_grid, title=title, cbar_label=cbar_label,
log_cbar = log_cbar, frmt_clb = frmt_clb, cmap = cmap, **kwargs)
return fig, ax, cbar, data_crs, gl
#plotting of epistemic uncertainty coefficients
def PlotCoeffCAMapSig(cont_latlondata, cmin=None, cmax=None, flag_grid=False, title=None, cbar_label=None, log_cbar = False, frmt_clb = '%.2f', **kwargs):
cmap = 'Purples_r'
fig, ax, cbar, data_crs, gl = PlotContourCAMap(cont_latlondata, cmin=cmin, cmax=cmax, flag_grid=flag_grid, title=title, cbar_label=cbar_label,
log_cbar = log_cbar, frmt_clb = frmt_clb, cmap = cmap, **kwargs)
return fig, ax, cbar, data_crs, gl
#plotting of median values of cells
def PlotCellsCAMapMed(cell_latlondata, cmin=None, cmax=None, flag_grid=False, title=None, cbar_label=None, log_cbar = False, frmt_clb = '%.2f',
alpha_v = .8, cell_size = 50):
cmap = 'seismic'
fig, ax, cbar, data_crs, gl = PlotCellsCAMap(cell_latlondata, cmin=cmin, cmax=cmax, flag_grid=flag_grid, title=title, cbar_label=cbar_label,
log_cbar=log_cbar, frmt_clb=frmt_clb,
alpha_v=alpha_v, cell_size=cell_size, cmap=cmap)
return fig, ax, cbar, data_crs, gl
#plotting of mono-color increasing values of cells
def PlotCellsCAMapInc(cell_latlondata, cmin=None, cmax=None, flag_grid=False, title=None, cbar_label=None, log_cbar = False, frmt_clb = '%.2f',
alpha_v = .8, cell_size = 50):
cmap = 'Reds'
fig, ax, cbar, data_crs, gl = PlotCellsCAMap(cell_latlondata, cmin=cmin, cmax=cmax, flag_grid=flag_grid, title=title, cbar_label=cbar_label,
log_cbar=log_cbar, frmt_clb=frmt_clb,
alpha_v=alpha_v, cell_size=cell_size, cmap=cmap)
return fig, ax, cbar, data_crs, gl
#plotting of epistemic uncertainty of cells
def PlotCellsCAMapSig(cell_latlondata, cmin=None, cmax=None, flag_grid=False, title=None, cbar_label=None, log_cbar = False, frmt_clb = '%.2f',
alpha_v = .8, cell_size = 50):
cmap = 'Purples_r'
fig, ax, cbar, data_crs, gl = PlotCellsCAMap(cell_latlondata, cmin=cmin, cmax=cmax, flag_grid=flag_grid, title=title, cbar_label=cbar_label,
log_cbar=log_cbar, frmt_clb=frmt_clb,
alpha_v=alpha_v, cell_size=cell_size, cmap=cmap )
return fig, ax, cbar, data_crs, gl
# Base plot function
#---- ---- ---- ---- ---- ---- ----
def PlotMap(lat_lims = None, lon_lims = None, flag_grid=False, title=None):
'''
PlotContourCAMap:
simplifed function to create a contour plot of the data in cont_latlondata
Input Arguments:
line_latlondata (np.array [n1,3]): contains the latitude, logitude and contour values
cont_latlondata = [lat, long, data]
cmin (double-opt): lower limit for color levels for contour plot
cmax (double-opt): upper limit for color levels for contour plot
title (str-opt): figure title
cbar_label (str-opt): contour plot color bar label
ptlevs (np.array-opt): color levels for points
pt_label (str-opt): points color bar label
log_cbar (bool-opt): if true use log-scale for contour plots
frmt_clb string format color bar ticks
Output Arguments:
'''
plt_res = '50m'
plt_scale = '50m'
#create figure
fig = plt.figure(figsize=(10, 10))
#fig = plt.figure(figsize=(15, 15))
#create basemap
data_crs = ccrs.PlateCarree()
ax = fig.add_subplot(1, 1, 1, projection=data_crs)
if lat_lims:
ax.set_xlim(lon_lims)
if lon_lims:
ax.set_ylim(lat_lims)
#add land zones
lands = cfeature.LAND
ax.add_feature(lands, zorder=1)
#add costal lines
ax.coastlines(resolution=plt_res, edgecolor='black', zorder=3);
#add state boundaries
states = cfeature.NaturalEarthFeature(category='cultural', name='admin_1_states_provinces_lines',
scale=plt_scale, facecolor='none')
ax.add_feature(states, edgecolor='black', zorder=4)
borders = cfeature.NaturalEarthFeature(category='cultural', name='admin_0_countries',
scale=plt_scale, facecolor='none')
ax.add_feature(borders, edgecolor='black', zorder=5)
#add oceans
oceans = cfeature.NaturalEarthFeature(category='physical', name='ocean', facecolor='lightblue',
scale=plt_scale)
ax.add_feature(oceans, zorder=2)
#add figure title
if (not title is None): plt.title(title, fontsize=25)
plt.xlabel('Latitude (deg)', fontsize=20)
plt.ylabel('Longitude (deg)', fontsize=20)
#grid lines
if flag_grid:
# gl = ax.gridlines(crs=ccrs.PlateCarree(), draw_labels=True)
gl = ax.gridlines(crs=ccrs.PlateCarree(), draw_labels=True,
linewidth=1, color='gray', alpha=0.5, linestyle='--')
gl.xlabels_top = False
gl.ylabels_right = False
else:
gl = None
# fig.show()
# fig.draw()
# fig.tight_layout()
return fig, ax, data_crs, gl
| 35,537 | 40.613583 | 154 | py |
ngmm_tools | ngmm_tools-master/Analyses/Python_lib/plotting/__init__.py | 0 | 0 | 0 | py |
|
ngmm_tools | ngmm_tools-master/Analyses/Python_lib/ground_motions/pylib_gmm_eas.py | # ba18.py
# Conversion of Jeff Bayless' MATLAB code to Python
# Including class ba18
# I've tried to avoid mixed UPPER and lower case variable names
# e.g. Mbreak, Rrup, Vsref
#arithmetic libraries
import numpy as np
import numpy.matlib
from scipy import linalg as scipylalg
from scipy import sparse as scipysp
#geographic coordinates
import pyproj
#statistics libraries
import pandas as pd
#geometric libraries
from shapely.geometry import Point as shp_pt, Polygon as shp_poly
def SlicingSparceMat(mat_sp, i_rows, j_col):
'''Slice sparse matrix'''
return np.array([mat_sp.getcol(i_r).toarray().flatten()[j_col] for i_r in i_rows])
def QuartCos(per, x0, x, flag_left = False):
y = np.cos( 2.*np.pi*(x-x0)/per )
if flag_left: y[np.logical_or(x < x0-per/4, x > x0)] = 0.
else: y[np.logical_or(x < x0, x > x0+per/4)] = 0.
return y
def QuadCosTapper(freq, freq_nerg):
#boxcar at intermediate frequencies
i_box = np.logical_and(freq >= freq_nerg.min(), freq <= freq_nerg.max())
y_box = np.zeros(len(freq))
y_box[i_box] = 1.
#quarter cosine left taper
per = 2 * freq_nerg.min()
y_tpl = QuartCos(per, freq_nerg.min(), freq, flag_left=True)
#quarter cosine right taper
per = 2 * freq_nerg.max()
y_tpr = QuartCos(per, freq_nerg.max(), freq)
#combined tapering function
y_tapper = np.array([y_box, y_tpl, y_tpr]).max(axis=0)
return y_tapper
def TriagTapper(freq, freq_nerg):
fn_min = freq_nerg.min()
fn_max = freq_nerg.max()
#triangular window
f_win = np.array([0.5*fn_min, fn_min, fn_max, 1.5*fn_max])
y_win = np.array([0., 1., 1., 0.])
#triangular tapering function
y_tapper = np.interp(np.log(freq), np.log(f_win), y_win)
return y_tapper
def ConvertPandasDf2NpArray(df_array):
array = df_array.values if isinstance(df_array, pd.DataFrame) or isinstance(df_array, pd.Series) else df_array
return array
class BA18:
def __init__(self, file=None):
'''
Constructor for this class
Read CSV file of BA18 coefficients, frequency range: 0.1 - 100 Hz
Parameters
----------
file : string, optional
file name for coefficients. The default is None.
'''
if file is None:
file = '/mnt/halcloud_nfs/glavrent/Research/Nonerg_CA_GMM/Analyses/Python_lib/ground_motions/Bayless_ModelCoefs.csv'
df = pd.read_csv(file, index_col=0)
df = df.head(301)
# Frequencies 0.1 - 24 Hz
self.freq = df.index.values
# Median FAS parameters
self.b1 = df.c1.values
self.b2 = df.c2.values
self.b3quantity = df['(c2-c3)/cn'].values
self.b3 = df.c3.values
self.bn = df.cn.values
self.bm = df.cM .values
self.b4 = df.c4.values
self.b5 = df.c5.values
self.b6 = df.c6.values
self.bhm = df.chm.values
self.b7 = df.c7.values
self.b8 = df.c8.values
self.b9 = df.c9.values
self.b10 = df.c10.values
self.b11a = df.c11a.values
self.b11b = df.c11b.values
self.b11c = df.c11c.values
self.b11d = df.c11d.values
self.b1a = df.c1a.values
self.b1a[239:] = 0
# Non-linear site parameters
self.f3 = df.f3.values
self.f4 = df.f4.values
self.f5 = df.f5.values
# Aleatory variability parameters
self.s1 = df.s1.values
self.s2 = df.s2.values
self.s3 = df.s3.values
self.s4 = df.s4.values
self.s5 = df.s5.values
self.s6 = df.s6.values
# Constants
self.b4a = -0.5
self.vsref = 1000
self.mbreak = 6.0
#bedrock anelastic attenuation
self.b7rock = self.b7.copy()
#frequency limits
# self.maxfreq = 23.988321
self.maxfreq = self.freq.max()
self.minfreq = self.freq.min()
def EasBase(self, mag, rrup, vs30, ztor, fnorm, z1, regid, flag_keep_b7 = True):
# note Z1 must be provided in km
z1ref = (1/1000) * np.exp(-7.67/4 * np.log((vs30**4+610**4)/(1360**4+610**4)) )
if vs30<=200:
self.b11 = self.b11a
if vs30>200 and vs30<=300:
self.b11 = self.b11b
if vs30>300 and vs30<=500:
self.b11 = self.b11c
if vs30>500:
self.b11 = self.b11d
if z1 is None or np.isnan(z1):
z1 = self.Z1(vs30, regid=1)
# Compute lnFAS by summing contributions, including linear site response
lnfas = self.b1 + self.b2*(mag-self.mbreak)
lnfas += self.b3quantity*np.log(1+np.exp(self.bn*(self.bm-mag)))
lnfas += self.b4*np.log(rrup+self.b5*np.cosh(self.b6*np.maximum(mag-self.bhm,0)))
lnfas += (self.b4a-self.b4) * np.log( np.sqrt(rrup**2+50**2) )
lnfas += self.b7 * rrup if flag_keep_b7 else 0.
lnfas += self.b8 * np.log( min(vs30,1000) / self.vsref )
lnfas += self.b9 * min(ztor,20)
lnfas += self.b10 * fnorm
lnfas += self.b11 * np.log( (min(z1,2) + 0.01) / (z1ref + 0.01) )
# this is the linear spectrum up to maxfreq=23.988321 Hz
maxfreq = 23.988321
imax = np.where(self.freq==maxfreq)[0][0]
fas_lin = np.exp(lnfas)
# Extrapolate to 100 Hz
fas_maxfreq = fas_lin[imax]
# Kappa
kappa = np.exp(-0.4*np.log(vs30/760)-3.5)
# Diminuition operator
D = np.exp(-np.pi*kappa*(self.freq[imax:] - maxfreq))
fas_lin = np.append(fas_lin[:imax], fas_maxfreq * D)
# Compute non-linear site response
# get the EAS_rock at 5 Hz (no c8, c11 terms)
vref=760
#row = df.iloc[df.index == 5.011872]
i5 = np.where(self.freq==5.011872)
lnfasrock5Hz = self.b1[i5]
lnfasrock5Hz += self.b2[i5]*(mag-self.mbreak)
lnfasrock5Hz += self.b3quantity[i5]*np.log(1+np.exp(self.bn[i5]*(self.bm[i5]-mag)))
lnfasrock5Hz += self.b4[i5]*np.log(rrup+self.b5[i5]*np.cosh(self.b6[i5]*max(mag-self.bhm[i5],0)))
lnfasrock5Hz += (self.b4a-self.b4[i5])*np.log(np.sqrt(rrup**2+50**2))
lnfasrock5Hz += self.b7rock[i5]*rrup
lnfasrock5Hz += self.b9[i5]*min(ztor,20)
lnfasrock5Hz += self.b10[i5]*fnorm
# Compute PGA_rock extimate from 5 Hz FAS
IR = np.exp(1.238+0.846*lnfasrock5Hz)
# apply the modified Hashash model
self.f2 = self.f4*( np.exp(self.f5*(min(vs30,vref)-360)) - np.exp(self.f5*(vref-360)) )
fnl0 = self.f2 * np.log((IR+self.f3)/self.f3)
fnl0[np.where(fnl0==min(fnl0))[0][0]:] = min(fnl0)
fas_nlin = np.exp( np.log(fas_lin) + fnl0 )
# Aleatory variability
if mag<4:
tau = self.s1
phi_s2s = self.s3
phi_ss = self.s5
if mag>6:
tau = self.s2
phi_s2s = self.s4
phi_ss = self.s6
if mag >= 4 and mag <= 6:
tau = self.s1 + ((self.s2-self.s1)/2)*(mag-4)
phi_s2s = self.s3 + ((self.s4-self.s3)/2)*(mag-4)
phi_ss = self.s5 + ((self.s6-self.s5)/2)*(mag-4)
sigma = np.sqrt(tau**2 + phi_s2s**2 + phi_ss**2 + self.b1a**2);
return self.freq, fas_nlin, fas_lin, sigma
def EasBaseArray(self, mag, rrup, vs30, ztor, fnorm, z1=None, regid=1, flag_keep_b7=True):
#convert eq parameters to np.arrays
mag = np.array([mag]).flatten()
rrup = np.array([rrup]).flatten()
vs30 = np.array([vs30]).flatten()
ztor = np.array([ztor]).flatten()
fnorm = np.array([fnorm]).flatten()
z1 = np.array([self.Z1(vs, regid) for vs in vs30]) if z1 is None else np.array([z1]).flatten()
#number of scenarios
npt = len(mag)
#input assertions
assert( np.all(npt == np.array([len(rrup),len(vs30),len(ztor),len(fnorm),len(z1)])) ),'Error. Inconsistent number of gmm parameters'
#compute fas for all scenarios
fas_nlin = list()
fas_lin = list()
sigma = list()
for k, (m, r, vs, zt, fn, z_1) in enumerate(zip(mag, rrup, vs30, ztor, fnorm, z1)):
ba18_base = self.EasBase(m, r, vs, zt, fn, z_1, regid, flag_keep_b7)[1:]
fas_nlin.append(ba18_base[0])
fas_lin.append(ba18_base[1])
sigma.append(ba18_base[2])
#combine them to np.arrays
fas_nlin = np.vstack(fas_nlin)
fas_lin = np.vstack(fas_lin)
sigma = np.vstack(sigma)
# if npt == 1 and flag_flatten:
# fas_nlin = fas_nlin.flatten()
# fas_lin = fas_lin.flatten()
# sigma = sigma.flatten()
#return self.EasBase(mag, rrup, vs30, ztor, fnorm, z1, regid, flag_keep_b7)
return self.freq, fas_nlin, fas_lin, sigma
def Eas(self, mag, rrup, vs30, ztor, fnorm, z1=None, regid=1, flag_keep_b7=True, flag_flatten=True):
'''
Computes BA18 EAS GMM for all frequencies
Parameters
----------
mag : real
moment magnitude [3-8].
rrup : real
Rupture distance in kilometers (km) [0-300].
vs30 : real
site-specific Vs30 = slowness-averaged shear wavespeed of upper 30 m (m/s) [120-1500].
ztor : real
depth to top of rupture (km) [0-20].
fnorm : real
1 for normal faults and 0 for all other faulting types (no units) [0 or 1].
z1 : real, optional
site-specific depth to shear wavespeed of 1 km/s (km) [0-2]. The default is =None.
regid : int, optional
DESCRIPTION. The default is =1.
Returns
-------
freq : np.array
frequency array.
fas_nlin : np.array
fas array with nonlinear site response.
fas_lin : np.array
fas array with linear site response.
sigma : np.array
standard deviation array.
'''
#return self.EasBase(mag, rrup, vs30, ztor, fnorm, z1, regid, flag_keep_b7)
# return self.EasBaseArray(mag, rrup, vs30, ztor, fnorm, z1, regid, flag_keep_b7, flag_flatten)
freq, fas_nlin, fas_lin, sigma = self.EasBaseArray(mag, rrup, vs30, ztor, fnorm, z1, regid, flag_keep_b7)
#flatten arrays if only one datapoint
if fas_nlin.shape[0] == 1 and flag_flatten:
fas_nlin = fas_nlin.flatten()
fas_lin = fas_lin.flatten()
sigma = sigma.flatten()
return freq, fas_nlin, fas_lin, sigma
def EasF(self, freq, mag, rrup, vs30, ztor, fnorm, z1=None, regid=1, flag_keep_b7 = True, flag_flatten=True):
'''
Computes BA18 EAS GMM for frequency of interest
Parameters
----------
mag : real
moment magnitude [3-8].
rrup : real
Rupture distance in kilometers (km) [0-300].
vs30 : real
site-specific Vs30 = slowness-averaged shear wavespeed of upper 30 m (m/s) [120-1500].
ztor : real
depth to top of rupture (km) [0-20].
fnorm : real
1 for normal faults and 0 for all other faulting types (no units) [0 or 1].
z1 : real, optional
site-specific depth to shear wavespeed of 1 km/s (km) [0-2]. The default is =None.
regid : int, optional
DESCRIPTION. The default is =1.
Returns
-------
freq : real
frequency of interest.
fas_nlin : real
fas with nonlinear site response for frequency of interest.
fas_lin : real
fas with linear site response for frequency of interest.
sigma : real
standard deviation of frequency of interest.
'''
#convert freq to numpy array
freq = np.array([freq]).flatten()
#frequency tolerance
f_tol = 1e-4
#compute fas for all frequencies
freq_all, fas_all, fas_lin_all, sig_all = self.EasBaseArray(mag, rrup, vs30, ztor, fnorm, z1, regid, flag_keep_b7)
#find eas for frequency of interest
if np.all([np.isclose(f, freq_all, f_tol).any() for f in freq]):
# i_f = np.array([np.where(np.isclose(f, freq_all, f_tol))[0] for f in freq]).flatten()
i_f = np.array([np.argmin(np.abs(f-freq_all)) for f in freq]).flatten()
freq = freq_all[i_f]
fas = fas_all[:,i_f]
fas_lin = fas_lin_all[:,i_f]
sigma = sig_all[:,i_f]
else:
fas = np.vstack([np.exp(np.interp(np.log(np.abs(freq)), np.log(freq_all), np.log(fas), left=-np.nan, right=-np.nan)) for fas in fas_all])
fas_lin = np.vstack([np.exp(np.interp(np.log(np.abs(freq)), np.log(freq_all), np.log(fas_l), left=-np.nan, right=-np.nan)) for fas_l in fas_lin_all])
sigma = np.vstack([ np.interp(np.log(np.abs(freq)), np.log(freq_all), sig, left=-np.nan, right=-np.nan) for sig in sig_all])
#if one scenario flatten arrays
if fas.shape[0] == 1 and flag_flatten:
fas = fas.flatten()
fas_lin = fas_lin.flatten()
sigma = sigma.flatten()
return fas, fas_lin, sigma
def GetFreq(self):
return np.array(self.freq)
def Z1(self, vs30, regid=1):
'''
Compute Z1.0 based on Vs30 for CA and JP
Parameters
----------
vs30 : real
Time average shear-wave velocity.
regid : int, optional
Region ID. The default is 1.
Returns
-------
real
Depth to a shear wave velocity of 1000m/sec.
'''
if regid == 1: #CA
z_1 = -7.67/4. * np.log((vs30**4+610.**4)/(1360.**4+610.**4))
elif regid == 10: #JP
z_1 = -5.23/4. * np.log((vs30**4+412.**4)/(1360.**4+412.**4))
return 1/1000*np.exp(z_1)
| 14,209 | 36.005208 | 161 | py |
ngmm_tools | ngmm_tools-master/Analyses/Python_lib/ground_motions/pylib_Willis15CA_Vs30.py | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Tue Feb 2 19:01:47 2021
@author: glavrent
"""
#load variables
import pathlib
import numpy as np
import rasterio
class Willis15Vs30CA:
def __init__(self, fname_vs30map_med=None, fname_vs30map_sig=None):
#file path
root = pathlib.Path(__file__).parent
#vs30 data filenames
fname_vs30map_med = '/mnt/halcloud_nfs/glavrent/Research/Other_projects/VS30_CA/data/California_vs30_Wills15_hybrid_7p5c.tif' if fname_vs30map_med is None else fname_vs30map_med
fname_vs30map_sig = '/mnt/halcloud_nfs/glavrent/Research/Other_projects/VS30_CA/data/California_vs30_Wills15_hybrid_7p5c_sd.tif' if fname_vs30map_sig is None else fname_vs30map_sig
#load vs30 data
# self.vs30map_med = rasterio.open(root / 'data/California_vs30_Wills15_hybrid_7p5c.tif')
# self.vs30map_sig = rasterio.open(root / 'data/California_vs30_Wills15_hybrid_7p5c_sd.tif')
self.vs30map_med = rasterio.open( fname_vs30map_med )
self.vs30map_sig = rasterio.open( fname_vs30map_sig )
def lookup(self, lonlats):
return (
np.fromiter(self.vs30map_med.sample(lonlats, 1), np.float),
np.fromiter(self.vs30map_sig.sample(lonlats, 1), np.float)
)
def test_lookup(self):
medians, stds = list(self.lookup([(-122.258, 37.875), (-122.295, 37.895)]))
np.testing.assert_allclose(medians, [733.4, 351.9], rtol=0.01)
np.testing.assert_allclose(stds, [0.432, 0.219], rtol=0.01)
| 1,564 | 36.261905 | 188 | py |
ngmm_tools | ngmm_tools-master/Analyses/Python_lib/ground_motions/__init__.py | 0 | 0 | 0 | py |
|
ngmm_tools | ngmm_tools-master/Analyses/Python_lib/ground_motions/pylib_NGMM_prediction.py | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Sat Aug 20 14:54:54 2022
@author: glavrent
"""
# Packages
# ---------------------------
#arithmetic libraries
import numpy as np
from scipy import linalg as scipylinalg
from sklearn.gaussian_process.kernels import Matern
#user functions
import pylib_kernels as pylib_kern
import pylib_cell_dist as pylib_cells
# Non-ergodic GMM effects prediction
# ---------------------------
def PredictNErgEffects(n_samp, nerg_coeff_info, df_scen_predict, df_nerg_coeffs,
nerg_catten_info=None, df_cell_info=None, df_nerg_cellatten=None):
'''
Predict non-egodic ground motion effects
Parameters
----------
n_samp : int
Number of samples.
nerg_coeff_info : dict
Non-ergodic coefficient information dictionary.
df_scen_predict : pd.dataframe
Prediction scenarios.
df_nerg_coeffs : pd.dataframe
Regressed non-ergodic coefficients .
nerg_catten_info : dict, optional
cell-specific anelastic attenuation information dictionary. The default is None.
df_cell_info : pd.dataframe, optional
Cell info dataframe. The default is None.
df_nerg_cellatten : pd.dataframe, optional
Regressed anelastic attenuation coefficients. The default is None.
Returns
-------
nerg_effects_prdct_samp : np.array
Samples of total non-ergodic effects.
nerg_vcm_prdct_samp : TYPE
Samples of spatially varying component of non-ergodic effects.
nerg_atten_prdct_samp : TYPE
Samples of anelastic attenuation component of non-ergodic effects.
nerg_effects_prdct_mu : TYPE
Mean of total non-ergodic effects.
nerg_effects_prdct_sig : TYPE
Standard deviation of total non-ergodic effects.
nerg_vcm_cmp : list
List with individual components of spatially varying non-ergodic effects.
nerg_atten_cmp : list
List with individual components of anelast attenuation.
'''
#number of prediction scenarios
n_predict = len(df_scen_predict)
# VCM component
# --- --- --- ---
#initialize vcm samples
nerg_vcm_prdct_samp = np.zeros(shape=(n_predict,n_samp))
nerg_vcm_prdct_mu = np.zeros(shape=n_predict)
nerg_vcm_prdct_var = np.zeros(shape=n_predict)
nerg_vcm_cmp = {}
#iterate over non-ergodic coefficients
for nerg_c in nerg_coeff_info:
#kernel type
k_type = nerg_coeff_info[nerg_c]['kernel_type']
#hyper-parameters
if 'hyp' in nerg_coeff_info[nerg_c]:
hyp_param = nerg_coeff_info[nerg_c]['hyp']
hyp_mean_c = hyp_param['mean_c'] if (('mean_c' in hyp_param) and (not hyp_param['mean_c'] is None)) else 0
hyp_ell = hyp_param['ell'] if (('ell' in hyp_param) and (not hyp_param['ell'] is None)) else 0
hyp_omega = hyp_param['omega'] if (('omega' in hyp_param) and (not hyp_param['omega'] is None)) else 0
hyp_pi = hyp_param['pi'] if (('pi' in hyp_param) and (not hyp_param['pi'] is None)) else 0
hyp_nu = hyp_param['nu'] if (('nu' in hyp_param) and (not hyp_param['nu'] is None)) else 0
#mean and std of non-ergodic coefficients at known locations
c_mean_train = df_nerg_coeffs.loc[:,nerg_coeff_info[nerg_c]['coeff'][0]].values
c_sig_train = df_nerg_coeffs.loc[:,nerg_coeff_info[nerg_c]['coeff'][1]].values
#non-ergodic coefficient scaling
c_scl = np.ones(n_predict) if nerg_coeff_info[nerg_c]['scaling'] is None else df_nerg_coeffs.loc[:,nerg_coeff_info[nerg_c]['scaling']].values
if k_type == 0: #constan
assert(len(np.unique(c_mean_train))==1)
#mean and std of non-ergodic coefficient
c_mean_train = c_mean_train[0]
c_sig_train = c_sig_train[0]
#draw random samples
c_prdct_samp = np.random.normal(loc=c_mean_train, scale=c_sig_train, size=n_samp)
#sample non-ergodic coefficient for prediction scenarios
c_prdct_samp = np.full((n_predict,n_samp), c_prdct_samp)
#mean and sigma
c_prdct_mu = np.full(n_predict, c_mean_train)
c_prdct_sig = np.full(n_predict, c_sig_train)
if k_type == 1: #group
#group ids in training data
id_train = df_nerg_coeffs.loc[:,nerg_coeff_info[nerg_c]['cor_info']].values
id_train, idx_train = np.unique(id_train, axis=0, return_index=True)
#group ids in prediction data
id_prdct = df_scen_predict.loc[:,nerg_coeff_info[nerg_c]['cor_info']].values
id_prdct, inv_prdct = np.unique(id_prdct, axis=0, return_inverse=True)
#mean and std of non-ergodic coefficient
c_mean_train = c_mean_train[idx_train]
c_sig_train = c_sig_train[idx_train]
#compute mean and cov of non-erg coeffs for prediction scenarios
c_prdct_mu, _, c_prdct_cov = pylib_kern.PredictGroupKern(id_prdct, id_train,
c_train_mu=c_mean_train, c_train_sig=c_sig_train,
hyp_mean_c=hyp_mean_c, hyp_omega=hyp_omega)
#sample non-ergodic coefficient for prediction scenarios
c_prdct_samp = MVNRnd(mean=c_prdct_mu, cov=c_prdct_cov, n_samp=n_samp)
c_prdct_samp = c_prdct_samp[inv_prdct,:]
#mean and sigma
c_prdct_mu = c_prdct_mu[inv_prdct]
c_prdct_sig = np.sqrt( np.diag(c_prdct_cov) )[inv_prdct]
if k_type == 2: #exponetial
#coordinates of training data
t_train = df_nerg_coeffs.loc[:,nerg_coeff_info[nerg_c]['cor_info']].values
t_train, idx_train = np.unique(t_train, axis=0, return_index=True)
#coordinates of prediction data
t_prdct = df_scen_predict.loc[:,nerg_coeff_info[nerg_c]['cor_info']].values
t_prdct, inv_prdct = np.unique(t_prdct, axis=0, return_inverse=True)
#mean and std of non-ergodic coefficient
c_mean_train = c_mean_train[idx_train]
c_sig_train = c_sig_train[idx_train]
#compute mean and cov of non-erg coeffs for prediction scenarios
c_prdct_mu, _, c_prdct_cov = pylib_kern.PredictExpKern(t_prdct, t_train,
c_train_mu=c_mean_train, c_train_sig=c_sig_train,
hyp_mean_c=hyp_mean_c, hyp_ell=hyp_ell, hyp_omega=hyp_omega, hyp_pi=hyp_pi)
#sample non-ergodic coefficient for prediction scenarios
c_prdct_samp = MVNRnd(mean=c_prdct_mu, cov=c_prdct_cov, n_samp=n_samp)
c_prdct_samp = c_prdct_samp[inv_prdct,:]
#mean and sigma
c_prdct_mu = c_prdct_mu[inv_prdct]
c_prdct_sig = np.sqrt( np.diag(c_prdct_cov) )[inv_prdct]
if k_type == 3: #squared exponetial
#coordinates of training data
t_train = df_nerg_coeffs.loc[:,nerg_coeff_info[nerg_c]['cor_info']].values
t_train, idx_train = np.unique(t_train, axis=0, return_index=True)
#coordinates of prediction data
t_prdct = df_scen_predict.loc[:,nerg_coeff_info[nerg_c]['cor_info']].values
t_prdct, inv_prdct = np.unique(t_prdct, axis=0, return_inverse=True)
#mean and std of non-ergodic coefficient
c_mean_train = c_mean_train[idx_train]
c_sig_train = c_sig_train[idx_train]
#compute mean and cov of non-erg coeffs for prediction scenarios
c_prdct_mu, _, c_prdct_cov = pylib_kern.PredictSqExpKern(t_prdct, t_train,
c_train_mu=c_mean_train, c_train_sig=c_sig_train,
hyp_mean_c=hyp_mean_c, hyp_ell=hyp_ell, hyp_omega=hyp_omega, hyp_pi=hyp_pi)
#sample non-ergodic coefficient for prediction scenarios
c_prdct_samp = MVNRnd(mean=c_prdct_mu, cov=c_prdct_cov, n_samp=n_samp)
c_prdct_samp = c_prdct_samp[inv_prdct,:]
#mean and sigma
c_prdct_mu = c_prdct_mu[inv_prdct]
c_prdct_sig = np.sqrt( np.diag(c_prdct_cov) )[inv_prdct]
if k_type == 4: #Matern kernel function
#coordinates of training data
t_train = df_nerg_coeffs.loc[:,nerg_coeff_info[nerg_c]['cor_info']].values
t_train, idx_train = np.unique(t_train, axis=0, return_index=True)
#coordinates of prediction data
t_prdct = df_scen_predict.loc[:,nerg_coeff_info[nerg_c]['cor_info']].values
t_prdct, inv_prdct = np.unique(t_prdct, axis=0, return_inverse=True)
#mean and std of non-ergodic coefficient
c_mean_train = c_mean_train[idx_train]
c_sig_train = c_sig_train[idx_train]
#compute mean and cov of non-erg coeffs for prediction scenarios
c_prdct_mu, _, c_prdct_cov = pylib_kern.PredictMaternKern(t_prdct, t_train,
c_train_mu=c_mean_train, c_train_sig=c_sig_train,
hyp_mean_c=hyp_mean_c, hyp_ell=hyp_ell, hyp_omega=hyp_omega, hyp_pi=hyp_pi,
hyp_nu=hyp_nu)
#sample non-ergodic coefficient for prediction scenarios
c_prdct_samp = MVNRnd(mean=c_prdct_mu, cov=c_prdct_cov, n_samp=n_samp)
c_prdct_samp = c_prdct_samp[inv_prdct,:]
#mean and sigma
c_prdct_mu = c_prdct_mu[inv_prdct]
c_prdct_sig = np.sqrt( np.diag(c_prdct_cov) )[inv_prdct]
#add contribution of non-ergodic effect
nerg_vcm_prdct_samp += c_scl[:,np.newaxis] * c_prdct_samp
#mean and std contribution of non-ergodic effect
nerg_vcm_prdct_mu += c_scl * c_prdct_mu
nerg_vcm_prdct_var += c_scl**2 * c_prdct_sig**2
#summarize individual components
nerg_vcm_cmp[nerg_c] = [c_scl * c_prdct_mu, c_scl * c_prdct_sig, c_scl[:,np.newaxis] * c_prdct_samp]
# Anelastic attenuation
# --- --- --- ---
#initialize anelastic attenuation
nerg_atten_prdct_samp = np.zeros(shape=(n_predict,n_samp))
nerg_atten_prdct_mu = np.zeros(shape=n_predict)
nerg_atten_prdct_var = np.zeros(shape=n_predict)
nerg_atten_cmp = {}
if not nerg_catten_info is None:
#cell edge coordinates for path seg calculation
ct4dist = df_cell_info.loc[:,['q1X', 'q1Y', 'q1Z', 'q8X', 'q8Y', 'q8Z']].values
#cell limts
c_lmax = ct4dist[:,[3,4,5]].max(axis=0)
c_lmin = ct4dist[:,[0,1,2]].min(axis=0)
#compute cell-path
cell_path = np.zeros([n_predict, len(df_cell_info)])
for j, (rsn, scn_p) in enumerate(df_scen_predict.iterrows()):
pt1 = scn_p[['eqX','eqY','eqZ']].values.astype(float)
pt2 = np.hstack([scn_p[['staX','staY']].values, 0]).astype(float)
#check limits
assert(np.logical_and(pt1>=c_lmin, pt1<=c_lmax).all()),'Error. Eq outside cell domain for rsn: %i'%rsn
assert(np.logical_and(pt2>=c_lmin, pt2<=c_lmax).all()),'Error. Sta outside cell domain for rsn: %i'%rsn
#cell paths for pt1 - pt2
cell_path[j,:] = pylib_cells.ComputeDistGridCells(pt1,pt2,ct4dist, flagUTM=True)
#keep only cells with non-zero paths
ca_valid = cell_path.sum(axis=0) > 0
cell_path = cell_path[:,ca_valid]
df_cell_info = df_cell_info.loc[ca_valid,:]
#iterate over anelastic attenuation components
for nerg_ca in nerg_catten_info:
#kernel type
k_type = nerg_catten_info[nerg_ca]['kernel_type']
#mean and std anelastic attenuation cells
ca_mean_train = df_nerg_cellatten.loc[:,nerg_catten_info[nerg_ca]['catten'][0]].values
ca_sig_train = df_nerg_cellatten.loc[:,nerg_catten_info[nerg_ca]['catten'][1]].values
#hyper-parameters
hyp_param = nerg_catten_info[nerg_ca]['hyp']
hyp_mean_ca = hyp_param['mean_ca'] if (('mean_ca' in hyp_param) and (not hyp_param['mean_ca'] is None)) else 0
hyp_ell = hyp_param['ell'] if (('ell' in hyp_param) and (not hyp_param['ell'] is None)) else 0
hyp_ell1 = hyp_param['ell1'] if (('ell1' in hyp_param) and (not hyp_param['ell1'] is None)) else np.nan
hyp_ell2 = hyp_param['ell2'] if (('ell2' in hyp_param) and (not hyp_param['ell2'] is None)) else np.nan
hyp_omega = hyp_param['omega'] if (('omega' in hyp_param) and (not hyp_param['omega'] is None)) else 0
hyp_omega1 = hyp_param['omega1'] if (('omega1' in hyp_param) and (not hyp_param['omega1'] is None)) else np.nan
hyp_omega2 = hyp_param['omega2'] if (('omega2' in hyp_param) and (not hyp_param['omega2'] is None)) else np.nan
hyp_pi = hyp_param['pi'] if (('pi' in hyp_param) and (not hyp_param['pi'] is None)) else 0
hyp_nu = hyp_param['nu'] if (('nu' in hyp_param) and (not hyp_param['nu'] is None)) else 0
#select kernel function
if k_type == 1: #independent cells
#cell ids in training data
# cid_train = df_nerg_cellatten.loc[:,nerg_catten_info[nerg_ca]['cor_info']].values
cid_train = df_nerg_cellatten.index.values
#cell ids in prediction data
# cid_prdct = df_cell_info.loc[:,nerg_catten_info[nerg_ca]['cor_info']].values
cid_prdct = df_cell_info.index.values
#compute mean and cov of cell anelastic coeffs for prediction scenarios
ca_prdct_mu, _, ca_prdct_cov = pylib_kern.PredictGroupKern(cid_prdct, cid_train,
c_train_mu=ca_mean_train, c_train_sig=ca_sig_train,
hyp_mean_c=hyp_mean_ca , hyp_omega=hyp_omega)
if k_type == 2: #exponetial
#cell coordinates of training data
ct_train = df_nerg_cellatten.loc[:,nerg_catten_info[nerg_ca]['cor_info']].values
#cell coordinates of prediction data
ct_prdct = df_cell_info.loc[:,nerg_catten_info[nerg_ca]['cor_info']].values
#compute mean and cov of cell anelastic coeffs for prediction scenarios
ca_prdct_mu, _, ca_prdct_cov = pylib_kern.PredictExpKern(ct_prdct, ct_train,
c_train_mu=ca_mean_train, c_train_sig=ca_sig_train,
hyp_mean_c=hyp_mean_ca, hyp_ell=hyp_ell, hyp_omega=hyp_omega, hyp_pi=hyp_pi)
if k_type == 3: #squared exponetial
#cell coordinates of training data
ct_train = df_nerg_cellatten.loc[:,nerg_catten_info[nerg_ca]['cor_info']].values
#cell coordinates of prediction data
ct_prdct = df_cell_info.loc[:,nerg_catten_info[nerg_ca]['cor_info']].values
#compute mean and cov of cell anelastic coeffs for prediction scenarios
ca_prdct_mu, _, ca_prdct_cov = pylib_kern.PredictSqExpKern(ct_prdct, ct_train,
c_train_mu=ca_mean_train, c_train_sig=ca_sig_train,
hyp_mean_c=hyp_mean_ca, hyp_ell=hyp_ell, hyp_omega=hyp_omega, hyp_pi=hyp_pi)
if k_type == 4: #Matern
#cell coordinates of training data
ct_train = df_nerg_cellatten.loc[:,nerg_catten_info[nerg_ca]['cor_info']].values
#cell coordinates of prediction data
ct_prdct = df_cell_info.loc[:,nerg_catten_info[nerg_ca]['cor_info']].values
#compute mean and cov of cell anelastic coeffs for prediction scenarios
ca_prdct_mu, _, ca_prdct_cov = pylib_kern.PredictSqMaternKern(ct_prdct, ct_train,
c_train_mu=ca_mean_train, c_train_sig=ca_sig_train,
hyp_mean_c=hyp_mean_ca, hyp_ell=hyp_ell, hyp_omega=hyp_omega, hyp_pi=hyp_pi)
if k_type == 5: #exponetial and spatially independent composite
#cell coordinates of training data
ct_train = df_nerg_cellatten.loc[:,nerg_catten_info[nerg_ca]['cor_info']].values
#cell coordinates of prediction data
ct_prdct = df_cell_info.loc[:,nerg_catten_info[nerg_ca]['cor_info']].values
#compute mean and cov of cell anelastic coeffs for prediction scenarios
ca_prdct_mu, _, ca_prdct_cov = pylib_kern.PredictNegExpSptInptKern(ct_prdct, ct_train,
c_train_mu=ca_mean_train, c_train_sig=ca_sig_train,
hyp_mean_c=hyp_mean_ca, hyp_ell1=hyp_ell1, hyp_omega1=hyp_omega1,
hyp_omega2=hyp_omega2, hyp_pi=hyp_pi)
#sample cell-specific anelastic coefficients for prediction scenarios
ca_prdct_samp = MVNRnd(mean=ca_prdct_mu, cov=ca_prdct_cov, n_samp=n_samp)
ca_prdct_sig = np.sqrt( np.diag(ca_prdct_cov) )
#effect of anelastic attenuation
nerg_atten_prdct_samp += cell_path @ ca_prdct_samp
nerg_atten_prdct_mu += cell_path @ ca_prdct_mu
nerg_atten_prdct_var += np.square(cell_path) @ ca_prdct_sig**2
#summarize individual anelastic components
nerg_atten_cmp[nerg_ca] = [cell_path @ ca_prdct_mu, np.sqrt(np.square(cell_path) @ ca_prdct_sig**2),
cell_path @ ca_prdct_samp]
#total non-ergodic effects
nerg_effects_prdct_samp = nerg_vcm_prdct_samp + nerg_atten_prdct_samp
nerg_effects_prdct_mu = nerg_vcm_prdct_mu + nerg_atten_prdct_mu
nerg_effects_prdct_sig = np.sqrt(nerg_vcm_prdct_var + nerg_atten_prdct_var)
return nerg_effects_prdct_samp, nerg_vcm_prdct_samp, nerg_atten_prdct_samp, \
nerg_effects_prdct_mu, nerg_effects_prdct_sig, \
nerg_vcm_cmp, nerg_atten_cmp
# Multivariate normal distribution random samples
#--------------------------------------
def MVNRnd(mean = None, cov = None, seed = None, n_samp = None, flag_sp = False, flag_list = False):
'''
Draw random samples from a Multivariable Normal distribution
Parameters
----------
mean : np.array(n), optional
Mean array. The default is None.
cov : np.array(n,n), optional
Covariance Matrix. The default is None.
seed : int, optional
Seed number of random number generator. The default is None.
n_samp : int, optional
Number of samples. The default is None.
flag_sp : boolean, optional
Sparse covariance matrix flag; if sparse flag_sp = True. The default is False.
flag_list : boolean, optional
Flag returning output as list. The default is False.
Returns
-------
samp
Sampled values.
'''
#if not already covert to list
if flag_list:
seed_list = seed if not seed is None else [None]
else:
seed_list = [seed]
#number of dimensions
n_dim = len(mean) if not mean is None else cov.shape[0]
assert(cov.shape == (n_dim,n_dim)),'Error. Inconsistent size of mean array and covariance matrix'
#set mean array to zero if not given
if mean is None: mean = np.zeros(n_dim)
#compute L D L' decomposition
if flag_sp: cov = cov.toarray()
L, D, _ = scipylinalg.ldl(cov)
assert( not np.count_nonzero(D - np.diag(np.diagonal(D))) ),'Error. D not diagonal'
assert( np.all(np.diag(D) > -1e-1) ),'Error. D diagonal is negative'
#extract diagonal from D matrix, set to zero any negative entries due to bad conditioning
d = np.diagonal(D).copy()
d[d<0] = 0
#compute Q matrix
Q = L @ np.diag(np.sqrt(d))
#generate random sample
samp_list = list()
for k, seed in enumerate(seed_list):
#genereate seed numbers if not given
if seed is None: seed = np.random.standard_normal(size=(n_dim, n_samp))
#generate random multi-normal random samples
samp = Q @ (seed )
samp += mean[:,np.newaxis] if samp.ndim > 1 else mean
#summarize samples
samp_list.append( samp )
return samp_list if flag_list else samp_list[0]
| 21,359 | 54.051546 | 154 | py |
ngmm_tools | ngmm_tools-master/Analyses/Python_lib/ground_motions/pylib_cell_dist.py | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Sun May 3 17:25:10 2020
@author: glavrent
"""
#load libraries
import numpy as np
import geopy.distance as geopydist
def ComputeDistUnGridCells(pt1, pt2, cells, diffx, diffy, flagUTM=False):
'''
Compute the path distances of uniformly gridded cells
Parameters
----------
pt1 : np.array(3)
Latitude, Longitude, elevation coordinates of first point.
pt2 : np.array(3)
Latitude, Longitude, elevation coordinates of second point.
cells : np.array(n_cells, 4)
Cell coordinates: Cartesian or LatLon
Latitude, Longitude, bottom and top elevation of cells
[x, y, elev_bot, elev_top]
Lat Lon coordinates:
Latitude, Longitude, bottom and top elevation of cells
[lon, lat, elev_bot, elev_top]
diffx : real
Latitude interval of cells.
diffy : real
Longitude interval of cells.
Returns
-------
dm : np.array(n_cells)
Distance path on each cell.
'''
#import pdb; pdb.set_trace()
#grid points
x_grid = np.unique(cells[:, 0])
y_grid = np.unique(cells[:, 1])
z_grid = np.unique(cells[:, 2])
## find x,y,z grid points which are between source and site
x_v = np.sort([pt1[0], pt2[0]])
x_g_pts = x_grid[(x_v[0] <= x_grid) & (x_grid < x_v[1])]
y_v = np.sort([pt1[1], pt2[1]])
y_g_pts = y_grid[(y_v[0] <= y_grid) & (y_grid < y_v[1])]
z_v = np.sort([pt1[2], pt2[2]])
z_g_pts = z_grid[(z_v[0] <= z_grid) & (z_grid < z_v[1])]
#p1-pt2 vector
vec = np.subtract(pt1, pt2)
# intersection points for x
normal = [1, 0, 0];
ptx = np.ones(len(x_g_pts) * 3)
if len(x_g_pts) > 0:
ptx = ptx.reshape(len(x_g_pts), 3)
for i, xv in enumerate(x_g_pts):
ptplane = [xv, y_grid[0], 0]
d = np.divide(np.dot(np.subtract(ptplane,pt1),normal), np.dot(vec,normal))
pt = pt1 + d * vec
ptx[i] = pt
else:
ptx = [[-999, -999, -999]]
# intersection points for y
normal = [0, 1, 0];
pty = np.ones(len(y_g_pts) * 3)
if len(y_g_pts) > 0:
pty = pty.reshape(len(y_g_pts), 3)
for i, yv in enumerate(y_g_pts):
ptplane = [x_grid[0], yv, 0]
d = np.divide(np.dot(np.subtract(ptplane,pt1),normal), np.dot(vec,normal))
pt = pt1 + d * vec
pty[i] = pt
else:
pty = [[-999, -999, -999]]
# intersection points for z
normal = [0, 0, 1]
ptz = np.ones(len(z_g_pts) * 3)
if len(z_g_pts) > 0:
ptz = ptz.reshape(len(z_g_pts), 3)
for i, zv in enumerate(z_g_pts):
ptplane = [x_grid[0], y_grid[0], zv]
d = np.divide(np.dot(np.subtract(ptplane,pt1),normal), np.dot(vec,normal))
pt = pt1 + d * vec
ptz[i] = pt
else:
ptz = [[-999, -999, -999]]
#summarize all intersection points
ptall = np.concatenate(([pt1], [pt2], ptx, pty, ptz))
ptall = ptall[(ptall[:, 0] != -999) & (ptall[:, 1] != -999) & (ptall[:, 2] != -999)]
ptall = np.unique(ptall, axis=0)
if pt1[0] != pt2[0]:
ptall = ptall[ptall[:, 0].argsort()] #sort points by x coordinate
else:
ptall = ptall[ptall[:, 1].argsort()] #sort points by y coordinate
#cell ids
id_cells = np.arange(len(cells))
#compute cell distance
idx = np.zeros(len(ptall)-1)
distances = np.ones(len(ptall)-1)
for i in range(len(ptall) - 1):
p1 = ptall[i] #first intersection point
p2 = ptall[i+1] #second intersection point
#cell indices of cells where the first intersection point belongs
idx1 = id_cells[(cells[:, 0] <= p1[0]) & (p1[0] <= cells[:, 0] + diffx) & \
(cells[:, 1] <= p1[1]) & (p1[1] <= cells[:, 1] + diffy) & \
(cells[:, 2] <= p1[2]) & (p1[2] <= cells[:, 3])]
#cell indices of cells where the second intersection point belongs
idx2 = id_cells[(cells[:, 0] <= p2[0]) & (p2[0] <= cells[:, 0] + diffx) & \
(cells[:, 1] <= p2[1]) & (p2[1] <= cells[:, 1] + diffy) & \
(cells[:, 2] <= p2[2]) & (p2[2] <= cells[:, 3])]
#common indices of first and second int points
idx[i] = np.intersect1d(idx1, idx2)
#compute path distance
if not flagUTM:
dxy = geopydist.distance(ptall[i,(1,0)],ptall[i + 1,(1,0)]).km
else:
dxy = np.linalg.norm(ptall[i,0:1] - ptall[i + 1,0:1])
dz = ptall[i,2] - ptall[i + 1,2]
distances[i] = np.sqrt(dxy** 2 + dz ** 2)
dm = np.zeros(len(cells))
dm[idx.astype(int)] = distances
return dm
def ComputeDistGridCells(pt1, pt2, cells, flagUTM=False):
'''
Compute the path distances of gridded cells
Parameters
----------
pt1 : np.array(3)
Latitude, Longitude, elevation coordinates of first point.
pt2 : np.array(3)
Latitude, Longitude, elevation coordinates of second point.
cells : np.array(n_cells, 6)
Latitude, Longitude, elevation of bottom left (q1) and top right (q8) corrners of cells
[q1_lat, q1_lon, q1_elev, q8_lat, q8_lon, q8_elev]
diffx : real
Latitude interval of cells.
diffy : real
Longitude interval of cells.
Returns
-------
dm : np.array(n_cells)
Distance path on each cell.
'''
#import pdb; pdb.set_trace()
#grid points
x_grid = np.unique(cells[:, 0])
y_grid = np.unique(cells[:, 1])
z_grid = np.unique(cells[:, 2])
## find x,y,z grid points which are between source and site
x_v = np.sort([pt1[0], pt2[0]])
x_g_pts = x_grid[(x_v[0] <= x_grid) & (x_grid < x_v[1])]
y_v = np.sort([pt1[1], pt2[1]])
y_g_pts = y_grid[(y_v[0] <= y_grid) & (y_grid < y_v[1])]
z_v = np.sort([pt1[2], pt2[2]])
z_g_pts = z_grid[(z_v[0] <= z_grid) & (z_grid < z_v[1])]
#p1-pt2 vector
vec = np.subtract(pt1, pt2)
# intersection points for x
normal = [1, 0, 0];
ptx = np.ones(len(x_g_pts) * 3)
if len(x_g_pts) > 0:
ptx = ptx.reshape(len(x_g_pts), 3)
for i, xv in enumerate(x_g_pts):
ptplane = [xv, y_grid[0], 0]
d = np.divide(np.dot(np.subtract(ptplane,pt1),normal), np.dot(vec,normal))
pt = pt1 + d * vec
ptx[i] = pt
else:
ptx = [[-999, -999, -999]]
# intersection points for y
normal = [0, 1, 0];
pty = np.ones(len(y_g_pts) * 3)
if len(y_g_pts) > 0:
pty = pty.reshape(len(y_g_pts), 3)
for i, yv in enumerate(y_g_pts):
ptplane = [x_grid[0], yv, 0]
d = np.divide(np.dot(np.subtract(ptplane,pt1),normal), np.dot(vec,normal))
pt = pt1 + d * vec
pty[i] = pt
else:
pty = [[-999, -999, -999]]
# intersection points for z
normal = [0, 0, 1]
ptz = np.ones(len(z_g_pts) * 3)
if len(z_g_pts) > 0:
ptz = ptz.reshape(len(z_g_pts), 3)
for i, zv in enumerate(z_g_pts):
ptplane = [x_grid[0], y_grid[0], zv]
d = np.divide(np.dot(np.subtract(ptplane,pt1),normal), np.dot(vec,normal))
pt = pt1 + d * vec
ptz[i] = pt
else:
ptz = [[-999, -999, -999]]
#summarize all intersection points
ptall = np.concatenate(([pt1], [pt2], ptx, pty, ptz))
ptall = ptall[(ptall[:, 0] != -999) & (ptall[:, 1] != -999) & (ptall[:, 2] != -999)]
#ptall = np.unique(ptall.round, axis=0, return_index=True)
_, i_ptall_unq = np.unique(ptall.round(decimals=7), axis=0, return_index=True)
ptall = ptall[i_ptall_unq,:]
# if pt1[0] != pt2[0]:
if abs(pt1[0] - pt2[0]) > 1e-6:
ptall = ptall[ptall[:, 0].argsort()]
else:
ptall = ptall[ptall[:, 1].argsort()]
#compute cell distance
id_cells = np.arange(len(cells))
idx = np.ones(len(ptall)-1)
distances = np.ones(len(ptall)-1)
for i in range(len(ptall) - 1):
p1 = ptall[i] #first intersection point
p2 = ptall[i+1] #second intersection point
#cell indices where the first point belongs
tol = 1e-9
idx1 = id_cells[(cells[:, 0]-tol <= p1[0]) & (p1[0] <= cells[:, 3]+tol) & \
(cells[:, 1]-tol <= p1[1]) & (p1[1] <= cells[:, 4]+tol) & \
(cells[:, 2]-tol <= p1[2]) & (p1[2] <= cells[:, 5]+tol)]
#cell indices where the second point belongs
idx2 = id_cells[(cells[:, 0]-tol <= p2[0]) & (p2[0] <= cells[:, 3]+tol) & \
(cells[:, 1]-tol <= p2[1]) & (p2[1] <= cells[:, 4]+tol) & \
(cells[:, 2]-tol <= p2[2]) & (p2[2] <= cells[:, 5]+tol)]
#common indices of first and second point
try:
idx[i] = np.intersect1d(idx1, idx2)
except ValueError:
print('i_pt: ', i)
print('idx1: ', idx1)
print('idx2: ', idx2)
print('p1: ', p1)
print('p2: ', p2)
# import pdb; pdb.set_trace()
raise
#compute path distance
if not flagUTM:
dxy = geopydist.distance(ptall[i,(1,0)],ptall[i + 1,(1,0)]).km
else:
dxy = np.linalg.norm(ptall[i,0:2] - ptall[i + 1,0:2])
dz = ptall[i,2] - ptall[i + 1,2]
distances[i] = np.sqrt(dxy** 2 + dz ** 2)
dm = np.zeros(len(cells))
dm[idx.astype(int)] = distances
return dm
| 9,551 | 32.75265 | 95 | py |
ngmm_tools | ngmm_tools-master/Analyses/Python_lib/ground_motions/pylib_kernels.py | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Sat Aug 20 13:52:51 2022
@author: glavrent
"""
# Packages
# ---------------------------
#arithmetic libraries
import numpy as np
from scipy import linalg as scipylinalg
from sklearn.gaussian_process.kernels import Matern
# Kernel Functions
#--------------------------------------
# group kernel function
#--- --- --- --- --- --- --- ---
def KernelGroup(grp_1, grp_2, hyp_omega = 0, delta = 1e-9):
'''
Compute kernel function for perfect correlation between group variables
Parameters
----------
grp_1 : np.array
IDs for first group.
grp_2 : np.array
IDs for second group.
hyp_omega : non-negative real, optional
Scale of kernel function. The default is 0.
delta : non-negative real, optional
Diagonal widening. The default is 1e-9.
Returns
-------
cov_mat : np.array
Covariance Matrix.
'''
#tolerance for station id comparison
r_tol = np.min([0.01/np.max([np.abs(grp_1).max(), np.abs(grp_2).max()]), 1e-11])
#number of grid nodes
n_pt_1 = grp_1.shape[0]
n_pt_2 = grp_2.shape[0]
#number of dimensions
n_dim = grp_1.ndim
#create cov. matrix
cov_mat = np.zeros([n_pt_1,n_pt_2]) #initialize
if n_dim == 1:
for i in range(n_pt_1):
cov_mat[i,:] = hyp_omega**2 * np.isclose(grp_1[i], grp_2, rtol=r_tol).flatten()
else:
for i in range(n_pt_1):
cov_mat[i,:] = hyp_omega**2 * (scipylinalg.norm(grp_1[i] - grp_2, axis=1) < r_tol)
if n_pt_1 == n_pt_2:
for i in range(n_pt_1):
cov_mat[i,i] += delta
return cov_mat
# exponential kernel
#--- --- --- --- --- --- --- ---
def KernelExp(t_1, t_2, hyp_ell = 0, hyp_omega = 0, hyp_pi = 0, delta = 1e-9):
'''
Compute exponential kernel function
Parameters
----------
t_1 : np.array
Coordinates of first group.
t_2 : np.array
Coordinates of second group.
hyp_ell : non-negative real, optional
Correlation length. The default is 0.
hyp_omega : non-negative real, optional
Scale of kernel function. The default is 0.
hyp_pi : non-negative real, optional
Constant of kernel function. The default is 0.
delta : non-negative real, optional
Diagonal widening. The default is 1e-9.
Returns
-------
cov_mat : np.array
Covariance Matrix.
'''
#number of grid nodes
n_pt_1 = t_1.shape[0]
n_pt_2 = t_2.shape[0]
#number of dimensions
n_dim = t_1.ndim
#create cov. matrix
cov_mat = np.zeros([n_pt_1,n_pt_2]) #initialize
for i in range(n_pt_1):
dist = scipylinalg.norm(t_1[i] - t_2,axis=1) if n_dim > 1 else np.abs(t_1[i] - t_2)
cov_mat[i,:] = hyp_pi**2 + hyp_omega**2 * np.exp(- dist/hyp_ell)
if n_pt_1 == n_pt_2:
for i in range(n_pt_1):
cov_mat[i,i] += delta
return cov_mat
# squared exponential kernel
#--- --- --- --- --- --- --- ---
def KernelSqExp(t_1, t_2, hyp_ell = 0, hyp_omega = 0, hyp_pi = 0, delta = 1e-9):
'''
Compute squared exponential kernel function
Parameters
----------
t_1 : np.array
Coordinates of first group.
t_2 : np.array
Coordinates of second group.
hyp_ell : non-negative real, optional
Correlation length. The default is 0.
hyp_omega : non-negative real, optional
Scale of kernel function. The default is 0.
hyp_pi : non-negative real, optional
Constant of kernel function. The default is 0.
delta : non-negative real, optional
Diagonal widening. The default is 1e-9.
Returns
-------
cov_mat : np.array
Covariance Matrix.
'''
#number of grid nodes
n_pt_1 = t_1.shape[0]
n_pt_2 = t_2.shape[0]
#number of dimensions
n_dim = t_1.ndim
#create cov. matrix
cov_mat = np.zeros([n_pt_1,n_pt_2]) #initialize
for i in range(n_pt_1):
dist = scipylinalg.norm(t_1[i] - t_2,axis=1) if n_dim > 1 else np.abs(t_1[i] - t_2)
cov_mat[i,:] = hyp_pi**2 + hyp_omega**2 * np.exp(- dist**2/hyp_ell**2)
if n_pt_1 == n_pt_2:
for i in range(n_pt_1):
cov_mat[i,i] += delta
return cov_mat
# matern exponential kernel
#--- --- --- --- --- --- --- ---
def MaternKernel(t_1, t_2, hyp_ell = 0, hyp_omega = 0, hyp_pi = 0, hyp_nu=1.5, delta = 1e-9):
'''
Compute Matern kernel function
Parameters
----------
t_1 : np.array
Coordinates of first group.
t_2 : np.array
Coordinates of second group.
hyp_ell : non-negative real, optional
Correlation length. The default is 0.
hyp_omega : non-negative real, optional
Scale of kernel function. The default is 0.
hyp_pi : non-negative real, optional
Constant of kernel function. The default is 0.
hyp_nu : non-negative real, optional
Smoothness parameter. The default is 1.5.
delta : non-negative real, optional
Diagonal widening. The default is 1e-9.
Returns
-------
cov_mat : np.array
Covariance Matrix.
'''
#number of grid nodes
n_pt_1 = t_1.shape[0]
n_pt_2 = t_2.shape[0]
#number of dimensions
n_dim = t_1.ndim
#distance matrix
dist_mat = np.array([scipylinalg.norm(t1 - t_2, axis=1) if n_dim > 1 else np.abs(t1 - t_2)
for t1 in t_1])
#create cov. matrix
cov_mat = hyp_omega**2 * Matern(nu=hyp_nu, length_scale=hyp_ell)(0, dist_mat.ravel()[:, np.newaxis]).reshape(dist_mat.shape)
cov_mat += hyp_pi**2
if n_pt_1 == n_pt_2:
for i in range(n_pt_1):
cov_mat[i,i] += delta
return cov_mat
# composite exponential kernel and spatially independent
#--- --- --- --- --- --- --- ---
def KernelNegExpSptInpt(t_1, t_2, hyp_ell1 = 0, hyp_omega1 = 0, hyp_omega2 = 0, hyp_pi = 0, delta = 1e-9):
'''
Compute composite kernel function, with negative exponential and
spatially idependent components
Parameters
----------
t_1 : np.array
Coordinates of first group.
t_2 : np.array
Coordinates of second group.
hyp_ell1 : non-negative real, optional
Correlation length of neg. exponential component. The default is 0.
hyp_omega1 : non-negative real, optional
Scale of neg. exponential component. The default is 0.
hyp_omega2 : non-negative real, optional
Scale of spatially independent component. The default is 0.
hyp_pi : non-negative real, optional
Constant of kernel function. The default is 0.
delta : non-negative real, optional
Diagonal widening. The default is 1e-9.
Returns
-------
cov_mat : TYPE
DESCRIPTION.
'''
#number of grid nodes
n_pt_1 = t_1.shape[0]
n_pt_2 = t_2.shape[0]
#negative exponetial component
cov_mat = KernelExp(t_1, t_2, hyp_ell=hyp_ell1, hyp_omega=hyp_omega1, hyp_pi=hyp_pi, delta=1e-9)
#spatially independent component
cov_mat += KernelGroup(t_1, t_2, hyp_omega=hyp_omega2, delta=0)
return cov_mat
# Predictive Functions
#--------------------------------------
# predict coeffs with group kernel function
#--- --- --- --- --- --- --- ---
def PredictGroupKern(g_prdct, g_train, c_train_mu, c_train_sig = None,
hyp_mean_c = 0, hyp_omega = 0, delta = 1e-9):
'''
Predict conditional coefficients based on group kernel function.
Parameters
----------
g_prdct : np.array
Group IDs of prediction cases.
g_train : np.array
Group IDs of training cases.
c_train_mu : np.array
Mean values of non-ergodic coefficient of training cases.
c_train_sig : np.array, optional
Standard deviations of non-ergodic coefficient of training cases. The default is None.
hyp_mean_c : real, optional
Mean of non-ergodic coefficient. The default is 0.
hyp_omega : non-negative real, optional
Scale of kernel function. The default is 0.
delta : non-negative real, optional
Diagonal widening. The default is 1e-9.
Returns
-------
c_prdct_mu : np.array
Mean value of non-ergodic coefficient for prediction cases.
c_prdct_sig : np.array
Standard deviations of non-ergodic coefficient for prediction cases.
c_prdct_cov : np.array
Covariance matrix of non-ergodic coefficient for prediction cases.
'''
#remove mean effect from training coefficients
c_train_mu = c_train_mu - hyp_mean_c
#uncertainty in training data
if c_train_sig is None: c_train_sig = np.zeros(len(c_train_mu))
c_train_cov = np.diag(c_train_sig**2) if c_train_sig.ndim == 1 else c_train_sig
#covariance between training data
K = KernelGroup(g_train, g_train, hyp_omega=hyp_omega, delta=delta)
#covariance between data and new locations
k = KernelGroup(g_prdct, g_train, hyp_omega=hyp_omega, delta=0)
#covariance between new locations
k_star = KernelGroup(g_prdct, g_prdct, hyp_omega=hyp_omega, delta=0)
#inverse of covariance matrix
K_inv = scipylinalg.inv(K)
#product of k * K^-1
kK_inv = k.dot(K_inv)
#posterior mean and variance at new locations
c_prdct_mu = kK_inv.dot(c_train_mu)
c_prdct_cov = k_star - kK_inv.dot(k.transpose()) + kK_inv.dot( c_train_cov.dot(kK_inv.transpose()) )
#posterior standard dev. at new locations
c_prdct_sig = np.sqrt(np.diag(c_prdct_cov))
#add mean effect from training coefficients
c_prdct_mu += hyp_mean_c
return c_prdct_mu, c_prdct_sig, c_prdct_cov
# predict coeffs with exponential kernel
#--- --- --- --- --- --- --- ---
def PredictExpKern(t_prdct, t_train, c_train_mu, c_train_sig = None,
hyp_mean_c = 0, hyp_ell = 0, hyp_omega = 0, hyp_pi = 0, delta = 1e-9):
'''
Predict conditional coefficients based on exponential kernel function.
Parameters
----------
t_prdct : np.array
Coordinates of prediction cases.
t_train : np.array
Coordinates of training cases.
c_train_mu : np.array
Mean values of non-ergodic coefficient of training cases.
c_train_sig : np.array, optional
Standard deviations of non-ergodic coefficient of training cases. The default is None.
hyp_mean_c : real, optional
Mean of non-ergodic coefficient. The default is 0.
hyp_ell : non-negative real, optional
Correlation length of kernel function.. The default is 0.
hyp_omega : non-negative real, optional
Scale of kernel function. The default is 0.
hyp_pi : postive real, optional
Constant of kernel function. The default is 0.
delta : non-negative real, optional
Diagonal widening. The default is 1e-9.
Returns
-------
c_prdct_mu : np.array
Mean value of non-ergodic coefficient for prediction cases.
c_prdct_sig : np.array
Standard deviations of non-ergodic coefficient for prediction cases.
c_prdct_cov : np.array
Covariance matrix of non-ergodic coefficient for prediction cases.
'''
#remove mean effect from training coefficients
c_train_mu = c_train_mu - hyp_mean_c
#uncertainty in training data
if c_train_sig is None: c_train_sig = np.zeros(len(c_train_mu))
c_train_cov = np.diag(c_train_sig**2) if c_train_sig.ndim == 1 else c_train_sig
#covariance between training data
K = KernelExp(t_train, t_train, hyp_ell=hyp_ell, hyp_omega=hyp_omega, hyp_pi=hyp_pi, delta=delta)
#covariance between data and new locations
k = KernelExp(t_prdct, t_train, hyp_ell=hyp_ell, hyp_omega=hyp_omega, hyp_pi=hyp_pi, delta=0)
#covariance between new locations
k_star = KernelExp(t_prdct, t_prdct, hyp_ell=hyp_ell, hyp_omega=hyp_omega, hyp_pi=hyp_pi, delta=0)
#inverse of covariance matrix
K_inv = scipylinalg.inv(K)
#product of k * K^-1
kK_inv = k.dot(K_inv)
#posterior mean and variance at new locations
c_prdct_mu = kK_inv.dot(c_train_mu)
c_prdct_cov = k_star - kK_inv.dot(k.transpose()) + kK_inv.dot( c_train_cov.dot(kK_inv.transpose()) )
#posterior standard dev. at new locations
c_prdct_sig = np.sqrt(np.diag(c_prdct_cov))
#add mean effect from training coefficients
c_prdct_mu += hyp_mean_c
return c_prdct_mu, c_prdct_sig, c_prdct_cov
# predict coeffs with squared exponential kernel
#--- --- --- --- --- --- --- ---
def PredictSqExpKern(t_prdct, t_train, c_train_mu, c_train_sig = None,
hyp_mean_c = 0, hyp_ell = 0, hyp_omega = 0, hyp_pi = 0, delta = 1e-9):
'''
Predict conditional coefficients based on squared exponential kernel function.
Parameters
----------
t_prdct : np.array
Coordinates of prediction cases.
t_train : np.array
Coordinates of training cases.
c_train_mu : np.array
Mean values of non-ergodic coefficient of training cases.
c_train_sig : np.array, optional
Standard deviations of non-ergodic coefficient of training cases. The default is None.
hyp_mean_c : real, optional
Mean of non-ergodic coefficient. The default is 0.
hyp_ell : non-negative real, optional
Correlation length of kernel function.. The default is 0.
hyp_omega : non-negative real, optional
Scale of kernel function. The default is 0.
hyp_pi : postive real, optional
Constant of kernel function. The default is 0.
delta : non-negative real, optional
Diagonal widening. The default is 1e-9.
Returns
-------
c_prdct_mu : np.array
Mean value of non-ergodic coefficient for prediction cases.
c_prdct_sig : np.array
Standard deviations of non-ergodic coefficient for prediction cases.
c_prdct_cov : np.array
Covariance matrix of non-ergodic coefficient for prediction cases.
'''
#remove mean effect from training coefficients
c_train_mu = c_train_mu - hyp_mean_c
#uncertainty in training data
if c_train_sig is None: c_train_sig = np.zeros(len(c_train_mu))
c_train_cov = np.diag(c_train_sig**2) if c_train_sig.ndim == 1 else c_train_sig
#covariance between training data
K = KernelNegExpSptInpt(t_train, t_train, hyp_ell=hyp_ell, hyp_omega=hyp_omega, hyp_pi=hyp_pi, delta=delta)
#covariance between data and new locations
k = KernelNegExpSptInpt(t_prdct, t_train, hyp_ell=hyp_ell, hyp_omega=hyp_omega, hyp_pi=hyp_pi, delta=0)
#covariance between new locations
k_star = KernelNegExpSptInpt(t_prdct, t_prdct, hyp_ell=hyp_ell, hyp_omega=hyp_omega, hyp_pi=hyp_pi, delta=0)
#inverse of covariance matrix
K_inv = scipylinalg.inv(K)
#product of k * K^-1
kK_inv = k.dot(K_inv)
#posterior mean and variance at new locations
c_prdct_mu = kK_inv.dot(c_train_mu)
c_prdct_cov = k_star - kK_inv.dot(k.transpose()) + kK_inv.dot( c_train_cov.dot(kK_inv.transpose()) )
#posterior standard dev. at new locations
c_prdct_sig = np.sqrt(np.diag(c_prdct_cov))
#add mean effect from training coefficients
c_prdct_mu += hyp_mean_c
return c_prdct_mu, c_prdct_sig, c_prdct_cov
# predict coeffs with Matern kernel
#--- --- --- --- --- --- --- ---
def PredictMaternKern(t_prdct, t_train, c_train_mu, c_train_sig = None,
hyp_mean_c = 0, hyp_ell = 0, hyp_omega = 0, hyp_pi = 0, hyp_nu=1.5,
delta = 1e-9):
'''
Predict conditional coefficients based on Matern kernel function.
Parameters
----------
t_prdct : np.array
Coordinates of prediction cases.
t_train : np.array
Coordinates of training cases.
c_train_mu : np.array
Mean values of non-ergodic coefficient of training cases.
c_train_sig : np.array, optional
Standard deviations of non-ergodic coefficient of training cases. The default is None.
hyp_mean_c : real, optional
Mean of non-ergodic coefficient. The default is 0.
hyp_ell : non-negative real, optional
Correlation length of kernel function.. The default is 0.
hyp_omega : non-negative real, optional
Scale of kernel function. The default is 0.
hyp_pi : postive real, optional
Constant of kernel function. The default is 0.
hyp_nu: positive real, optional
Smoothness parameter. The default is 1.5.
delta : non-negative real, optional
Diagonal widening. The default is 1e-9.
Returns
-------
c_prdct_mu : np.array
Mean value of non-ergodic coefficient for prediction cases.
c_prdct_sig : np.array
Standard deviations of non-ergodic coefficient for prediction cases.
c_prdct_cov : np.array
Covariance matrix of non-ergodic coefficient for prediction cases.
'''
#remove mean effect from training coefficients
c_train_mu = c_train_mu - hyp_mean_c
#uncertainty in training data
if c_train_sig is None: c_train_sig = np.zeros(len(c_train_mu))
c_train_cov = np.diag(c_train_sig**2) if c_train_sig.ndim == 1 else c_train_sig
#covariance between training data
K = MaternKernel(t_train, t_train, hyp_ell=hyp_ell, hyp_omega=hyp_omega, hyp_pi=hyp_pi, hyp_nu=hyp_nu, delta=delta)
#covariance between data and new locations
k = MaternKernel(t_prdct, t_train, hyp_ell=hyp_ell, hyp_omega=hyp_omega, hyp_pi=hyp_pi, hyp_nu=hyp_nu, delta=0)
#covariance between new locations
k_star = MaternKernel(t_prdct, t_prdct, hyp_ell=hyp_ell, hyp_omega=hyp_omega, hyp_pi=hyp_pi, hyp_nu=hyp_nu, delta=0)
#inverse of covariance matrix
K_inv = scipylinalg.inv(K)
#product of k * K^-1
kK_inv = k.dot(K_inv)
#posterior mean and variance at new locations
c_prdct_mu = kK_inv.dot(c_train_mu)
c_prdct_cov = k_star - kK_inv.dot(k.transpose()) + kK_inv.dot( c_train_cov.dot(kK_inv.transpose()) )
#posterior standard dev. at new locations
c_prdct_sig = np.sqrt(np.diag(c_prdct_cov))
#add mean effect from training coefficients
c_prdct_mu += hyp_mean_c
return c_prdct_mu, c_prdct_sig, c_prdct_cov
# predict coeffs with composite exponential and spatially independent kernel function
#--- --- --- --- --- --- --- ---
def PredictNegExpSptInptKern(t_prdct, t_train, c_train_mu, c_train_sig = None,
hyp_mean_c = 0, hyp_ell1 = 0, hyp_omega1 = 0,
hyp_omega2 = 0, hyp_pi = 0, delta = 1e-9):
'''
Predict conditional coefficients based on composite exponential and
spatially independent kernel function.
Parameters
----------
t_prdct : np.array
Coordinates of prediction cases.
t_train : np.array
Coordinates of training cases.
c_train_mu : np.array
Mean values of non-ergodic coefficient of training cases.
c_train_sig : np.array, optional
Standard deviations of non-ergodic coefficient of training cases. The default is None.
hyp_mean_c : real, optional
Mean of non-ergodic coefficient. The default is 0.
hyp_ell1 : non-negative real, optional
Correlation length of negative exponential kernel function. The default is 0.
hyp_omega1 : non-negative real, optional
Scale of negative exponential kernel function. The default is 0.
hyp_omega2 : non-negative real, optional
Scale of spatially independent kernel function. The default is 0.
hyp_pi : postive real, optional
Constant of kernel function. The default is 0.
delta : non-negative real, optional
Diagonal widening. The default is 1e-9.
Returns
-------
c_prdct_mu : np.array
Mean value of non-ergodic coefficient for prediction cases.
c_prdct_sig : np.array
Standard deviations of non-ergodic coefficient for prediction cases.
c_prdct_cov : np.array
Covariance matrix of non-ergodic coefficient for prediction cases.
'''
#remove mean effect from training coefficients
c_train_mu = c_train_mu - hyp_mean_c
#uncertainty in training data
if c_train_sig is None: c_train_sig = np.zeros(len(c_train_mu))
c_train_cov = np.diag(c_train_sig**2) if c_train_sig.ndim == 1 else c_train_sig
#covariance between training data
K = KernelNegExpSptInpt(t_train, t_train, hyp_ell1=hyp_ell1, hyp_omega1=hyp_omega1,
hyp_omega2=hyp_omega2, hyp_pi=hyp_pi, delta=delta)
#covariance between data and new locations
k = KernelNegExpSptInpt(t_prdct, t_train, hyp_ell1=hyp_ell1, hyp_omega1=hyp_omega1,
hyp_omega2=hyp_omega2, hyp_pi=hyp_pi, delta=0)
#covariance between new locations
k_star = KernelNegExpSptInpt(t_prdct, t_prdct, hyp_ell1=hyp_ell1, hyp_omega1=hyp_omega1,
hyp_omega2=hyp_omega2, hyp_pi=hyp_pi, delta=0)
#inverse of covariance matrix
K_inv = scipylinalg.inv(K)
#product of k * K^-1
kK_inv = k.dot(K_inv)
#posterior mean and variance at new locations
c_prdct_mu = kK_inv.dot(c_train_mu)
c_prdct_cov = k_star - kK_inv.dot(k.transpose()) + kK_inv.dot( c_train_cov.dot(kK_inv.transpose()) )
#posterior standard dev. at new locations
c_prdct_sig = np.sqrt(np.diag(c_prdct_cov))
#add mean effect from training coefficients
c_prdct_mu += hyp_mean_c
return c_prdct_mu, c_prdct_sig, c_prdct_cov | 21,651 | 34.966777 | 128 | py |
ngmm_tools | ngmm_tools-master/Analyses/Python_lib/QGIS/pylib_QGIS.py | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Tue May 19 11:04:00 2020
@author: glavrent
"""
#load libraries
#load GIS
from qgis.core import QgsVectorLayer, QgsPointXY
from qgis.core import QgsField, QgsFeature, QgsGeometry, QgsVectorFileWriter, QgsFeatureSink
from qgis.PyQt.QtCore import QVariant
def EQLayer(eq_data):
'''
Create earthquake source layer for QGIS
Parameters
----------
eq_data : pd.dataframe
Dataframe for rupture points with fields:
eqid, region, mag, SOF, Ztor, eqLat, eqLon
Returns
-------
eq_layer : TYPE
QGIS layer with earthquake sources.
'''
#create qgis layer for earthquake sources
eq_layer = QgsVectorLayer("Point", "eq_pts", "memory")
eq_pr = eq_layer.dataProvider()
eq_pr.addAttributes([QgsField("eqid", QVariant.Int),
QgsField("region", QVariant.Int),
QgsField("mag", QVariant.Double),
QgsField("SOF", QVariant.Int),
QgsField("Ztor", QVariant.Double),
QgsField("eqLat", QVariant.Double),
QgsField("eqLon", QVariant.Double)])
#iterate over earthquakes, add on layer
eq_layer.startEditing()
for eq in eq_data.iterrows():
#earthquake info
eq_info = eq[1][['eqid','region','mag','SOF','Ztor']].tolist()
eq_latlon = eq[1][['eqLat','eqLon']].tolist()
#define feature, earthquake
eq_f = QgsFeature()
eq_f.setGeometry(QgsGeometry.fromPointXY(QgsPointXY(eq_latlon[1],eq_latlon[0])))
eq_f.setAttributes(eq_info + eq_latlon)
#add earthquake in layer
eq_pr.addFeatures([eq_f])
#commit changes
eq_layer.commitChanges()
#update displacement layer
eq_layer.updateExtents()
return eq_layer
def STALayer(sta_data):
'''
Create station layer for QGIS
Parameters
----------
sta_data : pd.dataframe
Dataframe for rupture points with fields:
'ssn','region','Vs30','Z1.0','StaLat','StaLon'
eqid','region','mag','SOF','eqLat','eqLon'
Returns
-------
sta_layer : TYPE
QGIS layer with station points.
'''
#create qgis layer for station locations
sta_layer = QgsVectorLayer("Point", "sta_pts", "memory")
sta_pr = sta_layer.dataProvider()
sta_pr.addAttributes([QgsField("ssn", QVariant.Int),
QgsField("region", QVariant.Int),
QgsField("Vs30", QVariant.Double),
QgsField("Z1.0", QVariant.Double),
QgsField("staLat", QVariant.Double),
QgsField("staLon", QVariant.Double)])
#iterate over station, add on layer
sta_layer.startEditing()
for sta in sta_data.iterrows():
#earthquake info
sta_info = sta[1][['ssn','region','Vs30','Z1.0']].tolist()
sta_latlon = sta[1][['staLat','staLon']].tolist()
#define feature, earthquake
sta_f = QgsFeature()
sta_f.setGeometry(QgsGeometry.fromPointXY(QgsPointXY(sta_latlon[1],sta_latlon[0])))
sta_f.setAttributes(sta_info + sta_latlon)
#add earthquake in layer
sta_pr.addFeatures([sta_f])
#commit changes
sta_layer.commitChanges()
#update displacement layer
sta_layer.updateExtents()
return sta_layer
| 3,513 | 31.841121 | 92 | py |
ngmm_tools | ngmm_tools-master/Analyses/Python_lib/QGIS/__init__.py | 0 | 0 | 0 | py |
|
ngmm_tools | ngmm_tools-master/Analyses/Python_lib/regression/pylib_stats.py | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Tue Mar 15 13:56:13 2022
@author: glavrent
Other python statistics functions
"""
#imprort libraries
import numpy as np
def CalcRMS(samp_q, samp_p):
'''
Compute root mean square error between observation samples (samp_p) and
model samples (samp_p)
Parameters
----------
samp_q : np.array()
Model Samples.
samp_p : np.array()
Data Samples.
Returns
-------
real
root mean square error
'''
#errors
e = samp_q - samp_p
return np.sqrt(np.mean(e**2))
def CalcLKDivergece(samp_q, samp_p):
'''
Compute Kullback–Leibler divergence of observation samples (samp_p) based
on model samples (samp_p)
Parameters
----------
samp_q : np.array()
Model Samples.
samp_p : np.array()
Data Samples.
Returns
-------
real
Kullback–Leibler divergence.
'''
#create histogram bins
_, hist_bins = np.histogram(np.concatenate([samp_p,samp_q]))
#count of p and q distribution
p, _ = np.histogram(samp_p, bins=hist_bins)
q, _ = np.histogram(samp_q, bins=hist_bins)
#remove bins empty in any dist, otherwise kl= +/- inf
i_empty_bins = np.logical_or(p==0, q==0)
p = p[~i_empty_bins]
q = q[~i_empty_bins]
#normalize to compute probabilites
p = p/p.sum()
q = q/q.sum()
return sum(p[i] * np.log2(p[i]/q[i]) for i in range(len(p)))
| 1,508 | 19.671233 | 78 | py |
ngmm_tools | ngmm_tools-master/Analyses/Python_lib/regression/__init__.py | 0 | 0 | 0 | py |
|
ngmm_tools | ngmm_tools-master/Analyses/Python_lib/regression/pystan/regression_pystan_model3_uncorr_cells_unbounded_hyp.py | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Tue Jul 13 18:22:15 2021
@author: glavrent
"""
#load variables
import os
import pathlib
import glob
import re #regular expression package
import pickle
from joblib import cpu_count
#arithmetic libraries
import numpy as np
#statistics libraries
import pandas as pd
#plot libraries
import matplotlib as mpl
import matplotlib.pyplot as plt
from matplotlib.ticker import AutoLocator as plt_autotick
import arviz as az
mpl.use('agg')
def RunStan(df_flatfile, df_cellinfo, df_celldist, stan_model_fname,
out_fname, out_dir, res_name='res', c_2_erg=0, c_3_erg=0, c_a_erg=0,
runstan_flag=True,
n_iter=600, n_chains=4,
adapt_delta=0.8, max_treedepth=10,
pystan_ver=2, pystan_parallel=False):
'''
Run full Bayessian regression in Stan. Non-ergodic model includes: a spatially
varying earthquake constant, a spatially varying site constant, a spatially
independent site constant, and uncorrelated anelastic attenuation.
Parameters
----------
df_flatfile : pd.DataFrame
Input data frame containing total residuals, eq and site coordinates.
df_cellinfo : pd.DataFrame
Dataframe with coordinates of anelastic attenuation cells.
df_celldist : pd.DataFrame
Datafame with cell path distances of all records in df_flatfile.
stan_model_fname : string
File name for stan model.
out_fname : string
File name for output files.
out_dir : string
Output directory.
res_name : string, optional
Column name for total residuals. The default is 'res'.
c_2_erg : double, optional
Value of ergodic geometrical spreading coefficient. The default is 0.
c_3_erg : double, optional
Value of ergodic Vs30 coefficient. The default is 0.
c_a_erg : double, optional
Value of ergodic anelatic attenuation coefficient. Used as mean of cell
specific anelastic attenuation prior distribution. The default is 0.
n_iter : integer, optional
Number of stan samples. The default is 600.
n_chains : integer, optional
Number of MCMC chains. The default is 4.
runstan_flag : bool, optional
Flag for running stan. If true run regression, if false read past regression
output and summarize non-ergodic parameters. The default is True.
adapt_delta : double, optional
Target average proposal acceptance probability for adaptation. The default is 0.8.
max_treedepth : integer, optional
Maximum number of evaluations for each iteration (2^max_treedepth). The default is 10.
pystan_ver : integer, optional
Version of pystan to run. The default is 2.
pystan_parallel : bool, optional
Flag for using multithreaded option in STAN. The default is False.
Returns
-------
None.
'''
#number of cores
n_cpu = max(cpu_count() -1,1)
## Read Data
#============================
#read stan model
with open(stan_model_fname, "r") as f:
stan_model_code = f.read()
## Preprocess Input Data
#============================
#set rsn column as dataframe index, skip if rsn already the index
if not df_flatfile.index.name == 'rsn':
df_flatfile.set_index('rsn', drop=True, inplace=True)
if not df_celldist.index.name == 'rsn':
df_celldist.set_index('rsn', drop=True, inplace=True)
#set cellid column as dataframe index, skip if cellid already the index
if not df_cellinfo.index.name == 'cellid':
df_cellinfo.set_index('cellid', drop=True, inplace=True)
#number of data
n_data = len(df_flatfile)
#earthquake data
data_eq_all = df_flatfile[['eqid','mag','eqX', 'eqY']].values
_, eq_idx, eq_inv = np.unique(df_flatfile[['eqid']], axis=0, return_inverse=True, return_index=True)
data_eq = data_eq_all[eq_idx,:]
X_eq = data_eq[:,[2,3]] #earthquake coordinates
#create earthquake ids for all records (1 to n_eq)
eq_id = eq_inv + 1
n_eq = len(data_eq)
#station data
data_sta_all = df_flatfile[['ssn','Vs30','x_3','staX','staY']].values
_, sta_idx, sta_inv = np.unique( df_flatfile[['ssn']].values, axis = 0, return_inverse=True, return_index=True)
data_sta = data_sta_all[sta_idx,:]
X_sta = data_sta[:,[3,4]] #station coordinates
#create station indices for all records (1 to n_sta)
sta_id = sta_inv + 1
n_sta = len(data_sta)
#ground-motion observations
y_data = df_flatfile[res_name].to_numpy().copy()
#geometrical spreading covariates
x_2 = df_flatfile['x_2'].values
#vs30 covariates
x_3 = df_flatfile['x_3'].values[sta_idx]
#cell data
#reorder and only keep records included in the flatfile
df_celldist = df_celldist.reindex(df_flatfile.index)
#cell info
cell_ids_all = df_cellinfo.index
cell_names_all = df_cellinfo.cellname
#cell distance matrix
celldist_all = df_celldist[cell_names_all] #cell-distance matrix with all cells
#find cell with more than one paths
i_cells_valid = np.where(celldist_all.sum(axis=0) > 0)[0] #valid cells with more than one path
cell_ids_valid = cell_ids_all[i_cells_valid]
cell_names_valid = cell_names_all[i_cells_valid]
celldist_valid = celldist_all.loc[:,cell_names_valid] #cell-distance with only non-zero cells
#number of cells
n_cell = celldist_all.shape[1]
n_cell_valid = celldist_valid.shape[1]
#cell coordinates
X_cells_valid = df_cellinfo.loc[i_cells_valid,['mptX','mptY']].values
#print Rrup missfits
print('max R_rup misfit', (df_flatfile.Rrup.values - celldist_valid.sum(axis=1)).abs().max())
stan_data = {'N': n_data,
'NEQ': n_eq,
'NSTAT': n_sta,
'NCELL': n_cell_valid,
'eq': eq_id, #earthquake id
'stat': sta_id, #station id
'rec_mu': np.zeros(y_data.shape),
'Y': y_data,
'x_2': x_2,
'x_3': x_3,
'c_2_erg': c_2_erg,
'c_3_erg': c_3_erg,
'c_a_erg': c_a_erg,
'X_e': X_eq, #earthquake coordinates
'X_s': X_sta, #station coordinates
'X_c': X_cells_valid,
'RC': celldist_valid.to_numpy(),
}
stan_data_fname = out_fname + '_stan_data' + '.Rdata'
## Run Stan, fit model
#============================
#number of cores
n_cpu = max(cpu_count() -1,1)
#filename for STAN regression raw output file saved as pkl
stan_fit_fname = out_dir + out_fname + '_stan_fit' + '.pkl'
#run stan
if runstan_flag:
#control paramters
control_stan = {'adapt_delta':adapt_delta, 'max_treedepth':max_treedepth}
if pystan_ver == 2:
import pystan
if (not pystan_parallel) or n_cpu<=n_chains:
#compile
stan_model = pystan.StanModel(model_code=stan_model_code)
#full Bayesian statistics
stan_fit = stan_model.sampling(data=stan_data, iter=n_iter, chains = n_chains, refresh=10, control = control_stan)
else:
#number of cores per chain
n_cpu_chain = int(np.floor(n_cpu/n_chains))
#multi-processing arguments
os.environ['STAN_NUM_THREADS'] = str(n_cpu_chain)
extra_compile_args = ['-pthread', '-DSTAN_THREADS']
#compile
stan_model = pystan.StanModel(model_code=stan_model_code, extra_compile_args=extra_compile_args)
#full Bayesian statistics
stan_fit = stan_model.sampling(data=stan_data, iter=n_iter, chains = n_chains, refresh=1, control = control_stan)
elif pystan_ver == 3:
import nest_asyncio
import stan
nest_asyncio.apply()
#compile
stan_model = stan.build(stan_model_code, data=stan_data, random_seed=1)
#full Bayesian statistics
stan_fit = stan_model.sample(num_chains=n_chains, num_samples=n_iter, max_depth=max_treedepth, delta=adapt_delta)
#save stan model and fit
pathlib.Path(out_dir).mkdir(parents=True, exist_ok=True)
with open(stan_fit_fname, "wb") as f:
pickle.dump({'model' : stan_model, 'fit' : stan_fit}, f, protocol=-1)
else:
#load model and fit for postprocessing if has already been executed
with open(stan_fit_fname, "rb") as f:
data_dict = pickle.load(f)
stan_fit = data_dict['fit']
stan_model = data_dict['model']
del data_dict
## Postprocessing Data
#============================
## Extract posterior samples
# ---------------------------
#hyper-parameters
col_names_hyp = ['dc_0','mu_2p','mu_3s',
'ell_1e', 'ell_1as', 'omega_1e', 'omega_1as', 'omega_1bs',
'ell_2p', 'ell_3s', 'omega_2p', 'omega_3s',
'mu_cap', 'omega_cap',
'phi_0','tau_0']
#non-ergodic terms
col_names_dc_1e = ['dc_1e.%i'%(k) for k in range(n_eq)]
col_names_dc_1as = ['dc_1as.%i'%(k) for k in range(n_sta)]
col_names_dc_1bs = ['dc_1bs.%i'%(k) for k in range(n_sta)]
col_names_c_2p = ['c_2p.%i'%(k) for k in range(n_eq)]
col_names_c_3s = ['c_3s.%i'%(k) for k in range(n_sta)]
col_names_dB = ['dB.%i'%(k) for k in range(n_eq)]
col_names_cap = ['c_cap.%i'%(c_id) for c_id in cell_ids_valid]
col_names_all = (col_names_hyp + col_names_dc_1e + col_names_dc_1as + col_names_dc_1bs +
col_names_c_2p + col_names_c_3s + col_names_cap + col_names_dB)
#summarize raw posterior distributions
stan_posterior = np.stack([stan_fit[c_n].flatten() for c_n in col_names_hyp], axis=1)
#adjustment terms
if pystan_ver == 2:
stan_posterior = np.concatenate((stan_posterior, stan_fit['dc_1e']), axis=1)
stan_posterior = np.concatenate((stan_posterior, stan_fit['dc_1as']), axis=1)
stan_posterior = np.concatenate((stan_posterior, stan_fit['dc_1bs']), axis=1)
stan_posterior = np.concatenate((stan_posterior, stan_fit['c_2p']), axis=1)
stan_posterior = np.concatenate((stan_posterior, stan_fit['c_3s']), axis=1)
stan_posterior = np.concatenate((stan_posterior, stan_fit['c_cap']), axis=1)
stan_posterior = np.concatenate((stan_posterior, stan_fit['dB']), axis=1)
else:
stan_posterior = np.concatenate((stan_posterior, stan_fit['dc_1e'].T), axis=1)
stan_posterior = np.concatenate((stan_posterior, stan_fit['dc_1as'].T), axis=1)
stan_posterior = np.concatenate((stan_posterior, stan_fit['dc_1bs'].T), axis=1)
stan_posterior = np.concatenate((stan_posterior, stan_fit['c_2p'].T), axis=1)
stan_posterior = np.concatenate((stan_posterior, stan_fit['c_3s'].T), axis=1)
stan_posterior = np.concatenate((stan_posterior, stan_fit['c_cap'].T), axis=1)
stan_posterior = np.concatenate((stan_posterior, stan_fit['dB'].T), axis=1)
#save raw-posterior distribution
df_stan_posterior_raw = pd.DataFrame(stan_posterior, columns = col_names_all)
df_stan_posterior_raw.to_csv(out_dir + out_fname + '_stan_posterior_raw' + '.csv', index=False)
## Summarize hyper-parameters
# ---------------------------
#summarize posterior distributions of hyper-parameters
perc_array = np.array([0.05,0.25,0.5,0.75,0.95])
df_stan_hyp = df_stan_posterior_raw[col_names_hyp].quantile(perc_array)
df_stan_hyp = df_stan_hyp.append(df_stan_posterior_raw[col_names_hyp].mean(axis = 0), ignore_index=True)
df_stan_hyp.index = ['prc_%.2f'%(prc) for prc in perc_array]+['mean']
df_stan_hyp.to_csv(out_dir + out_fname + '_stan_hyperparameters' + '.csv', index=True)
#detailed posterior percentiles of posterior distributions
perc_array = np.arange(0.01,0.99,0.01)
df_stan_posterior = df_stan_posterior_raw[col_names_hyp].quantile(perc_array)
df_stan_posterior.index.name = 'prc'
df_stan_posterior .to_csv(out_dir + out_fname + '_stan_hyperposterior' + '.csv', index=True)
del col_names_dc_1e, col_names_dc_1as, col_names_dc_1bs, col_names_c_2p, col_names_c_3s, col_names_dB
del stan_posterior, col_names_all
## Sample spatially varying coefficients and predictions at record locations
# ---------------------------
# earthquake and station location in database
X_eq_all = df_flatfile[['eqX', 'eqY']].values
X_sta_all = df_flatfile[['staX','staY']].values
# GMM anelastic attenuation
#--- --- --- --- --- --- --- ---
cells_ca_mu = np.array([df_stan_posterior_raw.loc[:,'c_cap.%i'%(k)].mean() for k in cell_ids_valid])
cells_ca_med = np.array([df_stan_posterior_raw.loc[:,'c_cap.%i'%(k)].median() for k in cell_ids_valid])
cells_ca_sig = np.array([df_stan_posterior_raw.loc[:,'c_cap.%i'%(k)].std() for k in cell_ids_valid])
#effect of anelastic attenuation in GM
cells_LcA_mu = celldist_valid.values @ cells_ca_mu
cells_LcA_med = celldist_valid.values @ cells_ca_med
cells_LcA_sig = np.sqrt(np.square(celldist_valid.values) @ cells_ca_sig**2)
#summary attenuation cells
catten_summary = np.vstack((np.tile(c_a_erg, n_cell_valid),
cells_ca_mu,
cells_ca_med,
cells_ca_sig)).T
columns_names = ['c_a_erg','c_cap_mean','c_cap_med','c_cap_sig']
df_catten_summary = pd.DataFrame(catten_summary, columns = columns_names, index=df_cellinfo.index[i_cells_valid])
#create dataframe with summary attenuation cells
df_catten_summary = pd.merge(df_cellinfo[['cellname','mptLat','mptLon','mptX','mptY','mptZ','UTMzone']],
df_catten_summary, how='right', left_index=True, right_index=True)
df_catten_summary.to_csv(out_dir + out_fname + '_stan_catten' + '.csv', index=True)
# GMM coefficients
#--- --- --- --- --- --- --- ---
#constant shift coefficient
coeff_0_mu = df_stan_posterior_raw.loc[:,'dc_0'].mean() * np.ones(n_data)
coeff_0_med = df_stan_posterior_raw.loc[:,'dc_0'].median() * np.ones(n_data)
coeff_0_sig = df_stan_posterior_raw.loc[:,'dc_0'].std() * np.ones(n_data)
#spatially varying earthquake constant coefficient
coeff_1e_mu = np.array([df_stan_posterior_raw.loc[:,f'dc_1e.{k}'].mean() for k in range(n_eq)])
coeff_1e_mu = coeff_1e_mu[eq_inv]
coeff_1e_med = np.array([df_stan_posterior_raw.loc[:,f'dc_1e.{k}'].median() for k in range(n_eq)])
coeff_1e_med = coeff_1e_med[eq_inv]
coeff_1e_sig = np.array([df_stan_posterior_raw.loc[:,f'dc_1e.{k}'].std() for k in range(n_eq)])
coeff_1e_sig = coeff_1e_sig[eq_inv]
#site term constant covariance
coeff_1as_mu = np.array([df_stan_posterior_raw.loc[:,f'dc_1as.{k}'].mean() for k in range(n_sta)])
coeff_1as_mu = coeff_1as_mu[sta_inv]
coeff_1as_med = np.array([df_stan_posterior_raw.loc[:,f'dc_1as.{k}'].median() for k in range(n_sta)])
coeff_1as_med = coeff_1as_med[sta_inv]
coeff_1as_sig = np.array([df_stan_posterior_raw.loc[:,f'dc_1as.{k}'].std() for k in range(n_sta)])
coeff_1as_sig = coeff_1as_sig[sta_inv]
#spatially varying station constant covariance
coeff_1bs_mu = np.array([df_stan_posterior_raw.loc[:,f'dc_1bs.{k}'].mean() for k in range(n_sta)])
coeff_1bs_mu = coeff_1bs_mu[sta_inv]
coeff_1bs_med = np.array([df_stan_posterior_raw.loc[:,f'dc_1bs.{k}'].median() for k in range(n_sta)])
coeff_1bs_med = coeff_1bs_med[sta_inv]
coeff_1bs_sig = np.array([df_stan_posterior_raw.loc[:,f'dc_1bs.{k}'].std() for k in range(n_sta)])
coeff_1bs_sig = coeff_1bs_sig[sta_inv]
#spatially varying geometrical spreading coefficient
coeff_2p_mu = np.array([df_stan_posterior_raw.loc[:,f'c_2p.{k}'].mean() for k in range(n_eq)])
coeff_2p_mu = coeff_2p_mu[eq_inv]
coeff_2p_med = np.array([df_stan_posterior_raw.loc[:,f'c_2p.{k}'].median() for k in range(n_eq)])
coeff_2p_med = coeff_2p_med[eq_inv]
coeff_2p_sig = np.array([df_stan_posterior_raw.loc[:,f'c_2p.{k}'].std() for k in range(n_eq)])
coeff_2p_sig = coeff_2p_sig[eq_inv]
#spatially varying Vs30 coefficient
coeff_3s_mu = np.array([df_stan_posterior_raw.loc[:,f'c_3s.{k}'].mean() for k in range(n_sta)])
coeff_3s_mu = coeff_3s_mu[sta_inv]
coeff_3s_med = np.array([df_stan_posterior_raw.loc[:,f'c_3s.{k}'].median() for k in range(n_sta)])
coeff_3s_med = coeff_3s_med[sta_inv]
coeff_3s_sig = np.array([df_stan_posterior_raw.loc[:,f'c_3s.{k}'].std() for k in range(n_sta)])
coeff_3s_sig = coeff_3s_sig[sta_inv]
# aleatory variability
phi_0_array = np.array([df_stan_posterior_raw.phi_0.mean()]*X_sta_all.shape[0])
tau_0_array = np.array([df_stan_posterior_raw.tau_0.mean()]*X_sta_all.shape[0])
#dataframe with flatfile info
df_flatinfo = df_flatfile[['eqid','ssn','eqLat','eqLon','staLat','staLon','eqX','eqY','staX','staY','UTMzone']]
#summary coefficients
coeffs_summary = np.vstack((coeff_0_mu,
coeff_1e_mu,
coeff_1as_mu,
coeff_1bs_mu,
coeff_2p_mu,
coeff_3s_mu,
cells_LcA_mu,
coeff_0_med,
coeff_1e_med,
coeff_1as_med,
coeff_1bs_med,
coeff_2p_med,
coeff_3s_med,
cells_LcA_med,
coeff_0_sig,
coeff_1e_sig,
coeff_1as_sig,
coeff_1bs_sig,
coeff_2p_sig,
coeff_3s_sig,
cells_LcA_sig)).T
columns_names = ['dc_0_mean','dc_1e_mean','dc_1as_mean','dc_1bs_mean','c_2p_mean','c_3s_mean','Lc_ca_mean',
'dc_0_med', 'dc_1e_med', 'dc_1as_med', 'dc_1bs_med', 'c_2p_med', 'c_3s_med', 'Lc_ca_med',
'dc_0_sig', 'dc_1e_sig', 'dc_1as_sig', 'dc_1bs_sig', 'c_2p_sig', 'c_3s_sig', 'Lc_ca_sig']
df_coeffs_summary = pd.DataFrame(coeffs_summary, columns = columns_names, index=df_flatfile.index)
#create dataframe with summary coefficients
df_coeffs_summary = pd.merge(df_flatinfo, df_coeffs_summary, how='right', left_index=True, right_index=True)
df_coeffs_summary[['eqid','ssn']] = df_coeffs_summary[['eqid','ssn']].astype(int)
df_coeffs_summary.to_csv(out_dir + out_fname + '_stan_coefficients' + '.csv', index=True)
# GMM prediction
#--- --- --- --- --- --- --- ---
#mean prediction
y_mu = (coeff_0_mu + coeff_1e_mu + coeff_1as_mu + coeff_1bs_mu + coeff_2p_mu*x_2 + coeff_3s_mu*x_3[sta_inv] + cells_LcA_mu)
#compute residuals
res_tot = y_data - y_mu
#residuals computed directly from stan regression
res_between = [df_stan_posterior_raw.loc[:,f'dB.{k}'].mean() for k in range(n_eq)]
res_between = np.array([res_between[k] for k in (eq_inv).astype(int)])
res_within = res_tot - res_between
#summary predictions and residuals
predict_summary = np.vstack((y_mu, res_tot, res_between, res_within)).T
columns_names = ['nerg_mu','res_tot','res_between','res_within']
df_predict_summary = pd.DataFrame(predict_summary, columns = columns_names, index=df_flatfile.index)
#create dataframe with predictions and residuals
df_predict_summary = pd.merge(df_flatinfo, df_predict_summary, how='right', left_index=True, right_index=True)
df_predict_summary[['eqid','ssn']] = df_predict_summary[['eqid','ssn']].astype(int)
df_predict_summary.to_csv(out_dir + out_fname + '_stan_residuals' + '.csv', index=True)
## Summary regression
# ---------------------------
#save summary statistics
stan_summary_fname = out_dir + out_fname + '_stan_summary' + '.txt'
with open(stan_summary_fname, 'w') as f:
print(stan_fit, file=f)
#create and save trace plots
fig_dir = out_dir + 'summary_figs/'
#create figures directory if doesn't exit
pathlib.Path(fig_dir).mkdir(parents=True, exist_ok=True)
#create stan trace plots
for c_name in col_names_hyp:
fig = stan_fit.traceplot(c_name)
fig.savefig(fig_dir + out_fname + '_stan_traceplot_' + c_name + '.png')
#create trace plot with arviz
ax = az.plot_trace(stan_fit, var_names=c_name, figsize=(10,5) ).ravel()
ax[0].yaxis.set_major_locator(plt_autotick())
ax[0].set_xlabel('sample value')
ax[0].set_ylabel('frequency')
ax[0].set_title('')
ax[0].grid(axis='both')
ax[1].set_xlabel('iteration')
ax[1].set_ylabel('sample value')
ax[1].grid(axis='both')
ax[1].set_title('')
fig = ax[0].figure
fig.suptitle(c_name)
fig.savefig(fig_dir + out_fname + '_stan_traceplot_' + c_name + '_arviz' + '.png')
return None
| 21,718 | 46.944812 | 130 | py |
ngmm_tools | ngmm_tools-master/Analyses/Python_lib/regression/pystan/regression_pystan_model2_uncorr_cells_sparse_unbounded_hyp.py | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Tue Jul 13 18:22:15 2021
@author: glavrent
"""
#load variables
import os
import pathlib
import glob
import re #regular expression package
import pickle
from joblib import cpu_count
#arithmetic libraries
import numpy as np
from scipy import sparse
#statistics libraries
import pandas as pd
#plot libraries
import matplotlib as mpl
import matplotlib.pyplot as plt
from matplotlib.ticker import AutoLocator as plt_autotick
import arviz as az
mpl.use('agg')
def RunStan(df_flatfile, df_cellinfo, df_celldist, stan_model_fname,
out_fname, out_dir, res_name='res', c_a_erg=0,
runstan_flag=True,
n_iter=600, n_chains=4,
adapt_delta=0.8, max_treedepth=10,
pystan_ver=2, pystan_parallel=False):
'''
Run full Bayessian regression in Stan. Non-ergodic model includes: a spatially
varying earthquake constant, a spatially varying site constant, a spatially
independent site constant, and uncorrelated anelastic attenuation.
Parameters
----------
df_flatfile : pd.DataFrame
Input data frame containing total residuals, eq and site coordinates.
df_cellinfo : pd.DataFrame
Dataframe with coordinates of anelastic attenuation cells.
df_celldist : pd.DataFrame
Datafame with cell path distances of all records in df_flatfile.
stan_model_fname : string
File name for stan model.
out_fname : string
File name for output files.
out_dir : string
Output directory.
res_name : string, optional
Column name for total residuals. The default is 'res'.
c_a_erg : double, optional
Value of ergodic anelatic attenuation coefficient. Used as mean of cell
specific anelastic attenuation prior distribution. The default is 0.
n_iter : integer, optional
Number of stan samples. The default is 600.
n_chains : integer, optional
Number of MCMC chains. The default is 4.
runstan_flag : bool, optional
Flag for running stan. If true run regression, if false read past regression
output and summarize non-ergodic parameters. The default is True.
adapt_delta : double, optional
Target average proposal acceptance probability for adaptation. The default is 0.8.
max_treedepth : integer, optional
Maximum number of evaluations for each iteration (2^max_treedepth). The default is 10.
pystan_ver : integer, optional
Version of pystan to run. The default is 2.
pystan_parallel : bool, optional
Flag for using multithreaded option in STAN. The default is False.
Returns
-------
None.
'''
## Read Data
#============================
#read stan model
with open(stan_model_fname, "r") as f:
stan_model_code = f.read()
## Preprocess Input Data
#============================
#set rsn column as dataframe index, skip if rsn already the index
if not df_flatfile.index.name == 'rsn':
df_flatfile.set_index('rsn', drop=True, inplace=True)
if not df_celldist.index.name == 'rsn':
df_celldist.set_index('rsn', drop=True, inplace=True)
#set cellid column as dataframe index, skip if cellid already the index
if not df_cellinfo.index.name == 'cellid':
df_cellinfo.set_index('cellid', drop=True, inplace=True)
#number of data
n_data = len(df_flatfile)
#earthquake data
data_eq_all = df_flatfile[['eqid','mag','eqX', 'eqY']].values
_, eq_idx, eq_inv = np.unique(df_flatfile[['eqid']], axis=0, return_inverse=True, return_index=True)
data_eq = data_eq_all[eq_idx,:]
X_eq = data_eq[:,[2,3]] #earthquake coordinates
#create earthquake ids for all records (1 to n_eq)
eq_id = eq_inv + 1
n_eq = len(data_eq)
#station data
data_sta_all = df_flatfile[['ssn','Vs30','staX','staY']].values
_, sta_idx, sta_inv = np.unique( df_flatfile[['ssn']].values, axis = 0, return_inverse=True, return_index=True)
data_sta = data_sta_all[sta_idx,:]
X_sta = data_sta[:,[2,3]] #station coordinates
#create station indices for all records (1 to n_sta)
sta_id = sta_inv + 1
n_sta = len(data_sta)
#ground-motion observations
y_data = df_flatfile[res_name].to_numpy().copy()
#cell data
#reorder and only keep records included in the flatfile
df_celldist = df_celldist.reindex(df_flatfile.index)
#cell info
cell_ids_all = df_cellinfo.index
cell_names_all = df_cellinfo.cellname
#cell distance matrix
celldist_all = df_celldist[cell_names_all] #cell-distance matrix with all cells
#find cell with more than one paths
i_cells_valid = np.where(celldist_all.sum(axis=0) > 0)[0] #valid cells with more than one path
cell_ids_valid = cell_ids_all[i_cells_valid]
cell_names_valid = cell_names_all[i_cells_valid]
celldist_valid = celldist_all.loc[:,cell_names_valid].to_numpy() #cell-distance with only non-zero cells
celldist_valid_sp = sparse.csr_matrix(celldist_valid)
#number of cells
n_cell = celldist_all.shape[1]
n_cell_valid = celldist_valid.shape[1]
#cell coordinates
X_cells_valid = df_cellinfo.loc[i_cells_valid,['mptX','mptY']].values
#print Rrup missfits
print('max R_rup misfit', np.abs(df_flatfile.Rrup.values - celldist_valid.sum(axis=1)).max())
stan_data = {'N': n_data,
'NEQ': n_eq,
'NSTAT': n_sta,
'NCELL': n_cell_valid,
'NCELL_SP': len(celldist_valid_sp.data),
'eq': eq_id, #earthquake id
'stat': sta_id, #station id
'X_e': X_eq, #earthquake coordinates
'X_s': X_sta, #station coordinates
'X_c': X_cells_valid,
'rec_mu': np.zeros(y_data.shape),
'RC_val': celldist_valid_sp.data,
'RC_w': celldist_valid_sp.indices+1,
'RC_u': celldist_valid_sp.indptr+1,
'c_a_erg': c_a_erg,
'Y': y_data,
}
stan_data_fname = out_fname + '_stan_data' + '.Rdata'
## Run Stan, fit model
#============================
#number of cores
n_cpu = max(cpu_count() -1,1)
#filename for STAN regression raw output file saved as pkl
stan_fit_fname = out_dir + out_fname + '_stan_fit' + '.pkl'
#run stan
if runstan_flag:
#control paramters
control_stan = {'adapt_delta':adapt_delta, 'max_treedepth':max_treedepth}
if pystan_ver == 2:
import pystan
if (not pystan_parallel) or n_cpu<=n_chains:
#compile
stan_model = pystan.StanModel(model_code=stan_model_code)
#full Bayesian statistics
stan_fit = stan_model.sampling(data=stan_data, iter=n_iter, chains = n_chains, refresh=10, control = control_stan)
else:
#number of cores per chain
n_cpu_chain = int(np.floor(n_cpu/n_chains))
#multi-processing arguments
os.environ['STAN_NUM_THREADS'] = str(n_cpu_chain)
extra_compile_args = ['-pthread', '-DSTAN_THREADS']
#compile
stan_model = pystan.StanModel(model_code=stan_model_code, extra_compile_args=extra_compile_args)
#full Bayesian statistics
stan_fit = stan_model.sampling(data=stan_data, iter=n_iter, chains = n_chains, refresh=1, control = control_stan)
elif pystan_ver == 3:
import nest_asyncio
import stan
nest_asyncio.apply()
#compile
stan_model = stan.build(stan_model_code, data=stan_data, random_seed=1)
#full Bayesian statistics
stan_fit = stan_model.sample(num_chains=n_chains, num_samples=n_iter, max_depth=max_treedepth, delta=adapt_delta)
#save stan model and fit
pathlib.Path(out_dir).mkdir(parents=True, exist_ok=True)
with open(stan_fit_fname, "wb") as f:
pickle.dump({'model' : stan_model, 'fit' : stan_fit}, f, protocol=-1)
else:
#load model and fit for postprocessing if has already been executed
with open(stan_fit_fname, "rb") as f:
data_dict = pickle.load(f)
stan_fit = data_dict['fit']
stan_model = data_dict['model']
del data_dict
## Postprocessing Data
#============================
## Extract posterior samples
# ---------------------------
#hyper-parameters
col_names_hyp = ['dc_0','ell_1e', 'ell_1as', 'omega_1e', 'omega_1as', 'omega_1bs',
'mu_cap', 'omega_cap',
'phi_0','tau_0']
#non-ergodic terms
col_names_dc_1e = ['dc_1e.%i'%(k) for k in range(n_eq)]
col_names_dc_1as = ['dc_1as.%i'%(k) for k in range(n_sta)]
col_names_dc_1bs = ['dc_1bs.%i'%(k) for k in range(n_sta)]
col_names_dB = ['dB.%i'%(k) for k in range(n_eq)]
col_names_cap = ['c_cap.%i'%(c_id) for c_id in cell_ids_valid]
col_names_all = col_names_hyp + col_names_dc_1e + col_names_dc_1as + col_names_dc_1bs + col_names_cap + col_names_dB
#summarize raw posterior distributions
stan_posterior = np.stack([stan_fit[c_n].flatten() for c_n in col_names_hyp], axis=1)
#adjustment terms
if pystan_ver == 2:
stan_posterior = np.concatenate((stan_posterior, stan_fit['dc_1e']), axis=1)
stan_posterior = np.concatenate((stan_posterior, stan_fit['dc_1as']), axis=1)
stan_posterior = np.concatenate((stan_posterior, stan_fit['dc_1bs']), axis=1)
stan_posterior = np.concatenate((stan_posterior, stan_fit['c_cap']), axis=1)
stan_posterior = np.concatenate((stan_posterior, stan_fit['dB']), axis=1)
else:
stan_posterior = np.concatenate((stan_posterior, stan_fit['dc_1e'].T), axis=1)
stan_posterior = np.concatenate((stan_posterior, stan_fit['dc_1as'].T), axis=1)
stan_posterior = np.concatenate((stan_posterior, stan_fit['dc_1bs'].T), axis=1)
stan_posterior = np.concatenate((stan_posterior, stan_fit['c_cap'].T), axis=1)
stan_posterior = np.concatenate((stan_posterior, stan_fit['dB'].T), axis=1)
#save raw-posterior distribution
df_stan_posterior_raw = pd.DataFrame(stan_posterior, columns = col_names_all)
df_stan_posterior_raw.to_csv(out_dir + out_fname + '_stan_posterior_raw' + '.csv', index=False)
## Summarize hyper-parameters
# ---------------------------
#summarize posterior distributions of hyper-parameters
perc_array = np.array([0.05,0.25,0.5,0.75,0.95])
df_stan_hyp = df_stan_posterior_raw[col_names_hyp].quantile(perc_array)
df_stan_hyp = df_stan_hyp.append(df_stan_posterior_raw[col_names_hyp].mean(axis = 0), ignore_index=True)
df_stan_hyp.index = ['prc_%.2f'%(prc) for prc in perc_array]+['mean']
df_stan_hyp.to_csv(out_dir + out_fname + '_stan_hyperparameters' + '.csv', index=True)
#detailed posterior percentiles of posterior distributions
perc_array = np.arange(0.01,0.99,0.01)
df_stan_posterior = df_stan_posterior_raw[col_names_hyp].quantile(perc_array)
df_stan_posterior.index.name = 'prc'
df_stan_posterior .to_csv(out_dir + out_fname + '_stan_hyperposterior' + '.csv', index=True)
del col_names_dc_1e, col_names_dc_1as, col_names_dc_1bs, col_names_dB
del stan_posterior, col_names_all
## Sample spatially varying coefficients and predictions at record locations
# ---------------------------
# earthquake and station location in database
X_eq_all = df_flatfile[['eqX', 'eqY']].values
X_sta_all = df_flatfile[['staX','staY']].values
# GMM anelastic attenuation
#--- --- --- --- --- --- --- ---
cells_ca_mu = np.array([df_stan_posterior_raw.loc[:,'c_cap.%i'%(k)].mean() for k in cell_ids_valid])
cells_ca_med = np.array([df_stan_posterior_raw.loc[:,'c_cap.%i'%(k)].median() for k in cell_ids_valid])
cells_ca_sig = np.array([df_stan_posterior_raw.loc[:,'c_cap.%i'%(k)].std() for k in cell_ids_valid])
#effect of anelastic attenuation in GM
cells_LcA_mu = celldist_valid_sp @ cells_ca_mu
cells_LcA_med = celldist_valid_sp @ cells_ca_med
cells_LcA_sig = np.sqrt(celldist_valid_sp.power(2) @ cells_ca_sig**2)
#summary attenuation cells
catten_summary = np.vstack((np.tile(c_a_erg, n_cell_valid),
cells_ca_mu,
cells_ca_med,
cells_ca_sig)).T
columns_names = ['c_a_erg','c_cap_mean','c_cap_med','c_cap_sig']
df_catten_summary = pd.DataFrame(catten_summary, columns = columns_names, index=df_cellinfo.index[i_cells_valid])
#create dataframe with summary attenuation cells
df_catten_summary = pd.merge(df_cellinfo[['cellname','mptLat','mptLon','mptX','mptY','mptZ','UTMzone']],
df_catten_summary, how='right', left_index=True, right_index=True)
df_catten_summary.to_csv(out_dir + out_fname + '_stan_catten' + '.csv', index=True)
# GMM coefficients
#--- --- --- --- --- --- --- ---
#constant shift coefficient
coeff_0_mu = df_stan_posterior_raw.loc[:,'dc_0'].mean() * np.ones(n_data)
coeff_0_med = df_stan_posterior_raw.loc[:,'dc_0'].median() * np.ones(n_data)
coeff_0_sig = df_stan_posterior_raw.loc[:,'dc_0'].std() * np.ones(n_data)
#spatially varying earthquake constant coefficient
coeff_1e_mu = np.array([df_stan_posterior_raw.loc[:,f'dc_1e.{k}'].mean() for k in range(n_eq)])
coeff_1e_mu = coeff_1e_mu[eq_inv]
coeff_1e_med = np.array([df_stan_posterior_raw.loc[:,f'dc_1e.{k}'].median() for k in range(n_eq)])
coeff_1e_med = coeff_1e_med[eq_inv]
coeff_1e_sig = np.array([df_stan_posterior_raw.loc[:,f'dc_1e.{k}'].std() for k in range(n_eq)])
coeff_1e_sig = coeff_1e_sig[eq_inv]
#site term constant covariance
coeff_1as_mu = np.array([df_stan_posterior_raw.loc[:,f'dc_1as.{k}'].mean() for k in range(n_sta)])
coeff_1as_mu = coeff_1as_mu[sta_inv]
coeff_1as_med = np.array([df_stan_posterior_raw.loc[:,f'dc_1as.{k}'].median() for k in range(n_sta)])
coeff_1as_med = coeff_1as_med[sta_inv]
coeff_1as_sig = np.array([df_stan_posterior_raw.loc[:,f'dc_1as.{k}'].std() for k in range(n_sta)])
coeff_1as_sig = coeff_1as_sig[sta_inv]
#spatially varying station constant covariance
coeff_1bs_mu = np.array([df_stan_posterior_raw.loc[:,f'dc_1bs.{k}'].mean() for k in range(n_sta)])
coeff_1bs_mu = coeff_1bs_mu[sta_inv]
coeff_1bs_med = np.array([df_stan_posterior_raw.loc[:,f'dc_1bs.{k}'].median() for k in range(n_sta)])
coeff_1bs_med = coeff_1bs_med[sta_inv]
coeff_1bs_sig = np.array([df_stan_posterior_raw.loc[:,f'dc_1bs.{k}'].std() for k in range(n_sta)])
coeff_1bs_sig = coeff_1bs_sig[sta_inv]
# aleatory variability
phi_0_array = np.array([df_stan_posterior_raw.phi_0.mean()]*X_sta_all.shape[0])
tau_0_array = np.array([df_stan_posterior_raw.tau_0.mean()]*X_sta_all.shape[0])
#initiaize flatfile for sumamry of non-erg coefficinets and residuals
df_flatinfo = df_flatfile[['eqid','ssn','eqLat','eqLon','staLat','staLon','eqX','eqY','staX','staY','UTMzone']]
#summary coefficients
coeffs_summary = np.vstack((coeff_0_mu,
coeff_1e_mu,
coeff_1as_mu,
coeff_1bs_mu,
cells_LcA_mu,
coeff_0_med,
coeff_1e_med,
coeff_1as_med,
coeff_1bs_med,
cells_LcA_med,
coeff_0_sig,
coeff_1e_sig,
coeff_1as_sig,
coeff_1bs_sig,
cells_LcA_sig)).T
columns_names = ['dc_0_mean','dc_1e_mean','dc_1as_mean','dc_1bs_mean','Lc_ca_mean',
'dc_0_med', 'dc_1e_med', 'dc_1as_med', 'dc_1bs_med', 'Lc_ca_med',
'dc_0_sig', 'dc_1e_sig', 'dc_1as_sig', 'dc_1bs_sig', 'Lc_ca_sig']
df_coeffs_summary = pd.DataFrame(coeffs_summary, columns = columns_names, index=df_flatfile.index)
#create dataframe with summary coefficients
df_coeffs_summary = pd.merge(df_flatinfo, df_coeffs_summary, how='right', left_index=True, right_index=True)
df_coeffs_summary[['eqid','ssn']] = df_coeffs_summary[['eqid','ssn']].astype(int)
df_coeffs_summary.to_csv(out_dir + out_fname + '_stan_coefficients' + '.csv', index=True)
# GMM prediction
#--- --- --- --- --- --- --- ---
#mean prediction
y_mu = (coeff_0_mu + coeff_1e_mu + coeff_1as_mu + coeff_1bs_mu + cells_LcA_mu)
#compute residuals
res_tot = y_data - y_mu
#residuals computed directly from stan regression
res_between = [df_stan_posterior_raw.loc[:,f'dB.{k}'].mean() for k in range(n_eq)]
res_between = np.array([res_between[k] for k in (eq_inv).astype(int)])
res_within = res_tot - res_between
#summary predictions and residuals
predict_summary = np.vstack((y_mu, res_tot, res_between, res_within)).T
columns_names = ['nerg_mu','res_tot','res_between','res_within']
df_predict_summary = pd.DataFrame(predict_summary, columns = columns_names, index=df_flatfile.index)
#create dataframe with predictions and residuals
df_predict_summary = pd.merge(df_flatinfo, df_predict_summary, how='right', left_index=True, right_index=True)
df_predict_summary[['eqid','ssn']] = df_predict_summary[['eqid','ssn']].astype(int)
df_predict_summary.to_csv(out_dir + out_fname + '_stan_residuals' + '.csv', index=True)
## Summary regression
# ---------------------------
#save summary statistics
stan_summary_fname = out_dir + out_fname + '_stan_summary' + '.txt'
with open(stan_summary_fname, 'w') as f:
print(stan_fit, file=f)
#create and save trace plots
fig_dir = out_dir + 'summary_figs/'
#create figures directory if doesn't exit
pathlib.Path(fig_dir).mkdir(parents=True, exist_ok=True)
#create stan trace plots
for c_name in col_names_hyp:
#create trace plot with arviz
ax = az.plot_trace(stan_fit, var_names=c_name, figsize=(10,5) ).ravel()
ax[0].yaxis.set_major_locator(plt_autotick())
ax[0].set_xlabel('sample value')
ax[0].set_ylabel('frequency')
ax[0].set_title('')
ax[0].grid(axis='both')
ax[1].set_xlabel('iteration')
ax[1].set_ylabel('sample value')
ax[1].grid(axis='both')
ax[1].set_title('')
fig = ax[0].figure
fig.suptitle(c_name)
fig.savefig(fig_dir + out_fname + '_stan_traceplot_' + c_name + '_arviz' + '.png')
return None
| 19,298 | 46.185819 | 130 | py |
ngmm_tools | ngmm_tools-master/Analyses/Python_lib/regression/pystan/regression_pystan_model1_unbounded_hyp.py | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Tue Jul 13 18:22:15 2021
@author: glavrent
"""
#imprort libraries
import os
import pathlib
import glob
import re #regular expression package
import pickle
from joblib import cpu_count
#arithmetic libraries
import numpy as np
#statistics libraries
import pandas as pd
#plot libraries
import matplotlib as mpl
import matplotlib.pyplot as plt
from matplotlib.ticker import AutoLocator as plt_autotick
import arviz as az
mpl.use('agg')
def RunStan(df_flatfile, stan_model_fname,
out_fname, out_dir, res_name='res',
runstan_flag=True,
n_iter=600, n_chains=4,
adapt_delta=0.8, max_treedepth=10,
pystan_ver=2, pystan_parallel=False):
'''
Run full Bayessian regression in Stan. Non-ergodic model includes: a spatially
varying earthquake constant, a spatially varying site constant, and a spatially
independent site constant.
Parameters
----------
df_flatfile : pd.DataFrame
Input data frame containing total residuals, eq and site coordinates.
stan_model_fname : string
File name for stan model.
out_fname : string
File name for output files.
out_dir : string
Output directory.
res_name : string, optional
Column name for total residuals. The default is 'res'.
runstan_flag : bool, optional
Flag for running stan. If true run regression, if false read past regression
output and summarize non-ergodic parameters. The default is True.
n_iter : integer, optional
Number of stan samples. The default is 600.
n_chains : integer, optional
Number of MCMC chains. The default is 4.
adapt_delta : double, optional
Target average proposal acceptance probability for adaptation. The default is 0.8.
max_treedepth : integer, optional
Maximum number of evaluations for each iteration (2^max_treedepth). The default is 10.
pystan_ver : integer, optional
Version of pystan to run. The default is 2.
pystan_parallel : bool, optional
Flag for using multithreaded option in STAN. The default is False.
Returns
-------
None.
'''
## Read Data
#============================
#read stan model
with open(stan_model_fname, "r") as f:
stan_model_code = f.read()
## Preprocess Input Data
#============================
#set rsn column as dataframe index, skip if rsn already the index
if not df_flatfile.index.name == 'rsn':
df_flatfile.set_index('rsn', drop=True, inplace=True)
#number of data
n_data = len(df_flatfile)
#earthquake data
data_eq_all = df_flatfile[['eqid','mag','eqX', 'eqY']].values
_, eq_idx, eq_inv = np.unique(df_flatfile[['eqid']].values, axis=0, return_inverse=True, return_index=True)
data_eq = data_eq_all[eq_idx,:]
X_eq = data_eq[:,[2,3]] #earthquake coordinates
#create earthquake ids for all records (1 to n_eq)
eq_id = eq_inv + 1
n_eq = len(data_eq)
#verify no collocated events
eq_dist_min = np.min([np.linalg.norm(x_eq - np.delete(X_eq,k, axis=0), axis=1).min() for k, x_eq in enumerate(X_eq) ])
assert(eq_dist_min > 5e-5),'Error. Singular covariance matrix due to collocated events'
#station data
data_sta_all = df_flatfile[['ssn','Vs30','staX','staY']].values
_, sta_idx, sta_inv = np.unique( df_flatfile[['ssn']].values, axis = 0, return_inverse=True, return_index=True)
data_sta = data_sta_all[sta_idx,:]
X_sta = data_sta[:,[2,3]] #station coordinates
#create station indices for all records (1 to n_sta)
sta_id = sta_inv + 1
n_sta = len(data_sta)
#verify no collocated stations
sta_dist_min = np.min([np.linalg.norm(x_sta - np.delete(X_sta,k, axis=0), axis=1).min() for k, x_sta in enumerate(X_sta) ])
assert(sta_dist_min > 5e-5),'Error. Singular covariance matrix due to collocated stations'
#ground-motion observations
y_data = df_flatfile[res_name].to_numpy().copy()
#stan data
stan_data = {'N': n_data,
'NEQ': n_eq,
'NSTAT': n_sta,
'eq': eq_id, #earthquake id
'stat': sta_id, #station id
'X_e': X_eq, #earthquake coordinates
'X_s': X_sta, #station coordinates
'rec_mu': np.zeros(y_data.shape),
'Y': y_data,
}
stan_data_fname = out_fname + '_stan_data' + '.Rdata'
## Run Stan, fit model
#============================
#number of cores
n_cpu = max(cpu_count() -1,1)
#filename for STAN regression raw output file saved as pkl
stan_fit_fname = out_dir + out_fname + '_stan_fit' + '.pkl'
#run stan
if runstan_flag:
#control paramters
control_stan = {'adapt_delta':adapt_delta, 'max_treedepth':max_treedepth}
if pystan_ver == 2:
import pystan
if (not pystan_parallel) or n_cpu<=n_chains:
#compile
stan_model = pystan.StanModel(model_code=stan_model_code)
#full Bayesian statistics
stan_fit = stan_model.sampling(data=stan_data, iter=n_iter, chains = n_chains, refresh=10, control = control_stan)
else:
#number of cores per chain
n_cpu_chain = int(np.floor(n_cpu/n_chains))
#multi-processing arguments
os.environ['STAN_NUM_THREADS'] = str(n_cpu_chain)
extra_compile_args = ['-pthread', '-DSTAN_THREADS']
#compile
stan_model = pystan.StanModel(model_code=stan_model_code, extra_compile_args=extra_compile_args)
#full Bayesian statistics
stan_fit = stan_model.sampling(data=stan_data, iter=n_iter, chains = n_chains, refresh=1, control = control_stan)
elif pystan_ver == 3:
import nest_asyncio
import stan
nest_asyncio.apply()
#compile
stan_model = stan.build(stan_model_code, data=stan_data, random_seed=1)
#full Bayesian statistics
stan_fit = stan_model.sample(num_chains=n_chains, num_samples=n_iter, max_depth=max_treedepth, delta=adapt_delta)
#save stan model and fit
pathlib.Path(out_dir).mkdir(parents=True, exist_ok=True)
with open(stan_fit_fname, "wb") as f:
pickle.dump({'model' : stan_model, 'fit' : stan_fit}, f, protocol=-1)
else:
#load model and fit for postprocessing if has already been executed
with open(stan_fit_fname, "rb") as f:
data_dict = pickle.load(f)
stan_fit = data_dict['fit']
stan_model = data_dict['model']
del data_dict
## Postprocessing Data
#============================
## Extract posterior samples
# ---------------------------
#hyper-parameters
col_names_hyp = ['dc_0','ell_1e', 'ell_1as', 'omega_1e', 'omega_1as', 'omega_1bs',
'phi_0','tau_0']
#non-ergodic terms
col_names_dc_1e = ['dc_1e.%i'%(k) for k in range(n_eq)]
col_names_dc_1as = ['dc_1as.%i'%(k) for k in range(n_sta)]
col_names_dc_1bs = ['dc_1bs.%i'%(k) for k in range(n_sta)]
col_names_dB = ['dB.%i'%(k) for k in range(n_eq)]
col_names_all = col_names_hyp + col_names_dc_1e + col_names_dc_1as + col_names_dc_1bs + col_names_dB
#summarize raw posterior distributions
stan_posterior = np.stack([stan_fit[c_n].flatten() for c_n in col_names_hyp], axis=1)
#adjustment terms
if pystan_ver == 2:
stan_posterior = np.concatenate((stan_posterior, stan_fit['dc_1e']), axis=1)
stan_posterior = np.concatenate((stan_posterior, stan_fit['dc_1as']), axis=1)
stan_posterior = np.concatenate((stan_posterior, stan_fit['dc_1bs']), axis=1)
stan_posterior = np.concatenate((stan_posterior, stan_fit['dB']), axis=1)
else:
stan_posterior = np.concatenate((stan_posterior, stan_fit['dc_1e'].T), axis=1)
stan_posterior = np.concatenate((stan_posterior, stan_fit['dc_1as'].T), axis=1)
stan_posterior = np.concatenate((stan_posterior, stan_fit['dc_1bs'].T), axis=1)
stan_posterior = np.concatenate((stan_posterior, stan_fit['dB'].T), axis=1)
#save raw-posterior distribution
df_stan_posterior_raw = pd.DataFrame(stan_posterior, columns = col_names_all)
df_stan_posterior_raw.to_csv(out_dir + out_fname + '_stan_posterior_raw' + '.csv', index=False)
## Summarize hyper-parameters
# ---------------------------
#summarize posterior distributions of hyper-parameters
perc_array = np.array([0.05,0.25,0.5,0.75,0.95])
df_stan_hyp = df_stan_posterior_raw[col_names_hyp].quantile(perc_array)
df_stan_hyp = df_stan_hyp.append(df_stan_posterior_raw[col_names_hyp].mean(axis = 0), ignore_index=True)
df_stan_hyp.index = ['prc_%.2f'%(prc) for prc in perc_array]+['mean']
df_stan_hyp.to_csv(out_dir + out_fname + '_stan_hyperparameters' + '.csv', index=True)
#detailed posterior percentiles of posterior distributions
perc_array = np.arange(0.01,0.99,0.01)
df_stan_posterior = df_stan_posterior_raw[col_names_hyp].quantile(perc_array)
df_stan_posterior.index.name = 'prc'
df_stan_posterior .to_csv(out_dir + out_fname + '_stan_hyperposterior' + '.csv', index=True)
del col_names_dc_1e, col_names_dc_1as, col_names_dc_1bs, col_names_dB
del stan_posterior, col_names_all
## Sample spatially varying coefficients and predictions at record locations
# ---------------------------
# earthquake and station location in database
X_eq_all = df_flatfile[['eqX', 'eqY']].values
X_sta_all = df_flatfile[['staX','staY']].values
# GMM coefficients
#--- --- --- --- --- --- --- ---
#constant shift coefficient
coeff_0_mu = df_stan_posterior_raw.loc[:,'dc_0'].mean() * np.ones(n_data)
coeff_0_med = df_stan_posterior_raw.loc[:,'dc_0'].median() * np.ones(n_data)
coeff_0_sig = df_stan_posterior_raw.loc[:,'dc_0'].std() * np.ones(n_data)
#spatially varying earthquake constant coefficient
coeff_1e_mu = np.array([df_stan_posterior_raw.loc[:,f'dc_1e.{k}'].mean() for k in range(n_eq)])
coeff_1e_mu = coeff_1e_mu[eq_inv]
coeff_1e_med = np.array([df_stan_posterior_raw.loc[:,f'dc_1e.{k}'].median() for k in range(n_eq)])
coeff_1e_med = coeff_1e_med[eq_inv]
coeff_1e_sig = np.array([df_stan_posterior_raw.loc[:,f'dc_1e.{k}'].std() for k in range(n_eq)])
coeff_1e_sig = coeff_1e_sig[eq_inv]
#site term constant covariance
coeff_1as_mu = np.array([df_stan_posterior_raw.loc[:,f'dc_1as.{k}'].mean() for k in range(n_sta)])
coeff_1as_mu = coeff_1as_mu[sta_inv]
coeff_1as_med = np.array([df_stan_posterior_raw.loc[:,f'dc_1as.{k}'].median() for k in range(n_sta)])
coeff_1as_med = coeff_1as_med[sta_inv]
coeff_1as_sig = np.array([df_stan_posterior_raw.loc[:,f'dc_1as.{k}'].std() for k in range(n_sta)])
coeff_1as_sig = coeff_1as_sig[sta_inv]
#spatially varying station constant covariance
coeff_1bs_mu = np.array([df_stan_posterior_raw.loc[:,f'dc_1bs.{k}'].mean() for k in range(n_sta)])
coeff_1bs_mu = coeff_1bs_mu[sta_inv]
coeff_1bs_med = np.array([df_stan_posterior_raw.loc[:,f'dc_1bs.{k}'].median() for k in range(n_sta)])
coeff_1bs_med = coeff_1bs_med[sta_inv]
coeff_1bs_sig = np.array([df_stan_posterior_raw.loc[:,f'dc_1bs.{k}'].std() for k in range(n_sta)])
coeff_1bs_sig = coeff_1bs_sig[sta_inv]
# aleatory variability
phi_0_array = np.array([df_stan_posterior_raw.phi_0.mean()]*X_sta_all.shape[0])
tau_0_array = np.array([df_stan_posterior_raw.tau_0.mean()]*X_sta_all.shape[0])
#initiaize flatfile for sumamry of non-erg coefficinets and residuals
df_flatinfo = df_flatfile[['eqid','ssn','eqLat','eqLon','staLat','staLon','eqX','eqY','staX','staY','UTMzone']]
#summarize non-ergodic coefficients
coeffs_summary = np.vstack((coeff_0_mu,
coeff_1e_mu,
coeff_1as_mu,
coeff_1bs_mu,
coeff_0_med,
coeff_1e_med,
coeff_1as_med,
coeff_1bs_med,
coeff_0_sig,
coeff_1e_sig,
coeff_1as_sig,
coeff_1bs_sig)).T
columns_names = ['dc_0_mean','dc_1e_mean','dc_1as_mean','dc_1bs_mean',
'dc_0_med', 'dc_1e_med', 'dc_1as_med', 'dc_1bs_med',
'dc_0_sig', 'dc_1e_sig', 'dc_1as_sig', 'dc_1bs_sig']
df_coeffs_summary = pd.DataFrame(coeffs_summary, columns = columns_names, index=df_flatfile.index)
#create dataframe with summary coefficients
df_coeffs_summary = pd.merge(df_flatinfo, df_coeffs_summary, how='right', left_index=True, right_index=True)
df_coeffs_summary[['eqid','ssn']] = df_coeffs_summary[['eqid','ssn']].astype(int)
df_coeffs_summary.to_csv(out_dir + out_fname + '_stan_coefficients' + '.csv', index=True)
# GMM prediction and residuals
#--- --- --- --- --- --- --- ---
#mean prediction
y_mu = (coeff_0_mu + coeff_1e_mu + coeff_1as_mu + coeff_1bs_mu)
#compute residuals
res_tot = y_data - y_mu
#residuals computed directly from stan regression
res_between = [df_stan_posterior_raw.loc[:,f'dB.{k}'].mean() for k in range(n_eq)]
res_between = np.array([res_between[k] for k in (eq_inv).astype(int)])
res_within = res_tot - res_between
#summarize predictions and residuals
predict_summary = np.vstack((y_mu, res_tot, res_between, res_within)).T
columns_names = ['nerg_mu','res_tot','res_between','res_within']
df_predict_summary = pd.DataFrame(predict_summary, columns = columns_names, index=df_flatfile.index)
#create dataframe with predictions and residuals
df_predict_summary = pd.merge(df_flatinfo, df_predict_summary, how='right', left_index=True, right_index=True)
df_predict_summary[['eqid','ssn']] = df_predict_summary[['eqid','ssn']].astype(int)
df_predict_summary.to_csv(out_dir + out_fname + '_stan_residuals' + '.csv', index=True)
## Summary regression
# ---------------------------
#save summary statistics
stan_summary_fname = out_dir + out_fname + '_stan_summary' + '.txt'
with open(stan_summary_fname, 'w') as f:
print(stan_fit, file=f)
#create and save trace plots
fig_dir = out_dir + 'summary_figs/'
#create figures directory if doesn't exit
pathlib.Path(fig_dir).mkdir(parents=True, exist_ok=True)
#create stan trace plots
for c_name in col_names_hyp:
#create trace plot with arviz
ax = az.plot_trace(stan_fit, var_names=c_name, figsize=(10,5) ).ravel()
ax[0].yaxis.set_major_locator(plt_autotick())
ax[0].set_xlabel('sample value')
ax[0].set_ylabel('frequency')
ax[0].set_title('')
ax[0].grid(axis='both')
ax[1].set_xlabel('iteration')
ax[1].set_ylabel('sample value')
ax[1].grid(axis='both')
ax[1].set_title('')
fig = ax[0].figure
fig.suptitle(c_name)
fig.savefig(fig_dir + out_fname + '_stan_traceplot_' + c_name + '_arviz' + '.png')
return None
| 15,691 | 44.616279 | 130 | py |
ngmm_tools | ngmm_tools-master/Analyses/Python_lib/regression/pystan/regression_pystan_model2_uncorr_cells_unbounded_hyp.py | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Tue Jul 13 18:22:15 2021
@author: glavrent
"""
#load variables
import os
import pathlib
import glob
import re #regular expression package
import pickle
from joblib import cpu_count
#arithmetic libraries
import numpy as np
#statistics libraries
import pandas as pd
#plot libraries
import matplotlib as mpl
import matplotlib.pyplot as plt
from matplotlib.ticker import AutoLocator as plt_autotick
import arviz as az
mpl.use('agg')
def RunStan(df_flatfile, df_cellinfo, df_celldist, stan_model_fname,
out_fname, out_dir, res_name='res', c_a_erg=0,
runstan_flag=True,
n_iter=600, n_chains=4,
adapt_delta=0.8, max_treedepth=10,
pystan_ver=2, pystan_parallel=False):
'''
Run full Bayessian regression in Stan. Non-ergodic model includes: a spatially
varying earthquake constant, a spatially varying site constant, a spatially
independent site constant, and uncorrelated anelastic attenuation.
Parameters
----------
df_flatfile : pd.DataFrame
Input data frame containing total residuals, eq and site coordinates.
df_cellinfo : pd.DataFrame
Dataframe with coordinates of anelastic attenuation cells.
df_celldist : pd.DataFrame
Datafame with cell path distances of all records in df_flatfile.
stan_model_fname : string
File name for stan model.
out_fname : string
File name for output files.
out_dir : string
Output directory.
res_name : string, optional
Column name for total residuals. The default is 'res'.
c_a_erg : double, optional
Value of ergodic anelatic attenuation coefficient. Used as mean of cell
specific anelastic attenuation prior distribution. The default is 0.
n_iter : integer, optional
Number of stan samples. The default is 600.
n_chains : integer, optional
Number of MCMC chains. The default is 4.
runstan_flag : bool, optional
Flag for running stan. If true run regression, if false read past regression
output and summarize non-ergodic parameters. The default is True.
adapt_delta : double, optional
Target average proposal acceptance probability for adaptation. The default is 0.8.
max_treedepth : integer, optional
Maximum number of evaluations for each iteration (2^max_treedepth). The default is 10.
pystan_ver : integer, optional
Version of pystan to run. The default is 2.
pystan_parallel : bool, optional
Flag for using multithreaded option in STAN. The default is False.
Returns
-------
None.
'''
## Read Data
#============================
#read stan model
with open(stan_model_fname, "r") as f:
stan_model_code = f.read()
## Preprocess Input Data
#============================
#set rsn column as dataframe index, skip if rsn already the index
if not df_flatfile.index.name == 'rsn':
df_flatfile.set_index('rsn', drop=True, inplace=True)
if not df_celldist.index.name == 'rsn':
df_celldist.set_index('rsn', drop=True, inplace=True)
#set cellid column as dataframe index, skip if cellid already the index
if not df_cellinfo.index.name == 'cellid':
df_cellinfo.set_index('cellid', drop=True, inplace=True)
#number of data
n_data = len(df_flatfile)
#earthquake data
data_eq_all = df_flatfile[['eqid','mag','eqX', 'eqY']].values
_, eq_idx, eq_inv = np.unique(df_flatfile[['eqid']], axis=0, return_inverse=True, return_index=True)
data_eq = data_eq_all[eq_idx,:]
X_eq = data_eq[:,[2,3]] #earthquake coordinates
#create earthquake ids for all records (1 to n_eq)
eq_id = eq_inv + 1
n_eq = len(data_eq)
#station data
data_sta_all = df_flatfile[['ssn','Vs30','staX','staY']].values
_, sta_idx, sta_inv = np.unique( df_flatfile[['ssn']].values, axis = 0, return_inverse=True, return_index=True)
data_sta = data_sta_all[sta_idx,:]
X_sta = data_sta[:,[2,3]] #station coordinates
#create station indices for all records (1 to n_sta)
sta_id = sta_inv + 1
n_sta = len(data_sta)
#ground-motion observations
y_data = df_flatfile[res_name].to_numpy().copy()
#cell data
#reorder and only keep records included in the flatfile
df_celldist = df_celldist.reindex(df_flatfile.index)
#cell info
cell_ids_all = df_cellinfo.index
cell_names_all = df_cellinfo.cellname
#cell distance matrix
celldist_all = df_celldist[cell_names_all] #cell-distance matrix with all cells
#find cell with more than one paths
i_cells_valid = np.where(celldist_all.sum(axis=0) > 0)[0] #valid cells with more than one path
cell_ids_valid = cell_ids_all[i_cells_valid]
cell_names_valid = cell_names_all[i_cells_valid]
celldist_valid = celldist_all.loc[:,cell_names_valid] #cell-distance with only non-zero cells
#number of cells
n_cell = celldist_all.shape[1]
n_cell_valid = celldist_valid.shape[1]
#cell coordinates
X_cells_valid = df_cellinfo.loc[i_cells_valid,['mptX','mptY']].values
#print Rrup missfits
print('max R_rup misfit', (df_flatfile.Rrup.values - celldist_valid.sum(axis=1)).abs().max())
stan_data = {'N': n_data,
'NEQ': n_eq,
'NSTAT': n_sta,
'NCELL': n_cell_valid,
'eq': eq_id, #earthquake id
'stat': sta_id, #station id
'X_e': X_eq, #earthquake coordinates
'X_s': X_sta, #station coordinates
'X_c': X_cells_valid,
'rec_mu': np.zeros(y_data.shape),
'RC': celldist_valid.to_numpy(),
'c_a_erg': c_a_erg,
'Y': y_data,
}
stan_data_fname = out_fname + '_stan_data' + '.Rdata'
## Run Stan, fit model
#============================
#number of cores
n_cpu = max(cpu_count() -1,1)
#filename for STAN regression raw output file saved as pkl
stan_fit_fname = out_dir + out_fname + '_stan_fit' + '.pkl'
#run stan
if runstan_flag:
#control paramters
control_stan = {'adapt_delta':adapt_delta, 'max_treedepth':max_treedepth}
if pystan_ver == 2:
import pystan
if (not pystan_parallel) or n_cpu<=n_chains:
#compile
stan_model = pystan.StanModel(model_code=stan_model_code)
#full Bayesian statistics
stan_fit = stan_model.sampling(data=stan_data, iter=n_iter, chains = n_chains, refresh=10, control = control_stan)
else:
#number of cores per chain
n_cpu_chain = int(np.floor(n_cpu/n_chains))
#multi-processing arguments
os.environ['STAN_NUM_THREADS'] = str(n_cpu_chain)
extra_compile_args = ['-pthread', '-DSTAN_THREADS']
#compile
stan_model = pystan.StanModel(model_code=stan_model_code, extra_compile_args=extra_compile_args)
#full Bayesian statistics
stan_fit = stan_model.sampling(data=stan_data, iter=n_iter, chains = n_chains, refresh=1, control = control_stan)
elif pystan_ver == 3:
import nest_asyncio
import stan
nest_asyncio.apply()
#compile
stan_model = stan.build(stan_model_code, data=stan_data, random_seed=1)
#full Bayesian statistics
stan_fit = stan_model.sample(num_chains=n_chains, num_samples=n_iter, max_depth=max_treedepth, delta=adapt_delta)
#save stan model and fit
pathlib.Path(out_dir).mkdir(parents=True, exist_ok=True)
with open(stan_fit_fname, "wb") as f:
pickle.dump({'model' : stan_model, 'fit' : stan_fit}, f, protocol=-1)
else:
#load model and fit for postprocessing if has already been executed
with open(stan_fit_fname, "rb") as f:
data_dict = pickle.load(f)
stan_fit = data_dict['fit']
stan_model = data_dict['model']
del data_dict
## Postprocessing Data
#============================
## Extract posterior samples
# ---------------------------
#hyper-parameters
col_names_hyp = ['dc_0','ell_1e', 'ell_1as', 'omega_1e', 'omega_1as', 'omega_1bs',
'mu_cap', 'omega_cap',
'phi_0','tau_0']
#non-ergodic terms
col_names_dc_1e = ['dc_1e.%i'%(k) for k in range(n_eq)]
col_names_dc_1as = ['dc_1as.%i'%(k) for k in range(n_sta)]
col_names_dc_1bs = ['dc_1bs.%i'%(k) for k in range(n_sta)]
col_names_dB = ['dB.%i'%(k) for k in range(n_eq)]
col_names_cap = ['c_cap.%i'%(c_id) for c_id in cell_ids_valid]
col_names_all = col_names_hyp + col_names_dc_1e + col_names_dc_1as + col_names_dc_1bs + col_names_cap + col_names_dB
#summarize raw posterior distributions
stan_posterior = np.stack([stan_fit[c_n].flatten() for c_n in col_names_hyp], axis=1)
#adjustment terms
if pystan_ver == 2:
stan_posterior = np.concatenate((stan_posterior, stan_fit['dc_1e']), axis=1)
stan_posterior = np.concatenate((stan_posterior, stan_fit['dc_1as']), axis=1)
stan_posterior = np.concatenate((stan_posterior, stan_fit['dc_1bs']), axis=1)
stan_posterior = np.concatenate((stan_posterior, stan_fit['c_cap']), axis=1)
stan_posterior = np.concatenate((stan_posterior, stan_fit['dB']), axis=1)
else:
stan_posterior = np.concatenate((stan_posterior, stan_fit['dc_1e'].T), axis=1)
stan_posterior = np.concatenate((stan_posterior, stan_fit['dc_1as'].T), axis=1)
stan_posterior = np.concatenate((stan_posterior, stan_fit['dc_1bs'].T), axis=1)
stan_posterior = np.concatenate((stan_posterior, stan_fit['c_cap'].T), axis=1)
stan_posterior = np.concatenate((stan_posterior, stan_fit['dB'].T), axis=1)
#save raw-posterior distribution
df_stan_posterior_raw = pd.DataFrame(stan_posterior, columns = col_names_all)
df_stan_posterior_raw.to_csv(out_dir + out_fname + '_stan_posterior_raw' + '.csv', index=False)
## Summarize hyper-parameters
# ---------------------------
#summarize posterior distributions of hyper-parameters
perc_array = np.array([0.05,0.25,0.5,0.75,0.95])
df_stan_hyp = df_stan_posterior_raw[col_names_hyp].quantile(perc_array)
df_stan_hyp = df_stan_hyp.append(df_stan_posterior_raw[col_names_hyp].mean(axis = 0), ignore_index=True)
df_stan_hyp.index = ['prc_%.2f'%(prc) for prc in perc_array]+['mean']
df_stan_hyp.to_csv(out_dir + out_fname + '_stan_hyperparameters' + '.csv', index=True)
#detailed posterior percentiles of posterior distributions
perc_array = np.arange(0.01,0.99,0.01)
df_stan_posterior = df_stan_posterior_raw[col_names_hyp].quantile(perc_array)
df_stan_posterior.index.name = 'prc'
df_stan_posterior .to_csv(out_dir + out_fname + '_stan_hyperposterior' + '.csv', index=True)
del col_names_dc_1e, col_names_dc_1as, col_names_dc_1bs, col_names_dB
del stan_posterior, col_names_all
## Sample spatially varying coefficients and predictions at record locations
# ---------------------------
# earthquake and station location in database
X_eq_all = df_flatfile[['eqX', 'eqY']].values
X_sta_all = df_flatfile[['staX','staY']].values
# GMM anelastic attenuation
#--- --- --- --- --- --- --- ---
cells_ca_mu = np.array([df_stan_posterior_raw.loc[:,'c_cap.%i'%(k)].mean() for k in cell_ids_valid])
cells_ca_med = np.array([df_stan_posterior_raw.loc[:,'c_cap.%i'%(k)].median() for k in cell_ids_valid])
cells_ca_sig = np.array([df_stan_posterior_raw.loc[:,'c_cap.%i'%(k)].std() for k in cell_ids_valid])
#effect of anelastic attenuation in GM
cells_LcA_mu = celldist_valid.values @ cells_ca_mu
cells_LcA_med = celldist_valid.values @ cells_ca_med
cells_LcA_sig = np.sqrt(np.square(celldist_valid.values) @ cells_ca_sig**2)
#summary attenuation cells
catten_summary = np.vstack((np.tile(c_a_erg, n_cell_valid),
cells_ca_mu,
cells_ca_med,
cells_ca_sig)).T
columns_names = ['c_a_erg','c_cap_mean','c_cap_med','c_cap_sig']
df_catten_summary = pd.DataFrame(catten_summary, columns = columns_names, index=df_cellinfo.index[i_cells_valid])
#create dataframe with summary attenuation cells
df_catten_summary = pd.merge(df_cellinfo[['cellname','mptLat','mptLon','mptX','mptY','mptZ','UTMzone']],
df_catten_summary, how='right', left_index=True, right_index=True)
df_catten_summary.to_csv(out_dir + out_fname + '_stan_catten' + '.csv', index=True)
# GMM coefficients
#--- --- --- --- --- --- --- ---
#constant shift coefficient
coeff_0_mu = df_stan_posterior_raw.loc[:,'dc_0'].mean() * np.ones(n_data)
coeff_0_med = df_stan_posterior_raw.loc[:,'dc_0'].median() * np.ones(n_data)
coeff_0_sig = df_stan_posterior_raw.loc[:,'dc_0'].std() * np.ones(n_data)
#spatially varying earthquake constant coefficient
coeff_1e_mu = np.array([df_stan_posterior_raw.loc[:,f'dc_1e.{k}'].mean() for k in range(n_eq)])
coeff_1e_mu = coeff_1e_mu[eq_inv]
coeff_1e_med = np.array([df_stan_posterior_raw.loc[:,f'dc_1e.{k}'].median() for k in range(n_eq)])
coeff_1e_med = coeff_1e_med[eq_inv]
coeff_1e_sig = np.array([df_stan_posterior_raw.loc[:,f'dc_1e.{k}'].std() for k in range(n_eq)])
coeff_1e_sig = coeff_1e_sig[eq_inv]
#site term constant covariance
coeff_1as_mu = np.array([df_stan_posterior_raw.loc[:,f'dc_1as.{k}'].mean() for k in range(n_sta)])
coeff_1as_mu = coeff_1as_mu[sta_inv]
coeff_1as_med = np.array([df_stan_posterior_raw.loc[:,f'dc_1as.{k}'].median() for k in range(n_sta)])
coeff_1as_med = coeff_1as_med[sta_inv]
coeff_1as_sig = np.array([df_stan_posterior_raw.loc[:,f'dc_1as.{k}'].std() for k in range(n_sta)])
coeff_1as_sig = coeff_1as_sig[sta_inv]
#spatially varying station constant covariance
coeff_1bs_mu = np.array([df_stan_posterior_raw.loc[:,f'dc_1bs.{k}'].mean() for k in range(n_sta)])
coeff_1bs_mu = coeff_1bs_mu[sta_inv]
coeff_1bs_med = np.array([df_stan_posterior_raw.loc[:,f'dc_1bs.{k}'].median() for k in range(n_sta)])
coeff_1bs_med = coeff_1bs_med[sta_inv]
coeff_1bs_sig = np.array([df_stan_posterior_raw.loc[:,f'dc_1bs.{k}'].std() for k in range(n_sta)])
coeff_1bs_sig = coeff_1bs_sig[sta_inv]
# aleatory variability
phi_0_array = np.array([df_stan_posterior_raw.phi_0.mean()]*X_sta_all.shape[0])
tau_0_array = np.array([df_stan_posterior_raw.tau_0.mean()]*X_sta_all.shape[0])
#initiaize flatfile for sumamry of non-erg coefficinets and residuals
df_flatinfo = df_flatfile[['eqid','ssn','eqLat','eqLon','staLat','staLon','eqX','eqY','staX','staY','UTMzone']]
#summary coefficients
coeffs_summary = np.vstack((coeff_0_mu,
coeff_1e_mu,
coeff_1as_mu,
coeff_1bs_mu,
cells_LcA_mu,
coeff_0_med,
coeff_1e_med,
coeff_1as_med,
coeff_1bs_med,
cells_LcA_med,
coeff_0_sig,
coeff_1e_sig,
coeff_1as_sig,
coeff_1bs_sig,
cells_LcA_sig)).T
columns_names = ['dc_0_mean','dc_1e_mean','dc_1as_mean','dc_1bs_mean','Lc_ca_mean',
'dc_0_med', 'dc_1e_med', 'dc_1as_med', 'dc_1bs_med', 'Lc_ca_med',
'dc_0_sig', 'dc_1e_sig', 'dc_1as_sig', 'dc_1bs_sig', 'Lc_ca_sig']
df_coeffs_summary = pd.DataFrame(coeffs_summary, columns = columns_names, index=df_flatfile.index)
#create dataframe with summary coefficients
df_coeffs_summary = pd.merge(df_flatinfo, df_coeffs_summary, how='right', left_index=True, right_index=True)
df_coeffs_summary[['eqid','ssn']] = df_coeffs_summary[['eqid','ssn']].astype(int)
df_coeffs_summary.to_csv(out_dir + out_fname + '_stan_coefficients' + '.csv', index=True)
# GMM prediction
#--- --- --- --- --- --- --- ---
#mean prediction
y_mu = (coeff_0_mu + coeff_1e_mu + coeff_1as_mu + coeff_1bs_mu + cells_LcA_mu)
#compute residuals
res_tot = y_data - y_mu
#residuals computed directly from stan regression
res_between = [df_stan_posterior_raw.loc[:,f'dB.{k}'].mean() for k in range(n_eq)]
res_between = np.array([res_between[k] for k in (eq_inv).astype(int)])
res_within = res_tot - res_between
#summary predictions and residuals
predict_summary = np.vstack((y_mu, res_tot, res_between, res_within)).T
columns_names = ['nerg_mu','res_tot','res_between','res_within']
df_predict_summary = pd.DataFrame(predict_summary, columns = columns_names, index=df_flatfile.index)
#create dataframe with predictions and residuals
df_predict_summary = pd.merge(df_flatinfo, df_predict_summary, how='right', left_index=True, right_index=True)
df_predict_summary[['eqid','ssn']] = df_predict_summary[['eqid','ssn']].astype(int)
df_predict_summary.to_csv(out_dir + out_fname + '_stan_residuals' + '.csv', index=True)
## Summary regression
# ---------------------------
#save summary statistics
stan_summary_fname = out_dir + out_fname + '_stan_summary' + '.txt'
with open(stan_summary_fname, 'w') as f:
print(stan_fit, file=f)
#create and save trace plots
fig_dir = out_dir + 'summary_figs/'
#create figures directory if doesn't exit
pathlib.Path(fig_dir).mkdir(parents=True, exist_ok=True)
#create stan trace plots
for c_name in col_names_hyp:
#create trace plot with arviz
ax = az.plot_trace(stan_fit, var_names=c_name, figsize=(10,5) ).ravel()
ax[0].yaxis.set_major_locator(plt_autotick())
ax[0].set_xlabel('sample value')
ax[0].set_ylabel('frequency')
ax[0].set_title('')
ax[0].grid(axis='both')
ax[1].set_xlabel('iteration')
ax[1].set_ylabel('sample value')
ax[1].grid(axis='both')
ax[1].set_title('')
fig = ax[0].figure
fig.suptitle(c_name)
fig.savefig(fig_dir + out_fname + '_stan_traceplot_' + c_name + '_arviz' + '.png')
return None
| 19,042 | 46.136139 | 130 | py |
ngmm_tools | ngmm_tools-master/Analyses/Python_lib/regression/pystan/regression_pystan_model2_corr_cells_unbounded_hyp.py | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Tue Jul 13 18:22:15 2021
@author: glavrent
"""
#load variables
import os
import pathlib
import glob
import re #regular expression package
import pickle
from joblib import cpu_count
#arithmetic libraries
import numpy as np
#statistics libraries
import pandas as pd
#plot libraries
import matplotlib as mpl
import matplotlib.pyplot as plt
from matplotlib.ticker import AutoLocator as plt_autotick
import arviz as az
mpl.use('agg')
def RunStan(df_flatfile, df_cellinfo, df_celldist, stan_model_fname,
out_fname, out_dir, res_name='res', c_a_erg=0,
runstan_flag=True,
n_iter=600, n_chains=4,
adapt_delta=0.8, max_treedepth=10,
pystan_ver=2, pystan_parallel=False):
'''
Run full Bayessian regression in Stan. Non-ergodic model includes: a spatially
varying earthquake constant, a spatially varying site constant, a spatially
independent site constant, and partially spatially correlated anelastic
attenuation.
Parameters
----------
df_flatfile : pd.DataFrame
Input data frame containing total residuals, eq and site coordinates.
df_cellinfo : pd.DataFrame
Dataframe with coordinates of anelastic attenuation cells.
df_celldist : pd.DataFrame
Datafame with cell path distances of all records in df_flatfile.
stan_model_fname : string
File name for stan model.
out_fname : string
File name for output files.
out_dir : string
Output directory.
res_name : string, optional
Column name for total residuals. The default is 'res'.
c_a_erg : double, optional
Value of ergodic anelatic attenuation coefficient. Used as mean of cell
specific anelastic attenuation prior distribution. The default is 0.
n_iter : integer, optional
Number of stan samples. The default is 600.
n_chains : integer, optional
Number of MCMC chains. The default is 4.
runstan_flag : bool, optional
Flag for running stan. If true run regression, if false read past regression
output and summarize non-ergodic parameters. The default is True.
adapt_delta : double, optional
Target average proposal acceptance probability for adaptation. The default is 0.8.
max_treedepth : integer, optional
Maximum number of evaluations for each iteration (2^max_treedepth). The default is 10.
pystan_ver : integer, optional
Version of pystan to run. The default is 2.
pystan_parallel : bool, optional
Flag for using multithreaded option in STAN. The default is False.
Returns
-------
None.
'''
## Read Data
#============================
#read stan model
with open(stan_model_fname, "r") as f:
stan_model_code = f.read()
## Preprocess Input Data
#============================
#set rsn column as dataframe index, skip if rsn already the index
if not df_flatfile.index.name == 'rsn':
df_flatfile.set_index('rsn', drop=True, inplace=True)
if not df_celldist.index.name == 'rsn':
df_celldist.set_index('rsn', drop=True, inplace=True)
#set cellid column as dataframe index, skip if cellid already the index
if not df_cellinfo.index.name == 'cellid':
df_cellinfo.set_index('cellid', drop=True, inplace=True)
#number of data
n_data = len(df_flatfile)
#earthquake data
data_eq_all = df_flatfile[['eqid','mag','eqX', 'eqY']].values
_, eq_idx, eq_inv = np.unique(df_flatfile[['eqid']], axis=0, return_inverse=True, return_index=True)
data_eq = data_eq_all[eq_idx,:]
X_eq = data_eq[:,[2,3]] #earthquake coordinates
#create earthquake ids for all records (1 to n_eq)
eq_id = eq_inv + 1
n_eq = len(data_eq)
#station data
data_sta_all = df_flatfile[['ssn','Vs30','staX','staY']].values
_, sta_idx, sta_inv = np.unique( df_flatfile[['ssn']].values, axis = 0, return_inverse=True, return_index=True)
data_sta = data_sta_all[sta_idx,:]
X_sta = data_sta[:,[2,3]] #station coordinates
#create station indices for all records (1 to n_sta)
sta_id = sta_inv + 1
n_sta = len(data_sta)
#ground-motion observations
y_data = df_flatfile[res_name].to_numpy().copy()
#cell data
#reorder and only keep records included in the flatfile
df_celldist = df_celldist.reindex(df_flatfile.index)
#cell info
cell_ids_all = df_cellinfo.index
cell_names_all = df_cellinfo.cellname
#cell distance matrix
celldist_all = df_celldist[cell_names_all] #cell-distance matrix with all cells
#find cell with more than one paths
i_cells_valid = np.where(celldist_all.sum(axis=0) > 0)[0] #valid cells with more than one path
cell_ids_valid = cell_ids_all[i_cells_valid]
cell_names_valid = cell_names_all[i_cells_valid]
celldist_valid = celldist_all.loc[:,cell_names_valid] #cell-distance with only non-zero cells
#number of cells
n_cell = celldist_all.shape[1]
n_cell_valid = celldist_valid.shape[1]
#cell coordinates
X_cells_valid = df_cellinfo.loc[i_cells_valid,['mptX','mptY']].values
#print Rrup missfits
print('max R_rup misfit', (df_flatfile.Rrup.values - celldist_valid.sum(axis=1)).abs().max())
stan_data = {'N': n_data,
'NEQ': n_eq,
'NSTAT': n_sta,
'NCELL': n_cell_valid,
'eq': eq_id, #earthquake id
'stat': sta_id, #station id
'X_e': X_eq, #earthquake coordinates
'X_s': X_sta, #station coordinates
'X_c': X_cells_valid,
'rec_mu': np.zeros(y_data.shape),
'RC': celldist_valid.to_numpy(),
'c_a_erg': c_a_erg,
'Y': y_data,
}
stan_data_fname = out_fname + '_stan_data' + '.Rdata'
## Run Stan, fit model
#============================
#number of cores
n_cpu = max(cpu_count() -1,1)
#filename for STAN regression raw output file saved as pkl
stan_fit_fname = out_dir + out_fname + '_stan_fit' + '.pkl'
#run stan
if runstan_flag:
#control paramters
control_stan = {'adapt_delta':adapt_delta, 'max_treedepth':max_treedepth}
if pystan_ver == 2:
import pystan
if (not pystan_parallel) or n_cpu<=n_chains:
#compile
stan_model = pystan.StanModel(model_code=stan_model_code)
#full Bayesian statistics
stan_fit = stan_model.sampling(data=stan_data, iter=n_iter, chains = n_chains, refresh=10, control = control_stan)
else:
#number of cores per chain
n_cpu_chain = int(np.floor(n_cpu/n_chains))
#multi-processing arguments
os.environ['STAN_NUM_THREADS'] = str(n_cpu_chain)
extra_compile_args = ['-pthread', '-DSTAN_THREADS']
#compile
stan_model = pystan.StanModel(model_code=stan_model_code, extra_compile_args=extra_compile_args)
#full Bayesian statistics
stan_fit = stan_model.sampling(data=stan_data, iter=n_iter, chains = n_chains, refresh=1, control = control_stan)
elif pystan_ver == 3:
import nest_asyncio
import stan
nest_asyncio.apply()
#compile
stan_model = stan.build(stan_model_code, data=stan_data, random_seed=1)
#full Bayesian statistics
stan_fit = stan_model.sample(num_chains=n_chains, num_samples=n_iter, max_depth=max_treedepth, delta=adapt_delta)
#save stan model and fit
pathlib.Path(out_dir).mkdir(parents=True, exist_ok=True)
with open(stan_fit_fname, "wb") as f:
pickle.dump({'model' : stan_model, 'fit' : stan_fit}, f, protocol=-1)
else:
#load model and fit for postprocessing if has already been executed
with open(stan_fit_fname, "rb") as f:
data_dict = pickle.load(f)
stan_fit = data_dict['fit']
stan_model = data_dict['model']
del data_dict
## Postprocessing Data
#============================
## Extract posterior samples
# ---------------------------
#hyper-parameters
col_names_hyp = ['dc_0','ell_1e', 'ell_1as', 'omega_1e', 'omega_1as', 'omega_1bs',
'mu_cap', 'ell_ca1p', 'omega_ca1p', 'omega_ca2p',
'phi_0','tau_0']
#non-ergodic terms
col_names_dc_1e = ['dc_1e.%i'%(k) for k in range(n_eq)]
col_names_dc_1as = ['dc_1as.%i'%(k) for k in range(n_sta)]
col_names_dc_1bs = ['dc_1bs.%i'%(k) for k in range(n_sta)]
col_names_dB = ['dB.%i'%(k) for k in range(n_eq)]
col_names_cap = ['c_cap.%i'%(c_id) for c_id in cell_ids_valid]
col_names_all = col_names_hyp + col_names_dc_1e + col_names_dc_1as + col_names_dc_1bs + col_names_cap + col_names_dB
#summarize raw posterior distributions
stan_posterior = np.stack([stan_fit[c_n].flatten() for c_n in col_names_hyp], axis=1)
#adjustment terms
if pystan_ver == 2:
stan_posterior = np.concatenate((stan_posterior, stan_fit['dc_1e']), axis=1)
stan_posterior = np.concatenate((stan_posterior, stan_fit['dc_1as']), axis=1)
stan_posterior = np.concatenate((stan_posterior, stan_fit['dc_1bs']), axis=1)
stan_posterior = np.concatenate((stan_posterior, stan_fit['c_cap']), axis=1)
stan_posterior = np.concatenate((stan_posterior, stan_fit['dB']), axis=1)
else:
stan_posterior = np.concatenate((stan_posterior, stan_fit['dc_1e'].T), axis=1)
stan_posterior = np.concatenate((stan_posterior, stan_fit['dc_1as'].T), axis=1)
stan_posterior = np.concatenate((stan_posterior, stan_fit['dc_1bs'].T), axis=1)
stan_posterior = np.concatenate((stan_posterior, stan_fit['c_cap'].T), axis=1)
stan_posterior = np.concatenate((stan_posterior, stan_fit['dB'].T), axis=1)
#save raw-posterior distribution
df_stan_posterior_raw = pd.DataFrame(stan_posterior, columns = col_names_all)
df_stan_posterior_raw.to_csv(out_dir + out_fname + '_stan_posterior_raw' + '.csv', index=False)
## Summarize hyper-parameters
# ---------------------------
#summarize posterior distributions of hyper-parameters
perc_array = np.array([0.05,0.25,0.5,0.75,0.95])
df_stan_hyp = df_stan_posterior_raw[col_names_hyp].quantile(perc_array)
df_stan_hyp = df_stan_hyp.append(df_stan_posterior_raw[col_names_hyp].mean(axis = 0), ignore_index=True)
df_stan_hyp.index = ['prc_%.2f'%(prc) for prc in perc_array]+['mean']
df_stan_hyp.to_csv(out_dir + out_fname + '_stan_hyperparameters' + '.csv', index=True)
#detailed posterior percentiles of posterior distributions
perc_array = np.arange(0.01,0.99,0.01)
df_stan_posterior = df_stan_posterior_raw[col_names_hyp].quantile(perc_array)
df_stan_posterior.index.name = 'prc'
df_stan_posterior .to_csv(out_dir + out_fname + '_stan_hyperposterior' + '.csv', index=True)
del col_names_dc_1e, col_names_dc_1as, col_names_dc_1bs, col_names_dB
del stan_posterior, col_names_all
## Sample spatially varying coefficients and predictions at record locations
# ---------------------------
# earthquake and station location in database
X_eq_all = df_flatfile[['eqX', 'eqY']].values
X_sta_all = df_flatfile[['staX','staY']].values
# GMM anelastic attenuation
#--- --- --- --- --- --- --- ---
cells_ca_mu = np.array([df_stan_posterior_raw.loc[:,'c_cap.%i'%(k)].mean() for k in cell_ids_valid])
cells_ca_med = np.array([df_stan_posterior_raw.loc[:,'c_cap.%i'%(k)].median() for k in cell_ids_valid])
cells_ca_sig = np.array([df_stan_posterior_raw.loc[:,'c_cap.%i'%(k)].std() for k in cell_ids_valid])
#effect of anelastic attenuation in GM
cells_LcA_mu = celldist_valid.values @ cells_ca_mu
cells_LcA_med = celldist_valid.values @ cells_ca_med
cells_LcA_sig = np.sqrt(np.square(celldist_valid.values) @ cells_ca_sig**2)
#summary attenuation cells
catten_summary = np.vstack((np.tile(c_a_erg, n_cell_valid),
cells_ca_mu,
cells_ca_med,
cells_ca_sig)).T
columns_names = ['c_a_erg','c_cap_mean','c_cap_med','c_cap_sig']
df_catten_summary = pd.DataFrame(catten_summary, columns = columns_names, index=df_cellinfo.index[i_cells_valid])
#create dataframe with summary attenuation cells
df_catten_summary = pd.merge(df_cellinfo[['cellname','mptLat','mptLon','mptX','mptY','mptZ','UTMzone']],
df_catten_summary, how='right', left_index=True, right_index=True)
df_catten_summary.to_csv(out_dir + out_fname + '_stan_catten' + '.csv', index=True)
# GMM coefficients
#--- --- --- --- --- --- --- ---
#constant shift coefficient
coeff_0_mu = df_stan_posterior_raw.loc[:,'dc_0'].mean() * np.ones(n_data)
coeff_0_med = df_stan_posterior_raw.loc[:,'dc_0'].median() * np.ones(n_data)
coeff_0_sig = df_stan_posterior_raw.loc[:,'dc_0'].std() * np.ones(n_data)
#spatially varying earthquake constant coefficient
coeff_1e_mu = np.array([df_stan_posterior_raw.loc[:,f'dc_1e.{k}'].mean() for k in range(n_eq)])
coeff_1e_mu = coeff_1e_mu[eq_inv]
coeff_1e_med = np.array([df_stan_posterior_raw.loc[:,f'dc_1e.{k}'].median() for k in range(n_eq)])
coeff_1e_med = coeff_1e_med[eq_inv]
coeff_1e_sig = np.array([df_stan_posterior_raw.loc[:,f'dc_1e.{k}'].std() for k in range(n_eq)])
coeff_1e_sig = coeff_1e_sig[eq_inv]
#site term constant covariance
coeff_1as_mu = np.array([df_stan_posterior_raw.loc[:,f'dc_1as.{k}'].mean() for k in range(n_sta)])
coeff_1as_mu = coeff_1as_mu[sta_inv]
coeff_1as_med = np.array([df_stan_posterior_raw.loc[:,f'dc_1as.{k}'].median() for k in range(n_sta)])
coeff_1as_med = coeff_1as_med[sta_inv]
coeff_1as_sig = np.array([df_stan_posterior_raw.loc[:,f'dc_1as.{k}'].std() for k in range(n_sta)])
coeff_1as_sig = coeff_1as_sig[sta_inv]
#spatially varying station constant covariance
coeff_1bs_mu = np.array([df_stan_posterior_raw.loc[:,f'dc_1bs.{k}'].mean() for k in range(n_sta)])
coeff_1bs_mu = coeff_1bs_mu[sta_inv]
coeff_1bs_med = np.array([df_stan_posterior_raw.loc[:,f'dc_1bs.{k}'].median() for k in range(n_sta)])
coeff_1bs_med = coeff_1bs_med[sta_inv]
coeff_1bs_sig = np.array([df_stan_posterior_raw.loc[:,f'dc_1bs.{k}'].std() for k in range(n_sta)])
coeff_1bs_sig = coeff_1bs_sig[sta_inv]
# aleatory variability
phi_0_array = np.array([df_stan_posterior_raw.phi_0.mean()]*X_sta_all.shape[0])
tau_0_array = np.array([df_stan_posterior_raw.tau_0.mean()]*X_sta_all.shape[0])
#initiaize flatfile for sumamry of non-erg coefficinets and residuals
df_flatinfo = df_flatfile[['eqid','ssn','eqLat','eqLon','staLat','staLon','eqX','eqY','staX','staY','UTMzone']]
#summary coefficients
coeffs_summary = np.vstack((coeff_0_mu,
coeff_1e_mu,
coeff_1as_mu,
coeff_1bs_mu,
cells_LcA_mu,
coeff_0_med,
coeff_1e_med,
coeff_1as_med,
coeff_1bs_med,
cells_LcA_med,
coeff_0_sig,
coeff_1e_sig,
coeff_1as_sig,
coeff_1bs_sig,
cells_LcA_sig)).T
columns_names = ['dc_0_mean','dc_1e_mean','dc_1as_mean','dc_1bs_mean','Lc_ca_mean',
'dc_0_med', 'dc_1e_med', 'dc_1as_med', 'dc_1bs_med', 'Lc_ca_med',
'dc_0_sig', 'dc_1e_sig', 'dc_1as_sig', 'dc_1bs_sig', 'Lc_ca_sig']
df_coeffs_summary = pd.DataFrame(coeffs_summary, columns = columns_names, index=df_flatfile.index)
#create dataframe with summary coefficients
df_coeffs_summary = pd.merge(df_flatinfo, df_coeffs_summary, how='right', left_index=True, right_index=True)
df_coeffs_summary[['eqid','ssn']] = df_coeffs_summary[['eqid','ssn']].astype(int)
df_coeffs_summary.to_csv(out_dir + out_fname + '_stan_coefficients' + '.csv', index=True)
# GMM prediction
#--- --- --- --- --- --- --- ---
#mean prediction
y_mu = (coeff_0_mu + coeff_1e_mu + coeff_1as_mu + coeff_1bs_mu + cells_LcA_mu)
#compute residuals
res_tot = y_data - y_mu
#residuals computed directly from stan regression
res_between = [df_stan_posterior_raw.loc[:,f'dB.{k}'].mean() for k in range(n_eq)]
res_between = np.array([res_between[k] for k in (eq_inv).astype(int)])
res_within = res_tot - res_between
#summary predictions and residuals
predict_summary = np.vstack((y_mu, res_tot, res_between, res_within)).T
columns_names = ['nerg_mu','res_tot','res_between','res_within']
df_predict_summary = pd.DataFrame(predict_summary, columns = columns_names, index=df_flatfile.index)
#create dataframe with predictions and residuals
df_predict_summary = pd.merge(df_flatinfo, df_predict_summary, how='right', left_index=True, right_index=True)
df_predict_summary[['eqid','ssn']] = df_predict_summary[['eqid','ssn']].astype(int)
df_predict_summary.to_csv(out_dir + out_fname + '_stan_residuals' + '.csv', index=True)
## Summary regression
# ---------------------------
#save summary statistics
stan_summary_fname = out_dir + out_fname + '_stan_summary' + '.txt'
with open(stan_summary_fname, 'w') as f:
print(stan_fit, file=f)
#create and save trace plots
fig_dir = out_dir + 'summary_figs/'
#create figures directory if doesn't exit
pathlib.Path(fig_dir).mkdir(parents=True, exist_ok=True)
#create stan trace plots
for c_name in col_names_hyp:
#create trace plot with arviz
ax = az.plot_trace(stan_fit, var_names=c_name, figsize=(10,5) ).ravel()
ax[0].yaxis.set_major_locator(plt_autotick())
ax[0].set_xlabel('sample value')
ax[0].set_ylabel('frequency')
ax[0].set_title('')
ax[0].grid(axis='both')
ax[1].set_xlabel('iteration')
ax[1].set_ylabel('sample value')
ax[1].grid(axis='both')
ax[1].set_title('')
fig = ax[0].figure
fig.suptitle(c_name)
fig.savefig(fig_dir + out_fname + '_stan_traceplot_' + c_name + '_arviz' + '.png')
return None
| 19,072 | 46.093827 | 130 | py |
ngmm_tools | ngmm_tools-master/Analyses/Python_lib/regression/pystan/regression_pystan_model2_corr_cells_sparse_unbounded_hyp.py | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Tue Jul 13 18:22:15 2021
@author: glavrent
"""
#load variables
import os
import pathlib
import glob
import re #regular expression package
import pickle
from joblib import cpu_count
#arithmetic libraries
import numpy as np
from scipy import sparse
#statistics libraries
import pandas as pd
#plot libraries
import matplotlib as mpl
import matplotlib.pyplot as plt
from matplotlib.ticker import AutoLocator as plt_autotick
import arviz as az
mpl.use('agg')
def RunStan(df_flatfile, df_cellinfo, df_celldist, stan_model_fname,
out_fname, out_dir, res_name='res', c_a_erg=0,
runstan_flag=True,
n_iter=600, n_chains=4,
adapt_delta=0.8, max_treedepth=10,
pystan_ver=2, pystan_parallel=False):
'''
Run full Bayessian regression in Stan. Non-ergodic model includes: a spatially
varying earthquake constant, a spatially varying site constant, a spatially
independent site constant, and partially spatially correlated anelastic
attenuation.
Parameters
----------
df_flatfile : pd.DataFrame
Input data frame containing total residuals, eq and site coordinates.
df_cellinfo : pd.DataFrame
Dataframe with coordinates of anelastic attenuation cells.
df_celldist : pd.DataFrame
Datafame with cell path distances of all records in df_flatfile.
stan_model_fname : string
File name for stan model.
out_fname : string
File name for output files.
out_dir : string
Output directory.
res_name : string, optional
Column name for total residuals. The default is 'res'.
c_a_erg : double, optional
Value of ergodic anelatic attenuation coefficient. Used as mean of cell
specific anelastic attenuation prior distribution. The default is 0.
n_iter : integer, optional
Number of stan samples. The default is 600.
n_chains : integer, optional
Number of MCMC chains. The default is 4.
runstan_flag : bool, optional
Flag for running stan. If true run regression, if false read past regression
output and summarize non-ergodic parameters. The default is True.
adapt_delta : double, optional
Target average proposal acceptance probability for adaptation. The default is 0.8.
max_treedepth : integer, optional
Maximum number of evaluations for each iteration (2^max_treedepth). The default is 10.
pystan_ver : integer, optional
Version of pystan to run. The default is 2.
pystan_parallel : bool, optional
Flag for using multithreaded option in STAN. The default is False.
Returns
-------
None.
'''
## Read Data
#============================
#read stan model
with open(stan_model_fname, "r") as f:
stan_model_code = f.read()
## Preprocess Input Data
#============================
#set rsn column as dataframe index, skip if rsn already the index
if not df_flatfile.index.name == 'rsn':
df_flatfile.set_index('rsn', drop=True, inplace=True)
if not df_celldist.index.name == 'rsn':
df_celldist.set_index('rsn', drop=True, inplace=True)
#set cellid column as dataframe index, skip if cellid already the index
if not df_cellinfo.index.name == 'cellid':
df_cellinfo.set_index('cellid', drop=True, inplace=True)
#number of data
n_data = len(df_flatfile)
#earthquake data
data_eq_all = df_flatfile[['eqid','mag','eqX', 'eqY']].values
_, eq_idx, eq_inv = np.unique(df_flatfile[['eqid']], axis=0, return_inverse=True, return_index=True)
data_eq = data_eq_all[eq_idx,:]
X_eq = data_eq[:,[2,3]] #earthquake coordinates
#create earthquake ids for all records (1 to n_eq)
eq_id = eq_inv + 1
n_eq = len(data_eq)
#station data
data_sta_all = df_flatfile[['ssn','Vs30','staX','staY']].values
_, sta_idx, sta_inv = np.unique( df_flatfile[['ssn']].values, axis = 0, return_inverse=True, return_index=True)
data_sta = data_sta_all[sta_idx,:]
X_sta = data_sta[:,[2,3]] #station coordinates
#create station indices for all records (1 to n_sta)
sta_id = sta_inv + 1
n_sta = len(data_sta)
#ground-motion observations
y_data = df_flatfile[res_name].to_numpy().copy()
#cell data
#reorder and only keep records included in the flatfile
df_celldist = df_celldist.reindex(df_flatfile.index)
#cell info
cell_ids_all = df_cellinfo.index
cell_names_all = df_cellinfo.cellname
#cell distance matrix
celldist_all = df_celldist[cell_names_all] #cell-distance matrix with all cells
#find cell with more than one paths
i_cells_valid = np.where(celldist_all.sum(axis=0) > 0)[0] #valid cells with more than one path
cell_ids_valid = cell_ids_all[i_cells_valid]
cell_names_valid = cell_names_all[i_cells_valid]
celldist_valid = celldist_all.loc[:,cell_names_valid].to_numpy() #cell-distance with only non-zero cells
celldist_valid_sp = sparse.csr_matrix(celldist_valid)
#number of cells
n_cell = celldist_all.shape[1]
n_cell_valid = celldist_valid.shape[1]
#cell coordinates
X_cells_valid = df_cellinfo.loc[i_cells_valid,['mptX','mptY']].values
#print Rrup missfits
print('max R_rup misfit', np.abs(df_flatfile.Rrup.values - celldist_valid.sum(axis=1)).max())
stan_data = {'N': n_data,
'NEQ': n_eq,
'NSTAT': n_sta,
'NCELL': n_cell_valid,
'NCELL_SP': len(celldist_valid_sp.data),
'eq': eq_id, #earthquake id
'stat': sta_id, #station id
'X_e': X_eq, #earthquake coordinates
'X_s': X_sta, #station coordinates
'X_c': X_cells_valid,
'rec_mu': np.zeros(y_data.shape),
'RC_val': celldist_valid_sp.data,
'RC_w': celldist_valid_sp.indices+1,
'RC_u': celldist_valid_sp.indptr+1,
'c_a_erg': c_a_erg,
'Y': y_data,
}
stan_data_fname = out_fname + '_stan_data' + '.Rdata'
## Run Stan, fit model
#============================
#number of cores
n_cpu = max(cpu_count() -1,1)
#filename for STAN regression raw output file saved as pkl
stan_fit_fname = out_dir + out_fname + '_stan_fit' + '.pkl'
#run stan
if runstan_flag:
#control paramters
control_stan = {'adapt_delta':adapt_delta, 'max_treedepth':max_treedepth}
if pystan_ver == 2:
import pystan
if (not pystan_parallel) or n_cpu<=n_chains:
#compile
stan_model = pystan.StanModel(model_code=stan_model_code)
#full Bayesian statistics
stan_fit = stan_model.sampling(data=stan_data, iter=n_iter, chains = n_chains, refresh=10, control = control_stan)
else:
#number of cores per chain
n_cpu_chain = int(np.floor(n_cpu/n_chains))
#multi-processing arguments
os.environ['STAN_NUM_THREADS'] = str(n_cpu_chain)
extra_compile_args = ['-pthread', '-DSTAN_THREADS']
#compile
stan_model = pystan.StanModel(model_code=stan_model_code, extra_compile_args=extra_compile_args)
#full Bayesian statistics
stan_fit = stan_model.sampling(data=stan_data, iter=n_iter, chains = n_chains, refresh=1, control = control_stan)
elif pystan_ver == 3:
import nest_asyncio
import stan
nest_asyncio.apply()
#compile
stan_model = stan.build(stan_model_code, data=stan_data, random_seed=1)
#full Bayesian statistics
stan_fit = stan_model.sample(num_chains=n_chains, num_samples=n_iter, max_depth=max_treedepth, delta=adapt_delta)
#save stan model and fit
pathlib.Path(out_dir).mkdir(parents=True, exist_ok=True)
with open(stan_fit_fname, "wb") as f:
pickle.dump({'model' : stan_model, 'fit' : stan_fit}, f, protocol=-1)
else:
#load model and fit for postprocessing if has already been executed
with open(stan_fit_fname, "rb") as f:
data_dict = pickle.load(f)
stan_fit = data_dict['fit']
stan_model = data_dict['model']
del data_dict
## Postprocessing Data
#============================
## Extract posterior samples
# ---------------------------
#hyper-parameters
col_names_hyp = ['dc_0','ell_1e', 'ell_1as', 'omega_1e', 'omega_1as', 'omega_1bs',
'mu_cap', 'ell_ca1p', 'omega_ca1p', 'omega_ca2p',
'phi_0','tau_0']
#non-ergodic terms
col_names_dc_1e = ['dc_1e.%i'%(k) for k in range(n_eq)]
col_names_dc_1as = ['dc_1as.%i'%(k) for k in range(n_sta)]
col_names_dc_1bs = ['dc_1bs.%i'%(k) for k in range(n_sta)]
col_names_dB = ['dB.%i'%(k) for k in range(n_eq)]
col_names_cap = ['c_cap.%i'%(c_id) for c_id in cell_ids_valid]
col_names_all = col_names_hyp + col_names_dc_1e + col_names_dc_1as + col_names_dc_1bs + col_names_cap + col_names_dB
#summarize raw posterior distributions
stan_posterior = np.stack([stan_fit[c_n].flatten() for c_n in col_names_hyp], axis=1)
#adjustment terms
if pystan_ver == 2:
stan_posterior = np.concatenate((stan_posterior, stan_fit['dc_1e']), axis=1)
stan_posterior = np.concatenate((stan_posterior, stan_fit['dc_1as']), axis=1)
stan_posterior = np.concatenate((stan_posterior, stan_fit['dc_1bs']), axis=1)
stan_posterior = np.concatenate((stan_posterior, stan_fit['c_cap']), axis=1)
stan_posterior = np.concatenate((stan_posterior, stan_fit['dB']), axis=1)
else:
stan_posterior = np.concatenate((stan_posterior, stan_fit['dc_1e'].T), axis=1)
stan_posterior = np.concatenate((stan_posterior, stan_fit['dc_1as'].T), axis=1)
stan_posterior = np.concatenate((stan_posterior, stan_fit['dc_1bs'].T), axis=1)
stan_posterior = np.concatenate((stan_posterior, stan_fit['c_cap'].T), axis=1)
stan_posterior = np.concatenate((stan_posterior, stan_fit['dB'].T), axis=1)
#save raw-posterior distribution
df_stan_posterior_raw = pd.DataFrame(stan_posterior, columns = col_names_all)
df_stan_posterior_raw.to_csv(out_dir + out_fname + '_stan_posterior_raw' + '.csv', index=False)
## Summarize hyper-parameters
# ---------------------------
#summarize posterior distributions of hyper-parameters
perc_array = np.array([0.05,0.25,0.5,0.75,0.95])
df_stan_hyp = df_stan_posterior_raw[col_names_hyp].quantile(perc_array)
df_stan_hyp = df_stan_hyp.append(df_stan_posterior_raw[col_names_hyp].mean(axis = 0), ignore_index=True)
df_stan_hyp.index = ['prc_%.2f'%(prc) for prc in perc_array]+['mean']
df_stan_hyp.to_csv(out_dir + out_fname + '_stan_hyperparameters' + '.csv', index=True)
#detailed posterior percentiles of posterior distributions
perc_array = np.arange(0.01,0.99,0.01)
df_stan_posterior = df_stan_posterior_raw[col_names_hyp].quantile(perc_array)
df_stan_posterior.index.name = 'prc'
df_stan_posterior .to_csv(out_dir + out_fname + '_stan_hyperposterior' + '.csv', index=True)
del col_names_dc_1e, col_names_dc_1as, col_names_dc_1bs, col_names_dB
del stan_posterior, col_names_all
## Sample spatially varying coefficients and predictions at record locations
# ---------------------------
# earthquake and station location in database
X_eq_all = df_flatfile[['eqX', 'eqY']].values
X_sta_all = df_flatfile[['staX','staY']].values
# GMM anelastic attenuation
#--- --- --- --- --- --- --- ---
cells_ca_mu = np.array([df_stan_posterior_raw.loc[:,'c_cap.%i'%(k)].mean() for k in cell_ids_valid])
cells_ca_med = np.array([df_stan_posterior_raw.loc[:,'c_cap.%i'%(k)].median() for k in cell_ids_valid])
cells_ca_sig = np.array([df_stan_posterior_raw.loc[:,'c_cap.%i'%(k)].std() for k in cell_ids_valid])
#effect of anelastic attenuation in GM
cells_LcA_mu = celldist_valid_sp @ cells_ca_mu
cells_LcA_med = celldist_valid_sp @ cells_ca_med
cells_LcA_sig = np.sqrt(celldist_valid_sp.power(2) @ cells_ca_sig**2)
#summary attenuation cells
catten_summary = np.vstack((np.tile(c_a_erg, n_cell_valid),
cells_ca_mu,
cells_ca_med,
cells_ca_sig)).T
columns_names = ['c_a_erg','c_cap_mean','c_cap_med','c_cap_sig']
df_catten_summary = pd.DataFrame(catten_summary, columns = columns_names, index=df_cellinfo.index[i_cells_valid])
#create dataframe with summary attenuation cells
df_catten_summary = pd.merge(df_cellinfo[['cellname','mptLat','mptLon','mptX','mptY','mptZ','UTMzone']],
df_catten_summary, how='right', left_index=True, right_index=True)
df_catten_summary.to_csv(out_dir + out_fname + '_stan_catten' + '.csv', index=True)
# GMM coefficients
#--- --- --- --- --- --- --- ---
#constant shift coefficient
coeff_0_mu = df_stan_posterior_raw.loc[:,'dc_0'].mean() * np.ones(n_data)
coeff_0_med = df_stan_posterior_raw.loc[:,'dc_0'].median() * np.ones(n_data)
coeff_0_sig = df_stan_posterior_raw.loc[:,'dc_0'].std() * np.ones(n_data)
#spatially varying earthquake constant coefficient
coeff_1e_mu = np.array([df_stan_posterior_raw.loc[:,f'dc_1e.{k}'].mean() for k in range(n_eq)])
coeff_1e_mu = coeff_1e_mu[eq_inv]
coeff_1e_med = np.array([df_stan_posterior_raw.loc[:,f'dc_1e.{k}'].median() for k in range(n_eq)])
coeff_1e_med = coeff_1e_med[eq_inv]
coeff_1e_sig = np.array([df_stan_posterior_raw.loc[:,f'dc_1e.{k}'].std() for k in range(n_eq)])
coeff_1e_sig = coeff_1e_sig[eq_inv]
#site term constant covariance
coeff_1as_mu = np.array([df_stan_posterior_raw.loc[:,f'dc_1as.{k}'].mean() for k in range(n_sta)])
coeff_1as_mu = coeff_1as_mu[sta_inv]
coeff_1as_med = np.array([df_stan_posterior_raw.loc[:,f'dc_1as.{k}'].median() for k in range(n_sta)])
coeff_1as_med = coeff_1as_med[sta_inv]
coeff_1as_sig = np.array([df_stan_posterior_raw.loc[:,f'dc_1as.{k}'].std() for k in range(n_sta)])
coeff_1as_sig = coeff_1as_sig[sta_inv]
#spatially varying station constant covariance
coeff_1bs_mu = np.array([df_stan_posterior_raw.loc[:,f'dc_1bs.{k}'].mean() for k in range(n_sta)])
coeff_1bs_mu = coeff_1bs_mu[sta_inv]
coeff_1bs_med = np.array([df_stan_posterior_raw.loc[:,f'dc_1bs.{k}'].median() for k in range(n_sta)])
coeff_1bs_med = coeff_1bs_med[sta_inv]
coeff_1bs_sig = np.array([df_stan_posterior_raw.loc[:,f'dc_1bs.{k}'].std() for k in range(n_sta)])
coeff_1bs_sig = coeff_1bs_sig[sta_inv]
# aleatory variability
phi_0_array = np.array([df_stan_posterior_raw.phi_0.mean()]*X_sta_all.shape[0])
tau_0_array = np.array([df_stan_posterior_raw.tau_0.mean()]*X_sta_all.shape[0])
#initiaize flatfile for sumamry of non-erg coefficinets and residuals
df_flatinfo = df_flatfile[['eqid','ssn','eqLat','eqLon','staLat','staLon','eqX','eqY','staX','staY','UTMzone']]
#summary coefficients
coeffs_summary = np.vstack((coeff_0_mu,
coeff_1e_mu,
coeff_1as_mu,
coeff_1bs_mu,
cells_LcA_mu,
coeff_0_med,
coeff_1e_med,
coeff_1as_med,
coeff_1bs_med,
cells_LcA_med,
coeff_0_sig,
coeff_1e_sig,
coeff_1as_sig,
coeff_1bs_sig,
cells_LcA_sig)).T
columns_names = ['dc_0_mean','dc_1e_mean','dc_1as_mean','dc_1bs_mean','Lc_ca_mean',
'dc_0_med', 'dc_1e_med', 'dc_1as_med', 'dc_1bs_med', 'Lc_ca_med',
'dc_0_sig', 'dc_1e_sig', 'dc_1as_sig', 'dc_1bs_sig', 'Lc_ca_sig']
df_coeffs_summary = pd.DataFrame(coeffs_summary, columns = columns_names, index=df_flatfile.index)
#create dataframe with summary coefficients
df_coeffs_summary = pd.merge(df_flatinfo, df_coeffs_summary, how='right', left_index=True, right_index=True)
df_coeffs_summary[['eqid','ssn']] = df_coeffs_summary[['eqid','ssn']].astype(int)
df_coeffs_summary.to_csv(out_dir + out_fname + '_stan_coefficients' + '.csv', index=True)
# GMM prediction
#--- --- --- --- --- --- --- ---
#mean prediction
y_mu = (coeff_0_mu + coeff_1e_mu + coeff_1as_mu + coeff_1bs_mu + cells_LcA_mu)
#compute residuals
res_tot = y_data - y_mu
#residuals computed directly from stan regression
res_between = [df_stan_posterior_raw.loc[:,f'dB.{k}'].mean() for k in range(n_eq)]
res_between = np.array([res_between[k] for k in (eq_inv).astype(int)])
res_within = res_tot - res_between
#summary predictions and residuals
predict_summary = np.vstack((y_mu, res_tot, res_between, res_within)).T
columns_names = ['nerg_mu','res_tot','res_between','res_within']
df_predict_summary = pd.DataFrame(predict_summary, columns = columns_names, index=df_flatfile.index)
#create dataframe with predictions and residuals
df_predict_summary = pd.merge(df_flatinfo, df_predict_summary, how='right', left_index=True, right_index=True)
df_predict_summary[['eqid','ssn']] = df_predict_summary[['eqid','ssn']].astype(int)
df_predict_summary.to_csv(out_dir + out_fname + '_stan_residuals' + '.csv', index=True)
## Summary regression
# ---------------------------
#save summary statistics
stan_summary_fname = out_dir + out_fname + '_stan_summary' + '.txt'
with open(stan_summary_fname, 'w') as f:
print(stan_fit, file=f)
#create and save trace plots
fig_dir = out_dir + 'summary_figs/'
#create figures directory if doesn't exit
pathlib.Path(fig_dir).mkdir(parents=True, exist_ok=True)
#create stan trace plots
for c_name in col_names_hyp:
#create trace plot with arviz
ax = az.plot_trace(stan_fit, var_names=c_name, figsize=(10,5) ).ravel()
ax[0].yaxis.set_major_locator(plt_autotick())
ax[0].set_xlabel('sample value')
ax[0].set_ylabel('frequency')
ax[0].set_title('')
ax[0].grid(axis='both')
ax[1].set_xlabel('iteration')
ax[1].set_ylabel('sample value')
ax[1].grid(axis='both')
ax[1].set_title('')
fig = ax[0].figure
fig.suptitle(c_name)
fig.savefig(fig_dir + out_fname + '_stan_traceplot_' + c_name + '_arviz' + '.png')
return None
| 19,326 | 46.139024 | 130 | py |
ngmm_tools | ngmm_tools-master/Analyses/Python_lib/regression/pystan/regression_pystan_model3_corr_cells_unbounded_hyp.py | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Tue Jul 13 18:22:15 2021
@author: glavrent
"""
#load variables
import os
import pathlib
import glob
import re #regular expression package
import pickle
from joblib import cpu_count
#arithmetic libraries
import numpy as np
#statistics libraries
import pandas as pd
#plot libraries
import matplotlib as mpl
import matplotlib.pyplot as plt
from matplotlib.ticker import AutoLocator as plt_autotick
import arviz as az
mpl.use('agg')
def RunStan(df_flatfile, df_cellinfo, df_celldist, stan_model_fname,
out_fname, out_dir, res_name='res', c_2_erg=0, c_3_erg=0, c_a_erg=0,
runstan_flag=True,
n_iter=600, n_chains=4,
adapt_delta=0.8, max_treedepth=10,
pystan_ver=2, pystan_parallel=False):
'''
Run full Bayessian regression in Stan. Non-ergodic model includes: a spatially
varying earthquake constant, a spatially varying site constant, a spatially
independent site constant, and partially spatially correlated anelastic
attenuation.
Parameters
----------
df_flatfile : pd.DataFrame
Input data frame containing total residuals, eq and site coordinates.
df_cellinfo : pd.DataFrame
Dataframe with coordinates of anelastic attenuation cells.
df_celldist : pd.DataFrame
Datafame with cell path distances of all records in df_flatfile.
stan_model_fname : string
File name for stan model.
out_fname : string
File name for output files.
out_dir : string
Output directory.
res_name : string, optional
Column name for total residuals. The default is 'res'.
c_2_erg : double, optional
Value of ergodic geometrical spreading coefficient. The default is 0.
c_3_erg : double, optional
Value of ergodic Vs30 coefficient. The default is 0.
c_a_erg : double, optional
Value of ergodic anelatic attenuation coefficient. Used as mean of cell
specific anelastic attenuation prior distribution. The default is 0.
n_iter : integer, optional
Number of stan samples. The default is 600.
n_chains : integer, optional
Number of MCMC chains. The default is 4.
runstan_flag : bool, optional
Flag for running stan. If true run regression, if false read past regression
output and summarize non-ergodic parameters. The default is True.
adapt_delta : double, optional
Target average proposal acceptance probability for adaptation. The default is 0.8.
max_treedepth : integer, optional
Maximum number of evaluations for each iteration (2^max_treedepth). The default is 10.
pystan_ver : integer, optional
Version of pystan to run. The default is 2.
pystan_parallel : bool, optional
Flag for using multithreaded option in STAN. The default is False.
Returns
-------
None.
'''
#number of cores
n_cpu = max(cpu_count() -1,1)
## Read Data
#============================
#read stan model
with open(stan_model_fname, "r") as f:
stan_model_code = f.read()
## Preprocess Input Data
#============================
#set rsn column as dataframe index, skip if rsn already the index
if not df_flatfile.index.name == 'rsn':
df_flatfile.set_index('rsn', drop=True, inplace=True)
if not df_celldist.index.name == 'rsn':
df_celldist.set_index('rsn', drop=True, inplace=True)
#set cellid column as dataframe index, skip if cellid already the index
if not df_cellinfo.index.name == 'cellid':
df_cellinfo.set_index('cellid', drop=True, inplace=True)
#number of data
n_data = len(df_flatfile)
#earthquake data
data_eq_all = df_flatfile[['eqid','mag','eqX', 'eqY']].values
_, eq_idx, eq_inv = np.unique(df_flatfile[['eqid']], axis=0, return_inverse=True, return_index=True)
data_eq = data_eq_all[eq_idx,:]
X_eq = data_eq[:,[2,3]] #earthquake coordinates
#create earthquake ids for all records (1 to n_eq)
eq_id = eq_inv + 1
n_eq = len(data_eq)
#station data
data_sta_all = df_flatfile[['ssn','Vs30','x_3','staX','staY']].values
_, sta_idx, sta_inv = np.unique( df_flatfile[['ssn']].values, axis = 0, return_inverse=True, return_index=True)
data_sta = data_sta_all[sta_idx,:]
X_sta = data_sta[:,[3,4]] #station coordinates
#create station indices for all records (1 to n_sta)
sta_id = sta_inv + 1
n_sta = len(data_sta)
#geometrical spreading covariates
x_2 = df_flatfile['x_2'].values
#vs30 covariates
x_3 = df_flatfile['x_3'].values[sta_idx]
#ground-motion observations
y_data = df_flatfile[res_name].to_numpy().copy()
#cell data
#reorder and only keep records included in the flatfile
df_celldist = df_celldist.reindex(df_flatfile.index)
#cell info
cell_ids_all = df_cellinfo.index
cell_names_all = df_cellinfo.cellname
#cell distance matrix
celldist_all = df_celldist[cell_names_all] #cell-distance matrix with all cells
#find cell with more than one paths
i_cells_valid = np.where(celldist_all.sum(axis=0) > 0)[0] #valid cells with more than one path
cell_ids_valid = cell_ids_all[i_cells_valid]
cell_names_valid = cell_names_all[i_cells_valid]
celldist_valid = celldist_all.loc[:,cell_names_valid] #cell-distance with only non-zero cells
#number of cells
n_cell = celldist_all.shape[1]
n_cell_valid = celldist_valid.shape[1]
#cell coordinates
X_cells_valid = df_cellinfo.loc[i_cells_valid,['mptX','mptY']].values
#print Rrup missfits
print('max R_rup misfit', (df_flatfile.Rrup.values - celldist_valid.sum(axis=1)).abs().max())
stan_data = {'N': n_data,
'NEQ': n_eq,
'NSTAT': n_sta,
'NCELL': n_cell_valid,
'eq': eq_id, #earthquake id
'stat': sta_id, #station id
'rec_mu': np.zeros(y_data.shape),
'Y': y_data,
'x_2': x_2,
'x_3': x_3,
'c_2_erg': c_2_erg,
'c_3_erg': c_3_erg,
'c_a_erg': c_a_erg,
'X_e': X_eq, #earthquake coordinates
'X_s': X_sta, #station coordinates
'X_c': X_cells_valid,
'RC': celldist_valid.to_numpy(),
}
stan_data_fname = out_fname + '_stan_data' + '.Rdata'
## Run Stan, fit model
#============================
#number of cores
n_cpu = max(cpu_count() -1,1)
#filename for STAN regression raw output file saved as pkl
stan_fit_fname = out_dir + out_fname + '_stan_fit' + '.pkl'
#run stan
if runstan_flag:
#control paramters
control_stan = {'adapt_delta':adapt_delta, 'max_treedepth':max_treedepth}
if pystan_ver == 2:
import pystan
if (not pystan_parallel) or n_cpu<=n_chains:
#compile
stan_model = pystan.StanModel(model_code=stan_model_code)
#full Bayesian statistics
stan_fit = stan_model.sampling(data=stan_data, iter=n_iter, chains = n_chains, refresh=10, control = control_stan)
else:
#number of cores per chain
n_cpu_chain = int(np.floor(n_cpu/n_chains))
#multi-processing arguments
os.environ['STAN_NUM_THREADS'] = str(n_cpu_chain)
extra_compile_args = ['-pthread', '-DSTAN_THREADS']
#compile
stan_model = pystan.StanModel(model_code=stan_model_code, extra_compile_args=extra_compile_args)
#full Bayesian statistics
stan_fit = stan_model.sampling(data=stan_data, iter=n_iter, chains = n_chains, refresh=1, control = control_stan)
elif pystan_ver == 3:
import nest_asyncio
import stan
nest_asyncio.apply()
#compile
stan_model = stan.build(stan_model_code, data=stan_data, random_seed=1)
#full Bayesian statistics
stan_fit = stan_model.sample(num_chains=n_chains, num_samples=n_iter, max_depth=max_treedepth, delta=adapt_delta)
#save stan model and fit
pathlib.Path(out_dir).mkdir(parents=True, exist_ok=True)
with open(stan_fit_fname, "wb") as f:
pickle.dump({'model' : stan_model, 'fit' : stan_fit}, f, protocol=-1)
else:
#load model and fit for postprocessing if has already been executed
with open(stan_fit_fname, "rb") as f:
data_dict = pickle.load(f)
stan_fit = data_dict['fit']
stan_model = data_dict['model']
del data_dict
## Postprocessing Data
#============================
## Extract posterior samples
# ---------------------------
#hyper-parameters
col_names_hyp = ['dc_0','mu_2p','mu_3s',
'ell_1e', 'ell_1as', 'omega_1e', 'omega_1as', 'omega_1bs',
'ell_2p', 'ell_3s', 'omega_2p', 'omega_3s',
'mu_cap', 'ell_ca1p', 'omega_ca1p', 'omega_ca2p',
'phi_0','tau_0']
#non-ergodic terms
col_names_dc_1e = ['dc_1e.%i'%(k) for k in range(n_eq)]
col_names_dc_1as = ['dc_1as.%i'%(k) for k in range(n_sta)]
col_names_dc_1bs = ['dc_1bs.%i'%(k) for k in range(n_sta)]
col_names_c_2p = ['c_2p.%i'%(k) for k in range(n_eq)]
col_names_c_3s = ['c_3s.%i'%(k) for k in range(n_sta)]
col_names_dB = ['dB.%i'%(k) for k in range(n_eq)]
col_names_cap = ['c_cap.%i'%(c_id) for c_id in cell_ids_valid]
col_names_all = (col_names_hyp + col_names_dc_1e + col_names_dc_1as + col_names_dc_1bs +
col_names_c_2p + col_names_c_3s + col_names_cap + col_names_dB)
#summarize raw posterior distributions
stan_posterior = np.stack([stan_fit[c_n].flatten() for c_n in col_names_hyp], axis=1)
#adjustment terms
if pystan_ver == 2:
stan_posterior = np.concatenate((stan_posterior, stan_fit['dc_1e']), axis=1)
stan_posterior = np.concatenate((stan_posterior, stan_fit['dc_1as']), axis=1)
stan_posterior = np.concatenate((stan_posterior, stan_fit['dc_1bs']), axis=1)
stan_posterior = np.concatenate((stan_posterior, stan_fit['c_2p']), axis=1)
stan_posterior = np.concatenate((stan_posterior, stan_fit['c_3s']), axis=1)
stan_posterior = np.concatenate((stan_posterior, stan_fit['c_cap']), axis=1)
stan_posterior = np.concatenate((stan_posterior, stan_fit['dB']), axis=1)
else:
stan_posterior = np.concatenate((stan_posterior, stan_fit['dc_1e'].T), axis=1)
stan_posterior = np.concatenate((stan_posterior, stan_fit['dc_1as'].T), axis=1)
stan_posterior = np.concatenate((stan_posterior, stan_fit['dc_1bs'].T), axis=1)
stan_posterior = np.concatenate((stan_posterior, stan_fit['c_2p'].T), axis=1)
stan_posterior = np.concatenate((stan_posterior, stan_fit['c_3s'].T), axis=1)
stan_posterior = np.concatenate((stan_posterior, stan_fit['c_cap'].T), axis=1)
stan_posterior = np.concatenate((stan_posterior, stan_fit['dB'].T), axis=1)
#save raw-posterior distribution
df_stan_posterior_raw = pd.DataFrame(stan_posterior, columns = col_names_all)
df_stan_posterior_raw.to_csv(out_dir + out_fname + '_stan_posterior_raw' + '.csv', index=False)
## Summarize hyper-parameters
# ---------------------------
#summarize posterior distributions of hyper-parameters
perc_array = np.array([0.05,0.25,0.5,0.75,0.95])
df_stan_hyp = df_stan_posterior_raw[col_names_hyp].quantile(perc_array)
df_stan_hyp = df_stan_hyp.append(df_stan_posterior_raw[col_names_hyp].mean(axis = 0), ignore_index=True)
df_stan_hyp.index = ['prc_%.2f'%(prc) for prc in perc_array]+['mean']
df_stan_hyp.to_csv(out_dir + out_fname + '_stan_hyperparameters' + '.csv', index=True)
#detailed posterior percentiles of posterior distributions
perc_array = np.arange(0.01,0.99,0.01)
df_stan_posterior = df_stan_posterior_raw[col_names_hyp].quantile(perc_array)
df_stan_posterior.index.name = 'prc'
df_stan_posterior .to_csv(out_dir + out_fname + '_stan_hyperposterior' + '.csv', index=True)
del col_names_dc_1e, col_names_dc_1as, col_names_dc_1bs, col_names_c_2p, col_names_c_3s, col_names_dB
del stan_posterior, col_names_all
## Sample spatially varying coefficients and predictions at record locations
# ---------------------------
# earthquake and station location in database
X_eq_all = df_flatfile[['eqX', 'eqY']].values
X_sta_all = df_flatfile[['staX','staY']].values
# GMM anelastic attenuation
#--- --- --- --- --- --- --- ---
cells_ca_mu = np.array([df_stan_posterior_raw.loc[:,'c_cap.%i'%(k)].mean() for k in cell_ids_valid])
cells_ca_med = np.array([df_stan_posterior_raw.loc[:,'c_cap.%i'%(k)].median() for k in cell_ids_valid])
cells_ca_sig = np.array([df_stan_posterior_raw.loc[:,'c_cap.%i'%(k)].std() for k in cell_ids_valid])
#effect of anelastic attenuation in GM
cells_LcA_mu = celldist_valid.values @ cells_ca_mu
cells_LcA_med = celldist_valid.values @ cells_ca_med
cells_LcA_sig = np.sqrt(np.square(celldist_valid.values) @ cells_ca_sig**2)
#summary attenuation cells
catten_summary = np.vstack((np.tile(c_a_erg, n_cell_valid),
cells_ca_mu,
cells_ca_med,
cells_ca_sig)).T
columns_names = ['c_a_erg','c_cap_mean','c_cap_med','c_cap_sig']
df_catten_summary = pd.DataFrame(catten_summary, columns = columns_names, index=df_cellinfo.index[i_cells_valid])
#create dataframe with summary attenuation cells
df_catten_summary = pd.merge(df_cellinfo[['cellname','mptLat','mptLon','mptX','mptY','mptZ','UTMzone']],
df_catten_summary, how='right', left_index=True, right_index=True)
df_catten_summary.to_csv(out_dir + out_fname + '_stan_catten' + '.csv', index=True)
# GMM coefficients
#--- --- --- --- --- --- --- ---
#constant shift coefficient
coeff_0_mu = df_stan_posterior_raw.loc[:,'dc_0'].mean() * np.ones(n_data)
coeff_0_med = df_stan_posterior_raw.loc[:,'dc_0'].median() * np.ones(n_data)
coeff_0_sig = df_stan_posterior_raw.loc[:,'dc_0'].std() * np.ones(n_data)
#spatially varying earthquake constant coefficient
coeff_1e_mu = np.array([df_stan_posterior_raw.loc[:,f'dc_1e.{k}'].mean() for k in range(n_eq)])
coeff_1e_mu = coeff_1e_mu[eq_inv]
coeff_1e_med = np.array([df_stan_posterior_raw.loc[:,f'dc_1e.{k}'].median() for k in range(n_eq)])
coeff_1e_med = coeff_1e_med[eq_inv]
coeff_1e_sig = np.array([df_stan_posterior_raw.loc[:,f'dc_1e.{k}'].std() for k in range(n_eq)])
coeff_1e_sig = coeff_1e_sig[eq_inv]
#site term constant covariance
coeff_1as_mu = np.array([df_stan_posterior_raw.loc[:,f'dc_1as.{k}'].mean() for k in range(n_sta)])
coeff_1as_mu = coeff_1as_mu[sta_inv]
coeff_1as_med = np.array([df_stan_posterior_raw.loc[:,f'dc_1as.{k}'].median() for k in range(n_sta)])
coeff_1as_med = coeff_1as_med[sta_inv]
coeff_1as_sig = np.array([df_stan_posterior_raw.loc[:,f'dc_1as.{k}'].std() for k in range(n_sta)])
coeff_1as_sig = coeff_1as_sig[sta_inv]
#spatially varying station constant covariance
coeff_1bs_mu = np.array([df_stan_posterior_raw.loc[:,f'dc_1bs.{k}'].mean() for k in range(n_sta)])
coeff_1bs_mu = coeff_1bs_mu[sta_inv]
coeff_1bs_med = np.array([df_stan_posterior_raw.loc[:,f'dc_1bs.{k}'].median() for k in range(n_sta)])
coeff_1bs_med = coeff_1bs_med[sta_inv]
coeff_1bs_sig = np.array([df_stan_posterior_raw.loc[:,f'dc_1bs.{k}'].std() for k in range(n_sta)])
coeff_1bs_sig = coeff_1bs_sig[sta_inv]
#spatially varying geometrical spreading coefficient
coeff_2p_mu = np.array([df_stan_posterior_raw.loc[:,f'c_2p.{k}'].mean() for k in range(n_eq)])
coeff_2p_mu = coeff_2p_mu[eq_inv]
coeff_2p_med = np.array([df_stan_posterior_raw.loc[:,f'c_2p.{k}'].median() for k in range(n_eq)])
coeff_2p_med = coeff_2p_med[eq_inv]
coeff_2p_sig = np.array([df_stan_posterior_raw.loc[:,f'c_2p.{k}'].std() for k in range(n_eq)])
coeff_2p_sig = coeff_2p_sig[eq_inv]
#spatially varying Vs30 coefficient
coeff_3s_mu = np.array([df_stan_posterior_raw.loc[:,f'c_3s.{k}'].mean() for k in range(n_sta)])
coeff_3s_mu = coeff_3s_mu[sta_inv]
coeff_3s_med = np.array([df_stan_posterior_raw.loc[:,f'c_3s.{k}'].median() for k in range(n_sta)])
coeff_3s_med = coeff_3s_med[sta_inv]
coeff_3s_sig = np.array([df_stan_posterior_raw.loc[:,f'c_3s.{k}'].std() for k in range(n_sta)])
coeff_3s_sig = coeff_3s_sig[sta_inv]
# aleatory variability
phi_0_array = np.array([df_stan_posterior_raw.phi_0.mean()]*X_sta_all.shape[0])
tau_0_array = np.array([df_stan_posterior_raw.tau_0.mean()]*X_sta_all.shape[0])
#initiaize flatfile for sumamry of non-erg coefficinets and residuals
df_flatinfo = df_flatfile[['eqid','ssn','eqLat','eqLon','staLat','staLon','eqX','eqY','staX','staY','UTMzone']]
#summary coefficients
coeffs_summary = np.vstack((coeff_0_mu,
coeff_1e_mu,
coeff_1as_mu,
coeff_1bs_mu,
coeff_2p_mu,
coeff_3s_mu,
cells_LcA_mu,
coeff_0_med,
coeff_1e_med,
coeff_1as_med,
coeff_1bs_med,
coeff_2p_med,
coeff_3s_med,
cells_LcA_med,
coeff_0_sig,
coeff_1e_sig,
coeff_1as_sig,
coeff_1bs_sig,
coeff_2p_sig,
coeff_3s_sig,
cells_LcA_sig)).T
columns_names = ['dc_0_mean','dc_1e_mean','dc_1as_mean','dc_1bs_mean','c_2p_mean','c_3s_mean','Lc_ca_mean',
'dc_0_med', 'dc_1e_med', 'dc_1as_med', 'dc_1bs_med', 'c_2p_med', 'c_3s_med', 'Lc_ca_med',
'dc_0_sig', 'dc_1e_sig', 'dc_1as_sig', 'dc_1bs_sig', 'c_2p_sig', 'c_3s_sig', 'Lc_ca_sig']
df_coeffs_summary = pd.DataFrame(coeffs_summary, columns = columns_names, index=df_flatfile.index)
#create dataframe with summary coefficients
df_coeffs_summary = pd.merge(df_flatinfo, df_coeffs_summary, how='right', left_index=True, right_index=True)
df_coeffs_summary[['eqid','ssn']] = df_coeffs_summary[['eqid','ssn']].astype(int)
df_coeffs_summary.to_csv(out_dir + out_fname + '_stan_coefficients' + '.csv', index=True)
# GMM prediction
#--- --- --- --- --- --- --- ---
#mean prediction
y_mu = (coeff_0_mu + coeff_1e_mu + coeff_1as_mu + coeff_1bs_mu + coeff_2p_mu*x_2 + coeff_3s_mu*x_3[sta_inv] + cells_LcA_mu)
#compute residuals
res_tot = y_data - y_mu
#residuals computed directly from stan regression
res_between = [df_stan_posterior_raw.loc[:,f'dB.{k}'].mean() for k in range(n_eq)]
res_between = np.array([res_between[k] for k in (eq_inv).astype(int)])
res_within = res_tot - res_between
#summary predictions and residuals
predict_summary = np.vstack((y_mu, res_tot, res_between, res_within)).T
columns_names = ['nerg_mu','res_tot','res_between','res_within']
df_predict_summary = pd.DataFrame(predict_summary, columns = columns_names, index=df_flatfile.index)
#create dataframe with predictions and residuals
df_predict_summary = pd.merge(df_flatinfo, df_predict_summary, how='right', left_index=True, right_index=True)
df_predict_summary[['eqid','ssn']] = df_predict_summary[['eqid','ssn']].astype(int)
df_predict_summary.to_csv(out_dir + out_fname + '_stan_residuals' + '.csv', index=True)
## Summary regression
# ---------------------------
#save summary statistics
stan_summary_fname = out_dir + out_fname + '_stan_summary' + '.txt'
with open(stan_summary_fname, 'w') as f:
print(stan_fit, file=f)
#create and save trace plots
fig_dir = out_dir + 'summary_figs/'
#create figures directory if doesn't exit
pathlib.Path(fig_dir).mkdir(parents=True, exist_ok=True)
#create stan trace plots
for c_name in col_names_hyp:
#create trace plot with arviz
ax = az.plot_trace(stan_fit, var_names=c_name, figsize=(10,5) ).ravel()
ax[0].yaxis.set_major_locator(plt_autotick())
ax[0].set_xlabel('sample value')
ax[0].set_ylabel('frequency')
ax[0].set_title('')
ax[0].grid(axis='both')
ax[1].set_xlabel('iteration')
ax[1].set_ylabel('sample value')
ax[1].grid(axis='both')
ax[1].set_title('')
fig = ax[0].figure
fig.suptitle(c_name)
fig.savefig(fig_dir + out_fname + '_stan_traceplot_' + c_name + '_arviz' + '.png')
return None
| 21,689 | 46.986726 | 130 | py |
ngmm_tools | ngmm_tools-master/Analyses/Python_lib/regression/cmdstan/regression_cmdstan_model1_unbounded_hyp.py | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Tue Jul 13 18:22:15 2021
@author: glavrent
"""
#load variables
import pathlib
from joblib import cpu_count
#arithmetic libraries
import numpy as np
#statistics libraries
import pandas as pd
#plot libraries
import matplotlib as mpl
from matplotlib.ticker import AutoLocator as plt_autotick
import arviz as az
mpl.use('agg')
#stan library
import cmdstanpy
def RunStan(df_flatfile, stan_model_fname,
out_fname, out_dir, res_name='res',
n_iter_warmup=300, n_iter_sampling=300, n_chains=4,
max_treedepth=10, adapt_delta=0.80,
stan_parallel=False):
'''
Run full Bayessian regression in Stan. Non-ergodic model includes: a spatially
varying earthquake constant, a spatially varying site constant, and a spatially
independent site constant.
Parameters
----------
df_flatfile : pd.DataFrame
Input data frame containing total residuals, eq and site coordinates.
stan_model_fname : string
File name for stan model.
out_fname : string
File name for output files.
out_dir : string
Output directory.
res_name : string, optional
Column name for total residuals. The default is 'res'.
n_iter_warmup : integer, optional
Number of burn out MCMC samples. The default is 300.
n_iter_sampling : integer, optional
Number of MCMC samples for computing the posterior distributions. The default is 300.
n_chains : integer, optional
Number of MCMC chains. The default is 4.
adapt_delta : double, optional
Target average proposal acceptance probability for adaptation. The default is 0.8.
max_treedepth : integer, optional
Maximum number of evaluations for each iteration (2^max_treedepth). The default is 10.
stan_parallel : bool, optional
Flag for using multithreaded option in STAN. The default is False.
Returns
-------
None.
'''
## Preprocess Input Data
#============================
#set rsn column as dataframe index, skip if rsn already the index
if not df_flatfile.index.name == 'rsn':
df_flatfile.set_index('rsn', drop=True, inplace=True)
#number of data
n_data = len(df_flatfile)
#earthquake data
data_eq_all = df_flatfile[['eqid','mag','eqX', 'eqY']].values
_, eq_idx, eq_inv = np.unique(df_flatfile[['eqid']].values, axis=0, return_inverse=True, return_index=True)
data_eq = data_eq_all[eq_idx,:]
X_eq = data_eq[:,[2,3]] #earthquake coordinates
#create earthquake ids for all records (1 to n_eq)
eq_id = eq_inv + 1
n_eq = len(data_eq)
#verify no collocated events
eq_dist_min = np.min([np.linalg.norm(x_eq - np.delete(X_eq,k, axis=0), axis=1).min() for k, x_eq in enumerate(X_eq) ])
assert(eq_dist_min > 5e-5),'Error. Singular covariance matrix due to collocated events'
#station data
data_sta_all = df_flatfile[['ssn','Vs30','staX','staY']].values
_, sta_idx, sta_inv = np.unique( df_flatfile[['ssn']].values, axis = 0, return_inverse=True, return_index=True)
data_sta = data_sta_all[sta_idx,:]
X_sta = data_sta[:,[2,3]] #station coordinates
#create station indices for all records (1 to n_sta)
sta_id = sta_inv + 1
n_sta = len(data_sta)
#verify no collocated stations
sta_dist_min = np.min([np.linalg.norm(x_sta - np.delete(X_sta,k, axis=0), axis=1).min() for k, x_sta in enumerate(X_sta) ])
assert(sta_dist_min > 5e-5),'Error. Singular covariance matrix due to collocated stations'
#ground-motion observations
y_data = df_flatfile[res_name].to_numpy().copy()
#stan data
stan_data = {'N': n_data,
'NEQ': n_eq,
'NSTAT': n_sta,
'eq': eq_id, #earthquake id
'stat': sta_id, #station id
'X_e': X_eq, #earthquake coordinates
'X_s': X_sta, #station coordinates
'rec_mu': np.zeros(y_data.shape),
'Y': y_data,
}
stan_data_fname = out_dir + out_fname + '_stan_data' + '.json'
#create output directory
pathlib.Path(out_dir).mkdir(parents=True, exist_ok=True)
#write as json file
cmdstanpy.utils.write_stan_json(stan_data_fname, stan_data)
## Run Stan, fit model
#============================
#number of cores
n_cpu = max(cpu_count() -1,1)
#run stan
if (not stan_parallel) or n_cpu<=n_chains:
#compile stan model
stan_model = cmdstanpy.CmdStanModel(stan_file=stan_model_fname)
stan_model.compile(force=True)
#run full MCMC sampler
stan_fit = stan_model.sample(data=stan_data_fname, chains=n_chains,
iter_warmup=n_iter_warmup, iter_sampling=n_iter_sampling,
seed=1, max_treedepth=max_treedepth, adapt_delta=adapt_delta,
show_progress=True, output_dir=out_dir+'stan_fit/')
else:
#compile stan model
stan_model = cmdstanpy.CmdStanModel(stan_file=stan_model_fname, cpp_options={"STAN_THREADS": True})
stan_model.compile(force=True)
#number of cores per chain
n_cpu_chain = int(np.floor(n_cpu/n_chains))
#run full MCMC sampler
stan_fit = stan_model.sample(data=stan_data_fname, chains=n_chains, threads_per_chain=n_cpu_chain,
iter_warmup=n_iter_warmup, iter_sampling=n_iter_sampling,
seed=1, max_treedepth=max_treedepth, adapt_delta=adapt_delta,
show_progress=True, output_dir=out_dir+'stan_fit/')
## Postprocessing Data
#============================
## Extract posterior samples
# ---------------------------
#hyper-parameters
col_names_hyp = ['dc_0','ell_1e', 'ell_1as', 'omega_1e', 'omega_1as', 'omega_1bs',
'phi_0','tau_0']
#non-ergodic terms
col_names_dc_1e = ['dc_1e.%i'%(k) for k in range(n_eq)]
col_names_dc_1as = ['dc_1as.%i'%(k) for k in range(n_sta)]
col_names_dc_1bs = ['dc_1bs.%i'%(k) for k in range(n_sta)]
col_names_dB = ['dB.%i'%(k) for k in range(n_eq)]
col_names_all = col_names_hyp + col_names_dc_1e + col_names_dc_1as + col_names_dc_1bs + col_names_dB
#summarize raw posterior distributions
stan_posterior = np.stack([stan_fit.stan_variable(c_n) for c_n in col_names_hyp], axis=1)
#adjustment terms
stan_posterior = np.concatenate((stan_posterior, stan_fit.stan_variable('dc_1e')), axis=1)
stan_posterior = np.concatenate((stan_posterior, stan_fit.stan_variable('dc_1as')), axis=1)
stan_posterior = np.concatenate((stan_posterior, stan_fit.stan_variable('dc_1bs')), axis=1)
stan_posterior = np.concatenate((stan_posterior, stan_fit.stan_variable('dB')), axis=1)
#save raw-posterior distribution
df_stan_posterior_raw = pd.DataFrame(stan_posterior, columns = col_names_all)
df_stan_posterior_raw.to_csv(out_dir + out_fname + '_stan_posterior_raw' + '.csv', index=False)
## Summarize hyper-parameters
# ---------------------------
#summarize posterior distributions of hyper-parameters
perc_array = np.array([0.05,0.25,0.5,0.75,0.95])
df_stan_hyp = df_stan_posterior_raw[col_names_hyp].quantile(perc_array)
df_stan_hyp = df_stan_hyp.append(df_stan_posterior_raw[col_names_hyp].mean(axis = 0), ignore_index=True)
df_stan_hyp.index = ['prc_%.2f'%(prc) for prc in perc_array]+['mean']
df_stan_hyp.to_csv(out_dir + out_fname + '_stan_hyperparameters' + '.csv', index=True)
#detailed posterior percentiles of posterior distributions
perc_array = np.arange(0.01,0.99,0.01)
df_stan_posterior = df_stan_posterior_raw[col_names_hyp].quantile(perc_array)
df_stan_posterior.index.name = 'prc'
df_stan_posterior.to_csv(out_dir + out_fname + '_stan_hyperposterior' + '.csv', index=True)
del col_names_dc_1e, col_names_dc_1as, col_names_dc_1bs, col_names_dB
del stan_posterior, col_names_all
## Sample spatially varying coefficients and predictions at record locations
# ---------------------------
# earthquake and station location in database
X_eq_all = df_flatfile[['eqX', 'eqY']].values
X_sta_all = df_flatfile[['staX','staY']].values
# GMM coefficients
#--- --- --- --- --- --- --- ---
#constant shift coefficient
coeff_0_mu = df_stan_posterior_raw.loc[:,'dc_0'].mean() * np.ones(n_data)
coeff_0_med = df_stan_posterior_raw.loc[:,'dc_0'].median() * np.ones(n_data)
coeff_0_sig = df_stan_posterior_raw.loc[:,'dc_0'].std() * np.ones(n_data)
#spatially varying earthquake constant coefficient
coeff_1e_mu = np.array([df_stan_posterior_raw.loc[:,f'dc_1e.{k}'].mean() for k in range(n_eq)])
coeff_1e_mu = coeff_1e_mu[eq_inv]
coeff_1e_med = np.array([df_stan_posterior_raw.loc[:,f'dc_1e.{k}'].median() for k in range(n_eq)])
coeff_1e_med = coeff_1e_med[eq_inv]
coeff_1e_sig = np.array([df_stan_posterior_raw.loc[:,f'dc_1e.{k}'].std() for k in range(n_eq)])
coeff_1e_sig = coeff_1e_sig[eq_inv]
#site term constant covariance
coeff_1as_mu = np.array([df_stan_posterior_raw.loc[:,f'dc_1as.{k}'].mean() for k in range(n_sta)])
coeff_1as_mu = coeff_1as_mu[sta_inv]
coeff_1as_med = np.array([df_stan_posterior_raw.loc[:,f'dc_1as.{k}'].median() for k in range(n_sta)])
coeff_1as_med = coeff_1as_med[sta_inv]
coeff_1as_sig = np.array([df_stan_posterior_raw.loc[:,f'dc_1as.{k}'].std() for k in range(n_sta)])
coeff_1as_sig = coeff_1as_sig[sta_inv]
#spatially varying station constant covariance
coeff_1bs_mu = np.array([df_stan_posterior_raw.loc[:,f'dc_1bs.{k}'].mean() for k in range(n_sta)])
coeff_1bs_mu = coeff_1bs_mu[sta_inv]
coeff_1bs_med = np.array([df_stan_posterior_raw.loc[:,f'dc_1bs.{k}'].median() for k in range(n_sta)])
coeff_1bs_med = coeff_1bs_med[sta_inv]
coeff_1bs_sig = np.array([df_stan_posterior_raw.loc[:,f'dc_1bs.{k}'].std() for k in range(n_sta)])
coeff_1bs_sig = coeff_1bs_sig[sta_inv]
# aleatory variability
phi_0_array = np.array([df_stan_posterior_raw.phi_0.mean()]*X_sta_all.shape[0])
tau_0_array = np.array([df_stan_posterior_raw.tau_0.mean()]*X_sta_all.shape[0])
#initiaize flatfile for sumamry of non-erg coefficinets and residuals
df_flatinfo = df_flatfile[['eqid','ssn','eqLat','eqLon','staLat','staLon','eqX','eqY','staX','staY','UTMzone']]
#summary coefficients
coeffs_summary = np.vstack((coeff_0_mu,
coeff_1e_mu,
coeff_1as_mu,
coeff_1bs_mu,
coeff_0_med,
coeff_1e_med,
coeff_1as_med,
coeff_1bs_med,
coeff_0_sig,
coeff_1e_sig,
coeff_1as_sig,
coeff_1bs_sig)).T
columns_names = ['dc_0_mean','dc_1e_mean','dc_1as_mean','dc_1bs_mean',
'dc_0_med', 'dc_1e_med', 'dc_1as_med', 'dc_1bs_med',
'dc_0_sig', 'dc_1e_sig', 'dc_1as_sig', 'dc_1bs_sig']
df_coeffs_summary = pd.DataFrame(coeffs_summary, columns = columns_names, index=df_flatfile.index)
#create dataframe with summary coefficients
df_coeffs_summary = pd.merge(df_flatinfo, df_coeffs_summary, how='right', left_index=True, right_index=True)
df_coeffs_summary[['eqid','ssn']] = df_coeffs_summary[['eqid','ssn']].astype(int)
df_coeffs_summary.to_csv(out_dir + out_fname + '_stan_coefficients' + '.csv', index=True)
# GMM prediction
#--- --- --- --- --- --- --- ---
#mean prediction
y_mu = (coeff_0_mu + coeff_1e_mu + coeff_1as_mu + coeff_1bs_mu)
#compute residuals
res_tot = y_data - y_mu
#residuals computed directly from stan regression
res_between = [df_stan_posterior_raw.loc[:,f'dB.{k}'].mean() for k in range(n_eq)]
res_between = np.array([res_between[k] for k in (eq_inv).astype(int)])
res_within = res_tot - res_between
#summary predictions and residuals
predict_summary = np.vstack((y_mu, res_tot, res_between, res_within)).T
columns_names = ['nerg_mu','res_tot','res_between','res_within']
df_predict_summary = pd.DataFrame(predict_summary, columns = columns_names, index=df_flatfile.index)
#create dataframe with predictions and residuals
df_predict_summary = pd.merge(df_flatinfo, df_predict_summary, how='right', left_index=True, right_index=True)
df_predict_summary[['eqid','ssn']] = df_predict_summary[['eqid','ssn']].astype(int)
df_predict_summary.to_csv(out_dir + out_fname + '_stan_residuals' + '.csv', index=True)
## Summary regression
# ---------------------------
#save summary statistics
stan_summary_fname = out_dir + out_fname + '_stan_summary' + '.txt'
with open(stan_summary_fname, 'w') as f:
print(stan_fit.summary(), file=f)
#create and save trace plots
fig_dir = out_dir + 'summary_figs/'
#create figures directory if doesn't exit
pathlib.Path(fig_dir).mkdir(parents=True, exist_ok=True)
#create stan trace plots
stan_az_fit = az.from_cmdstanpy(stan_fit)
# stan_az_fit = az.from_cmdstanpy(stan_fit, posterior_predictive='Y')
for c_name in col_names_hyp:
#create trace plot with arviz
ax = az.plot_trace(stan_az_fit, var_names=c_name, figsize=(10,5) ).ravel()
ax[0].yaxis.set_major_locator(plt_autotick())
ax[0].set_xlabel('sample value')
ax[0].set_ylabel('frequency')
ax[0].set_title('')
ax[0].grid(axis='both')
ax[1].set_xlabel('iteration')
ax[1].set_ylabel('sample value')
ax[1].grid(axis='both')
ax[1].set_title('')
fig = ax[0].figure
fig.suptitle(c_name)
fig.savefig(fig_dir + out_fname + '_stan_traceplot_' + c_name + '_arviz' + '.png')
return None
| 14,382 | 45.247588 | 127 | py |
ngmm_tools | ngmm_tools-master/Analyses/Python_lib/regression/cmdstan/regression_cmdstan_model2_corr_cells_sparse_unbounded_hyp.py | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Wed Dec 29 15:13:49 2021
@author: glavrent
"""
#load variables
import pathlib
from joblib import cpu_count
#arithmetic libraries
import numpy as np
from scipy import sparse
#statistics libraries
import pandas as pd
#plot libraries
import matplotlib as mpl
from matplotlib.ticker import AutoLocator as plt_autotick
import arviz as az
mpl.use('agg')
#stan library
import cmdstanpy
def RunStan(df_flatfile, df_cellinfo, df_celldist, stan_model_fname,
out_fname, out_dir, res_name='res', c_a_erg=0,
n_iter_warmup=300, n_iter_sampling=300, n_chains=4,
adapt_delta=0.8, max_treedepth=10,
stan_parallel=False):
'''
Run full Bayessian regression in Stan. Non-ergodic model includes: a spatially
varying earthquake constant, a spatially varying site constant, a spatially
independent site constant, and partially spatially correlated anelastic
attenuation.
Parameters
----------
df_flatfile : pd.DataFrame
Input data frame containing total residuals, eq and site coordinates.
df_cellinfo : pd.DataFrame
Dataframe with coordinates of anelastic attenuation cells.
df_celldist : pd.DataFrame
Datafame with cell path distances of all records in df_flatfile.
stan_model_fname : string
File name for stan model.
out_fname : string
File name for output files.
out_dir : string
Output directory.
res_name : string, optional
Column name for total residuals. The default is 'res'.
c_a_erg : double, optional
Value of ergodic anelatic attenuation coefficient. Used as mean of cell
specific anelastic attenuation prior distribution. The default is 0.
n_iter_warmup : integer, optional
Number of burn out MCMC samples. The default is 300.
n_iter_sampling : integer, optional
Number of MCMC samples for computing the posterior distributions. The default is 300.
n_chains : integer, optional
Number of MCMC chains. The default is 4.
adapt_delta : double, optional
Target average proposal acceptance probability for adaptation. The default is 0.8.
max_treedepth : integer, optional
Maximum number of evaluations for each iteration (2^max_treedepth). The default is 10.
stan_parallel : bool, optional
Flag for using multithreaded option in STAN. The default is False.
Returns
-------
None.
'''
## Preprocess Input Data
#============================
#set rsn column as dataframe index, skip if rsn already the index
if not df_flatfile.index.name == 'rsn':
df_flatfile.set_index('rsn', drop=True, inplace=True)
if not df_celldist.index.name == 'rsn':
df_celldist.set_index('rsn', drop=True, inplace=True)
#set cellid column as dataframe index, skip if cellid already the index
if not df_cellinfo.index.name == 'cellid':
df_cellinfo.set_index('cellid', drop=True, inplace=True)
#number of data
n_data = len(df_flatfile)
#earthquake data
data_eq_all = df_flatfile[['eqid','mag','eqX', 'eqY']].values
_, eq_idx, eq_inv = np.unique(df_flatfile[['eqid']], axis=0, return_inverse=True, return_index=True)
data_eq = data_eq_all[eq_idx,:]
X_eq = data_eq[:,[2,3]] #earthquake coordinates
#create earthquake ids for all records (1 to n_eq)
eq_id = eq_inv + 1
n_eq = len(data_eq)
#station data
data_sta_all = df_flatfile[['ssn','Vs30','staX','staY']].values
_, sta_idx, sta_inv = np.unique( df_flatfile[['ssn']].values, axis = 0, return_inverse=True, return_index=True)
data_sta = data_sta_all[sta_idx,:]
X_sta = data_sta[:,[2,3]] #station coordinates
#create station indices for all records (1 to n_sta)
sta_id = sta_inv + 1
n_sta = len(data_sta)
#ground-motion observations
y_data = df_flatfile[res_name].to_numpy().copy()
#cell data
#reorder and only keep records included in the flatfile
df_celldist = df_celldist.reindex(df_flatfile.index)
#cell info
cell_ids_all = df_cellinfo.index
cell_names_all = df_cellinfo.cellname
#cell distance matrix
celldist_all = df_celldist[cell_names_all] #cell-distance matrix with all cells
#find cell with more than one paths
i_cells_valid = np.where(celldist_all.sum(axis=0) > 0)[0] #valid cells with more than one path
cell_ids_valid = cell_ids_all[i_cells_valid]
cell_names_valid = cell_names_all[i_cells_valid]
celldist_valid = celldist_all.loc[:,cell_names_valid].to_numpy() #cell-distance with only non-zero cells
celldist_valid_sp = sparse.csr_matrix(celldist_valid)
#number of cells
n_cell = celldist_all.shape[1]
n_cell_valid = celldist_valid.shape[1]
#cell coordinates
X_cells_valid = df_cellinfo.loc[i_cells_valid,['mptX','mptY']].values
#print Rrup missfits
print('max R_rup misfit', np.abs(df_flatfile.Rrup.values - celldist_valid.sum(axis=1)).max())
stan_data = {'N': n_data,
'NEQ': n_eq,
'NSTAT': n_sta,
'NCELL': n_cell_valid,
'NCELL_SP': len(celldist_valid_sp.data),
'eq': eq_id, #earthquake id
'stat': sta_id, #station id
'X_e': X_eq, #earthquake coordinates
'X_s': X_sta, #station coordinates
'X_c': X_cells_valid,
'rec_mu': np.zeros(y_data.shape),
'RC_val': celldist_valid_sp.data,
'RC_w': celldist_valid_sp.indices+1,
'RC_u': celldist_valid_sp.indptr+1,
'c_a_erg': c_a_erg,
'Y': y_data,
}
stan_data_fname = out_dir + out_fname + '_stan_data' + '.json'
#create output directory
pathlib.Path(out_dir).mkdir(parents=True, exist_ok=True)
#write as json file
cmdstanpy.utils.write_stan_json(stan_data_fname, stan_data)
## Run Stan, fit model
#============================
#number of cores
n_cpu = max(cpu_count() -1,1)
#run stan
if (not stan_parallel) or n_cpu<=n_chains:
#compile stan model
stan_model = cmdstanpy.CmdStanModel(stan_file=stan_model_fname)
stan_model.compile(force=True)
#run full MCMC sampler
stan_fit = stan_model.sample(data=stan_data_fname, chains=n_chains,
iter_warmup=n_iter_warmup, iter_sampling=n_iter_sampling,
seed=1, max_treedepth=max_treedepth, adapt_delta=adapt_delta,
show_progress=True, output_dir=out_dir+'stan_fit/')
else:
#compile stan model
stan_model = cmdstanpy.CmdStanModel(stan_file=stan_model_fname, cpp_options={"STAN_THREADS": True})
stan_model.compile(force=True)
#number of cores per chain
n_cpu_chain = int(np.floor(n_cpu/n_chains))
#run full MCMC sampler
stan_fit = stan_model.sample(data=stan_data_fname, chains=n_chains, threads_per_chain=n_cpu_chain,
iter_warmup=n_iter_warmup, iter_sampling=n_iter_sampling,
seed=1, max_treedepth=max_treedepth, adapt_delta=adapt_delta,
show_progress=True, output_dir=out_dir+'stan_fit/')
## Postprocessing Data
#============================
## Extract posterior samples
# ---------------------------
#hyper-parameters
col_names_hyp = ['dc_0','ell_1e', 'ell_1as', 'omega_1e', 'omega_1as', 'omega_1bs',
'mu_cap', 'ell_ca1p', 'omega_ca1p', 'omega_ca2p',
'phi_0','tau_0']
#non-ergodic terms
col_names_dc_1e = ['dc_1e.%i'%(k) for k in range(n_eq)]
col_names_dc_1as = ['dc_1as.%i'%(k) for k in range(n_sta)]
col_names_dc_1bs = ['dc_1bs.%i'%(k) for k in range(n_sta)]
col_names_dB = ['dB.%i'%(k) for k in range(n_eq)]
col_names_cap = ['c_cap.%i'%(c_id) for c_id in cell_ids_valid]
col_names_all = col_names_hyp + col_names_dc_1e + col_names_dc_1as + col_names_dc_1bs + col_names_cap + col_names_dB
#summarize raw posterior distributions
stan_posterior = np.stack([stan_fit.stan_variable(c_n) for c_n in col_names_hyp], axis=1)
#adjustment terms
stan_posterior = np.concatenate((stan_posterior, stan_fit.stan_variable('dc_1e')), axis=1)
stan_posterior = np.concatenate((stan_posterior, stan_fit.stan_variable('dc_1as')), axis=1)
stan_posterior = np.concatenate((stan_posterior, stan_fit.stan_variable('dc_1bs')), axis=1)
stan_posterior = np.concatenate((stan_posterior, stan_fit.stan_variable('c_cap')), axis=1)
stan_posterior = np.concatenate((stan_posterior, stan_fit.stan_variable('dB')), axis=1)
#save raw-posterior distribution
df_stan_posterior_raw = pd.DataFrame(stan_posterior, columns = col_names_all)
df_stan_posterior_raw.to_csv(out_dir + out_fname + '_stan_posterior_raw' + '.csv', index=False)
## Summarize hyper-parameters
# ---------------------------
#summarize posterior distributions of hyper-parameters
perc_array = np.array([0.05,0.25,0.5,0.75,0.95])
df_stan_hyp = df_stan_posterior_raw[col_names_hyp].quantile(perc_array)
df_stan_hyp = df_stan_hyp.append(df_stan_posterior_raw[col_names_hyp].mean(axis = 0), ignore_index=True)
df_stan_hyp.index = ['prc_%.2f'%(prc) for prc in perc_array]+['mean']
df_stan_hyp.to_csv(out_dir + out_fname + '_stan_hyperparameters' + '.csv', index=True)
#detailed posterior percentiles of posterior distributions
perc_array = np.arange(0.01,0.99,0.01)
df_stan_posterior = df_stan_posterior_raw[col_names_hyp].quantile(perc_array)
df_stan_posterior.index.name = 'prc'
df_stan_posterior.to_csv(out_dir + out_fname + '_stan_hyperposterior' + '.csv', index=True)
del col_names_dc_1e, col_names_dc_1as, col_names_dc_1bs, col_names_dB
del stan_posterior, col_names_all
## Sample spatially varying coefficients and predictions at record locations
# ---------------------------
# earthquake and station location in database
X_eq_all = df_flatfile[['eqX', 'eqY']].values
X_sta_all = df_flatfile[['staX','staY']].values
# GMM anelastic attenuation
#--- --- --- --- --- --- --- ---
cells_ca_mu = np.array([df_stan_posterior_raw.loc[:,'c_cap.%i'%(k)].mean() for k in cell_ids_valid])
cells_ca_med = np.array([df_stan_posterior_raw.loc[:,'c_cap.%i'%(k)].median() for k in cell_ids_valid])
cells_ca_sig = np.array([df_stan_posterior_raw.loc[:,'c_cap.%i'%(k)].std() for k in cell_ids_valid])
#effect of anelastic attenuation in GM
cells_LcA_mu = celldist_valid_sp @ cells_ca_mu
cells_LcA_med = celldist_valid_sp @ cells_ca_med
cells_LcA_sig = np.sqrt(celldist_valid_sp.power(2) @ cells_ca_sig**2)
#summary attenuation cells
catten_summary = np.vstack((np.tile(c_a_erg, n_cell_valid),
cells_ca_mu,
cells_ca_med,
cells_ca_sig)).T
columns_names = ['c_a_erg','c_cap_mean','c_cap_med','c_cap_sig']
df_catten_summary = pd.DataFrame(catten_summary, columns = columns_names, index=df_cellinfo.index[i_cells_valid])
#create dataframe with summary attenuation cells
df_catten_summary = pd.merge(df_cellinfo[['cellname','mptLat','mptLon','mptX','mptY','mptZ','UTMzone']],
df_catten_summary, how='right', left_index=True, right_index=True)
df_catten_summary.to_csv(out_dir + out_fname + '_stan_catten' + '.csv', index=True)
# GMM coefficients
#--- --- --- --- --- --- --- ---
#constant shift coefficient
coeff_0_mu = df_stan_posterior_raw.loc[:,'dc_0'].mean() * np.ones(n_data)
coeff_0_med = df_stan_posterior_raw.loc[:,'dc_0'].median() * np.ones(n_data)
coeff_0_sig = df_stan_posterior_raw.loc[:,'dc_0'].std() * np.ones(n_data)
#spatially varying earthquake constant coefficient
coeff_1e_mu = np.array([df_stan_posterior_raw.loc[:,f'dc_1e.{k}'].mean() for k in range(n_eq)])
coeff_1e_mu = coeff_1e_mu[eq_inv]
coeff_1e_med = np.array([df_stan_posterior_raw.loc[:,f'dc_1e.{k}'].median() for k in range(n_eq)])
coeff_1e_med = coeff_1e_med[eq_inv]
coeff_1e_sig = np.array([df_stan_posterior_raw.loc[:,f'dc_1e.{k}'].std() for k in range(n_eq)])
coeff_1e_sig = coeff_1e_sig[eq_inv]
#site term constant covariance
coeff_1as_mu = np.array([df_stan_posterior_raw.loc[:,f'dc_1as.{k}'].mean() for k in range(n_sta)])
coeff_1as_mu = coeff_1as_mu[sta_inv]
coeff_1as_med = np.array([df_stan_posterior_raw.loc[:,f'dc_1as.{k}'].median() for k in range(n_sta)])
coeff_1as_med = coeff_1as_med[sta_inv]
coeff_1as_sig = np.array([df_stan_posterior_raw.loc[:,f'dc_1as.{k}'].std() for k in range(n_sta)])
coeff_1as_sig = coeff_1as_sig[sta_inv]
#spatially varying station constant covariance
coeff_1bs_mu = np.array([df_stan_posterior_raw.loc[:,f'dc_1bs.{k}'].mean() for k in range(n_sta)])
coeff_1bs_mu = coeff_1bs_mu[sta_inv]
coeff_1bs_med = np.array([df_stan_posterior_raw.loc[:,f'dc_1bs.{k}'].median() for k in range(n_sta)])
coeff_1bs_med = coeff_1bs_med[sta_inv]
coeff_1bs_sig = np.array([df_stan_posterior_raw.loc[:,f'dc_1bs.{k}'].std() for k in range(n_sta)])
coeff_1bs_sig = coeff_1bs_sig[sta_inv]
# aleatory variability
phi_0_array = np.array([df_stan_posterior_raw.phi_0.mean()]*X_sta_all.shape[0])
tau_0_array = np.array([df_stan_posterior_raw.tau_0.mean()]*X_sta_all.shape[0])
#dataframe with flatfile info
df_flatinfo = df_flatfile[['eqid','ssn','eqLat','eqLon','staLat','staLon','eqX','eqY','staX','staY','UTMzone']]
#summary coefficients
coeffs_summary = np.vstack((coeff_0_mu,
coeff_1e_mu,
coeff_1as_mu,
coeff_1bs_mu,
cells_LcA_mu,
coeff_0_med,
coeff_1e_med,
coeff_1as_med,
coeff_1bs_med,
cells_LcA_med,
coeff_0_sig,
coeff_1e_sig,
coeff_1as_sig,
coeff_1bs_sig,
cells_LcA_sig)).T
columns_names = ['dc_0_mean','dc_1e_mean','dc_1as_mean','dc_1bs_mean','Lc_ca_mean',
'dc_0_med', 'dc_1e_med', 'dc_1as_med', 'dc_1bs_med', 'Lc_ca_med',
'dc_0_sig', 'dc_1e_sig', 'dc_1as_sig', 'dc_1bs_sig', 'Lc_ca_sig']
df_coeffs_summary = pd.DataFrame(coeffs_summary, columns = columns_names, index=df_flatfile.index)
#create dataframe with summary coefficients
df_coeffs_summary = pd.merge(df_flatinfo, df_coeffs_summary, how='right', left_index=True, right_index=True)
df_coeffs_summary[['eqid','ssn']] = df_coeffs_summary[['eqid','ssn']].astype(int)
df_coeffs_summary.to_csv(out_dir + out_fname + '_stan_coefficients' + '.csv', index=True)
# GMM prediction
#--- --- --- --- --- --- --- ---
#mean prediction
y_mu = (coeff_0_mu + coeff_1e_mu + coeff_1as_mu + coeff_1bs_mu + cells_LcA_mu)
#compute residuals
res_tot = y_data - y_mu
#residuals computed directly from stan regression
res_between = [df_stan_posterior_raw.loc[:,f'dB.{k}'].mean() for k in range(n_eq)]
res_between = np.array([res_between[k] for k in (eq_inv).astype(int)])
res_within = res_tot - res_between
#summary predictions and residuals
predict_summary = np.vstack((y_mu, res_tot, res_between, res_within)).T
columns_names = ['nerg_mu','res_tot','res_between','res_within']
df_predict_summary = pd.DataFrame(predict_summary, columns = columns_names, index=df_flatfile.index)
#create dataframe with predictions and residuals
df_predict_summary = pd.merge(df_flatinfo, df_predict_summary, how='right', left_index=True, right_index=True)
df_predict_summary[['eqid','ssn']] = df_predict_summary[['eqid','ssn']].astype(int)
df_predict_summary.to_csv(out_dir + out_fname + '_stan_residuals' + '.csv', index=True)
## Summary regression
# ---------------------------
#save summary statistics
stan_summary_fname = out_dir + out_fname + '_stan_summary' + '.txt'
with open(stan_summary_fname, 'w') as f:
print(stan_fit, file=f)
#create and save trace plots
fig_dir = out_dir + 'summary_figs/'
#create figures directory if doesn't exit
pathlib.Path(fig_dir).mkdir(parents=True, exist_ok=True)
#create stan trace plots
stan_az_fit = az.from_cmdstanpy(stan_fit)
# stan_az_fit = az.from_cmdstanpy(stan_fit, posterior_predictive='Y')
for c_name in col_names_hyp:
#create trace plot with arviz
ax = az.plot_trace(stan_az_fit, var_names=c_name, figsize=(10,5) ).ravel()
ax[0].yaxis.set_major_locator(plt_autotick())
ax[0].set_xlabel('sample value')
ax[0].set_ylabel('frequency')
ax[0].set_title('')
ax[0].grid(axis='both')
ax[1].set_xlabel('iteration')
ax[1].set_ylabel('sample value')
ax[1].grid(axis='both')
ax[1].set_title('')
fig = ax[0].figure
fig.suptitle(c_name)
fig.savefig(fig_dir + out_fname + '_stan_traceplot_' + c_name + '_arviz' + '.png')
return None
| 17,927 | 46.808 | 120 | py |
ngmm_tools | ngmm_tools-master/Analyses/Python_lib/regression/cmdstan/regression_cmdstan_model2_uncorr_cells_unbounded_hyp.py | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Wed Dec 29 15:13:49 2021
@author: glavrent
"""
#load variables
import pathlib
from joblib import cpu_count
#arithmetic libraries
import numpy as np
#statistics libraries
import pandas as pd
#plot libraries
import matplotlib as mpl
from matplotlib.ticker import AutoLocator as plt_autotick
import arviz as az
mpl.use('agg')
#stan library
import cmdstanpy
def RunStan(df_flatfile, df_cellinfo, df_celldist, stan_model_fname,
out_fname, out_dir, res_name='res', c_a_erg=0,
n_iter_warmup=300, n_iter_sampling=300, n_chains=4,
adapt_delta=0.8, max_treedepth=10,
stan_parallel=False):
'''
Run full Bayessian regression in Stan. Non-ergodic model includes: a spatially
varying earthquake constant, a spatially varying site constant, a spatially
independent site constant, and uncorrelated anelastic attenuation.
Parameters
----------
df_flatfile : pd.DataFrame
Input data frame containing total residuals, eq and site coordinates.
df_cellinfo : pd.DataFrame
Dataframe with coordinates of anelastic attenuation cells.
df_celldist : pd.DataFrame
Datafame with cell path distances of all records in df_flatfile.
stan_model_fname : string
File name for stan model.
out_fname : string
File name for output files.
out_dir : string
Output directory.
res_name : string, optional
Column name for total residuals. The default is 'res'.
c_a_erg : double, optional
Value of ergodic anelatic attenuation coefficient. Used as mean of cell
specific anelastic attenuation prior distribution. The default is 0.
n_iter_warmup : integer, optional
Number of burn out MCMC samples. The default is 300.
n_iter_sampling : integer, optional
Number of MCMC samples for computing the posterior distributions. The default is 300.
n_chains : integer, optional
Number of MCMC chains. The default is 4.
adapt_delta : double, optional
Target average proposal acceptance probability for adaptation. The default is 0.8.
max_treedepth : integer, optional
Maximum number of evaluations for each iteration (2^max_treedepth). The default is 10.
pystan_ver : integer, optional
Version of pystan to run. The default is 2.
pystan_parallel : bool, optional
Flag for using multithreaded option in STAN. The default is False.
Returns
-------
None.
'''
## Preprocess Input Data
#============================
#set rsn column as dataframe index, skip if rsn already the index
if not df_flatfile.index.name == 'rsn':
df_flatfile.set_index('rsn', drop=True, inplace=True)
if not df_celldist.index.name == 'rsn':
df_celldist.set_index('rsn', drop=True, inplace=True)
#set cellid column as dataframe index, skip if cellid already the index
if not df_cellinfo.index.name == 'cellid':
df_cellinfo.set_index('cellid', drop=True, inplace=True)
#number of data
n_data = len(df_flatfile)
#earthquake data
data_eq_all = df_flatfile[['eqid','mag','eqX', 'eqY']].values
_, eq_idx, eq_inv = np.unique(df_flatfile[['eqid']], axis=0, return_inverse=True, return_index=True)
data_eq = data_eq_all[eq_idx,:]
X_eq = data_eq[:,[2,3]] #earthquake coordinates
#create earthquake ids for all records (1 to n_eq)
eq_id = eq_inv + 1
n_eq = len(data_eq)
#station data
data_sta_all = df_flatfile[['ssn','Vs30','staX','staY']].values
_, sta_idx, sta_inv = np.unique( df_flatfile[['ssn']].values, axis = 0, return_inverse=True, return_index=True)
data_sta = data_sta_all[sta_idx,:]
X_sta = data_sta[:,[2,3]] #station coordinates
#create station indices for all records (1 to n_sta)
sta_id = sta_inv + 1
n_sta = len(data_sta)
#ground-motion observations
y_data = df_flatfile[res_name].to_numpy().copy()
#cell data
#reorder and only keep records included in the flatfile
df_celldist = df_celldist.reindex(df_flatfile.index)
#cell info
cell_ids_all = df_cellinfo.index
cell_names_all = df_cellinfo.cellname
#cell distance matrix
celldist_all = df_celldist[cell_names_all] #cell-distance matrix with all cells
#find cell with more than one paths
i_cells_valid = np.where(celldist_all.sum(axis=0) > 0)[0] #valid cells with more than one path
cell_ids_valid = cell_ids_all[i_cells_valid]
cell_names_valid = cell_names_all[i_cells_valid]
celldist_valid = celldist_all.loc[:,cell_names_valid] #cell-distance with only non-zero cells
#number of cells
n_cell = celldist_all.shape[1]
n_cell_valid = celldist_valid.shape[1]
#cell coordinates
X_cells_valid = df_cellinfo.loc[i_cells_valid,['mptX','mptY']].values
#print Rrup missfits
print('max R_rup misfit', (df_flatfile.Rrup.values - celldist_valid.sum(axis=1)).abs().max())
stan_data = {'N': n_data,
'NEQ': n_eq,
'NSTAT': n_sta,
'NCELL': n_cell_valid,
'eq': eq_id, #earthquake id
'stat': sta_id, #station id
'X_e': X_eq, #earthquake coordinates
'X_s': X_sta, #station coordinates
'X_c': X_cells_valid,
'rec_mu': np.zeros(y_data.shape),
'RC': celldist_valid.to_numpy(),
'c_a_erg': c_a_erg,
'Y': y_data,
}
stan_data_fname = out_dir + out_fname + '_stan_data' + '.json'
#create output directory
pathlib.Path(out_dir).mkdir(parents=True, exist_ok=True)
#write as json file
cmdstanpy.utils.write_stan_json(stan_data_fname, stan_data)
## Run Stan, fit model
#============================
#number of cores
n_cpu = max(cpu_count() -1,1)
#run stan
if (not stan_parallel) or n_cpu<=n_chains:
#compile stan model
stan_model = cmdstanpy.CmdStanModel(stan_file=stan_model_fname)
stan_model.compile(force=True)
#run full MCMC sampler
stan_fit = stan_model.sample(data=stan_data_fname, chains=n_chains,
iter_warmup=n_iter_warmup, iter_sampling=n_iter_sampling,
seed=1, max_treedepth=max_treedepth, adapt_delta=adapt_delta,
show_progress=True, output_dir=out_dir+'stan_fit/')
else:
#compile stan model
stan_model = cmdstanpy.CmdStanModel(stan_file=stan_model_fname, cpp_options={"STAN_THREADS": True})
stan_model.compile(force=True)
#number of cores per chain
n_cpu_chain = int(np.floor(n_cpu/n_chains))
#run full MCMC sampler
stan_fit = stan_model.sample(data=stan_data_fname, chains=n_chains, threads_per_chain=n_cpu_chain,
iter_warmup=n_iter_warmup, iter_sampling=n_iter_sampling,
seed=1, max_treedepth=max_treedepth, adapt_delta=adapt_delta,
show_progress=True, output_dir=out_dir+'stan_fit/')
## Postprocessing Data
#============================
## Extract posterior samples
# ---------------------------
#hyper-parameters
col_names_hyp = ['dc_0','ell_1e', 'ell_1as', 'omega_1e', 'omega_1as', 'omega_1bs',
'mu_cap', 'omega_cap',
'phi_0','tau_0']
#non-ergodic terms
col_names_dc_1e = ['dc_1e.%i'%(k) for k in range(n_eq)]
col_names_dc_1as = ['dc_1as.%i'%(k) for k in range(n_sta)]
col_names_dc_1bs = ['dc_1bs.%i'%(k) for k in range(n_sta)]
col_names_dB = ['dB.%i'%(k) for k in range(n_eq)]
col_names_cap = ['c_cap.%i'%(c_id) for c_id in cell_ids_valid]
col_names_all = col_names_hyp + col_names_dc_1e + col_names_dc_1as + col_names_dc_1bs + col_names_cap + col_names_dB
#summarize raw posterior distributions
stan_posterior = np.stack([stan_fit.stan_variable(c_n) for c_n in col_names_hyp], axis=1)
#adjustment terms
stan_posterior = np.concatenate((stan_posterior, stan_fit.stan_variable('dc_1e')), axis=1)
stan_posterior = np.concatenate((stan_posterior, stan_fit.stan_variable('dc_1as')), axis=1)
stan_posterior = np.concatenate((stan_posterior, stan_fit.stan_variable('dc_1bs')), axis=1)
stan_posterior = np.concatenate((stan_posterior, stan_fit.stan_variable('c_cap')), axis=1)
stan_posterior = np.concatenate((stan_posterior, stan_fit.stan_variable('dB')), axis=1)
#save raw-posterior distribution
df_stan_posterior_raw = pd.DataFrame(stan_posterior, columns = col_names_all)
df_stan_posterior_raw.to_csv(out_dir + out_fname + '_stan_posterior_raw' + '.csv', index=False)
## Summarize hyper-parameters
# ---------------------------
#summarize posterior distributions of hyper-parameters
perc_array = np.array([0.05,0.25,0.5,0.75,0.95])
df_stan_hyp = df_stan_posterior_raw[col_names_hyp].quantile(perc_array)
df_stan_hyp = df_stan_hyp.append(df_stan_posterior_raw[col_names_hyp].mean(axis = 0), ignore_index=True)
df_stan_hyp.index = ['prc_%.2f'%(prc) for prc in perc_array]+['mean']
df_stan_hyp.to_csv(out_dir + out_fname + '_stan_hyperparameters' + '.csv', index=True)
#detailed posterior percentiles of posterior distributions
perc_array = np.arange(0.01,0.99,0.01)
df_stan_posterior = df_stan_posterior_raw[col_names_hyp].quantile(perc_array)
df_stan_posterior.index.name = 'prc'
df_stan_posterior .to_csv(out_dir + out_fname + '_stan_hyperposterior' + '.csv', index=True)
del col_names_dc_1e, col_names_dc_1as, col_names_dc_1bs, col_names_dB
del stan_posterior, col_names_all
## Sample spatially varying coefficients and predictions at record locations
# ---------------------------
# earthquake and station location in database
X_eq_all = df_flatfile[['eqX', 'eqY']].values
X_sta_all = df_flatfile[['staX','staY']].values
# GMM anelastic attenuation
#--- --- --- --- --- --- --- ---
cells_ca_mu = np.array([df_stan_posterior_raw.loc[:,'c_cap.%i'%(k)].mean() for k in cell_ids_valid])
cells_ca_med = np.array([df_stan_posterior_raw.loc[:,'c_cap.%i'%(k)].median() for k in cell_ids_valid])
cells_ca_sig = np.array([df_stan_posterior_raw.loc[:,'c_cap.%i'%(k)].std() for k in cell_ids_valid])
#effect of anelastic attenuation in GM
cells_LcA_mu = celldist_valid.values @ cells_ca_mu
cells_LcA_med = celldist_valid.values @ cells_ca_med
cells_LcA_sig = np.sqrt(np.square(celldist_valid.values) @ cells_ca_sig**2)
#summary attenuation cells
catten_summary = np.vstack((np.tile(c_a_erg, n_cell_valid),
cells_ca_mu,
cells_ca_med,
cells_ca_sig)).T
columns_names = ['c_a_erg','c_cap_mean','c_cap_med','c_cap_sig']
df_catten_summary = pd.DataFrame(catten_summary, columns = columns_names, index=df_cellinfo.index[i_cells_valid])
#create dataframe with summary attenuation cells
df_catten_summary = pd.merge(df_cellinfo[['cellname','mptLat','mptLon','mptX','mptY','mptZ','UTMzone']],
df_catten_summary, how='right', left_index=True, right_index=True)
df_catten_summary.to_csv(out_dir + out_fname + '_stan_catten' + '.csv', index=True)
# GMM coefficients
#--- --- --- --- --- --- --- ---
#constant shift coefficient
coeff_0_mu = df_stan_posterior_raw.loc[:,'dc_0'].mean() * np.ones(n_data)
coeff_0_med = df_stan_posterior_raw.loc[:,'dc_0'].median() * np.ones(n_data)
coeff_0_sig = df_stan_posterior_raw.loc[:,'dc_0'].std() * np.ones(n_data)
#spatially varying earthquake constant coefficient
coeff_1e_mu = np.array([df_stan_posterior_raw.loc[:,f'dc_1e.{k}'].mean() for k in range(n_eq)])
coeff_1e_mu = coeff_1e_mu[eq_inv]
coeff_1e_med = np.array([df_stan_posterior_raw.loc[:,f'dc_1e.{k}'].median() for k in range(n_eq)])
coeff_1e_med = coeff_1e_med[eq_inv]
coeff_1e_sig = np.array([df_stan_posterior_raw.loc[:,f'dc_1e.{k}'].std() for k in range(n_eq)])
coeff_1e_sig = coeff_1e_sig[eq_inv]
#site term constant covariance
coeff_1as_mu = np.array([df_stan_posterior_raw.loc[:,f'dc_1as.{k}'].mean() for k in range(n_sta)])
coeff_1as_mu = coeff_1as_mu[sta_inv]
coeff_1as_med = np.array([df_stan_posterior_raw.loc[:,f'dc_1as.{k}'].median() for k in range(n_sta)])
coeff_1as_med = coeff_1as_med[sta_inv]
coeff_1as_sig = np.array([df_stan_posterior_raw.loc[:,f'dc_1as.{k}'].std() for k in range(n_sta)])
coeff_1as_sig = coeff_1as_sig[sta_inv]
#spatially varying station constant covariance
coeff_1bs_mu = np.array([df_stan_posterior_raw.loc[:,f'dc_1bs.{k}'].mean() for k in range(n_sta)])
coeff_1bs_mu = coeff_1bs_mu[sta_inv]
coeff_1bs_med = np.array([df_stan_posterior_raw.loc[:,f'dc_1bs.{k}'].median() for k in range(n_sta)])
coeff_1bs_med = coeff_1bs_med[sta_inv]
coeff_1bs_sig = np.array([df_stan_posterior_raw.loc[:,f'dc_1bs.{k}'].std() for k in range(n_sta)])
coeff_1bs_sig = coeff_1bs_sig[sta_inv]
# aleatory variability
phi_0_array = np.array([df_stan_posterior_raw.phi_0.mean()]*X_sta_all.shape[0])
tau_0_array = np.array([df_stan_posterior_raw.tau_0.mean()]*X_sta_all.shape[0])
#initiaize flatfile for sumamry of non-erg coefficinets and residuals
df_flatinfo = df_flatfile[['eqid','ssn','eqLat','eqLon','staLat','staLon','eqX','eqY','staX','staY','UTMzone']]
#summary coefficients
coeffs_summary = np.vstack((coeff_0_mu,
coeff_1e_mu,
coeff_1as_mu,
coeff_1bs_mu,
cells_LcA_mu,
coeff_0_med,
coeff_1e_med,
coeff_1as_med,
coeff_1bs_med,
cells_LcA_med,
coeff_0_sig,
coeff_1e_sig,
coeff_1as_sig,
coeff_1bs_sig,
cells_LcA_sig)).T
columns_names = ['dc_0_mean','dc_1e_mean','dc_1as_mean','dc_1bs_mean','Lc_ca_mean',
'dc_0_med', 'dc_1e_med', 'dc_1as_med', 'dc_1bs_med', 'Lc_ca_med',
'dc_0_sig', 'dc_1e_sig', 'dc_1as_sig', 'dc_1bs_sig', 'Lc_ca_sig']
df_coeffs_summary = pd.DataFrame(coeffs_summary, columns = columns_names, index=df_flatfile.index)
#create dataframe with summary coefficients
df_coeffs_summary = pd.merge(df_flatinfo, df_coeffs_summary, how='right', left_index=True, right_index=True)
df_coeffs_summary[['eqid','ssn']] = df_coeffs_summary[['eqid','ssn']].astype(int)
df_coeffs_summary.to_csv(out_dir + out_fname + '_stan_coefficients' + '.csv', index=True)
# GMM prediction
#--- --- --- --- --- --- --- ---
#mean prediction
y_mu = (coeff_0_mu + coeff_1e_mu + coeff_1as_mu + coeff_1bs_mu + cells_LcA_mu)
#compute residuals
res_tot = y_data - y_mu
#residuals computed directly from stan regression
res_between = [df_stan_posterior_raw.loc[:,f'dB.{k}'].mean() for k in range(n_eq)]
res_between = np.array([res_between[k] for k in (eq_inv).astype(int)])
res_within = res_tot - res_between
#summary predictions and residuals
predict_summary = np.vstack((y_mu, res_tot, res_between, res_within)).T
columns_names = ['nerg_mu','res_tot','res_between','res_within']
df_predict_summary = pd.DataFrame(predict_summary, columns = columns_names, index=df_flatfile.index)
#create dataframe with predictions and residuals
df_predict_summary = pd.merge(df_flatinfo, df_predict_summary, how='right', left_index=True, right_index=True)
df_predict_summary[['eqid','ssn']] = df_predict_summary[['eqid','ssn']].astype(int)
df_predict_summary.to_csv(out_dir + out_fname + '_stan_residuals' + '.csv', index=True)
## Summary regression
# ---------------------------
#save summary statistics
stan_summary_fname = out_dir + out_fname + '_stan_summary' + '.txt'
with open(stan_summary_fname, 'w') as f:
print(stan_fit, file=f)
#create and save trace plots
fig_dir = out_dir + 'summary_figs/'
#create figures directory if doesn't exit
pathlib.Path(fig_dir).mkdir(parents=True, exist_ok=True)
#create stan trace plots
stan_az_fit = az.from_cmdstanpy(stan_fit)
# stan_az_fit = az.from_cmdstanpy(stan_fit, posterior_predictive='Y')
for c_name in col_names_hyp:
#create trace plot with arviz
ax = az.plot_trace(stan_az_fit, var_names=c_name, figsize=(10,5) ).ravel()
ax[0].yaxis.set_major_locator(plt_autotick())
ax[0].set_xlabel('sample value')
ax[0].set_ylabel('frequency')
ax[0].set_title('')
ax[0].grid(axis='both')
ax[1].set_xlabel('iteration')
ax[1].set_ylabel('sample value')
ax[1].grid(axis='both')
ax[1].set_title('')
fig = ax[0].figure
fig.suptitle(c_name)
fig.savefig(fig_dir + out_fname + '_stan_traceplot_' + c_name + '_arviz' + '.png')
return None
| 17,759 | 46.741935 | 120 | py |
ngmm_tools | ngmm_tools-master/Analyses/Python_lib/regression/cmdstan/regression_cmdstan_model3_uncorr_cells_unbounded_hyp.py | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Wed Dec 29 15:13:49 2021
@author: glavrent
"""
#load variables
import pathlib
from joblib import cpu_count
#arithmetic libraries
import numpy as np
#statistics libraries
import pandas as pd
#plot libraries
import matplotlib as mpl
from matplotlib.ticker import AutoLocator as plt_autotick
import arviz as az
mpl.use('agg')
#stan library
import cmdstanpy
def RunStan(df_flatfile, df_cellinfo, df_celldist, stan_model_fname,
out_fname, out_dir, res_name='res', c_2_erg=0, c_3_erg=0, c_a_erg=0,
n_iter_warmup=300, n_iter_sampling=300, n_chains=4,
adapt_delta=0.8, max_treedepth=10,
stan_parallel=False):
'''
Run full Bayessian regression in Stan. Non-ergodic model includes: a spatially
varying earthquake constant, a spatially varying site constant, a spatially
independent site constant, and uncorrelated anelastic attenuation.
Parameters
----------
df_flatfile : pd.DataFrame
Input data frame containing total residuals, eq and site coordinates.
df_cellinfo : pd.DataFrame
Dataframe with coordinates of anelastic attenuation cells.
df_celldist : pd.DataFrame
Datafame with cell path distances of all records in df_flatfile.
stan_model_fname : string
File name for stan model.
out_fname : string
File name for output files.
out_dir : string
Output directory.
res_name : string, optional
Column name for total residuals. The default is 'res'.
c_a_erg : double, optional
Value of ergodic anelatic attenuation coefficient. Used as mean of cell
specific anelastic attenuation prior distribution. The default is 0.
n_iter_warmup : integer, optional
Number of burn out MCMC samples. The default is 300.
n_iter_sampling : integer, optional
Number of MCMC samples for computing the posterior distributions. The default is 300.
n_chains : integer, optional
Number of MCMC chains. The default is 4.
adapt_delta : double, optional
Target average proposal acceptance probability for adaptation. The default is 0.8.
max_treedepth : integer, optional
Maximum number of evaluations for each iteration (2^max_treedepth). The default is 10.
pystan_ver : integer, optional
Version of pystan to run. The default is 2.
pystan_parallel : bool, optional
Flag for using multithreaded option in STAN. The default is False.
Returns
-------
None.
'''
## Preprocess Input Data
#============================
#set rsn column as dataframe index, skip if rsn already the index
if not df_flatfile.index.name == 'rsn':
df_flatfile.set_index('rsn', drop=True, inplace=True)
if not df_celldist.index.name == 'rsn':
df_celldist.set_index('rsn', drop=True, inplace=True)
#set cellid column as dataframe index, skip if cellid already the index
if not df_cellinfo.index.name == 'cellid':
df_cellinfo.set_index('cellid', drop=True, inplace=True)
#number of data
n_data = len(df_flatfile)
#earthquake data
data_eq_all = df_flatfile[['eqid','mag','eqX', 'eqY']].values
_, eq_idx, eq_inv = np.unique(df_flatfile[['eqid']], axis=0, return_inverse=True, return_index=True)
data_eq = data_eq_all[eq_idx,:]
X_eq = data_eq[:,[2,3]] #earthquake coordinates
#create earthquake ids for all records (1 to n_eq)
eq_id = eq_inv + 1
n_eq = len(data_eq)
#station data
data_sta_all = df_flatfile[['ssn','Vs30','x_3','staX','staY']].values
_, sta_idx, sta_inv = np.unique( df_flatfile[['ssn']].values, axis = 0, return_inverse=True, return_index=True)
data_sta = data_sta_all[sta_idx,:]
X_sta = data_sta[:,[3,4]] #station coordinates
#create station indices for all records (1 to n_sta)
sta_id = sta_inv + 1
n_sta = len(data_sta)
#ground-motion observations
y_data = df_flatfile[res_name].to_numpy().copy()
#geometrical spreading covariates
x_2 = df_flatfile['x_2'].values
#vs30 covariates
x_3 = df_flatfile['x_3'].values[sta_idx]
#cell data
#reorder and only keep records included in the flatfile
df_celldist = df_celldist.reindex(df_flatfile.index)
#cell info
cell_ids_all = df_cellinfo.index
cell_names_all = df_cellinfo.cellname
#cell distance matrix
celldist_all = df_celldist[cell_names_all] #cell-distance matrix with all cells
#find cell with more than one paths
i_cells_valid = np.where(celldist_all.sum(axis=0) > 0)[0] #valid cells with more than one path
cell_ids_valid = cell_ids_all[i_cells_valid]
cell_names_valid = cell_names_all[i_cells_valid]
celldist_valid = celldist_all.loc[:,cell_names_valid] #cell-distance with only non-zero cells
#number of cells
n_cell = celldist_all.shape[1]
n_cell_valid = celldist_valid.shape[1]
#cell coordinates
X_cells_valid = df_cellinfo.loc[i_cells_valid,['mptX','mptY']].values
#print Rrup missfits
print('max R_rup misfit', (df_flatfile.Rrup.values - celldist_valid.sum(axis=1)).abs().max())
stan_data = {'N': n_data,
'NEQ': n_eq,
'NSTAT': n_sta,
'NCELL': n_cell_valid,
'eq': eq_id, #earthquake id
'stat': sta_id, #station id
'rec_mu': np.zeros(y_data.shape),
'Y': y_data,
'x_2': x_2,
'x_3': x_3,
'c_2_erg': c_2_erg,
'c_3_erg': c_3_erg,
'c_a_erg': c_a_erg,
'X_e': X_eq, #earthquake coordinates
'X_s': X_sta, #station coordinates
'X_c': X_cells_valid,
'RC': celldist_valid.to_numpy(),
}
stan_data_fname = out_dir + out_fname + '_stan_data' + '.json'
#create output directory
pathlib.Path(out_dir).mkdir(parents=True, exist_ok=True)
#write as json file
cmdstanpy.utils.write_stan_json(stan_data_fname, stan_data)
## Run Stan, fit model
#============================
#number of cores
n_cpu = max(cpu_count() -1,1)
#run stan
if (not stan_parallel) or n_cpu<=n_chains:
#compile stan model
stan_model = cmdstanpy.CmdStanModel(stan_file=stan_model_fname)
stan_model.compile(force=True)
#run full MCMC sampler
stan_fit = stan_model.sample(data=stan_data_fname, chains=n_chains,
iter_warmup=n_iter_warmup, iter_sampling=n_iter_sampling,
seed=1, max_treedepth=max_treedepth, adapt_delta=adapt_delta,
show_progress=True, output_dir=out_dir+'stan_fit/')
else:
#compile stan model
stan_model = cmdstanpy.CmdStanModel(stan_file=stan_model_fname, cpp_options={"STAN_THREADS": True})
stan_model.compile(force=True)
#number of cores per chain
n_cpu_chain = int(np.floor(n_cpu/n_chains))
#run full MCMC sampler
stan_fit = stan_model.sample(data=stan_data_fname, chains=n_chains, threads_per_chain=n_cpu_chain,
iter_warmup=n_iter_warmup, iter_sampling=n_iter_sampling,
seed=1, max_treedepth=max_treedepth, adapt_delta=adapt_delta,
show_progress=True, output_dir=out_dir+'stan_fit/')
## Postprocessing Data
#============================
## Extract posterior samples
# ---------------------------
#hyper-parameters
col_names_hyp = ['dc_0','mu_2p','mu_3s',
'ell_1e', 'ell_1as', 'omega_1e', 'omega_1as', 'omega_1bs',
'ell_2p', 'ell_3s', 'omega_2p', 'omega_3s',
'mu_cap', 'omega_cap',
'phi_0','tau_0']
#non-ergodic terms
col_names_dc_1e = ['dc_1e.%i'%(k) for k in range(n_eq)]
col_names_dc_1as = ['dc_1as.%i'%(k) for k in range(n_sta)]
col_names_dc_1bs = ['dc_1bs.%i'%(k) for k in range(n_sta)]
col_names_c_2p = ['c_2p.%i'%(k) for k in range(n_eq)]
col_names_c_3s = ['c_3s.%i'%(k) for k in range(n_sta)]
col_names_dB = ['dB.%i'%(k) for k in range(n_eq)]
col_names_cap = ['c_cap.%i'%(c_id) for c_id in cell_ids_valid]
col_names_all = (col_names_hyp + col_names_dc_1e + col_names_dc_1as + col_names_dc_1bs +
col_names_c_2p + col_names_c_3s + col_names_cap + col_names_dB)
#sumarize raw posterior distributions
stan_posterior = np.stack([stan_fit.stan_variable(c_n) for c_n in col_names_hyp], axis=1)
#adjustment terms
stan_posterior = np.concatenate((stan_posterior, stan_fit.stan_variable('dc_1e')), axis=1)
stan_posterior = np.concatenate((stan_posterior, stan_fit.stan_variable('dc_1as')), axis=1)
stan_posterior = np.concatenate((stan_posterior, stan_fit.stan_variable('dc_1bs')), axis=1)
stan_posterior = np.concatenate((stan_posterior, stan_fit.stan_variable('c_2p')), axis=1)
stan_posterior = np.concatenate((stan_posterior, stan_fit.stan_variable('c_3s')), axis=1)
stan_posterior = np.concatenate((stan_posterior, stan_fit.stan_variable('c_cap')), axis=1)
stan_posterior = np.concatenate((stan_posterior, stan_fit.stan_variable('dB')), axis=1)
#save raw-posterior distribution
df_stan_posterior_raw = pd.DataFrame(stan_posterior, columns = col_names_all)
df_stan_posterior_raw.to_csv(out_dir + out_fname + '_stan_posterior_raw' + '.csv', index=False)
## Summarize hyper-parameters
# ---------------------------
#summarize posterior distributions of hyper-parameters
perc_array = np.array([0.05,0.25,0.5,0.75,0.95])
df_stan_hyp = df_stan_posterior_raw[col_names_hyp].quantile(perc_array)
df_stan_hyp = df_stan_hyp.append(df_stan_posterior_raw[col_names_hyp].mean(axis = 0), ignore_index=True)
df_stan_hyp.index = ['prc_%.2f'%(prc) for prc in perc_array]+['mean']
df_stan_hyp.to_csv(out_dir + out_fname + '_stan_hyperparameters' + '.csv', index=True)
#detailed posterior percentiles of posterior distributions
perc_array = np.arange(0.01,0.99,0.01)
df_stan_posterior = df_stan_posterior_raw[col_names_hyp].quantile(perc_array)
df_stan_posterior.index.name = 'prc'
df_stan_posterior .to_csv(out_dir + out_fname + '_stan_hyperposterior' + '.csv', index=True)
del col_names_dc_1e, col_names_dc_1as, col_names_dc_1bs, col_names_c_2p, col_names_c_3s, col_names_dB
del stan_posterior, col_names_all
## Sample spatially varying coefficients and predictions at record locations
# ---------------------------
# earthquake and station location in database
X_eq_all = df_flatfile[['eqX', 'eqY']].values
X_sta_all = df_flatfile[['staX','staY']].values
# GMM anelastic attenuation
#--- --- --- --- --- --- --- ---
cells_ca_mu = np.array([df_stan_posterior_raw.loc[:,'c_cap.%i'%(k)].mean() for k in cell_ids_valid])
cells_ca_med = np.array([df_stan_posterior_raw.loc[:,'c_cap.%i'%(k)].median() for k in cell_ids_valid])
cells_ca_sig = np.array([df_stan_posterior_raw.loc[:,'c_cap.%i'%(k)].std() for k in cell_ids_valid])
#effect of anelastic attenuation in GM
cells_LcA_mu = celldist_valid.values @ cells_ca_mu
cells_LcA_med = celldist_valid.values @ cells_ca_med
cells_LcA_sig = np.sqrt(np.square(celldist_valid.values) @ cells_ca_sig**2)
#summary attenuation cells
catten_summary = np.vstack((np.tile(c_a_erg, n_cell_valid),
cells_ca_mu,
cells_ca_med,
cells_ca_sig)).T
columns_names = ['c_a_erg','c_cap_mean','c_cap_med','c_cap_sig']
df_catten_summary = pd.DataFrame(catten_summary, columns = columns_names, index=df_cellinfo.index[i_cells_valid])
#create dataframe with summary attenuation cells
df_catten_summary = pd.merge(df_cellinfo[['cellname','mptLat','mptLon','mptX','mptY','mptZ','UTMzone']],
df_catten_summary, how='right', left_index=True, right_index=True)
df_catten_summary.to_csv(out_dir + out_fname + '_stan_catten' + '.csv', index=True)
# GMM coefficients
#--- --- --- --- --- --- --- ---
#constant shift coefficient
coeff_0_mu = df_stan_posterior_raw.loc[:,'dc_0'].mean() * np.ones(n_data)
coeff_0_med = df_stan_posterior_raw.loc[:,'dc_0'].median() * np.ones(n_data)
coeff_0_sig = df_stan_posterior_raw.loc[:,'dc_0'].std() * np.ones(n_data)
#spatially varying earthquake constant coefficient
coeff_1e_mu = np.array([df_stan_posterior_raw.loc[:,f'dc_1e.{k}'].mean() for k in range(n_eq)])
coeff_1e_mu = coeff_1e_mu[eq_inv]
coeff_1e_med = np.array([df_stan_posterior_raw.loc[:,f'dc_1e.{k}'].median() for k in range(n_eq)])
coeff_1e_med = coeff_1e_med[eq_inv]
coeff_1e_sig = np.array([df_stan_posterior_raw.loc[:,f'dc_1e.{k}'].std() for k in range(n_eq)])
coeff_1e_sig = coeff_1e_sig[eq_inv]
#site term constant covariance
coeff_1as_mu = np.array([df_stan_posterior_raw.loc[:,f'dc_1as.{k}'].mean() for k in range(n_sta)])
coeff_1as_mu = coeff_1as_mu[sta_inv]
coeff_1as_med = np.array([df_stan_posterior_raw.loc[:,f'dc_1as.{k}'].median() for k in range(n_sta)])
coeff_1as_med = coeff_1as_med[sta_inv]
coeff_1as_sig = np.array([df_stan_posterior_raw.loc[:,f'dc_1as.{k}'].std() for k in range(n_sta)])
coeff_1as_sig = coeff_1as_sig[sta_inv]
#spatially varying station constant covariance
coeff_1bs_mu = np.array([df_stan_posterior_raw.loc[:,f'dc_1bs.{k}'].mean() for k in range(n_sta)])
coeff_1bs_mu = coeff_1bs_mu[sta_inv]
coeff_1bs_med = np.array([df_stan_posterior_raw.loc[:,f'dc_1bs.{k}'].median() for k in range(n_sta)])
coeff_1bs_med = coeff_1bs_med[sta_inv]
coeff_1bs_sig = np.array([df_stan_posterior_raw.loc[:,f'dc_1bs.{k}'].std() for k in range(n_sta)])
coeff_1bs_sig = coeff_1bs_sig[sta_inv]
#spatially varying geometrical spreading coefficient
coeff_2p_mu = np.array([df_stan_posterior_raw.loc[:,f'c_2p.{k}'].mean() for k in range(n_eq)])
coeff_2p_mu = coeff_2p_mu[eq_inv]
coeff_2p_med = np.array([df_stan_posterior_raw.loc[:,f'c_2p.{k}'].median() for k in range(n_eq)])
coeff_2p_med = coeff_2p_med[eq_inv]
coeff_2p_sig = np.array([df_stan_posterior_raw.loc[:,f'c_2p.{k}'].std() for k in range(n_eq)])
coeff_2p_sig = coeff_2p_sig[eq_inv]
#spatially varying Vs30 coefficient
coeff_3s_mu = np.array([df_stan_posterior_raw.loc[:,f'c_3s.{k}'].mean() for k in range(n_sta)])
coeff_3s_mu = coeff_3s_mu[sta_inv]
coeff_3s_med = np.array([df_stan_posterior_raw.loc[:,f'c_3s.{k}'].median() for k in range(n_sta)])
coeff_3s_med = coeff_3s_med[sta_inv]
coeff_3s_sig = np.array([df_stan_posterior_raw.loc[:,f'c_3s.{k}'].std() for k in range(n_sta)])
coeff_3s_sig = coeff_3s_sig[sta_inv]
# aleatory variability
phi_0_array = np.array([df_stan_posterior_raw.phi_0.mean()]*X_sta_all.shape[0])
tau_0_array = np.array([df_stan_posterior_raw.tau_0.mean()]*X_sta_all.shape[0])
#dataframe with flatfile info
df_flatinfo = df_flatfile[['eqid','ssn','eqLat','eqLon','staLat','staLon','eqX','eqY','staX','staY','UTMzone']]
#summary coefficients
coeffs_summary = np.vstack((coeff_0_mu,
coeff_1e_mu,
coeff_1as_mu,
coeff_1bs_mu,
coeff_2p_mu,
coeff_3s_mu,
cells_LcA_mu,
coeff_0_med,
coeff_1e_med,
coeff_1as_med,
coeff_1bs_med,
coeff_2p_med,
coeff_3s_med,
cells_LcA_med,
coeff_0_sig,
coeff_1e_sig,
coeff_1as_sig,
coeff_1bs_sig,
coeff_2p_sig,
coeff_3s_sig,
cells_LcA_sig)).T
columns_names = ['dc_0_mean','dc_1e_mean','dc_1as_mean','dc_1bs_mean','c_2p_mean','c_3s_mean','Lc_ca_mean',
'dc_0_med', 'dc_1e_med', 'dc_1as_med', 'dc_1bs_med', 'c_2p_med', 'c_3s_med', 'Lc_ca_med',
'dc_0_sig', 'dc_1e_sig', 'dc_1as_sig', 'dc_1bs_sig', 'c_2p_sig', 'c_3s_sig', 'Lc_ca_sig']
df_coeffs_summary = pd.DataFrame(coeffs_summary, columns = columns_names, index=df_flatfile.index)
#create dataframe with summary coefficients
df_coeffs_summary = pd.merge(df_flatinfo, df_coeffs_summary, how='right', left_index=True, right_index=True)
df_coeffs_summary[['eqid','ssn']] = df_coeffs_summary[['eqid','ssn']].astype(int)
df_coeffs_summary.to_csv(out_dir + out_fname + '_stan_coefficients' + '.csv', index=True)
# GMM prediction
#--- --- --- --- --- --- --- ---
#mean prediction
y_mu = (coeff_0_mu + coeff_1e_mu + coeff_1as_mu + coeff_1bs_mu + coeff_2p_mu*x_2 + coeff_3s_mu*x_3[sta_inv] + cells_LcA_mu)
#compute residuals
res_tot = y_data - y_mu
#residuals computed directly from stan regression
res_between = [df_stan_posterior_raw.loc[:,f'dB.{k}'].mean() for k in range(n_eq)]
res_between = np.array([res_between[k] for k in (eq_inv).astype(int)])
res_within = res_tot - res_between
#summary predictions and residuals
predict_summary = np.vstack((y_mu, res_tot, res_between, res_within)).T
columns_names = ['nerg_mu','res_tot','res_between','res_within']
df_predict_summary = pd.DataFrame(predict_summary, columns = columns_names, index=df_flatfile.index)
#create dataframe with predictions and residuals
df_predict_summary = pd.merge(df_flatinfo, df_predict_summary, how='right', left_index=True, right_index=True)
df_predict_summary[['eqid','ssn']] = df_predict_summary[['eqid','ssn']].astype(int)
df_predict_summary.to_csv(out_dir + out_fname + '_stan_residuals' + '.csv', index=True)
## Summary regression
# ---------------------------
#save summary statistics
stan_summary_fname = out_dir + out_fname + '_stan_summary' + '.txt'
with open(stan_summary_fname, 'w') as f:
print(stan_fit, file=f)
#create and save trace plots
fig_dir = out_dir + 'summary_figs/'
#create figures directory if doesn't exit
pathlib.Path(fig_dir).mkdir(parents=True, exist_ok=True)
#create stan trace plots
stan_az_fit = az.from_cmdstanpy(stan_fit)
# stan_az_fit = az.from_cmdstanpy(stan_fit, posterior_predictive='Y')
for c_name in col_names_hyp:
#create trace plot with arviz
ax = az.plot_trace(stan_az_fit, var_names=c_name, figsize=(10,5) ).ravel()
ax[0].yaxis.set_major_locator(plt_autotick())
ax[0].set_xlabel('sample value')
ax[0].set_ylabel('frequency')
ax[0].set_title('')
ax[0].grid(axis='both')
ax[1].set_xlabel('iteration')
ax[1].set_ylabel('sample value')
ax[1].grid(axis='both')
ax[1].set_title('')
fig = ax[0].figure
fig.suptitle(c_name)
fig.savefig(fig_dir + out_fname + '_stan_traceplot_' + c_name + '_arviz' + '.png')
return None
| 19,904 | 47.667482 | 128 | py |
ngmm_tools | ngmm_tools-master/Analyses/Python_lib/regression/cmdstan/regression_cmdstan_model3_uncorr_cells_sparse_unbounded_hyp.py | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Wed Dec 29 15:13:49 2021
@author: glavrent
"""
#load variables
import pathlib
from joblib import cpu_count
#arithmetic libraries
import numpy as np
from scipy import sparse
#statistics libraries
import pandas as pd
#plot libraries
import matplotlib as mpl
from matplotlib.ticker import AutoLocator as plt_autotick
import arviz as az
mpl.use('agg')
#stan library
import cmdstanpy
def RunStan(df_flatfile, df_cellinfo, df_celldist, stan_model_fname,
out_fname, out_dir, res_name='res', c_2_erg=0, c_3_erg=0, c_a_erg=0,
n_iter_warmup=300, n_iter_sampling=300, n_chains=4,
adapt_delta=0.8, max_treedepth=10,
stan_parallel=False):
'''
Run full Bayessian regression in Stan. Non-ergodic model includes: a spatially
varying earthquake constant, a spatially varying site constant, a spatially
independent site constant, and uncorrelated anelastic attenuation.
Parameters
----------
df_flatfile : pd.DataFrame
Input data frame containing total residuals, eq and site coordinates.
df_cellinfo : pd.DataFrame
Dataframe with coordinates of anelastic attenuation cells.
df_celldist : pd.DataFrame
Datafame with cell path distances of all records in df_flatfile.
stan_model_fname : string
File name for stan model.
out_fname : string
File name for output files.
out_dir : string
Output directory.
res_name : string, optional
Column name for total residuals. The default is 'res'.
c_a_erg : double, optional
Value of ergodic anelatic attenuation coefficient. Used as mean of cell
specific anelastic attenuation prior distribution. The default is 0.
n_iter_warmup : integer, optional
Number of burn out MCMC samples. The default is 300.
n_iter_sampling : integer, optional
Number of MCMC samples for computing the posterior distributions. The default is 300.
n_chains : integer, optional
Number of MCMC chains. The default is 4.
adapt_delta : double, optional
Target average proposal acceptance probability for adaptation. The default is 0.8.
max_treedepth : integer, optional
Maximum number of evaluations for each iteration (2^max_treedepth). The default is 10.
pystan_ver : integer, optional
Version of pystan to run. The default is 2.
pystan_parallel : bool, optional
Flag for using multithreaded option in STAN. The default is False.
Returns
-------
None.
'''
## Preprocess Input Data
#============================
#set rsn column as dataframe index, skip if rsn already the index
if not df_flatfile.index.name == 'rsn':
df_flatfile.set_index('rsn', drop=True, inplace=True)
if not df_celldist.index.name == 'rsn':
df_celldist.set_index('rsn', drop=True, inplace=True)
#set cellid column as dataframe index, skip if cellid already the index
if not df_cellinfo.index.name == 'cellid':
df_cellinfo.set_index('cellid', drop=True, inplace=True)
#number of data
n_data = len(df_flatfile)
#earthquake data
data_eq_all = df_flatfile[['eqid','mag','eqX', 'eqY']].values
_, eq_idx, eq_inv = np.unique(df_flatfile[['eqid']], axis=0, return_inverse=True, return_index=True)
data_eq = data_eq_all[eq_idx,:]
X_eq = data_eq[:,[2,3]] #earthquake coordinates
#create earthquake ids for all records (1 to n_eq)
eq_id = eq_inv + 1
n_eq = len(data_eq)
#station data
data_sta_all = df_flatfile[['ssn','Vs30','x_3','staX','staY']].values
_, sta_idx, sta_inv = np.unique( df_flatfile[['ssn']].values, axis = 0, return_inverse=True, return_index=True)
data_sta = data_sta_all[sta_idx,:]
X_sta = data_sta[:,[3,4]] #station coordinates
#create station indices for all records (1 to n_sta)
sta_id = sta_inv + 1
n_sta = len(data_sta)
#ground-motion observations
y_data = df_flatfile[res_name].to_numpy().copy()
#geometrical spreading covariates
x_2 = df_flatfile['x_2'].values
#vs30 covariates
x_3 = df_flatfile['x_3'].values[sta_idx]
#cell data
#reorder and only keep records included in the flatfile
df_celldist = df_celldist.reindex(df_flatfile.index)
#cell info
cell_ids_all = df_cellinfo.index
cell_names_all = df_cellinfo.cellname
#cell distance matrix
celldist_all = df_celldist[cell_names_all] #cell-distance matrix with all cells
#find cell with more than one paths
i_cells_valid = np.where(celldist_all.sum(axis=0) > 0)[0] #valid cells with more than one path
cell_ids_valid = cell_ids_all[i_cells_valid]
cell_names_valid = cell_names_all[i_cells_valid]
celldist_valid = celldist_all.loc[:,cell_names_valid] #cell-distance with only non-zero cells
celldist_valid_sp = sparse.csr_matrix(celldist_valid)
#number of cells
n_cell = celldist_all.shape[1]
n_cell_valid = celldist_valid.shape[1]
#cell coordinates
X_cells_valid = df_cellinfo.loc[i_cells_valid,['mptX','mptY']].values
#print Rrup missfits
print('max R_rup misfit', (df_flatfile.Rrup.values - celldist_valid.sum(axis=1)).abs().max())
stan_data = {'N': n_data,
'NEQ': n_eq,
'NSTAT': n_sta,
'NCELL': n_cell_valid,
'NCELL_SP': len(celldist_valid_sp.data),
'eq': eq_id, #earthquake id
'stat': sta_id, #station id
'rec_mu': np.zeros(y_data.shape),
'Y': y_data,
'x_2': x_2,
'x_3': x_3,
'c_2_erg': c_2_erg,
'c_3_erg': c_3_erg,
'c_a_erg': c_a_erg,
'X_e': X_eq, #earthquake coordinates
'X_s': X_sta, #station coordinates
'X_c': X_cells_valid,
'RC_val': celldist_valid_sp.data,
'RC_w': celldist_valid_sp.indices+1,
'RC_u': celldist_valid_sp.indptr+1,
}
stan_data_fname = out_dir + out_fname + '_stan_data' + '.json'
#create output directory
pathlib.Path(out_dir).mkdir(parents=True, exist_ok=True)
#write as json file
cmdstanpy.utils.write_stan_json(stan_data_fname, stan_data)
## Run Stan, fit model
#============================
#number of cores
n_cpu = max(cpu_count() -1,1)
#run stan
if (not stan_parallel) or n_cpu<=n_chains:
#compile stan model
stan_model = cmdstanpy.CmdStanModel(stan_file=stan_model_fname)
stan_model.compile(force=True)
#run full MCMC sampler
stan_fit = stan_model.sample(data=stan_data_fname, chains=n_chains,
iter_warmup=n_iter_warmup, iter_sampling=n_iter_sampling,
seed=1, max_treedepth=max_treedepth, adapt_delta=adapt_delta,
show_progress=True, output_dir=out_dir+'stan_fit/')
else:
#compile stan model
stan_model = cmdstanpy.CmdStanModel(stan_file=stan_model_fname, cpp_options={"STAN_THREADS": True})
stan_model.compile(force=True)
#number of cores per chain
n_cpu_chain = int(np.floor(n_cpu/n_chains))
#run full MCMC sampler
stan_fit = stan_model.sample(data=stan_data_fname, chains=n_chains, threads_per_chain=n_cpu_chain,
iter_warmup=n_iter_warmup, iter_sampling=n_iter_sampling,
seed=1, max_treedepth=max_treedepth, adapt_delta=adapt_delta,
show_progress=True, output_dir=out_dir+'stan_fit/')
## Postprocessing Data
#============================
## Extract posterior samples
# ---------------------------
#hyper-parameters
col_names_hyp = ['dc_0','mu_2p','mu_3s',
'ell_1e', 'ell_1as', 'omega_1e', 'omega_1as', 'omega_1bs',
'ell_2p', 'ell_3s', 'omega_2p', 'omega_3s',
'mu_cap', 'omega_cap',
'phi_0','tau_0']
#non-ergodic terms
col_names_dc_1e = ['dc_1e.%i'%(k) for k in range(n_eq)]
col_names_dc_1as = ['dc_1as.%i'%(k) for k in range(n_sta)]
col_names_dc_1bs = ['dc_1bs.%i'%(k) for k in range(n_sta)]
col_names_c_2p = ['c_2p.%i'%(k) for k in range(n_eq)]
col_names_c_3s = ['c_3s.%i'%(k) for k in range(n_sta)]
col_names_dB = ['dB.%i'%(k) for k in range(n_eq)]
col_names_cap = ['c_cap.%i'%(c_id) for c_id in cell_ids_valid]
col_names_all = (col_names_hyp + col_names_dc_1e + col_names_dc_1as + col_names_dc_1bs +
col_names_c_2p + col_names_c_3s + col_names_cap + col_names_dB)
#sumarize raw posterior distributions
stan_posterior = np.stack([stan_fit.stan_variable(c_n) for c_n in col_names_hyp], axis=1)
#adjustment terms
stan_posterior = np.concatenate((stan_posterior, stan_fit.stan_variable('dc_1e')), axis=1)
stan_posterior = np.concatenate((stan_posterior, stan_fit.stan_variable('dc_1as')), axis=1)
stan_posterior = np.concatenate((stan_posterior, stan_fit.stan_variable('dc_1bs')), axis=1)
stan_posterior = np.concatenate((stan_posterior, stan_fit.stan_variable('c_2p')), axis=1)
stan_posterior = np.concatenate((stan_posterior, stan_fit.stan_variable('c_3s')), axis=1)
stan_posterior = np.concatenate((stan_posterior, stan_fit.stan_variable('c_cap')), axis=1)
stan_posterior = np.concatenate((stan_posterior, stan_fit.stan_variable('dB')), axis=1)
#save raw-posterior distribution
df_stan_posterior_raw = pd.DataFrame(stan_posterior, columns = col_names_all)
df_stan_posterior_raw.to_csv(out_dir + out_fname + '_stan_posterior_raw' + '.csv', index=False)
## Summarize hyper-parameters
# ---------------------------
#summarize posterior distributions of hyper-parameters
perc_array = np.array([0.05,0.25,0.5,0.75,0.95])
df_stan_hyp = df_stan_posterior_raw[col_names_hyp].quantile(perc_array)
df_stan_hyp = df_stan_hyp.append(df_stan_posterior_raw[col_names_hyp].mean(axis = 0), ignore_index=True)
df_stan_hyp.index = ['prc_%.2f'%(prc) for prc in perc_array]+['mean']
df_stan_hyp.to_csv(out_dir + out_fname + '_stan_hyperparameters' + '.csv', index=True)
#detailed posterior percentiles of posterior distributions
perc_array = np.arange(0.01,0.99,0.01)
df_stan_posterior = df_stan_posterior_raw[col_names_hyp].quantile(perc_array)
df_stan_posterior.index.name = 'prc'
df_stan_posterior.to_csv(out_dir + out_fname + '_stan_hyperposterior' + '.csv', index=True)
del col_names_dc_1e, col_names_dc_1as, col_names_dc_1bs, col_names_c_2p, col_names_c_3s, col_names_dB
del stan_posterior, col_names_all
## Sample spatially varying coefficients and predictions at record locations
# ---------------------------
# earthquake and station location in database
X_eq_all = df_flatfile[['eqX', 'eqY']].values
X_sta_all = df_flatfile[['staX','staY']].values
# GMM anelastic attenuation
#--- --- --- --- --- --- --- ---
cells_ca_mu = np.array([df_stan_posterior_raw.loc[:,'c_cap.%i'%(k)].mean() for k in cell_ids_valid])
cells_ca_med = np.array([df_stan_posterior_raw.loc[:,'c_cap.%i'%(k)].median() for k in cell_ids_valid])
cells_ca_sig = np.array([df_stan_posterior_raw.loc[:,'c_cap.%i'%(k)].std() for k in cell_ids_valid])
#effect of anelastic attenuation in GM
cells_LcA_mu = celldist_valid_sp @ cells_ca_mu
cells_LcA_med = celldist_valid_sp @ cells_ca_med
cells_LcA_sig = np.sqrt(celldist_valid_sp.power(2) @ cells_ca_sig**2)
#summary attenuation cells
catten_summary = np.vstack((np.tile(c_a_erg, n_cell_valid),
cells_ca_mu,
cells_ca_med,
cells_ca_sig)).T
columns_names = ['c_a_erg','c_cap_mean','c_cap_med','c_cap_sig']
df_catten_summary = pd.DataFrame(catten_summary, columns = columns_names, index=df_cellinfo.index[i_cells_valid])
#create dataframe with summary attenuation cells
df_catten_summary = pd.merge(df_cellinfo[['cellname','mptLat','mptLon','mptX','mptY','mptZ','UTMzone']],
df_catten_summary, how='right', left_index=True, right_index=True)
df_catten_summary.to_csv(out_dir + out_fname + '_stan_catten' + '.csv', index=True)
# GMM coefficients
#--- --- --- --- --- --- --- ---
#constant shift coefficient
coeff_0_mu = df_stan_posterior_raw.loc[:,'dc_0'].mean() * np.ones(n_data)
coeff_0_med = df_stan_posterior_raw.loc[:,'dc_0'].median() * np.ones(n_data)
coeff_0_sig = df_stan_posterior_raw.loc[:,'dc_0'].std() * np.ones(n_data)
#spatially varying earthquake constant coefficient
coeff_1e_mu = np.array([df_stan_posterior_raw.loc[:,f'dc_1e.{k}'].mean() for k in range(n_eq)])
coeff_1e_mu = coeff_1e_mu[eq_inv]
coeff_1e_med = np.array([df_stan_posterior_raw.loc[:,f'dc_1e.{k}'].median() for k in range(n_eq)])
coeff_1e_med = coeff_1e_med[eq_inv]
coeff_1e_sig = np.array([df_stan_posterior_raw.loc[:,f'dc_1e.{k}'].std() for k in range(n_eq)])
coeff_1e_sig = coeff_1e_sig[eq_inv]
#site term constant covariance
coeff_1as_mu = np.array([df_stan_posterior_raw.loc[:,f'dc_1as.{k}'].mean() for k in range(n_sta)])
coeff_1as_mu = coeff_1as_mu[sta_inv]
coeff_1as_med = np.array([df_stan_posterior_raw.loc[:,f'dc_1as.{k}'].median() for k in range(n_sta)])
coeff_1as_med = coeff_1as_med[sta_inv]
coeff_1as_sig = np.array([df_stan_posterior_raw.loc[:,f'dc_1as.{k}'].std() for k in range(n_sta)])
coeff_1as_sig = coeff_1as_sig[sta_inv]
#spatially varying station constant covariance
coeff_1bs_mu = np.array([df_stan_posterior_raw.loc[:,f'dc_1bs.{k}'].mean() for k in range(n_sta)])
coeff_1bs_mu = coeff_1bs_mu[sta_inv]
coeff_1bs_med = np.array([df_stan_posterior_raw.loc[:,f'dc_1bs.{k}'].median() for k in range(n_sta)])
coeff_1bs_med = coeff_1bs_med[sta_inv]
coeff_1bs_sig = np.array([df_stan_posterior_raw.loc[:,f'dc_1bs.{k}'].std() for k in range(n_sta)])
coeff_1bs_sig = coeff_1bs_sig[sta_inv]
#spatially varying geometrical spreading coefficient
coeff_2p_mu = np.array([df_stan_posterior_raw.loc[:,f'c_2p.{k}'].mean() for k in range(n_eq)])
coeff_2p_mu = coeff_2p_mu[eq_inv]
coeff_2p_med = np.array([df_stan_posterior_raw.loc[:,f'c_2p.{k}'].median() for k in range(n_eq)])
coeff_2p_med = coeff_2p_med[eq_inv]
coeff_2p_sig = np.array([df_stan_posterior_raw.loc[:,f'c_2p.{k}'].std() for k in range(n_eq)])
coeff_2p_sig = coeff_2p_sig[eq_inv]
#spatially varying Vs30 coefficient
coeff_3s_mu = np.array([df_stan_posterior_raw.loc[:,f'c_3s.{k}'].mean() for k in range(n_sta)])
coeff_3s_mu = coeff_3s_mu[sta_inv]
coeff_3s_med = np.array([df_stan_posterior_raw.loc[:,f'c_3s.{k}'].median() for k in range(n_sta)])
coeff_3s_med = coeff_3s_med[sta_inv]
coeff_3s_sig = np.array([df_stan_posterior_raw.loc[:,f'c_3s.{k}'].std() for k in range(n_sta)])
coeff_3s_sig = coeff_3s_sig[sta_inv]
# aleatory variability
phi_0_array = np.array([df_stan_posterior_raw.phi_0.mean()]*X_sta_all.shape[0])
tau_0_array = np.array([df_stan_posterior_raw.tau_0.mean()]*X_sta_all.shape[0])
#dataframe with flatfile info
df_flatinfo = df_flatfile[['eqid','ssn','eqLat','eqLon','staLat','staLon','eqX','eqY','staX','staY','UTMzone']]
#summary coefficients
coeffs_summary = np.vstack((coeff_0_mu,
coeff_1e_mu,
coeff_1as_mu,
coeff_1bs_mu,
coeff_2p_mu,
coeff_3s_mu,
cells_LcA_mu,
coeff_0_med,
coeff_1e_med,
coeff_1as_med,
coeff_1bs_med,
coeff_2p_med,
coeff_3s_med,
cells_LcA_med,
coeff_0_sig,
coeff_1e_sig,
coeff_1as_sig,
coeff_1bs_sig,
coeff_2p_sig,
coeff_3s_sig,
cells_LcA_sig)).T
columns_names = ['dc_0_mean','dc_1e_mean','dc_1as_mean','dc_1bs_mean','c_2p_mean','c_3s_mean','Lc_ca_mean',
'dc_0_med', 'dc_1e_med', 'dc_1as_med', 'dc_1bs_med', 'c_2p_med', 'c_3s_med', 'Lc_ca_med',
'dc_0_sig', 'dc_1e_sig', 'dc_1as_sig', 'dc_1bs_sig', 'c_2p_sig', 'c_3s_sig', 'Lc_ca_sig']
df_coeffs_summary = pd.DataFrame(coeffs_summary, columns = columns_names, index=df_flatfile.index)
#create dataframe with summary coefficients
df_coeffs_summary = pd.merge(df_flatinfo, df_coeffs_summary, how='right', left_index=True, right_index=True)
df_coeffs_summary[['eqid','ssn']] = df_coeffs_summary[['eqid','ssn']].astype(int)
df_coeffs_summary.to_csv(out_dir + out_fname + '_stan_coefficients' + '.csv', index=True)
# GMM prediction
#--- --- --- --- --- --- --- ---
#mean prediction
y_mu = (coeff_0_mu + coeff_1e_mu + coeff_1as_mu + coeff_1bs_mu + coeff_2p_mu*x_2 + coeff_3s_mu*x_3[sta_inv] + cells_LcA_mu)
#compute residuals
res_tot = y_data - y_mu
#residuals computed directly from stan regression
res_between = [df_stan_posterior_raw.loc[:,f'dB.{k}'].mean() for k in range(n_eq)]
res_between = np.array([res_between[k] for k in (eq_inv).astype(int)])
res_within = res_tot - res_between
#summary predictions and residuals
predict_summary = np.vstack((y_mu, res_tot, res_between, res_within)).T
columns_names = ['nerg_mu','res_tot','res_between','res_within']
df_predict_summary = pd.DataFrame(predict_summary, columns = columns_names, index=df_flatfile.index)
#create dataframe with predictions and residuals
df_predict_summary = pd.merge(df_flatinfo, df_predict_summary, how='right', left_index=True, right_index=True)
df_predict_summary[['eqid','ssn']] = df_predict_summary[['eqid','ssn']].astype(int)
df_predict_summary.to_csv(out_dir + out_fname + '_stan_residuals' + '.csv', index=True)
## Summary regression
# ---------------------------
#save summary statistics
stan_summary_fname = out_dir + out_fname + '_stan_summary' + '.txt'
with open(stan_summary_fname, 'w') as f:
print(stan_fit, file=f)
#create and save trace plots
fig_dir = out_dir + 'summary_figs/'
#create figures directory if doesn't exit
pathlib.Path(fig_dir).mkdir(parents=True, exist_ok=True)
#create stan trace plots
stan_az_fit = az.from_cmdstanpy(stan_fit)
# stan_az_fit = az.from_cmdstanpy(stan_fit, posterior_predictive='Y')
for c_name in col_names_hyp:
#create trace plot with arviz
ax = az.plot_trace(stan_az_fit, var_names=c_name, figsize=(10,5) ).ravel()
ax[0].yaxis.set_major_locator(plt_autotick())
ax[0].set_xlabel('sample value')
ax[0].set_ylabel('frequency')
ax[0].set_title('')
ax[0].grid(axis='both')
ax[1].set_xlabel('iteration')
ax[1].set_ylabel('sample value')
ax[1].grid(axis='both')
ax[1].set_title('')
fig = ax[0].figure
fig.suptitle(c_name)
fig.savefig(fig_dir + out_fname + '_stan_traceplot_' + c_name + '_arviz' + '.png')
return None
| 20,144 | 47.65942 | 128 | py |
ngmm_tools | ngmm_tools-master/Analyses/Python_lib/regression/cmdstan/regression_cmdstan_model2_corr_cells_unbounded_hyp.py | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Wed Dec 29 15:13:49 2021
@author: glavrent
"""
#load variables
import pathlib
from joblib import cpu_count
#arithmetic libraries
import numpy as np
#statistics libraries
import pandas as pd
#plot libraries
import matplotlib as mpl
from matplotlib.ticker import AutoLocator as plt_autotick
import arviz as az
mpl.use('agg')
#stan library
import cmdstanpy
def RunStan(df_flatfile, df_cellinfo, df_celldist, stan_model_fname,
out_fname, out_dir, res_name='res', c_a_erg=0,
n_iter_warmup=300, n_iter_sampling=300, n_chains=4,
adapt_delta=0.8, max_treedepth=10,
stan_parallel=False):
'''
Run full Bayessian regression in Stan. Non-ergodic model includes: a spatially
varying earthquake constant, a spatially varying site constant, a spatially
independent site constant, and partially spatially correlated anelastic
attenuation.
Parameters
----------
df_flatfile : pd.DataFrame
Input data frame containing total residuals, eq and site coordinates.
df_cellinfo : pd.DataFrame
Dataframe with coordinates of anelastic attenuation cells.
df_celldist : pd.DataFrame
Datafame with cell path distances of all records in df_flatfile.
stan_model_fname : string
File name for stan model.
out_fname : string
File name for output files.
out_dir : string
Output directory.
res_name : string, optional
Column name for total residuals. The default is 'res'.
c_a_erg : double, optional
Value of ergodic anelatic attenuation coefficient. Used as mean of cell
specific anelastic attenuation prior distribution. The default is 0.
n_iter_warmup : integer, optional
Number of burn out MCMC samples. The default is 300.
n_iter_sampling : integer, optional
Number of MCMC samples for computing the posterior distributions. The default is 300.
n_chains : integer, optional
Number of MCMC chains. The default is 4.
adapt_delta : double, optional
Target average proposal acceptance probability for adaptation. The default is 0.8.
max_treedepth : integer, optional
Maximum number of evaluations for each iteration (2^max_treedepth). The default is 10.
stan_parallel : bool, optional
Flag for using multithreaded option in STAN. The default is False.
Returns
-------
None.
'''
## Preprocess Input Data
#============================
#set rsn column as dataframe index, skip if rsn already the index
if not df_flatfile.index.name == 'rsn':
df_flatfile.set_index('rsn', drop=True, inplace=True)
if not df_celldist.index.name == 'rsn':
df_celldist.set_index('rsn', drop=True, inplace=True)
#set cellid column as dataframe index, skip if cellid already the index
if not df_cellinfo.index.name == 'cellid':
df_cellinfo.set_index('cellid', drop=True, inplace=True)
#number of data
n_data = len(df_flatfile)
#earthquake data
data_eq_all = df_flatfile[['eqid','mag','eqX', 'eqY']].values
_, eq_idx, eq_inv = np.unique(df_flatfile[['eqid']], axis=0, return_inverse=True, return_index=True)
data_eq = data_eq_all[eq_idx,:]
X_eq = data_eq[:,[2,3]] #earthquake coordinates
#create earthquake ids for all records (1 to n_eq)
eq_id = eq_inv + 1
n_eq = len(data_eq)
#station data
data_sta_all = df_flatfile[['ssn','Vs30','staX','staY']].values
_, sta_idx, sta_inv = np.unique( df_flatfile[['ssn']].values, axis = 0, return_inverse=True, return_index=True)
data_sta = data_sta_all[sta_idx,:]
X_sta = data_sta[:,[2,3]] #station coordinates
#create station indices for all records (1 to n_sta)
sta_id = sta_inv + 1
n_sta = len(data_sta)
#ground-motion observations
y_data = df_flatfile[res_name].to_numpy().copy()
#cell data
#reorder and only keep records included in the flatfile
df_celldist = df_celldist.reindex(df_flatfile.index)
#cell info
cell_ids_all = df_cellinfo.index
cell_names_all = df_cellinfo.cellname
#cell distance matrix
celldist_all = df_celldist[cell_names_all] #cell-distance matrix with all cells
#find cell with more than one paths
i_cells_valid = np.where(celldist_all.sum(axis=0) > 0)[0] #valid cells with more than one path
cell_ids_valid = cell_ids_all[i_cells_valid]
cell_names_valid = cell_names_all[i_cells_valid]
celldist_valid = celldist_all.loc[:,cell_names_valid] #cell-distance with only non-zero cells
#number of cells
n_cell = celldist_all.shape[1]
n_cell_valid = celldist_valid.shape[1]
#cell coordinates
X_cells_valid = df_cellinfo.loc[i_cells_valid,['mptX','mptY']].values
#print Rrup missfits
print('max R_rup misfit', (df_flatfile.Rrup.values - celldist_valid.sum(axis=1)).abs().max())
stan_data = {'N': n_data,
'NEQ': n_eq,
'NSTAT': n_sta,
'NCELL': n_cell_valid,
'eq': eq_id, #earthquake id
'stat': sta_id, #station id
'X_e': X_eq, #earthquake coordinates
'X_s': X_sta, #station coordinates
'X_c': X_cells_valid,
'rec_mu': np.zeros(y_data.shape),
'RC': celldist_valid.to_numpy(),
'c_a_erg': c_a_erg,
'Y': y_data,
}
stan_data_fname = out_dir + out_fname + '_stan_data' + '.json'
#create output directory
pathlib.Path(out_dir).mkdir(parents=True, exist_ok=True)
#write as json file
cmdstanpy.utils.write_stan_json(stan_data_fname, stan_data)
## Run Stan, fit model
#============================
#number of cores
n_cpu = max(cpu_count() -1,1)
#run stan
if (not stan_parallel) or n_cpu<=n_chains:
#compile stan model
stan_model = cmdstanpy.CmdStanModel(stan_file=stan_model_fname)
stan_model.compile(force=True)
#run full MCMC sampler
stan_fit = stan_model.sample(data=stan_data_fname, chains=n_chains,
iter_warmup=n_iter_warmup, iter_sampling=n_iter_sampling,
seed=1, max_treedepth=max_treedepth, adapt_delta=adapt_delta,
show_progress=True, output_dir=out_dir+'stan_fit/')
else:
#compile stan model
stan_model = cmdstanpy.CmdStanModel(stan_file=stan_model_fname, cpp_options={"STAN_THREADS": True})
stan_model.compile(force=True)
#number of cores per chain
n_cpu_chain = int(np.floor(n_cpu/n_chains))
#run full MCMC sampler
stan_fit = stan_model.sample(data=stan_data_fname, chains=n_chains, threads_per_chain=n_cpu_chain,
iter_warmup=n_iter_warmup, iter_sampling=n_iter_sampling,
seed=1, max_treedepth=max_treedepth, adapt_delta=adapt_delta,
show_progress=True, output_dir=out_dir+'stan_fit/')
## Postprocessing Data
#============================
## Extract posterior samples
# ---------------------------
#hyper-parameters
col_names_hyp = ['dc_0','ell_1e', 'ell_1as', 'omega_1e', 'omega_1as', 'omega_1bs',
'mu_cap', 'ell_ca1p', 'omega_ca1p', 'omega_ca2p',
'phi_0','tau_0']
#non-ergodic terms
col_names_dc_1e = ['dc_1e.%i'%(k) for k in range(n_eq)]
col_names_dc_1as = ['dc_1as.%i'%(k) for k in range(n_sta)]
col_names_dc_1bs = ['dc_1bs.%i'%(k) for k in range(n_sta)]
col_names_dB = ['dB.%i'%(k) for k in range(n_eq)]
col_names_cap = ['c_cap.%i'%(c_id) for c_id in cell_ids_valid]
col_names_all = col_names_hyp + col_names_dc_1e + col_names_dc_1as + col_names_dc_1bs + col_names_cap + col_names_dB
#summarize raw posterior distributions
stan_posterior = np.stack([stan_fit.stan_variable(c_n) for c_n in col_names_hyp], axis=1)
#adjustment terms
stan_posterior = np.concatenate((stan_posterior, stan_fit.stan_variable('dc_1e')), axis=1)
stan_posterior = np.concatenate((stan_posterior, stan_fit.stan_variable('dc_1as')), axis=1)
stan_posterior = np.concatenate((stan_posterior, stan_fit.stan_variable('dc_1bs')), axis=1)
stan_posterior = np.concatenate((stan_posterior, stan_fit.stan_variable('c_cap')), axis=1)
stan_posterior = np.concatenate((stan_posterior, stan_fit.stan_variable('dB')), axis=1)
#save raw-posterior distribution
df_stan_posterior_raw = pd.DataFrame(stan_posterior, columns = col_names_all)
df_stan_posterior_raw.to_csv(out_dir + out_fname + '_stan_posterior_raw' + '.csv', index=False)
## Summarize hyper-parameters
# ---------------------------
#summarize posterior distributions of hyper-parameters
perc_array = np.array([0.05,0.25,0.5,0.75,0.95])
df_stan_hyp = df_stan_posterior_raw[col_names_hyp].quantile(perc_array)
df_stan_hyp = df_stan_hyp.append(df_stan_posterior_raw[col_names_hyp].mean(axis = 0), ignore_index=True)
df_stan_hyp.index = ['prc_%.2f'%(prc) for prc in perc_array]+['mean']
df_stan_hyp.to_csv(out_dir + out_fname + '_stan_hyperparameters' + '.csv', index=True)
#detailed posterior percentiles of posterior distributions
perc_array = np.arange(0.01,0.99,0.01)
df_stan_posterior = df_stan_posterior_raw[col_names_hyp].quantile(perc_array)
df_stan_posterior.index.name = 'prc'
df_stan_posterior.to_csv(out_dir + out_fname + '_stan_hyperposterior' + '.csv', index=True)
del col_names_dc_1e, col_names_dc_1as, col_names_dc_1bs, col_names_dB
del stan_posterior, col_names_all
## Sample spatially varying coefficients and predictions at record locations
# ---------------------------
# earthquake and station location in database
X_eq_all = df_flatfile[['eqX', 'eqY']].values
X_sta_all = df_flatfile[['staX','staY']].values
# GMM anelastic attenuation
#--- --- --- --- --- --- --- ---
cells_ca_mu = np.array([df_stan_posterior_raw.loc[:,'c_cap.%i'%(k)].mean() for k in cell_ids_valid])
cells_ca_med = np.array([df_stan_posterior_raw.loc[:,'c_cap.%i'%(k)].median() for k in cell_ids_valid])
cells_ca_sig = np.array([df_stan_posterior_raw.loc[:,'c_cap.%i'%(k)].std() for k in cell_ids_valid])
#effect of anelastic attenuation in GM
cells_LcA_mu = celldist_valid.values @ cells_ca_mu
cells_LcA_med = celldist_valid.values @ cells_ca_med
cells_LcA_sig = np.sqrt(np.square(celldist_valid.values) @ cells_ca_sig**2)
#summary attenuation cells
catten_summary = np.vstack((np.tile(c_a_erg, n_cell_valid),
cells_ca_mu,
cells_ca_med,
cells_ca_sig)).T
columns_names = ['c_a_erg','c_cap_mean','c_cap_med','c_cap_sig']
df_catten_summary = pd.DataFrame(catten_summary, columns = columns_names, index=df_cellinfo.index[i_cells_valid])
#create dataframe with summary attenuation cells
df_catten_summary = pd.merge(df_cellinfo[['cellname','mptLat','mptLon','mptX','mptY','mptZ','UTMzone']],
df_catten_summary, how='right', left_index=True, right_index=True)
df_catten_summary.to_csv(out_dir + out_fname + '_stan_catten' + '.csv', index=True)
# GMM coefficients
#--- --- --- --- --- --- --- ---
#constant shift coefficient
coeff_0_mu = df_stan_posterior_raw.loc[:,'dc_0'].mean() * np.ones(n_data)
coeff_0_med = df_stan_posterior_raw.loc[:,'dc_0'].median() * np.ones(n_data)
coeff_0_sig = df_stan_posterior_raw.loc[:,'dc_0'].std() * np.ones(n_data)
#spatially varying earthquake constant coefficient
coeff_1e_mu = np.array([df_stan_posterior_raw.loc[:,f'dc_1e.{k}'].mean() for k in range(n_eq)])
coeff_1e_mu = coeff_1e_mu[eq_inv]
coeff_1e_med = np.array([df_stan_posterior_raw.loc[:,f'dc_1e.{k}'].median() for k in range(n_eq)])
coeff_1e_med = coeff_1e_med[eq_inv]
coeff_1e_sig = np.array([df_stan_posterior_raw.loc[:,f'dc_1e.{k}'].std() for k in range(n_eq)])
coeff_1e_sig = coeff_1e_sig[eq_inv]
#site term constant covariance
coeff_1as_mu = np.array([df_stan_posterior_raw.loc[:,f'dc_1as.{k}'].mean() for k in range(n_sta)])
coeff_1as_mu = coeff_1as_mu[sta_inv]
coeff_1as_med = np.array([df_stan_posterior_raw.loc[:,f'dc_1as.{k}'].median() for k in range(n_sta)])
coeff_1as_med = coeff_1as_med[sta_inv]
coeff_1as_sig = np.array([df_stan_posterior_raw.loc[:,f'dc_1as.{k}'].std() for k in range(n_sta)])
coeff_1as_sig = coeff_1as_sig[sta_inv]
#spatially varying station constant covariance
coeff_1bs_mu = np.array([df_stan_posterior_raw.loc[:,f'dc_1bs.{k}'].mean() for k in range(n_sta)])
coeff_1bs_mu = coeff_1bs_mu[sta_inv]
coeff_1bs_med = np.array([df_stan_posterior_raw.loc[:,f'dc_1bs.{k}'].median() for k in range(n_sta)])
coeff_1bs_med = coeff_1bs_med[sta_inv]
coeff_1bs_sig = np.array([df_stan_posterior_raw.loc[:,f'dc_1bs.{k}'].std() for k in range(n_sta)])
coeff_1bs_sig = coeff_1bs_sig[sta_inv]
# aleatory variability
phi_0_array = np.array([df_stan_posterior_raw.phi_0.mean()]*X_sta_all.shape[0])
tau_0_array = np.array([df_stan_posterior_raw.tau_0.mean()]*X_sta_all.shape[0])
#dataframe with flatfile info
df_flatinfo = df_flatfile[['eqid','ssn','eqLat','eqLon','staLat','staLon','eqX','eqY','staX','staY','UTMzone']]
#summary coefficients
coeffs_summary = np.vstack((coeff_0_mu,
coeff_1e_mu,
coeff_1as_mu,
coeff_1bs_mu,
cells_LcA_mu,
coeff_0_med,
coeff_1e_med,
coeff_1as_med,
coeff_1bs_med,
cells_LcA_med,
coeff_0_sig,
coeff_1e_sig,
coeff_1as_sig,
coeff_1bs_sig,
cells_LcA_sig)).T
columns_names = ['dc_0_mean','dc_1e_mean','dc_1as_mean','dc_1bs_mean','Lc_ca_mean',
'dc_0_med', 'dc_1e_med', 'dc_1as_med', 'dc_1bs_med', 'Lc_ca_med',
'dc_0_sig', 'dc_1e_sig', 'dc_1as_sig', 'dc_1bs_sig', 'Lc_ca_sig']
df_coeffs_summary = pd.DataFrame(coeffs_summary, columns = columns_names, index=df_flatfile.index)
#create dataframe with summary coefficients
df_coeffs_summary = pd.merge(df_flatinfo, df_coeffs_summary, how='right', left_index=True, right_index=True)
df_coeffs_summary[['eqid','ssn']] = df_coeffs_summary[['eqid','ssn']].astype(int)
df_coeffs_summary.to_csv(out_dir + out_fname + '_stan_coefficients' + '.csv', index=True)
# GMM prediction
#--- --- --- --- --- --- --- ---
#mean prediction
y_mu = (coeff_0_mu + coeff_1e_mu + coeff_1as_mu + coeff_1bs_mu + cells_LcA_mu)
#compute residuals
res_tot = y_data - y_mu
#residuals computed directly from stan regression
res_between = [df_stan_posterior_raw.loc[:,f'dB.{k}'].mean() for k in range(n_eq)]
res_between = np.array([res_between[k] for k in (eq_inv).astype(int)])
res_within = res_tot - res_between
#summary predictions and residuals
predict_summary = np.vstack((y_mu, res_tot, res_between, res_within)).T
columns_names = ['nerg_mu','res_tot','res_between','res_within']
df_predict_summary = pd.DataFrame(predict_summary, columns = columns_names, index=df_flatfile.index)
#create dataframe with predictions and residuals
df_predict_summary = pd.merge(df_flatinfo, df_predict_summary, how='right', left_index=True, right_index=True)
df_predict_summary[['eqid','ssn']] = df_predict_summary[['eqid','ssn']].astype(int)
df_predict_summary.to_csv(out_dir + out_fname + '_stan_residuals' + '.csv', index=True)
## Summary regression
# ---------------------------
#save summary statistics
stan_summary_fname = out_dir + out_fname + '_stan_summary' + '.txt'
with open(stan_summary_fname, 'w') as f:
print(stan_fit, file=f)
#create and save trace plots
fig_dir = out_dir + 'summary_figs/'
#create figures directory if doesn't exit
pathlib.Path(fig_dir).mkdir(parents=True, exist_ok=True)
#create stan trace plots
stan_az_fit = az.from_cmdstanpy(stan_fit)
# stan_az_fit = az.from_cmdstanpy(stan_fit, posterior_predictive='Y')
for c_name in col_names_hyp:
#create trace plot with arviz
ax = az.plot_trace(stan_az_fit, var_names=c_name, figsize=(10,5) ).ravel()
ax[0].yaxis.set_major_locator(plt_autotick())
ax[0].set_xlabel('sample value')
ax[0].set_ylabel('frequency')
ax[0].set_title('')
ax[0].grid(axis='both')
ax[1].set_xlabel('iteration')
ax[1].set_ylabel('sample value')
ax[1].grid(axis='both')
ax[1].set_title('')
fig = ax[0].figure
fig.suptitle(c_name)
fig.savefig(fig_dir + out_fname + '_stan_traceplot_' + c_name + '_arviz' + '.png')
return None
| 17,672 | 46.764865 | 120 | py |
ngmm_tools | ngmm_tools-master/Analyses/Python_lib/regression/cmdstan/regression_cmdstan_model3_corr_cells_unbounded_hyp.py | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Wed Dec 29 15:13:49 2021
@author: glavrent
"""
#load variables
import pathlib
from joblib import cpu_count
#arithmetic libraries
import numpy as np
#statistics libraries
import pandas as pd
#plot libraries
import matplotlib as mpl
from matplotlib.ticker import AutoLocator as plt_autotick
import arviz as az
mpl.use('agg')
#stan library
import cmdstanpy
def RunStan(df_flatfile, df_cellinfo, df_celldist, stan_model_fname,
out_fname, out_dir, res_name='res', c_2_erg=0, c_3_erg=0, c_a_erg=0,
n_iter_warmup=300, n_iter_sampling=300, n_chains=4,
adapt_delta=0.8, max_treedepth=10,
stan_parallel=False):
'''
Run full Bayessian regression in Stan. Non-ergodic model includes: a spatially
varying earthquake constant, a spatially varying site constant, a spatially
independent site constant, and partially spatially correlated anelastic
attenuation.
Parameters
----------
df_flatfile : pd.DataFrame
Input data frame containing total residuals, eq and site coordinates.
df_cellinfo : pd.DataFrame
Dataframe with coordinates of anelastic attenuation cells.
df_celldist : pd.DataFrame
Datafame with cell path distances of all records in df_flatfile.
stan_model_fname : string
File name for stan model.
out_fname : string
File name for output files.
out_dir : string
Output directory.
res_name : string, optional
Column name for total residuals. The default is 'res'.
c_2_erg : double, optional
Value of ergodic geometrical spreading coefficient. The default is 0.
c_3_erg : double, optional
Value of ergodic Vs30 coefficient. The default is 0.
c_a_erg : double, optional
Value of ergodic anelatic attenuation coefficient. Used as mean of cell
specific anelastic attenuation prior distribution. The default is 0.
n_iter_warmup : integer, optional
Number of burn out MCMC samples. The default is 300.
n_iter_sampling : integer, optional
Number of MCMC samples for computing the posterior distributions. The default is 300.
n_chains : integer, optional
Number of MCMC chains. The default is 4.
adapt_delta : double, optional
Target average proposal acceptance probability for adaptation. The default is 0.8.
max_treedepth : integer, optional
Maximum number of evaluations for each iteration (2^max_treedepth). The default is 10.
stan_parallel : bool, optional
Flag for using multithreaded option in STAN. The default is False.
Returns
-------
None.
'''
## Preprocess Input Data
#============================
#set rsn column as dataframe index, skip if rsn already the index
if not df_flatfile.index.name == 'rsn':
df_flatfile.set_index('rsn', drop=True, inplace=True)
if not df_celldist.index.name == 'rsn':
df_celldist.set_index('rsn', drop=True, inplace=True)
#set cellid column as dataframe index, skip if cellid already the index
if not df_cellinfo.index.name == 'cellid':
df_cellinfo.set_index('cellid', drop=True, inplace=True)
#number of data
n_data = len(df_flatfile)
#earthquake data
data_eq_all = df_flatfile[['eqid','mag','eqX', 'eqY']].values
_, eq_idx, eq_inv = np.unique(df_flatfile[['eqid']], axis=0, return_inverse=True, return_index=True)
data_eq = data_eq_all[eq_idx,:]
X_eq = data_eq[:,[2,3]] #earthquake coordinates
#create earthquake ids for all records (1 to n_eq)
eq_id = eq_inv + 1
n_eq = len(data_eq)
#station data
data_sta_all = df_flatfile[['ssn','Vs30','x_3','staX','staY']].values
_, sta_idx, sta_inv = np.unique( df_flatfile[['ssn']].values, axis = 0, return_inverse=True, return_index=True)
data_sta = data_sta_all[sta_idx,:]
X_sta = data_sta[:,[3,4]] #station coordinates
#create station indices for all records (1 to n_sta)
sta_id = sta_inv + 1
n_sta = len(data_sta)
#geometrical spreading covariates
x_2 = df_flatfile['x_2'].values
#vs30 covariates
x_3 = df_flatfile['x_3'].values[sta_idx]
#ground-motion observations
y_data = df_flatfile[res_name].to_numpy().copy()
#cell data
#reorder and only keep records included in the flatfile
df_celldist = df_celldist.reindex(df_flatfile.index)
#cell info
cell_ids_all = df_cellinfo.index
cell_names_all = df_cellinfo.cellname
#cell distance matrix
celldist_all = df_celldist[cell_names_all] #cell-distance matrix with all cells
#find cell with more than one paths
i_cells_valid = np.where(celldist_all.sum(axis=0) > 0)[0] #valid cells with more than one path
cell_ids_valid = cell_ids_all[i_cells_valid]
cell_names_valid = cell_names_all[i_cells_valid]
celldist_valid = celldist_all.loc[:,cell_names_valid] #cell-distance with only non-zero cells
#number of cells
n_cell = celldist_all.shape[1]
n_cell_valid = celldist_valid.shape[1]
#cell coordinates
X_cells_valid = df_cellinfo.loc[i_cells_valid,['mptX','mptY']].values
#print Rrup missfits
print('max R_rup misfit', (df_flatfile.Rrup.values - celldist_valid.sum(axis=1)).abs().max())
stan_data = {'N': n_data,
'NEQ': n_eq,
'NSTAT': n_sta,
'NCELL': n_cell_valid,
'eq': eq_id, #earthquake id
'stat': sta_id, #station id
'rec_mu': np.zeros(y_data.shape),
'Y': y_data,
'x_2': x_2,
'x_3': x_3,
'c_2_erg': c_2_erg,
'c_3_erg': c_3_erg,
'c_a_erg': c_a_erg,
'X_e': X_eq, #earthquake coordinates
'X_s': X_sta, #station coordinates
'X_c': X_cells_valid,
'RC': celldist_valid.to_numpy(),
}
stan_data_fname = out_dir + out_fname + '_stan_data' + '.json'
#create output directory
pathlib.Path(out_dir).mkdir(parents=True, exist_ok=True)
#write as json file
cmdstanpy.utils.write_stan_json(stan_data_fname, stan_data)
## Run Stan, fit model
#============================
#number of cores
n_cpu = max(cpu_count() -1,1)
#run stan
if (not stan_parallel) or n_cpu<=n_chains:
#compile stan model
stan_model = cmdstanpy.CmdStanModel(stan_file=stan_model_fname)
stan_model.compile(force=True)
#run full MCMC sampler
stan_fit = stan_model.sample(data=stan_data_fname, chains=n_chains,
iter_warmup=n_iter_warmup, iter_sampling=n_iter_sampling,
seed=1, max_treedepth=max_treedepth, adapt_delta=adapt_delta,
show_progress=True, output_dir=out_dir+'stan_fit/')
else:
#compile stan model
stan_model = cmdstanpy.CmdStanModel(stan_file=stan_model_fname, cpp_options={"STAN_THREADS": True})
stan_model.compile(force=True)
#number of cores per chain
n_cpu_chain = int(np.floor(n_cpu/n_chains))
#run full MCMC sampler
stan_fit = stan_model.sample(data=stan_data_fname, chains=n_chains, threads_per_chain=n_cpu_chain,
iter_warmup=n_iter_warmup, iter_sampling=n_iter_sampling,
seed=1, max_treedepth=max_treedepth, adapt_delta=adapt_delta,
show_progress=True, output_dir=out_dir+'stan_fit/')
## Postprocessing Data
#============================
## Extract posterior samples
# ---------------------------
#hyper-parameters
col_names_hyp = ['dc_0','mu_2p','mu_3s',
'ell_1e', 'ell_1as', 'omega_1e', 'omega_1as', 'omega_1bs',
'ell_2p', 'ell_3s', 'omega_2p', 'omega_3s',
'mu_cap', 'ell_ca1p', 'omega_ca1p', 'omega_ca2p',
'phi_0','tau_0']
#non-ergodic terms
col_names_dc_1e = ['dc_1e.%i'%(k) for k in range(n_eq)]
col_names_dc_1as = ['dc_1as.%i'%(k) for k in range(n_sta)]
col_names_dc_1bs = ['dc_1bs.%i'%(k) for k in range(n_sta)]
col_names_c_2p = ['c_2p.%i'%(k) for k in range(n_eq)]
col_names_c_3s = ['c_3s.%i'%(k) for k in range(n_sta)]
col_names_dB = ['dB.%i'%(k) for k in range(n_eq)]
col_names_cap = ['c_cap.%i'%(c_id) for c_id in cell_ids_valid]
col_names_all = (col_names_hyp + col_names_dc_1e + col_names_dc_1as + col_names_dc_1bs +
col_names_c_2p + col_names_c_3s + col_names_cap + col_names_dB)
#summarize raw posterior distributions
stan_posterior = np.stack([stan_fit.stan_variable(c_n) for c_n in col_names_hyp], axis=1)
#adjustment terms
stan_posterior = np.concatenate((stan_posterior, stan_fit.stan_variable('dc_1e')), axis=1)
stan_posterior = np.concatenate((stan_posterior, stan_fit.stan_variable('dc_1as')), axis=1)
stan_posterior = np.concatenate((stan_posterior, stan_fit.stan_variable('dc_1bs')), axis=1)
stan_posterior = np.concatenate((stan_posterior, stan_fit.stan_variable('c_2p')), axis=1)
stan_posterior = np.concatenate((stan_posterior, stan_fit.stan_variable('c_3s')), axis=1)
stan_posterior = np.concatenate((stan_posterior, stan_fit.stan_variable('c_cap')), axis=1)
stan_posterior = np.concatenate((stan_posterior, stan_fit.stan_variable('dB')), axis=1)
#save raw-posterior distribution
df_stan_posterior_raw = pd.DataFrame(stan_posterior, columns = col_names_all)
df_stan_posterior_raw.to_csv(out_dir + out_fname + '_stan_posterior_raw' + '.csv', index=False)
## Summarize hyper-parameters
# ---------------------------
#summarize posterior distributions of hyper-parameters
perc_array = np.array([0.05,0.25,0.5,0.75,0.95])
df_stan_hyp = df_stan_posterior_raw[col_names_hyp].quantile(perc_array)
df_stan_hyp = df_stan_hyp.append(df_stan_posterior_raw[col_names_hyp].mean(axis = 0), ignore_index=True)
df_stan_hyp.index = ['prc_%.2f'%(prc) for prc in perc_array]+['mean']
df_stan_hyp.to_csv(out_dir + out_fname + '_stan_hyperparameters' + '.csv', index=True)
#detailed posterior percentiles of posterior distributions
perc_array = np.arange(0.01,0.99,0.01)
df_stan_posterior = df_stan_posterior_raw[col_names_hyp].quantile(perc_array)
df_stan_posterior.index.name = 'prc'
df_stan_posterior .to_csv(out_dir + out_fname + '_stan_hyperposterior' + '.csv', index=True)
del col_names_dc_1e, col_names_dc_1as, col_names_dc_1bs, col_names_c_2p, col_names_c_3s, col_names_dB
del stan_posterior, col_names_all
## Sample spatially varying coefficients and predictions at record locations
# ---------------------------
# earthquake and station location in database
X_eq_all = df_flatfile[['eqX', 'eqY']].values
X_sta_all = df_flatfile[['staX','staY']].values
# GMM anelastic attenuation
#--- --- --- --- --- --- --- ---
cells_ca_mu = np.array([df_stan_posterior_raw.loc[:,'c_cap.%i'%(k)].mean() for k in cell_ids_valid])
cells_ca_med = np.array([df_stan_posterior_raw.loc[:,'c_cap.%i'%(k)].median() for k in cell_ids_valid])
cells_ca_sig = np.array([df_stan_posterior_raw.loc[:,'c_cap.%i'%(k)].std() for k in cell_ids_valid])
#effect of anelastic attenuation in GM
cells_LcA_mu = celldist_valid.values @ cells_ca_mu
cells_LcA_med = celldist_valid.values @ cells_ca_med
cells_LcA_sig = np.sqrt(np.square(celldist_valid.values) @ cells_ca_sig**2)
#summary attenuation cells
catten_summary = np.vstack((np.tile(c_a_erg, n_cell_valid),
cells_ca_mu,
cells_ca_med,
cells_ca_sig)).T
columns_names = ['c_a_erg','c_cap_mean','c_cap_med','c_cap_sig']
df_catten_summary = pd.DataFrame(catten_summary, columns = columns_names, index=df_cellinfo.index[i_cells_valid])
#create dataframe with summary attenuation cells
df_catten_summary = pd.merge(df_cellinfo[['cellname','mptLat','mptLon','mptX','mptY','mptZ','UTMzone']],
df_catten_summary, how='right', left_index=True, right_index=True)
df_catten_summary.to_csv(out_dir + out_fname + '_stan_catten' + '.csv', index=True)
# GMM coefficients
#--- --- --- --- --- --- --- ---
#constant shift coefficient
coeff_0_mu = df_stan_posterior_raw.loc[:,'dc_0'].mean() * np.ones(n_data)
coeff_0_med = df_stan_posterior_raw.loc[:,'dc_0'].median() * np.ones(n_data)
coeff_0_sig = df_stan_posterior_raw.loc[:,'dc_0'].std() * np.ones(n_data)
#spatially varying earthquake constant coefficient
coeff_1e_mu = np.array([df_stan_posterior_raw.loc[:,f'dc_1e.{k}'].mean() for k in range(n_eq)])
coeff_1e_mu = coeff_1e_mu[eq_inv]
coeff_1e_med = np.array([df_stan_posterior_raw.loc[:,f'dc_1e.{k}'].median() for k in range(n_eq)])
coeff_1e_med = coeff_1e_med[eq_inv]
coeff_1e_sig = np.array([df_stan_posterior_raw.loc[:,f'dc_1e.{k}'].std() for k in range(n_eq)])
coeff_1e_sig = coeff_1e_sig[eq_inv]
#site term constant covariance
coeff_1as_mu = np.array([df_stan_posterior_raw.loc[:,f'dc_1as.{k}'].mean() for k in range(n_sta)])
coeff_1as_mu = coeff_1as_mu[sta_inv]
coeff_1as_med = np.array([df_stan_posterior_raw.loc[:,f'dc_1as.{k}'].median() for k in range(n_sta)])
coeff_1as_med = coeff_1as_med[sta_inv]
coeff_1as_sig = np.array([df_stan_posterior_raw.loc[:,f'dc_1as.{k}'].std() for k in range(n_sta)])
coeff_1as_sig = coeff_1as_sig[sta_inv]
#spatially varying station constant covariance
coeff_1bs_mu = np.array([df_stan_posterior_raw.loc[:,f'dc_1bs.{k}'].mean() for k in range(n_sta)])
coeff_1bs_mu = coeff_1bs_mu[sta_inv]
coeff_1bs_med = np.array([df_stan_posterior_raw.loc[:,f'dc_1bs.{k}'].median() for k in range(n_sta)])
coeff_1bs_med = coeff_1bs_med[sta_inv]
coeff_1bs_sig = np.array([df_stan_posterior_raw.loc[:,f'dc_1bs.{k}'].std() for k in range(n_sta)])
coeff_1bs_sig = coeff_1bs_sig[sta_inv]
#spatially varying geometrical spreading coefficient
coeff_2p_mu = np.array([df_stan_posterior_raw.loc[:,f'c_2p.{k}'].mean() for k in range(n_eq)])
coeff_2p_mu = coeff_2p_mu[eq_inv]
coeff_2p_med = np.array([df_stan_posterior_raw.loc[:,f'c_2p.{k}'].median() for k in range(n_eq)])
coeff_2p_med = coeff_2p_med[eq_inv]
coeff_2p_sig = np.array([df_stan_posterior_raw.loc[:,f'c_2p.{k}'].std() for k in range(n_eq)])
coeff_2p_sig = coeff_2p_sig[eq_inv]
#spatially varying Vs30 coefficient
coeff_3s_mu = np.array([df_stan_posterior_raw.loc[:,f'c_3s.{k}'].mean() for k in range(n_sta)])
coeff_3s_mu = coeff_3s_mu[sta_inv]
coeff_3s_med = np.array([df_stan_posterior_raw.loc[:,f'c_3s.{k}'].median() for k in range(n_sta)])
coeff_3s_med = coeff_3s_med[sta_inv]
coeff_3s_sig = np.array([df_stan_posterior_raw.loc[:,f'c_3s.{k}'].std() for k in range(n_sta)])
coeff_3s_sig = coeff_3s_sig[sta_inv]
# aleatory variability
phi_0_array = np.array([df_stan_posterior_raw.phi_0.mean()]*X_sta_all.shape[0])
tau_0_array = np.array([df_stan_posterior_raw.tau_0.mean()]*X_sta_all.shape[0])
#initiaize flatfile for sumamry of non-erg coefficinets and residuals
df_flatinfo = df_flatfile[['eqid','ssn','eqLat','eqLon','staLat','staLon','eqX','eqY','staX','staY','UTMzone']]
#summary coefficients
coeffs_summary = np.vstack((coeff_0_mu,
coeff_1e_mu,
coeff_1as_mu,
coeff_1bs_mu,
coeff_2p_mu,
coeff_3s_mu,
cells_LcA_mu,
coeff_0_med,
coeff_1e_med,
coeff_1as_med,
coeff_1bs_med,
coeff_2p_med,
coeff_3s_med,
cells_LcA_med,
coeff_0_sig,
coeff_1e_sig,
coeff_1as_sig,
coeff_1bs_sig,
coeff_2p_sig,
coeff_3s_sig,
cells_LcA_sig)).T
columns_names = ['dc_0_mean','dc_1e_mean','dc_1as_mean','dc_1bs_mean','c_2p_mean','c_3s_mean','Lc_ca_mean',
'dc_0_med', 'dc_1e_med', 'dc_1as_med', 'dc_1bs_med', 'c_2p_med', 'c_3s_med', 'Lc_ca_med',
'dc_0_sig', 'dc_1e_sig', 'dc_1as_sig', 'dc_1bs_sig', 'c_2p_sig', 'c_3s_sig', 'Lc_ca_sig']
df_coeffs_summary = pd.DataFrame(coeffs_summary, columns = columns_names, index=df_flatfile.index)
#create dataframe with summary coefficients
df_coeffs_summary = pd.merge(df_flatinfo, df_coeffs_summary, how='right', left_index=True, right_index=True)
df_coeffs_summary[['eqid','ssn']] = df_coeffs_summary[['eqid','ssn']].astype(int)
df_coeffs_summary.to_csv(out_dir + out_fname + '_stan_coefficients' + '.csv', index=True)
# GMM prediction
#--- --- --- --- --- --- --- ---
#mean prediction
y_mu = (coeff_0_mu + coeff_1e_mu + coeff_1as_mu + coeff_1bs_mu + coeff_2p_mu*x_2 + coeff_3s_mu*x_3[sta_inv] + cells_LcA_mu)
#compute residuals
res_tot = y_data - y_mu
#residuals computed directly from stan regression
res_between = [df_stan_posterior_raw.loc[:,f'dB.{k}'].mean() for k in range(n_eq)]
res_between = np.array([res_between[k] for k in (eq_inv).astype(int)])
res_within = res_tot - res_between
#summary predictions and residuals
predict_summary = np.vstack((y_mu, res_tot, res_between, res_within)).T
columns_names = ['nerg_mu','res_tot','res_between','res_within']
df_predict_summary = pd.DataFrame(predict_summary, columns = columns_names, index=df_flatfile.index)
#create dataframe with predictions and residuals
df_predict_summary = pd.merge(df_flatinfo, df_predict_summary, how='right', left_index=True, right_index=True)
df_predict_summary[['eqid','ssn']] = df_predict_summary[['eqid','ssn']].astype(int)
df_predict_summary.to_csv(out_dir + out_fname + '_stan_residuals' + '.csv', index=True)
## Summary regression
# ---------------------------
#save summary statistics
stan_summary_fname = out_dir + out_fname + '_stan_summary' + '.txt'
with open(stan_summary_fname, 'w') as f:
print(stan_fit, file=f)
#create and save trace plots
fig_dir = out_dir + 'summary_figs/'
#create figures directory if doesn't exit
pathlib.Path(fig_dir).mkdir(parents=True, exist_ok=True)
#create stan trace plots
stan_az_fit = az.from_cmdstanpy(stan_fit)
# stan_az_fit = az.from_cmdstanpy(stan_fit, posterior_predictive='Y')
for c_name in col_names_hyp:
#create trace plot with arviz
ax = az.plot_trace(stan_az_fit, var_names=c_name, figsize=(10,5) ).ravel()
ax[0].yaxis.set_major_locator(plt_autotick())
ax[0].set_xlabel('sample value')
ax[0].set_ylabel('frequency')
ax[0].set_title('')
ax[0].grid(axis='both')
ax[1].set_xlabel('iteration')
ax[1].set_ylabel('sample value')
ax[1].grid(axis='both')
ax[1].set_title('')
fig = ax[0].figure
fig.suptitle(c_name)
fig.savefig(fig_dir + out_fname + '_stan_traceplot_' + c_name + '_arviz' + '.png')
return None
| 20,112 | 47.699758 | 128 | py |
ngmm_tools | ngmm_tools-master/Analyses/Python_lib/regression/cmdstan/regression_cmdstan_model2_uncorr_cells_sparse_unbounded_hyp.py | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Wed Dec 29 15:13:49 2021
@author: glavrent
"""
#load variables
import pathlib
from joblib import cpu_count
#arithmetic libraries
import numpy as np
from scipy import sparse
#statistics libraries
import pandas as pd
#plot libraries
import matplotlib as mpl
from matplotlib.ticker import AutoLocator as plt_autotick
import arviz as az
mpl.use('agg')
#stan library
import cmdstanpy
def RunStan(df_flatfile, df_cellinfo, df_celldist, stan_model_fname,
out_fname, out_dir, res_name='res', c_a_erg=0,
n_iter_warmup=300, n_iter_sampling=300, n_chains=4,
adapt_delta=0.8, max_treedepth=10,
stan_parallel=False):
'''
Run full Bayessian regression in Stan. Non-ergodic model includes: a spatially
varying earthquake constant, a spatially varying site constant, a spatially
independent site constant, and uncorrelated anelastic attenuation.
Parameters
----------
df_flatfile : pd.DataFrame
Input data frame containing total residuals, eq and site coordinates.
df_cellinfo : pd.DataFrame
Dataframe with coordinates of anelastic attenuation cells.
df_celldist : pd.DataFrame
Datafame with cell path distances of all records in df_flatfile.
stan_model_fname : string
File name for stan model.
out_fname : string
File name for output files.
out_dir : string
Output directory.
res_name : string, optional
Column name for total residuals. The default is 'res'.
c_a_erg : double, optional
Value of ergodic anelatic attenuation coefficient. Used as mean of cell
specific anelastic attenuation prior distribution. The default is 0.
n_iter_warmup : integer, optional
Number of burn out MCMC samples. The default is 300.
n_iter_sampling : integer, optional
Number of MCMC samples for computing the posterior distributions. The default is 300.
n_chains : integer, optional
Number of MCMC chains. The default is 4.
adapt_delta : double, optional
Target average proposal acceptance probability for adaptation. The default is 0.8.
max_treedepth : integer, optional
Maximum number of evaluations for each iteration (2^max_treedepth). The default is 10.
pystan_ver : integer, optional
Version of pystan to run. The default is 2.
pystan_parallel : bool, optional
Flag for using multithreaded option in STAN. The default is False.
Returns
-------
None.
'''
## Preprocess Input Data
#============================
#set rsn column as dataframe index, skip if rsn already the index
if not df_flatfile.index.name == 'rsn':
df_flatfile.set_index('rsn', drop=True, inplace=True)
if not df_celldist.index.name == 'rsn':
df_celldist.set_index('rsn', drop=True, inplace=True)
#set cellid column as dataframe index, skip if cellid already the index
if not df_cellinfo.index.name == 'cellid':
df_cellinfo.set_index('cellid', drop=True, inplace=True)
#number of data
n_data = len(df_flatfile)
#earthquake data
data_eq_all = df_flatfile[['eqid','mag','eqX', 'eqY']].values
_, eq_idx, eq_inv = np.unique(df_flatfile[['eqid']], axis=0, return_inverse=True, return_index=True)
data_eq = data_eq_all[eq_idx,:]
X_eq = data_eq[:,[2,3]] #earthquake coordinates
#create earthquake ids for all records (1 to n_eq)
eq_id = eq_inv + 1
n_eq = len(data_eq)
#station data
data_sta_all = df_flatfile[['ssn','Vs30','staX','staY']].values
_, sta_idx, sta_inv = np.unique( df_flatfile[['ssn']].values, axis = 0, return_inverse=True, return_index=True)
data_sta = data_sta_all[sta_idx,:]
X_sta = data_sta[:,[2,3]] #station coordinates
#create station indices for all records (1 to n_sta)
sta_id = sta_inv + 1
n_sta = len(data_sta)
#ground-motion observations
y_data = df_flatfile[res_name].to_numpy().copy()
#cell data
#reorder and only keep records included in the flatfile
df_celldist = df_celldist.reindex(df_flatfile.index)
#cell info
cell_ids_all = df_cellinfo.index
cell_names_all = df_cellinfo.cellname
#cell distance matrix
celldist_all = df_celldist[cell_names_all] #cell-distance matrix with all cells
#find cell with more than one paths
i_cells_valid = np.where(celldist_all.sum(axis=0) > 0)[0] #valid cells with more than one path
cell_ids_valid = cell_ids_all[i_cells_valid]
cell_names_valid = cell_names_all[i_cells_valid]
celldist_valid = celldist_all.loc[:,cell_names_valid].to_numpy() #cell-distance with only non-zero cells
celldist_valid_sp = sparse.csr_matrix(celldist_valid)
#number of cells
n_cell = celldist_all.shape[1]
n_cell_valid = celldist_valid.shape[1]
#cell coordinates
X_cells_valid = df_cellinfo.loc[i_cells_valid,['mptX','mptY']].values
#print Rrup missfits
print('max R_rup misfit', np.abs(df_flatfile.Rrup.values - celldist_valid.sum(axis=1)).max())
stan_data = {'N': n_data,
'NEQ': n_eq,
'NSTAT': n_sta,
'NCELL': n_cell_valid,
'NCELL_SP': len(celldist_valid_sp.data),
'eq': eq_id, #earthquake id
'stat': sta_id, #station id
'X_e': X_eq, #earthquake coordinates
'X_s': X_sta, #station coordinates
'X_c': X_cells_valid,
'rec_mu': np.zeros(y_data.shape),
'RC_val': celldist_valid_sp.data,
'RC_w': celldist_valid_sp.indices+1,
'RC_u': celldist_valid_sp.indptr+1,
'c_a_erg': c_a_erg,
'Y': y_data,
}
stan_data_fname = out_dir + out_fname + '_stan_data' + '.json'
#create output directory
pathlib.Path(out_dir).mkdir(parents=True, exist_ok=True)
#write as json file
cmdstanpy.utils.write_stan_json(stan_data_fname, stan_data)
## Run Stan, fit model
#============================
#number of cores
n_cpu = max(cpu_count() -1,1)
#run stan
if (not stan_parallel) or n_cpu<=n_chains:
#compile stan model
stan_model = cmdstanpy.CmdStanModel(stan_file=stan_model_fname)
stan_model.compile(force=True)
#run full MCMC sampler
stan_fit = stan_model.sample(data=stan_data_fname, chains=n_chains,
iter_warmup=n_iter_warmup, iter_sampling=n_iter_sampling,
seed=1, max_treedepth=max_treedepth, adapt_delta=adapt_delta,
show_progress=True, output_dir=out_dir+'stan_fit/')
else:
#compile stan model
stan_model = cmdstanpy.CmdStanModel(stan_file=stan_model_fname, cpp_options={"STAN_THREADS": True})
stan_model.compile(force=True)
#number of cores per chain
n_cpu_chain = int(np.floor(n_cpu/n_chains))
#run full MCMC sampler
stan_fit = stan_model.sample(data=stan_data_fname, chains=n_chains, threads_per_chain=n_cpu_chain,
iter_warmup=n_iter_warmup, iter_sampling=n_iter_sampling,
seed=1, max_treedepth=max_treedepth, adapt_delta=adapt_delta,
show_progress=True, output_dir=out_dir+'stan_fit/')
## Postprocessing Data
#============================
## Extract posterior samples
# ---------------------------
#hyper-parameters
col_names_hyp = ['dc_0','ell_1e', 'ell_1as', 'omega_1e', 'omega_1as', 'omega_1bs',
'mu_cap', 'omega_cap',
'phi_0','tau_0']
#non-ergodic terms
col_names_dc_1e = ['dc_1e.%i'%(k) for k in range(n_eq)]
col_names_dc_1as = ['dc_1as.%i'%(k) for k in range(n_sta)]
col_names_dc_1bs = ['dc_1bs.%i'%(k) for k in range(n_sta)]
col_names_dB = ['dB.%i'%(k) for k in range(n_eq)]
col_names_cap = ['c_cap.%i'%(c_id) for c_id in cell_ids_valid]
col_names_all = col_names_hyp + col_names_dc_1e + col_names_dc_1as + col_names_dc_1bs + col_names_cap + col_names_dB
#summarize raw posterior distributions
stan_posterior = np.stack([stan_fit.stan_variable(c_n) for c_n in col_names_hyp], axis=1)
#adjustment terms
stan_posterior = np.concatenate((stan_posterior, stan_fit.stan_variable('dc_1e')), axis=1)
stan_posterior = np.concatenate((stan_posterior, stan_fit.stan_variable('dc_1as')), axis=1)
stan_posterior = np.concatenate((stan_posterior, stan_fit.stan_variable('dc_1bs')), axis=1)
stan_posterior = np.concatenate((stan_posterior, stan_fit.stan_variable('c_cap')), axis=1)
stan_posterior = np.concatenate((stan_posterior, stan_fit.stan_variable('dB')), axis=1)
#save raw-posterior distribution
df_stan_posterior_raw = pd.DataFrame(stan_posterior, columns = col_names_all)
df_stan_posterior_raw.to_csv(out_dir + out_fname + '_stan_posterior_raw' + '.csv', index=False)
## Summarize hyper-parameters
# ---------------------------
#summarize posterior distributions of hyper-parameters
perc_array = np.array([0.05,0.25,0.5,0.75,0.95])
df_stan_hyp = df_stan_posterior_raw[col_names_hyp].quantile(perc_array)
df_stan_hyp = df_stan_hyp.append(df_stan_posterior_raw[col_names_hyp].mean(axis = 0), ignore_index=True)
df_stan_hyp.index = ['prc_%.2f'%(prc) for prc in perc_array]+['mean']
df_stan_hyp.to_csv(out_dir + out_fname + '_stan_hyperparameters' + '.csv', index=True)
#detailed posterior percentiles of posterior distributions
perc_array = np.arange(0.01,0.99,0.01)
df_stan_posterior = df_stan_posterior_raw[col_names_hyp].quantile(perc_array)
df_stan_posterior.index.name = 'prc'
df_stan_posterior.to_csv(out_dir + out_fname + '_stan_hyperposterior' + '.csv', index=True)
del col_names_dc_1e, col_names_dc_1as, col_names_dc_1bs, col_names_dB
del stan_posterior, col_names_all
## Sample spatially varying coefficients and predictions at record locations
# ---------------------------
# earthquake and station location in database
X_eq_all = df_flatfile[['eqX', 'eqY']].values
X_sta_all = df_flatfile[['staX','staY']].values
# GMM anelastic attenuation
#--- --- --- --- --- --- --- ---
cells_ca_mu = np.array([df_stan_posterior_raw.loc[:,'c_cap.%i'%(k)].mean() for k in cell_ids_valid])
cells_ca_med = np.array([df_stan_posterior_raw.loc[:,'c_cap.%i'%(k)].median() for k in cell_ids_valid])
cells_ca_sig = np.array([df_stan_posterior_raw.loc[:,'c_cap.%i'%(k)].std() for k in cell_ids_valid])
#effect of anelastic attenuation in GM
cells_LcA_mu = celldist_valid_sp @ cells_ca_mu
cells_LcA_med = celldist_valid_sp @ cells_ca_med
cells_LcA_sig = np.sqrt(celldist_valid_sp.power(2) @ cells_ca_sig**2)
#summary attenuation cells
catten_summary = np.vstack((np.tile(c_a_erg, n_cell_valid),
cells_ca_mu,
cells_ca_med,
cells_ca_sig)).T
columns_names = ['c_a_erg','c_cap_mean','c_cap_med','c_cap_sig']
df_catten_summary = pd.DataFrame(catten_summary, columns = columns_names, index=df_cellinfo.index[i_cells_valid])
#create dataframe with summary attenuation cells
df_catten_summary = pd.merge(df_cellinfo[['cellname','mptLat','mptLon','mptX','mptY','mptZ','UTMzone']],
df_catten_summary, how='right', left_index=True, right_index=True)
df_catten_summary.to_csv(out_dir + out_fname + '_stan_catten' + '.csv', index=True)
# GMM coefficients
#--- --- --- --- --- --- --- ---
#constant shift coefficient
coeff_0_mu = df_stan_posterior_raw.loc[:,'dc_0'].mean() * np.ones(n_data)
coeff_0_med = df_stan_posterior_raw.loc[:,'dc_0'].median() * np.ones(n_data)
coeff_0_sig = df_stan_posterior_raw.loc[:,'dc_0'].std() * np.ones(n_data)
#spatially varying earthquake constant coefficient
coeff_1e_mu = np.array([df_stan_posterior_raw.loc[:,f'dc_1e.{k}'].mean() for k in range(n_eq)])
coeff_1e_mu = coeff_1e_mu[eq_inv]
coeff_1e_med = np.array([df_stan_posterior_raw.loc[:,f'dc_1e.{k}'].median() for k in range(n_eq)])
coeff_1e_med = coeff_1e_med[eq_inv]
coeff_1e_sig = np.array([df_stan_posterior_raw.loc[:,f'dc_1e.{k}'].std() for k in range(n_eq)])
coeff_1e_sig = coeff_1e_sig[eq_inv]
#site term constant covariance
coeff_1as_mu = np.array([df_stan_posterior_raw.loc[:,f'dc_1as.{k}'].mean() for k in range(n_sta)])
coeff_1as_mu = coeff_1as_mu[sta_inv]
coeff_1as_med = np.array([df_stan_posterior_raw.loc[:,f'dc_1as.{k}'].median() for k in range(n_sta)])
coeff_1as_med = coeff_1as_med[sta_inv]
coeff_1as_sig = np.array([df_stan_posterior_raw.loc[:,f'dc_1as.{k}'].std() for k in range(n_sta)])
coeff_1as_sig = coeff_1as_sig[sta_inv]
#spatially varying station constant covariance
coeff_1bs_mu = np.array([df_stan_posterior_raw.loc[:,f'dc_1bs.{k}'].mean() for k in range(n_sta)])
coeff_1bs_mu = coeff_1bs_mu[sta_inv]
coeff_1bs_med = np.array([df_stan_posterior_raw.loc[:,f'dc_1bs.{k}'].median() for k in range(n_sta)])
coeff_1bs_med = coeff_1bs_med[sta_inv]
coeff_1bs_sig = np.array([df_stan_posterior_raw.loc[:,f'dc_1bs.{k}'].std() for k in range(n_sta)])
coeff_1bs_sig = coeff_1bs_sig[sta_inv]
# aleatory variability
phi_0_array = np.array([df_stan_posterior_raw.phi_0.mean()]*X_sta_all.shape[0])
tau_0_array = np.array([df_stan_posterior_raw.tau_0.mean()]*X_sta_all.shape[0])
#initiaize flatfile for sumamry of non-erg coefficinets and residuals
df_flatinfo = df_flatfile[['eqid','ssn','eqLat','eqLon','staLat','staLon','eqX','eqY','staX','staY','UTMzone']]
#summary coefficients
coeffs_summary = np.vstack((coeff_0_mu,
coeff_1e_mu,
coeff_1as_mu,
coeff_1bs_mu,
cells_LcA_mu,
coeff_0_med,
coeff_1e_med,
coeff_1as_med,
coeff_1bs_med,
cells_LcA_med,
coeff_0_sig,
coeff_1e_sig,
coeff_1as_sig,
coeff_1bs_sig,
cells_LcA_sig)).T
columns_names = ['dc_0_mean','dc_1e_mean','dc_1as_mean','dc_1bs_mean','Lc_ca_mean',
'dc_0_med', 'dc_1e_med', 'dc_1as_med', 'dc_1bs_med', 'Lc_ca_med',
'dc_0_sig', 'dc_1e_sig', 'dc_1as_sig', 'dc_1bs_sig', 'Lc_ca_sig']
df_coeffs_summary = pd.DataFrame(coeffs_summary, columns = columns_names, index=df_flatfile.index)
#create dataframe with summary coefficients
df_coeffs_summary = pd.merge(df_flatinfo, df_coeffs_summary, how='right', left_index=True, right_index=True)
df_coeffs_summary[['eqid','ssn']] = df_coeffs_summary[['eqid','ssn']].astype(int)
df_coeffs_summary.to_csv(out_dir + out_fname + '_stan_coefficients' + '.csv', index=True)
# GMM prediction
#--- --- --- --- --- --- --- ---
#mean prediction
y_mu = (coeff_0_mu + coeff_1e_mu + coeff_1as_mu + coeff_1bs_mu + cells_LcA_mu)
#compute residuals
res_tot = y_data - y_mu
#residuals computed directly from stan regression
res_between = [df_stan_posterior_raw.loc[:,f'dB.{k}'].mean() for k in range(n_eq)]
res_between = np.array([res_between[k] for k in (eq_inv).astype(int)])
res_within = res_tot - res_between
#summary predictions and residuals
predict_summary = np.vstack((y_mu, res_tot, res_between, res_within)).T
columns_names = ['nerg_mu','res_tot','res_between','res_within']
df_predict_summary = pd.DataFrame(predict_summary, columns = columns_names, index=df_flatfile.index)
#create dataframe with predictions and residuals
df_predict_summary = pd.merge(df_flatinfo, df_predict_summary, how='right', left_index=True, right_index=True)
df_predict_summary[['eqid','ssn']] = df_predict_summary[['eqid','ssn']].astype(int)
df_predict_summary.to_csv(out_dir + out_fname + '_stan_residuals' + '.csv', index=True)
## Summary regression
# ---------------------------
#save summary statistics
stan_summary_fname = out_dir + out_fname + '_stan_summary' + '.txt'
with open(stan_summary_fname, 'w') as f:
print(stan_fit, file=f)
#create and save trace plots
fig_dir = out_dir + 'summary_figs/'
#create figures directory if doesn't exit
pathlib.Path(fig_dir).mkdir(parents=True, exist_ok=True)
#create stan trace plots
stan_az_fit = az.from_cmdstanpy(stan_fit)
# stan_az_fit = az.from_cmdstanpy(stan_fit, posterior_predictive='Y')
for c_name in col_names_hyp:
#create trace plot with arviz
ax = az.plot_trace(stan_az_fit, var_names=c_name, figsize=(10,5) ).ravel()
ax[0].yaxis.set_major_locator(plt_autotick())
ax[0].set_xlabel('sample value')
ax[0].set_ylabel('frequency')
ax[0].set_title('')
ax[0].grid(axis='both')
ax[1].set_xlabel('iteration')
ax[1].set_ylabel('sample value')
ax[1].grid(axis='both')
ax[1].set_title('')
fig = ax[0].figure
fig.suptitle(c_name)
fig.savefig(fig_dir + out_fname + '_stan_traceplot_' + c_name + '_arviz' + '.png')
return None
| 18,013 | 46.782493 | 120 | py |
ngmm_tools | ngmm_tools-master/Analyses/Python_lib/regression/cmdstan/regression_cmdstan_model3_corr_cells_sparse_unbounded_hyp.py | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Wed Dec 29 15:13:49 2021
@author: glavrent
"""
#load variables
import pathlib
from joblib import cpu_count
#arithmetic libraries
import numpy as np
from scipy import sparse
#statistics libraries
import pandas as pd
#plot libraries
import matplotlib as mpl
from matplotlib.ticker import AutoLocator as plt_autotick
import arviz as az
mpl.use('agg')
#stan library
import cmdstanpy
def RunStan(df_flatfile, df_cellinfo, df_celldist, stan_model_fname,
out_fname, out_dir, res_name='res', c_2_erg=0, c_3_erg=0, c_a_erg=0,
n_iter_warmup=300, n_iter_sampling=300, n_chains=4,
adapt_delta=0.8, max_treedepth=10,
stan_parallel=False):
'''
Run full Bayessian regression in Stan. Non-ergodic model includes: a spatially
varying earthquake constant, a spatially varying site constant, a spatially
independent site constant, and partially spatially correlated anelastic
attenuation.
Parameters
----------
df_flatfile : pd.DataFrame
Input data frame containing total residuals, eq and site coordinates.
df_cellinfo : pd.DataFrame
Dataframe with coordinates of anelastic attenuation cells.
df_celldist : pd.DataFrame
Datafame with cell path distances of all records in df_flatfile.
stan_model_fname : string
File name for stan model.
out_fname : string
File name for output files.
out_dir : string
Output directory.
res_name : string, optional
Column name for total residuals. The default is 'res'.
c_2_erg : double, optional
Value of ergodic geometrical spreading coefficient. The default is 0.
c_3_erg : double, optional
Value of ergodic Vs30 coefficient. The default is 0.
c_a_erg : double, optional
Value of ergodic anelatic attenuation coefficient. Used as mean of cell
specific anelastic attenuation prior distribution. The default is 0.
n_iter_warmup : integer, optional
Number of burn out MCMC samples. The default is 300.
n_iter_sampling : integer, optional
Number of MCMC samples for computing the posterior distributions. The default is 300.
n_chains : integer, optional
Number of MCMC chains. The default is 4.
adapt_delta : double, optional
Target average proposal acceptance probability for adaptation. The default is 0.8.
max_treedepth : integer, optional
Maximum number of evaluations for each iteration (2^max_treedepth). The default is 10.
stan_parallel : bool, optional
Flag for using multithreaded option in STAN. The default is False.
Returns
-------
None.
'''
## Preprocess Input Data
#============================
#set rsn column as dataframe index, skip if rsn already the index
if not df_flatfile.index.name == 'rsn':
df_flatfile.set_index('rsn', drop=True, inplace=True)
if not df_celldist.index.name == 'rsn':
df_celldist.set_index('rsn', drop=True, inplace=True)
#set cellid column as dataframe index, skip if cellid already the index
if not df_cellinfo.index.name == 'cellid':
df_cellinfo.set_index('cellid', drop=True, inplace=True)
#number of data
n_data = len(df_flatfile)
#earthquake data
data_eq_all = df_flatfile[['eqid','mag','eqX', 'eqY']].values
_, eq_idx, eq_inv = np.unique(df_flatfile[['eqid']], axis=0, return_inverse=True, return_index=True)
data_eq = data_eq_all[eq_idx,:]
X_eq = data_eq[:,[2,3]] #earthquake coordinates
#create earthquake ids for all records (1 to n_eq)
eq_id = eq_inv + 1
n_eq = len(data_eq)
#station data
data_sta_all = df_flatfile[['ssn','Vs30','x_3','staX','staY']].values
_, sta_idx, sta_inv = np.unique( df_flatfile[['ssn']].values, axis = 0, return_inverse=True, return_index=True)
data_sta = data_sta_all[sta_idx,:]
X_sta = data_sta[:,[3,4]] #station coordinates
#create station indices for all records (1 to n_sta)
sta_id = sta_inv + 1
n_sta = len(data_sta)
#geometrical spreading covariates
x_2 = df_flatfile['x_2'].values
#vs30 covariates
x_3 = df_flatfile['x_3'].values[sta_idx]
#ground-motion observations
y_data = df_flatfile[res_name].to_numpy().copy()
#cell data
#reorder and only keep records included in the flatfile
df_celldist = df_celldist.reindex(df_flatfile.index)
#cell info
cell_ids_all = df_cellinfo.index
cell_names_all = df_cellinfo.cellname
#cell distance matrix
celldist_all = df_celldist[cell_names_all] #cell-distance matrix with all cells
#find cell with more than one paths
i_cells_valid = np.where(celldist_all.sum(axis=0) > 0)[0] #valid cells with more than one path
cell_ids_valid = cell_ids_all[i_cells_valid]
cell_names_valid = cell_names_all[i_cells_valid]
celldist_valid = celldist_all.loc[:,cell_names_valid] #cell-distance with only non-zero cells
celldist_valid_sp = sparse.csr_matrix(celldist_valid)
#number of cells
n_cell = celldist_all.shape[1]
n_cell_valid = celldist_valid.shape[1]
#cell coordinates
X_cells_valid = df_cellinfo.loc[i_cells_valid,['mptX','mptY']].values
#print Rrup missfits
print('max R_rup misfit', (df_flatfile.Rrup.values - celldist_valid.sum(axis=1)).abs().max())
stan_data = {'N': n_data,
'NEQ': n_eq,
'NSTAT': n_sta,
'NCELL': n_cell_valid,
'NCELL_SP': len(celldist_valid_sp.data),
'eq': eq_id, #earthquake id
'stat': sta_id, #station id
'rec_mu': np.zeros(y_data.shape),
'Y': y_data,
'x_2': x_2,
'x_3': x_3,
'c_2_erg': c_2_erg,
'c_3_erg': c_3_erg,
'c_a_erg': c_a_erg,
'X_e': X_eq, #earthquake coordinates
'X_s': X_sta, #station coordinates
'X_c': X_cells_valid,
'RC_val': celldist_valid_sp.data,
'RC_w': celldist_valid_sp.indices+1,
'RC_u': celldist_valid_sp.indptr+1,
}
stan_data_fname = out_dir + out_fname + '_stan_data' + '.json'
#create output directory
pathlib.Path(out_dir).mkdir(parents=True, exist_ok=True)
#write as json file
cmdstanpy.utils.write_stan_json(stan_data_fname, stan_data)
## Run Stan, fit model
#============================
#number of cores
n_cpu = max(cpu_count() -1,1)
#run stan
if (not stan_parallel) or n_cpu<=n_chains:
#compile stan model
stan_model = cmdstanpy.CmdStanModel(stan_file=stan_model_fname)
stan_model.compile(force=True)
#run full MCMC sampler
stan_fit = stan_model.sample(data=stan_data_fname, chains=n_chains,
iter_warmup=n_iter_warmup, iter_sampling=n_iter_sampling,
seed=1, max_treedepth=max_treedepth, adapt_delta=adapt_delta,
show_progress=True, output_dir=out_dir+'stan_fit/')
else:
#compile stan model
stan_model = cmdstanpy.CmdStanModel(stan_file=stan_model_fname, cpp_options={"STAN_THREADS": True})
stan_model.compile(force=True)
#number of cores per chain
n_cpu_chain = int(np.floor(n_cpu/n_chains))
#run full MCMC sampler
stan_fit = stan_model.sample(data=stan_data_fname, chains=n_chains, threads_per_chain=n_cpu_chain,
iter_warmup=n_iter_warmup, iter_sampling=n_iter_sampling,
seed=1, max_treedepth=max_treedepth, adapt_delta=adapt_delta,
show_progress=True, output_dir=out_dir+'stan_fit/')
## Postprocessing Data
#============================
## Extract posterior samples
# ---------------------------
#hyper-parameters
col_names_hyp = ['dc_0','mu_2p','mu_3s',
'ell_1e', 'ell_1as', 'omega_1e', 'omega_1as', 'omega_1bs',
'ell_2p', 'ell_3s', 'omega_2p', 'omega_3s',
'mu_cap', 'ell_ca1p', 'omega_ca1p', 'omega_ca2p',
'phi_0','tau_0']
#non-ergodic terms
col_names_dc_1e = ['dc_1e.%i'%(k) for k in range(n_eq)]
col_names_dc_1as = ['dc_1as.%i'%(k) for k in range(n_sta)]
col_names_dc_1bs = ['dc_1bs.%i'%(k) for k in range(n_sta)]
col_names_c_2p = ['c_2p.%i'%(k) for k in range(n_eq)]
col_names_c_3s = ['c_3s.%i'%(k) for k in range(n_sta)]
col_names_dB = ['dB.%i'%(k) for k in range(n_eq)]
col_names_cap = ['c_cap.%i'%(c_id) for c_id in cell_ids_valid]
col_names_all = (col_names_hyp + col_names_dc_1e + col_names_dc_1as + col_names_dc_1bs +
col_names_c_2p + col_names_c_3s + col_names_cap + col_names_dB)
#summarize raw posterior distributions
stan_posterior = np.stack([stan_fit.stan_variable(c_n) for c_n in col_names_hyp], axis=1)
#adjustment terms
stan_posterior = np.concatenate((stan_posterior, stan_fit.stan_variable('dc_1e')), axis=1)
stan_posterior = np.concatenate((stan_posterior, stan_fit.stan_variable('dc_1as')), axis=1)
stan_posterior = np.concatenate((stan_posterior, stan_fit.stan_variable('dc_1bs')), axis=1)
stan_posterior = np.concatenate((stan_posterior, stan_fit.stan_variable('c_2p')), axis=1)
stan_posterior = np.concatenate((stan_posterior, stan_fit.stan_variable('c_3s')), axis=1)
stan_posterior = np.concatenate((stan_posterior, stan_fit.stan_variable('c_cap')), axis=1)
stan_posterior = np.concatenate((stan_posterior, stan_fit.stan_variable('dB')), axis=1)
#save raw-posterior distribution
df_stan_posterior_raw = pd.DataFrame(stan_posterior, columns = col_names_all)
df_stan_posterior_raw.to_csv(out_dir + out_fname + '_stan_posterior_raw' + '.csv', index=False)
## Summarize hyper-parameters
# ---------------------------
#summarize posterior distributions of hyper-parameters
perc_array = np.array([0.05,0.25,0.5,0.75,0.95])
df_stan_hyp = df_stan_posterior_raw[col_names_hyp].quantile(perc_array)
df_stan_hyp = df_stan_hyp.append(df_stan_posterior_raw[col_names_hyp].mean(axis = 0), ignore_index=True)
df_stan_hyp.index = ['prc_%.2f'%(prc) for prc in perc_array]+['mean']
df_stan_hyp.to_csv(out_dir + out_fname + '_stan_hyperparameters' + '.csv', index=True)
#detailed posterior percentiles of posterior distributions
perc_array = np.arange(0.01,0.99,0.01)
df_stan_posterior = df_stan_posterior_raw[col_names_hyp].quantile(perc_array)
df_stan_posterior.index.name = 'prc'
df_stan_posterior.to_csv(out_dir + out_fname + '_stan_hyperposterior' + '.csv', index=True)
del col_names_dc_1e, col_names_dc_1as, col_names_dc_1bs, col_names_c_2p, col_names_c_3s, col_names_dB
del stan_posterior, col_names_all
## Sample spatially varying coefficients and predictions at record locations
# ---------------------------
# earthquake and station location in database
X_eq_all = df_flatfile[['eqX', 'eqY']].values
X_sta_all = df_flatfile[['staX','staY']].values
# GMM anelastic attenuation
#--- --- --- --- --- --- --- ---
cells_ca_mu = np.array([df_stan_posterior_raw.loc[:,'c_cap.%i'%(k)].mean() for k in cell_ids_valid])
cells_ca_med = np.array([df_stan_posterior_raw.loc[:,'c_cap.%i'%(k)].median() for k in cell_ids_valid])
cells_ca_sig = np.array([df_stan_posterior_raw.loc[:,'c_cap.%i'%(k)].std() for k in cell_ids_valid])
#effect of anelastic attenuation in GM
cells_LcA_mu = celldist_valid_sp @ cells_ca_mu
cells_LcA_med = celldist_valid_sp @ cells_ca_med
cells_LcA_sig = np.sqrt(celldist_valid_sp.power(2) @ cells_ca_sig**2)
#summary attenuation cells
catten_summary = np.vstack((np.tile(c_a_erg, n_cell_valid),
cells_ca_mu,
cells_ca_med,
cells_ca_sig)).T
columns_names = ['c_a_erg','c_cap_mean','c_cap_med','c_cap_sig']
df_catten_summary = pd.DataFrame(catten_summary, columns = columns_names, index=df_cellinfo.index[i_cells_valid])
#create dataframe with summary attenuation cells
df_catten_summary = pd.merge(df_cellinfo[['cellname','mptLat','mptLon','mptX','mptY','mptZ','UTMzone']],
df_catten_summary, how='right', left_index=True, right_index=True)
df_catten_summary.to_csv(out_dir + out_fname + '_stan_catten' + '.csv', index=True)
# GMM coefficients
#--- --- --- --- --- --- --- ---
#constant shift coefficient
coeff_0_mu = df_stan_posterior_raw.loc[:,'dc_0'].mean() * np.ones(n_data)
coeff_0_med = df_stan_posterior_raw.loc[:,'dc_0'].median() * np.ones(n_data)
coeff_0_sig = df_stan_posterior_raw.loc[:,'dc_0'].std() * np.ones(n_data)
#spatially varying earthquake constant coefficient
coeff_1e_mu = np.array([df_stan_posterior_raw.loc[:,f'dc_1e.{k}'].mean() for k in range(n_eq)])
coeff_1e_mu = coeff_1e_mu[eq_inv]
coeff_1e_med = np.array([df_stan_posterior_raw.loc[:,f'dc_1e.{k}'].median() for k in range(n_eq)])
coeff_1e_med = coeff_1e_med[eq_inv]
coeff_1e_sig = np.array([df_stan_posterior_raw.loc[:,f'dc_1e.{k}'].std() for k in range(n_eq)])
coeff_1e_sig = coeff_1e_sig[eq_inv]
#site term constant covariance
coeff_1as_mu = np.array([df_stan_posterior_raw.loc[:,f'dc_1as.{k}'].mean() for k in range(n_sta)])
coeff_1as_mu = coeff_1as_mu[sta_inv]
coeff_1as_med = np.array([df_stan_posterior_raw.loc[:,f'dc_1as.{k}'].median() for k in range(n_sta)])
coeff_1as_med = coeff_1as_med[sta_inv]
coeff_1as_sig = np.array([df_stan_posterior_raw.loc[:,f'dc_1as.{k}'].std() for k in range(n_sta)])
coeff_1as_sig = coeff_1as_sig[sta_inv]
#spatially varying station constant covariance
coeff_1bs_mu = np.array([df_stan_posterior_raw.loc[:,f'dc_1bs.{k}'].mean() for k in range(n_sta)])
coeff_1bs_mu = coeff_1bs_mu[sta_inv]
coeff_1bs_med = np.array([df_stan_posterior_raw.loc[:,f'dc_1bs.{k}'].median() for k in range(n_sta)])
coeff_1bs_med = coeff_1bs_med[sta_inv]
coeff_1bs_sig = np.array([df_stan_posterior_raw.loc[:,f'dc_1bs.{k}'].std() for k in range(n_sta)])
coeff_1bs_sig = coeff_1bs_sig[sta_inv]
#spatially varying geometrical spreading coefficient
coeff_2p_mu = np.array([df_stan_posterior_raw.loc[:,f'c_2p.{k}'].mean() for k in range(n_eq)])
coeff_2p_mu = coeff_2p_mu[eq_inv]
coeff_2p_med = np.array([df_stan_posterior_raw.loc[:,f'c_2p.{k}'].median() for k in range(n_eq)])
coeff_2p_med = coeff_2p_med[eq_inv]
coeff_2p_sig = np.array([df_stan_posterior_raw.loc[:,f'c_2p.{k}'].std() for k in range(n_eq)])
coeff_2p_sig = coeff_2p_sig[eq_inv]
#spatially varying Vs30 coefficient
coeff_3s_mu = np.array([df_stan_posterior_raw.loc[:,f'c_3s.{k}'].mean() for k in range(n_sta)])
coeff_3s_mu = coeff_3s_mu[sta_inv]
coeff_3s_med = np.array([df_stan_posterior_raw.loc[:,f'c_3s.{k}'].median() for k in range(n_sta)])
coeff_3s_med = coeff_3s_med[sta_inv]
coeff_3s_sig = np.array([df_stan_posterior_raw.loc[:,f'c_3s.{k}'].std() for k in range(n_sta)])
coeff_3s_sig = coeff_3s_sig[sta_inv]
# aleatory variability
phi_0_array = np.array([df_stan_posterior_raw.phi_0.mean()]*X_sta_all.shape[0])
tau_0_array = np.array([df_stan_posterior_raw.tau_0.mean()]*X_sta_all.shape[0])
#initiaize flatfile for sumamry of non-erg coefficinets and residuals
df_flatinfo = df_flatfile[['eqid','ssn','eqLat','eqLon','staLat','staLon','eqX','eqY','staX','staY','UTMzone']]
#summary coefficients
coeffs_summary = np.vstack((coeff_0_mu,
coeff_1e_mu,
coeff_1as_mu,
coeff_1bs_mu,
coeff_2p_mu,
coeff_3s_mu,
cells_LcA_mu,
coeff_0_med,
coeff_1e_med,
coeff_1as_med,
coeff_1bs_med,
coeff_2p_med,
coeff_3s_med,
cells_LcA_med,
coeff_0_sig,
coeff_1e_sig,
coeff_1as_sig,
coeff_1bs_sig,
coeff_2p_sig,
coeff_3s_sig,
cells_LcA_sig)).T
columns_names = ['dc_0_mean','dc_1e_mean','dc_1as_mean','dc_1bs_mean','c_2p_mean','c_3s_mean','Lc_ca_mean',
'dc_0_med', 'dc_1e_med', 'dc_1as_med', 'dc_1bs_med', 'c_2p_med', 'c_3s_med', 'Lc_ca_med',
'dc_0_sig', 'dc_1e_sig', 'dc_1as_sig', 'dc_1bs_sig', 'c_2p_sig', 'c_3s_sig', 'Lc_ca_sig']
df_coeffs_summary = pd.DataFrame(coeffs_summary, columns = columns_names, index=df_flatfile.index)
#create dataframe with summary coefficients
df_coeffs_summary = pd.merge(df_flatinfo, df_coeffs_summary, how='right', left_index=True, right_index=True)
df_coeffs_summary[['eqid','ssn']] = df_coeffs_summary[['eqid','ssn']].astype(int)
df_coeffs_summary.to_csv(out_dir + out_fname + '_stan_coefficients' + '.csv', index=True)
# GMM prediction
#--- --- --- --- --- --- --- ---
#mean prediction
y_mu = (coeff_0_mu + coeff_1e_mu + coeff_1as_mu + coeff_1bs_mu + coeff_2p_mu*x_2 + coeff_3s_mu*x_3[sta_inv] + cells_LcA_mu)
#compute residuals
res_tot = y_data - y_mu
#residuals computed directly from stan regression
res_between = [df_stan_posterior_raw.loc[:,f'dB.{k}'].mean() for k in range(n_eq)]
res_between = np.array([res_between[k] for k in (eq_inv).astype(int)])
res_within = res_tot - res_between
#summary predictions and residuals
predict_summary = np.vstack((y_mu, res_tot, res_between, res_within)).T
columns_names = ['nerg_mu','res_tot','res_between','res_within']
df_predict_summary = pd.DataFrame(predict_summary, columns = columns_names, index=df_flatfile.index)
#create dataframe with predictions and residuals
df_predict_summary = pd.merge(df_flatinfo, df_predict_summary, how='right', left_index=True, right_index=True)
df_predict_summary[['eqid','ssn']] = df_predict_summary[['eqid','ssn']].astype(int)
df_predict_summary.to_csv(out_dir + out_fname + '_stan_residuals' + '.csv', index=True)
## Summary regression
# ---------------------------
#save summary statistics
stan_summary_fname = out_dir + out_fname + '_stan_summary' + '.txt'
with open(stan_summary_fname, 'w') as f:
print(stan_fit, file=f)
#create and save trace plots
fig_dir = out_dir + 'summary_figs/'
#create figures directory if doesn't exit
pathlib.Path(fig_dir).mkdir(parents=True, exist_ok=True)
#create stan trace plots
stan_az_fit = az.from_cmdstanpy(stan_fit)
# stan_az_fit = az.from_cmdstanpy(stan_fit, posterior_predictive='Y')
for c_name in col_names_hyp:
#create trace plot with arviz
ax = az.plot_trace(stan_az_fit, var_names=c_name, figsize=(10,5) ).ravel()
ax[0].yaxis.set_major_locator(plt_autotick())
ax[0].set_xlabel('sample value')
ax[0].set_ylabel('frequency')
ax[0].set_title('')
ax[0].grid(axis='both')
ax[1].set_xlabel('iteration')
ax[1].set_ylabel('sample value')
ax[1].grid(axis='both')
ax[1].set_title('')
fig = ax[0].figure
fig.suptitle(c_name)
fig.savefig(fig_dir + out_fname + '_stan_traceplot_' + c_name + '_arviz' + '.png')
return None
| 20,349 | 47.684211 | 128 | py |
ngmm_tools | ngmm_tools-master/Analyses/Prediction/create_scen_dataframe.py | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Sat Aug 20 17:26:17 2022
@author: glavrent
"""
#load variables
import os
import sys
import pathlib
#arithmetic libraries
import numpy as np
#statistics libraries
import pandas as pd
#geographic libraries
import pyproj
import geopy.distance
#user libraries
sys.path.insert(0,'../Python_lib/ground_motions')
from pylib_gmm_eas import BA18
ba18 = BA18()
# Define Problem
# ---------------------------
#structural period
freq = 5.0119
#earthquake scenario
mag = 7.0
vs30 = 400
sof = 'SS'
dip = 90
z_tor = 0
#color bar limits
cbar_lim = [np.log(1e-8),np.log(.06)]
#earthquake coordinates
scen_eq_latlon = [34.2, -116.9]
#utm zone
utm_zone = '11S'
#grid
grid_X_dxdy = [10, 10]
#scenario filename
fname_scen_predict = '../../Data/Prediction/scen_predict.csv'
# UTM projection
# ---------------------------
# projection system
utmProj = pyproj.Proj("+proj=utm +zone="+utm_zone+", +ellps=WGS84 +datum=WGS84 +units=m +no_defs")
#grid limits in UTM
grid_X_win = np.array([[-140, 3500], [780, 4700]])
#create coordinate grid
grid_x_edge = np.arange(grid_X_win[0,0],grid_X_win[1,0],grid_X_dxdy[0])
grid_y_edge = np.arange(grid_X_win[0,1],grid_X_win[1,1],grid_X_dxdy[0])
grid_x, grid_y = np.meshgrid(grid_x_edge, grid_y_edge)
#create coordinate array with all grid nodes
grid_X = np.vstack([grid_x.T.flatten(), grid_y.T.flatten()]).T
#compute lat/lon coordinate array
grid_latlon = np.fliplr(np.array([utmProj(g_x*1000, g_y*1000, inverse=True) for g_x, g_y in
zip(grid_X[:,0], grid_X[:,1])]))
n_gpt = len(grid_X)
#earthquake UTM coordinates
scen_eq_X = np.array(utmProj(scen_eq_latlon[1], scen_eq_latlon[0])) / 1000
#create earthquake and site ids
eqid_array = np.full(n_gpt, -1)
site_array = -1*(1+np.arange(n_gpt))
# Compute Ergodic Base Scaling
# ---------------------------
#compute distances
scen_dist_array = np.linalg.norm(grid_X - scen_eq_X, axis=1)
scen_dist_array = np.sqrt(scen_dist_array**2 + z_tor**2)
#scenarios of interest
scen_eas_nerg_scl = np.full(n_gpt, np.nan)
scen_eas_nerg_aleat = np.full(n_gpt, np.nan)
for k, d in enumerate(scen_dist_array):
fnorm = 1 if sof == 'SS' else 0
#median and aleatory
scen_eas_nerg_scl[k], _, scen_eas_nerg_aleat[k] = ba18.EasF(freq, mag, rrup=d, vs30=vs30, ztor=z_tor, fnorm=fnorm, flag_keep_b7 = False)
# Summarize Scenario Dataframe
# ---------------------------
df_scen_prdct = pd.DataFrame({'eqid':eqid_array, 'ssn':site_array,
'eqLat':np.full(n_gpt,scen_eq_latlon[0]), 'eqLon':np.full(n_gpt,scen_eq_latlon[0]),
'staLat':grid_latlon[:,0], 'staLon':grid_latlon[:,1],
'eqX':np.full(n_gpt,scen_eq_X[0]), 'eqY':np.full(n_gpt,scen_eq_X[1]), 'eqZ':np.full(n_gpt,-z_tor),
'staX':grid_X[:,0], 'staY':grid_X[:,1],
'erg_base':scen_eas_nerg_scl})
#save prediction scenarios
df_scen_prdct.to_csv(fname_scen_predict )
| 3,049 | 28.61165 | 140 | py |
ngmm_tools | ngmm_tools-master/Analyses/Code_Verification/synthetic_datasets/create_synthetic_ds1.py | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Thu Jul 1 21:25:34 2021
@author: glavrent
"""
# load libraries
import os
import pathlib
# arithmetic libraries
import numpy as np
# statistics libraries
import pandas as pd
# python interface to Stan for Bayesian inference
# for installation check https://pystan.readthedocs.io/en/latest/
import pystan
# set working directories
os.chdir(os.getcwd()) # change directory to current directory
# %% Define Input Data
# ======================================
# USER SETS THE INPUT FLATFILE NAMES AND PATH
# ++++++++++++++++++++++++++++++++++++++++
#input flatfile
# fname_flatfile = 'CatalogNGAWest3CA'
# fname_flatfile = 'CatalogNGAWest3CA_2013'
# fname_flatfile = 'CatalogNGAWest3NCA'
# fname_flatfile = 'CatalogNGAWest3SCA'
fname_flatfile = 'CatalogNGAWest3CALite'
dir_flatfile = '../../../Data/Validation/preprocessing/flatfiles/merged/'
# ++++++++++++++++++++++++++++++++++++++++
# USER SETS THE INPUT FLATFILE NAMES AND PATH
# ++++++++++++++++++++++++++++++++++++++++
fname_stan_model = 'create_synthetic_ds1.stan'
# ++++++++++++++++++++++++++++++++++++++++
# USER SETS THE OUTPUT FILE PATH AND NAME
# ++++++++++++++++++++++++++++++++++++++++
# output filename sufix
# synds_suffix = '_small_corr_len'
# synds_suffix = '_large_corr_len'
# output directories
dir_out = f'../../../Data/Validation/synthetic_datasets/ds1{synds_suffix}/'
# ++++++++++++++++++++++++++++++++++++++++
# user defines hyper parameters
# --------------------------------------
# number of synthetic data-sets
n_ds = 5
# number of chains and seed number in stan model
n_chains = 1
n_seed = 1
# define hyper-parameters
# omega_0: standard deviation for constant offset
# omega_1e: standard deviation for spatially varying earthquake constant
# omega_1as: standard deviation for spatially vayring site constant
# omega_1bs: standard deviation for independent site constant
# ell_1e: correlation lenght for spatially varying earthquake constant
# ell_1as: correlation lenght for spatially vayring site constant
# phi_0: within-event standard deviation
# tau_0: between-event standard deviation
# USER NEEDS TO SPECIFY HYPERPARAMETERS
# ++++++++++++++++++++++++++++++++++++++++
# # small correlation lengths
# hyp = {'omega_0': 0.1, 'omega_1e':0.1, 'omega_1as': 0.35, 'omega_1bs': 0.25,
# 'ell_1e':60, 'ell_1as':30, 'phi_0':0.4, 'tau_0':0.3 }
#
# #large correlation lengths
# hyp = {'omega_0': 0.1, 'omega_1e':0.2, 'omega_1as': 0.4, 'omega_1bs': 0.3,
# 'ell_1e':100, 'ell_1as':70, 'phi_0':0.4, 'tau_0':0.3 }
# ++++++++++++++++++++++++++++++++++++++++
# %% Load Data
# ======================================
#load flatfile
fullname_flatfile = dir_flatfile + fname_flatfile + '.csv'
df_flatfile = pd.read_csv(fullname_flatfile)
# %% Processing
# ======================================
# read earthquake and station data from the flatfile
n_rec = len(df_flatfile)
# read earthquake data
# earthquake IDs (eqid), magnitudes (mag), and coordinates (eqX,eqY)
# user may change these IDs based on the headers of the flatfile
data_eq_all = df_flatfile[['eqid','mag','eqX', 'eqY']].values
_, eq_idx, eq_inv = np.unique(df_flatfile[['eqid']], axis=0, return_index=True, return_inverse=True)
data_eq = data_eq_all[eq_idx,:]
X_eq = data_eq[:,[2,3]] #earthquake coordinates
# create earthquake ids for all recordings
eq_id = eq_inv + 1
n_eq = len(data_eq)
# read station data
# station IDs (ssn), Vs30, and coordinates (staX,staY)
# user may change these IDs based on the headers of the flatfile
data_stat_all = df_flatfile[['ssn','Vs30','staX','staY']].values
_, sta_idx, sta_inv = np.unique(df_flatfile[['ssn']].values, axis = 0, return_index=True, return_inverse=True)
data_stat = data_stat_all[sta_idx,:]
X_stat = data_stat[:,[2,3]] #station coordinates
# create station ids for all recordings
sta_id = sta_inv + 1
n_stat = len(data_stat)
# %% Stan
# ======================================
## Stan Data
# ---------------------------
stan_data = {'N': n_rec,
'NEQ': n_eq,
'NSTAT': n_stat,
'X_e': X_eq, #earthquake coordinates
'X_s': X_stat, #station coordinates
'eq': eq_id, #earthquake index
'stat': sta_id, #station index
'mu_gmm': np.zeros(n_rec),
#hyper-parameters of generated data-set
'omega_0': hyp['omega_0'],
'omega_1e': hyp['omega_1e'],
'omega_1as': hyp['omega_1as'],
'omega_1bs': hyp['omega_1bs'],
'ell_1e': hyp['ell_1e'],
'ell_1as': hyp['ell_1as'],
#aleatory terms
'phi_0': hyp['phi_0'],
'tau_0': hyp['tau_0']
}
## Compile and Run Stan model
# ---------------------------
# compile model
sm = pystan.StanModel(file=fname_stan_model)
# generate samples
fit = sm.sampling(data=stan_data, algorithm="Fixed_param", iter=n_ds, chains=n_chains, seed=n_seed)
# keep valid datasets
Y_nerg_med = fit['Y_nerg_med']
Y_aleat = fit['Y_aleat']
Y_tot = fit['Y_tot']
# %% Output
# ======================================
if not os.path.isdir(dir_out): pathlib.Path(dir_out).mkdir(parents=True, exist_ok=True)
# save generated data-sets
for k, (Y_nm, Y_t) in enumerate(zip(Y_nerg_med, Y_tot)):
#copy catalog info to synthetic data-set
df_synthetic_data = df_flatfile.copy()
#add residuals columns
df_synthetic_data.loc[:,'nerg_gm'] = Y_nm
df_synthetic_data.loc[:,'tot'] = Y_t
#add columns with sampled coefficients
df_synthetic_data.loc[:,'dc_0'] = fit['dc_0'][k]
df_synthetic_data.loc[:,'dc_1e'] = fit['dc_1e'][k][eq_inv]
df_synthetic_data.loc[:,'dc_1as'] = fit['dc_1as'][k][sta_inv]
df_synthetic_data.loc[:,'dc_1bs'] = fit['dc_1bs'][k][sta_inv]
#add columns aleatory terms
df_synthetic_data.loc[:,'dW'] = fit['dW'][k]
df_synthetic_data.loc[:,'dB'] = fit['dB'][k][eq_inv]
#create data-frame with synthetic dataset
fname_synthetic_data = dir_out + f'{fname_flatfile}_synthetic_data{synds_suffix}_Y{k+1}.csv'
df_synthetic_data.to_csv(fname_synthetic_data, index=False)
| 6,252 | 34.528409 | 110 | py |
ngmm_tools | ngmm_tools-master/Analyses/Code_Verification/synthetic_datasets/create_synthetic_ds3.py | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Sun Dec 26 15:47:17 2021
@author: glavrent
"""
# load libraries
import os
import pathlib
# arithmetic libraries
import numpy as np
# statistics libraries
import pandas as pd
# python interface to Stan for Bayesian inference
# for installation check https://pystan.readthedocs.io/en/latest/
import pystan
# %% set working directories
os.chdir(os.getcwd()) # change directory to current directory
# %% Define Input Data
# ======================================
# USER SETS THE INPUT FLATFILE NAMES AND PATH
# ++++++++++++++++++++++++++++++++++++++++
#input flatfile
# fname_flatfile = 'CatalogNGAWest3CA'
# fname_flatfile = 'CatalogNGAWest3CA_2013'
# fname_flatfile = 'CatalogNGAWest3NCA'
# fname_flatfile = 'CatalogNGAWest3SCA'
fname_flatfile = 'CatalogNGAWest3CALite'
dir_flatfile = '../../../Data/Validation/preprocessing/flatfiles/merged/'
# cell data
# fname_cellinfo = 'CatalogNGAWest3CA_cellinfo.csv'
# fname_celldist = 'CatalogNGAWest3CA_distancematrix.csv'
# fname_cellinfo = 'CatalogNGAWest3CA_2013_cellinfo.csv'
# fname_celldist = 'CatalogNGAWest3CA_2013_distancematrix.csv'
fname_cellinfo = 'CatalogNGAWest3CALite_cellinfo.csv'
fname_celldist = 'CatalogNGAWest3CALite_distancematrix.csv'
fname_celldist_sp = 'CatalogNGAWest3CALite_distancematrix_sparce.csv'
dir_celldata = '../../../Data/Validation/preprocessing/cell_distances/'
# ++++++++++++++++++++++++++++++++++++++++
# USER SETS THE INPUT FLATFILE NAMES AND PATH
# ++++++++++++++++++++++++++++++++++++++++
fname_stan_model = 'create_synthetic_ds3.stan'
# ++++++++++++++++++++++++++++++++++++++++
# USER SETS THE OUTPUT FILE PATH AND NAME
# ++++++++++++++++++++++++++++++++++++++++
# output filename sufix
# synds_suffix = '_small_corr_len'
# synds_suffix = '_large_corr_len'
# output directories
dir_out = f'../../../Data/Validation/synthetic_datasets/ds3{synds_suffix}/'
# ++++++++++++++++++++++++++++++++++++++++
# number of synthetic data-sets
n_dataset = 5
n_attempts = 500
# number of chains and seed number in stan model
n_chains = 1
n_seed = 1
# define hyper-parameters
# omega_0: standard deviation for constant offset
# omega_1e: standard deviation for spatially varying earthquake constant
# omega_1as: standard deviation for spatially varying site constant
# omega_1bs: standard deviation for independent site constant
# ell_1e: correlation length for spatially varying earthquake constant
# ell_1as: correlation length for spatially varying site constant
# c_2_erg: ergodic geometrical-spreading coefficient
# omega_2: standard deviation for shift in average geometrical-spreading
# omega_2p: standard deviation for spatially varying geometrical-spreading coefficient
# ell_2p: correlation length for spatially varying geometrical-spreading coefficient
# c_3_erg: ergodic Vs30 scaling coefficient
# omega_3: standard deviation for shift in average Vs30 scaling
# omega_3s: standard deviation for spatially varying Vs30 scaling
# ell_3s: correlation length for spatially varying Vs30 scaling
# c_cap_erg: erogodic cell-specific anelastic attenuation
# omega_cap_mu: standard deviation for constant offset of cell-specific anelastic attenuation
# omega_ca1p: standard deviation for spatially varying component of cell-specific anelastic attenuation
# omega_ca2p: standard deviation for spatially independent component of cell-specific anelastic attenuation
# ell_ca1p: correlation length for spatially varying component of cell-specific anelastic attenuation
# phi_0: within-event standard deviation
# tau_0: between-event standard deviation
# USER NEEDS TO SPECIFY HYPERPARAMETERS
# ++++++++++++++++++++++++++++++++++++++++
# # small correlation lengths
# hyp = {'omega_0': 0.1, 'omega_1e':0.1, 'omega_1as': 0.35, 'omega_1bs': 0.25,
# 'ell_1e':60, 'ell_1as':30,
# 'c_2_erg': -2.0,
# 'omega_2': 0.2,
# 'omega_2p': 0.15, 'ell_2p': 80,
# 'c_3_erg':-0.6,
# 'omega_3': 0.15,
# 'omega_3s': 0.15, 'ell_3s': 130,
# 'c_cap_erg': -0.011,
# 'omega_cap_mu': 0.005, 'omega_ca1p':0.004, 'omega_ca2p':0.002,
# 'ell_ca1p': 75,
# 'phi_0':0.3, 'tau_0':0.25 }
# # large correlation lengths
# hyp = {'omega_0': 0.1, 'omega_1e':0.2, 'omega_1as': 0.4, 'omega_1bs': 0.3,
# 'ell_1e':100, 'ell_1as':70,
# 'c_2_erg': -2.0,
# 'omega_2': 0.2,
# 'omega_2p': 0.15, 'ell_2p': 140,
# 'c_3_erg':-0.6,
# 'omega_3': 0.15,
# 'omega_3s': 0.15, 'ell_3s': 180,
# 'c_cap_erg': -0.02,
# 'omega_cap_mu': 0.008, 'omega_ca1p':0.005, 'omega_ca2p':0.003,
# 'ell_ca1p': 120,
# 'phi_0':0.3, 'tau_0':0.25}
# ++++++++++++++++++++++++++++++++++++++++
#psuedo depth term for mag saturation
h_M = 4
# %% Load Data
# ======================================
# load flatfile
fullname_flatfile = dir_flatfile + fname_flatfile + '.csv'
df_flatfile = pd.read_csv(fullname_flatfile)
# load celldata
df_cell_dist = pd.read_csv(dir_celldata + fname_celldist, index_col=0)
df_cell_dist_sp = pd.read_csv(dir_celldata + fname_celldist_sp)
df_cell_info = pd.read_csv(dir_celldata + fname_cellinfo)
# %% Processing
# ======================================
# read earthquake and station data from the flatfile
n_rec = len(df_flatfile)
# read earthquake data
# earthquake IDs (rqid), magnitudes (mag), and coordinates (eqX,eqY)
# user may change these IDs based on the headers of the flatfile
data_eq_all = df_flatfile[['eqid','mag','eqX', 'eqY']].values
_, eq_idx, eq_inv = np.unique(df_flatfile[['eqid']], axis=0, return_index=True, return_inverse=True)
data_eq = data_eq_all[eq_idx,:]
X_eq = data_eq[:,[2,3]] # earthquake coordinates
# create earthquake ids for all recordings
eq_id = eq_inv + 1
n_eq = len(data_eq)
# read station data
# station IDs (ssn), Vs30, and coordinates (staX,staY)
# user may change these IDs based on the headers of the flatfile
data_sta_all = df_flatfile[['ssn','Vs30','staX','staY']].values
_, sta_idx, sta_inv = np.unique(df_flatfile[['ssn']].values, axis = 0, return_index=True, return_inverse=True)
data_sta = data_sta_all[sta_idx,:]
X_sta = data_sta[:,[2,3]] # station coordinates
# create station ids for all recordings
sta_id = sta_inv + 1
n_sta = len(data_sta)
# geometrical spreading covariate
x_2 = np.log(np.sqrt(df_flatfile.Rrup.values**2 + h_M**2))
#vs30 covariate
x_3 = np.log(np.minimum(data_sta[:,1], 1000)/1000)
assert(~np.isnan(x_3).all()),'Error. Invalid Vs30 values'
# read cell data
n_cell = len(df_cell_info)
df_cell_dist = df_cell_dist.reindex(df_flatfile.rsn) #cell distance matrix for records in the synthetic data-set
# cell names
cells_names = df_cell_info.cellname.values
cells_id = df_cell_info.cellid.values
# cell distance matrix
cell_dmatrix = df_cell_dist.loc[:,cells_names].values
# cell coordinates
X_cell = df_cell_info[['mptX','mptY']].values
# valid cells
i_val_cells = cell_dmatrix.sum(axis=0) > 0
# %% Stan
# ======================================
## Stan Data
# ---------------------------
stan_data = {'N': n_rec,
'NEQ': n_eq,
'NSTAT': n_sta,
'NCELL': n_cell,
'eq': eq_id, #earthquake index
'stat': sta_id, #station index
'X_e': X_eq, #earthquake coordinates
'X_s': X_sta, #station coordinates
'X_c': X_cell, #cell coordinates
'RC': cell_dmatrix, #cell distances
'mu_gmm': np.zeros(n_rec),
#covariates
'x_2': x_2, #geometrical spreading
'x_3': x_3, #Vs30 scaling
#hyper-parameters of generated data-set
'omega_0': hyp['omega_0'],
'omega_1e': hyp['omega_1e'],
'omega_1as': hyp['omega_1as'],
'omega_1bs': hyp['omega_1bs'],
'ell_1e': hyp['ell_1e'],
'ell_1as': hyp['ell_1as'],
'c_2_erg': hyp['c_2_erg'],
'omega_2': hyp['omega_2'],
'omega_2p': hyp['omega_2p'],
'ell_2p': hyp['ell_2p'],
'c_3_erg': hyp['c_3_erg'],
'omega_3': hyp['omega_3'],
'omega_3s': hyp['omega_3s'],
'ell_3s': hyp['ell_3s'],
#anelastic attenuation
'c_cap_erg': hyp['c_cap_erg'],
'omega_cap_mu': hyp['omega_cap_mu'],
'omega_ca1p': hyp['omega_ca1p'],
'omega_ca2p': hyp['omega_ca2p'],
'ell_ca1p': hyp['ell_ca1p'],
#aleatory terms
'phi_0': hyp['phi_0'],
'tau_0': hyp['tau_0']
}
## Compile and Run Stan model
# ---------------------------
# compile model
sm = pystan.StanModel(file=fname_stan_model)
# generate samples
fit = sm.sampling(data=stan_data, algorithm="Fixed_param", iter=n_attempts, chains=n_chains, seed=n_seed)
# select only data-sets with negative anelastic attenuation coefficients
valid_dataset = np.array( n_attempts * [False] )
for k, (c_2p, c_cap) in enumerate(zip(fit['c_2p'], fit['c_cap'])):
valid_dataset[k] = np.all(c_2p <= 0 ) & np.all(c_cap <= 0 )
valid_dataset = np.where(valid_dataset)[0] #valid data-set ids
valid_dataset = valid_dataset[:min(n_dataset,len(valid_dataset))]
# keep valid datasets
Y_nerg_med = fit['Y_nerg_med'][valid_dataset]
Y_var_coeff = fit['Y_var_ceoff'][valid_dataset]
Y_inattent = fit['Y_inattent'][valid_dataset]
Y_aleat = fit['Y_aleat'][valid_dataset]
Y_tot = fit['Y_tot'][valid_dataset]
c_cap = fit['c_cap'][valid_dataset]
# %% Output
# ======================================
if not os.path.isdir(dir_out): pathlib.Path(dir_out).mkdir(parents=True, exist_ok=True)
# save generated data-sets
for k, (k_vds, Y_nm, Y_vc, Y_iatt, Y_t) in enumerate(zip(valid_dataset, Y_nerg_med, Y_var_coeff, Y_inattent, Y_tot)):
#copy catalog info to synthetic data-set
df_synthetic_data = df_flatfile.copy()
#add covariates
df_synthetic_data.loc[:,'x_2'] = x_2
df_synthetic_data.loc[:,'x_3'] = x_3[sta_inv]
#add residuals columns
df_synthetic_data.loc[:,'nerg_gm'] = Y_nm
df_synthetic_data.loc[:,'vcoeff'] = Y_vc
df_synthetic_data.loc[:,'inatten'] = Y_iatt
df_synthetic_data.loc[:,'tot'] = Y_t
#add columns with sampled coefficients
df_synthetic_data.loc[:,'dc_0'] = fit['dc_0'][k_vds]
df_synthetic_data.loc[:,'dc_1e'] = fit['dc_1e'][k_vds][eq_inv]
df_synthetic_data.loc[:,'dc_1as'] = fit['dc_1as'][k_vds][sta_inv]
df_synthetic_data.loc[:,'dc_1bs'] = fit['dc_1bs'][k_vds][sta_inv]
df_synthetic_data.loc[:,'c_2'] = fit['c_2_mu'][k_vds]
df_synthetic_data.loc[:,'c_2p'] = fit['c_2p'][k_vds][eq_inv]
df_synthetic_data.loc[:,'c_3'] = fit['c_3_mu'][k_vds]
df_synthetic_data.loc[:,'c_3s'] = fit['c_3s'][k_vds][sta_inv]
#add columns aleatory terms
df_synthetic_data.loc[:,'dW'] = fit['dW'][k_vds]
df_synthetic_data.loc[:,'dB'] = fit['dB'][k_vds][eq_inv]
#create data-frame with synthetic dataset
fname_synthetic_data = dir_out + f'{fname_flatfile}_synthetic_data{synds_suffix}_Y{k+1}.csv'
df_synthetic_data.to_csv(fname_synthetic_data, index=False)
# save coeffiicients
for k, (k_vds, c_ca) in enumerate(zip(valid_dataset, c_cap)):
#create synthetic cell dataset
df_synthetic_cell = df_cell_info.copy()
#cell specific anelastic attenuation
df_synthetic_cell.loc[:,'c_cap_mu'] = fit['c_cap_mu'][k_vds]
df_synthetic_cell.loc[:,'c_cap'] = c_ca
#create data-frame with cell specific dataset
fname_synthetic_atten = dir_out + f'{fname_flatfile}_synthetic_atten{synds_suffix}_Y{k+1}.csv'
df_synthetic_cell.to_csv(fname_synthetic_atten, index=False)
# save cell data
fname_cell_info = dir_out + f'{fname_flatfile}_cellinfo.csv'
fname_cell_dist = dir_out + f'{fname_flatfile}_distancematrix.csv'
fname_cell_dist_sp = dir_out + f'{fname_flatfile}_distancematrix_sparse.csv'
df_cell_info.to_csv(fname_cell_info, index=False)
df_cell_dist.to_csv(fname_cell_dist)
df_cell_dist_sp.to_csv(fname_cell_dist_sp, index=False)
| 12,361 | 40.206667 | 117 | py |
ngmm_tools | ngmm_tools-master/Analyses/Code_Verification/synthetic_datasets/create_synthetic_ds2.py | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Thu Jul 1 21:25:34 2021
@author: glavrent
"""
# load libraries
import os
import pathlib
# arithmetic libraries
import numpy as np
# statistics libraries
import pandas as pd
# python interface to Stan for Bayesian inference
# for installation check https://pystan.readthedocs.io/en/latest/
import pystan
# %% set working directories
os.chdir(os.getcwd()) # change directory to current directory
# %% Define Input Data
# ======================================
# USER SETS THE INPUT FLATFILE NAMES AND PATH
# ++++++++++++++++++++++++++++++++++++++++
#input flatfile
# fname_flatfile = 'CatalogNGAWest3CA'
# fname_flatfile = 'CatalogNGAWest3CA_2013'
# fname_flatfile = 'CatalogNGAWest3NCA'
# fname_flatfile = 'CatalogNGAWest3SCA'
fname_flatfile = 'CatalogNGAWest3CALite'
dir_flatfile = '../../../Data/Validation/preprocessing/flatfiles/merged/'
# cell data
# fname_cellinfo = 'CatalogNGAWest3CA_cellinfo.csv'
# fname_celldist = 'CatalogNGAWest3CA_distancematrix.csv'
# fname_cellinfo = 'CatalogNGAWest3CA_2013_cellinfo.csv'
# fname_celldist = 'CatalogNGAWest3CA_2013_distancematrix.csv'
fname_cellinfo = 'CatalogNGAWest3CALite_cellinfo.csv'
fname_celldist = 'CatalogNGAWest3CALite_distancematrix.csv'
fname_celldist_sp = 'CatalogNGAWest3CALite_distancematrix_sparce.csv'
dir_celldata = '../../../Data/Validation/preprocessing/cell_distances/'
# ++++++++++++++++++++++++++++++++++++++++
# USER SETS THE INPUT FLATFILE NAMES AND PATH
# ++++++++++++++++++++++++++++++++++++++++
fname_stan_model = 'create_synthetic_ds2.stan'
# ++++++++++++++++++++++++++++++++++++++++
# USER SETS THE OUTPUT FILE PATH AND NAME
# ++++++++++++++++++++++++++++++++++++++++
# output filename sufix
# synds_suffix = '_small_corr_len'
# synds_suffix = '_large_corr_len'
# output directories
dir_out = f'../../../Data/Validation/synthetic_datasets/ds2{synds_suffix}/'
# ++++++++++++++++++++++++++++++++++++++++
# number of synthetic data-sets
n_dataset = 5
n_attempts = 500
# number of chains and seed number in stan model
n_chains = 1
n_seed = 1
# define hyper-parameters
# omega_0: standard deviation for constant offset
# omega_1e: standard deviation for spatially varying earthquake constant
# omega_1as: standard deviation for spatially varying site constant
# omega_1bs: standard deviation for independent site constant
# ell_1e: correlation length for spatially varying earthquake constant
# ell_1as: correlation length for spatially varying site constant
# c_cap_erg: erogodic cell-specific anelastic attenuation
# omega_cap_mu: standard deviation for constant offset of cell-specific anelastic attenuation
# omega_ca1p: standard deviation for spatially varying component of cell-specific anelastic attenuation
# omega_ca2p: standard deviation for spatially independent component of cell-specific anelastic attenuation
# ell_ca1p: correlation length for spatially varying component of cell-specific anelastic attenuation
# phi_0: within-event standard deviation
# tau_0: between-event standard deviation
# USER NEEDS TO SPECIFY HYPERPARAMETERS
# ++++++++++++++++++++++++++++++++++++++++
# # small correlation lengths
# hyp = {'omega_0': 0.1, 'omega_1e':0.1, 'omega_1as': 0.35, 'omega_1bs': 0.25,
# 'ell_1e':60, 'ell_1as':30,
# 'c_cap_erg': -0.011,
# 'omega_cap_mu': 0.005, 'omega_ca1p':0.004, 'omega_ca2p':0.002,
# 'ell_ca1p': 75,
# 'phi_0':0.4, 'tau_0':0.3 }
#
# # large correlation lengths
# hyp = {'omega_0': 0.1, 'omega_1e':0.2, 'omega_1as': 0.4, 'omega_1bs': 0.3,
# 'ell_1e':100, 'ell_1as':70,
# 'c_cap_erg': -0.02,
# 'omega_cap_mu': 0.008, 'omega_ca1p':0.005, 'omega_ca2p':0.003,
# 'ell_ca1p': 120,
# 'phi_0':0.4, 'tau_0':0.3}
# ++++++++++++++++++++++++++++++++++++++++
# %% Load Data
# ======================================
# load flatfile
fullname_flatfile = dir_flatfile + fname_flatfile + '.csv'
df_flatfile = pd.read_csv(fullname_flatfile)
# load celldata
df_cell_dist = pd.read_csv(dir_celldata + fname_celldist, index_col=0)
df_cell_dist_sp = pd.read_csv(dir_celldata + fname_celldist_sp)
df_cell_info = pd.read_csv(dir_celldata + fname_cellinfo)
# %% Processing
# ======================================
# read earthquake and station data from the flatfile
n_rec = len(df_flatfile)
# read earthquake data
# earthquake IDs (rqid), magnitudes (mag), and coordinates (eqX,eqY)
# user may change these IDs based on the headers of the flatfile
data_eq_all = df_flatfile[['eqid','mag','eqX', 'eqY']].values
_, eq_idx, eq_inv = np.unique(df_flatfile[['eqid']], axis=0, return_index=True, return_inverse=True)
data_eq = data_eq_all[eq_idx,:]
X_eq = data_eq[:,[2,3]] # earthquake coordinates
# create earthquake ids for all recordings
eq_id = eq_inv + 1
n_eq = len(data_eq)
# read station data
# station IDs (ssn), Vs30, and coordinates (staX,staY)
# user may change these IDs based on the headers of the flatfile
data_sta_all = df_flatfile[['ssn','Vs30','staX','staY']].values
_, sta_idx, sta_inv = np.unique(df_flatfile[['ssn']].values, axis = 0, return_index=True, return_inverse=True)
data_sta = data_sta_all[sta_idx,:]
X_sta = data_sta[:,[2,3]] # station coordinates
# create station ids for all recordings
sta_id = sta_inv + 1
n_sta = len(data_sta)
# read cell data
n_cell = len(df_cell_info)
df_cell_dist = df_cell_dist.reindex(df_flatfile.rsn) #cell distance matrix for records in the synthetic data-set
# cell names
cells_names = df_cell_info.cellname.values
cells_id = df_cell_info.cellid.values
# cell distance matrix
cell_dmatrix = df_cell_dist.loc[:,cells_names].values
# cell coordinates
X_cell = df_cell_info[['mptX','mptY']].values
# valid cells
i_val_cells = cell_dmatrix.sum(axis=0) > 0
# %% Stan
# ======================================
## Stan Data
# ---------------------------
stan_data = {'N': n_rec,
'NEQ': n_eq,
'NSTAT': n_sta,
'NCELL': n_cell,
'eq': eq_id, #earthquake index
'stat': sta_id, #station index
'X_e': X_eq, #earthquake coordinates
'X_s': X_sta, #station coordinates
'X_c': X_cell, #cell coordinates
'RC': cell_dmatrix, #cell distances
'mu_gmm': np.zeros(n_rec),
#hyper-parameters of generated data-set
'omega_0': hyp['omega_0'],
'omega_1e': hyp['omega_1e'],
'omega_1as': hyp['omega_1as'],
'omega_1bs': hyp['omega_1bs'],
'ell_1e': hyp['ell_1e'],
'ell_1as': hyp['ell_1as'],
#anelastic attenuation
'c_cap_erg': hyp['c_cap_erg'],
'omega_cap_mu': hyp['omega_cap_mu'],
'omega_ca1p': hyp['omega_ca1p'],
'omega_ca2p': hyp['omega_ca2p'],
'ell_ca1p': hyp['ell_ca1p'],
#aleatory terms
'phi_0': hyp['phi_0'],
'tau_0': hyp['tau_0']
}
## Compile and Run Stan model
# ---------------------------
# compile model
sm = pystan.StanModel(file=fname_stan_model)
# generate samples
fit = sm.sampling(data=stan_data, algorithm="Fixed_param", iter=n_attempts, chains=n_chains, seed=n_seed)
# select only data-sets with negative anelastic attenuation coefficients
valid_dataset = np.array( n_attempts * [False] )
for k, c_cap in enumerate(fit['c_cap']):
valid_dataset[k] = np.all(c_cap <= 0 )
valid_dataset = np.where(valid_dataset)[0] #valid data-set ids
valid_dataset = valid_dataset[:min(n_dataset,len(valid_dataset))]
# keep valid datasets
Y_nerg_med = fit['Y_nerg_med'][valid_dataset]
Y_var_coeff = fit['Y_var_ceoff'][valid_dataset]
Y_inattent = fit['Y_inattent'][valid_dataset]
Y_aleat = fit['Y_aleat'][valid_dataset]
Y_tot = fit['Y_tot'][valid_dataset]
c_cap = fit['c_cap'][valid_dataset]
# %% Output
# ======================================
if not os.path.isdir(dir_out): pathlib.Path(dir_out).mkdir(parents=True, exist_ok=True)
# save generated data-sets
for k, (k_vds, Y_nm, Y_vc, Y_iatt, Y_t) in enumerate(zip(valid_dataset, Y_nerg_med, Y_var_coeff, Y_inattent, Y_tot)):
#copy catalog info to synthetic data-set
df_synthetic_data = df_flatfile.copy()
#add residuals columns
df_synthetic_data.loc[:,'nerg_gm'] = Y_nm
df_synthetic_data.loc[:,'vcoeff'] = Y_vc
df_synthetic_data.loc[:,'inatten'] = Y_iatt
df_synthetic_data.loc[:,'tot'] = Y_t
#add columns with sampled coefficients
df_synthetic_data.loc[:,'dc_0'] = fit['dc_0'][k_vds]
df_synthetic_data.loc[:,'dc_1e'] = fit['dc_1e'][k_vds][eq_inv]
df_synthetic_data.loc[:,'dc_1as'] = fit['dc_1as'][k_vds][sta_inv]
df_synthetic_data.loc[:,'dc_1bs'] = fit['dc_1bs'][k_vds][sta_inv]
#add columns aleatory terms
df_synthetic_data.loc[:,'dW'] = fit['dW'][k_vds]
df_synthetic_data.loc[:,'dB'] = fit['dB'][k_vds][eq_inv]
#create data-frame with synthetic dataset
fname_synthetic_data = dir_out + f'{fname_flatfile}_synthetic_data{synds_suffix}_Y{k+1}.csv'
df_synthetic_data.to_csv(fname_synthetic_data, index=False)
# save coeffiicients
for k, (k_vds, c_ca) in enumerate(zip(valid_dataset, c_cap)):
#create synthetic cell dataset
df_synthetic_cell = df_cell_info.copy()
#cell specific anelastic attenuation
df_synthetic_cell.loc[:,'c_cap_mu'] = fit['c_cap_mu'][k_vds]
df_synthetic_cell.loc[:,'c_cap'] = c_ca
#create data-frame with cell specific dataset
fname_synthetic_atten = dir_out + f'{fname_flatfile}_synthetic_atten{synds_suffix}_Y{k+1}.csv'
df_synthetic_cell.to_csv(fname_synthetic_atten, index=False)
# save cell data
fname_cell_info = dir_out + f'{fname_flatfile}_cellinfo.csv'
fname_cell_dist = dir_out + f'{fname_flatfile}_distancematrix.csv'
fname_cell_dist_sp = dir_out + f'{fname_flatfile}_distancematrix_sparse.csv'
df_cell_info.to_csv(fname_cell_info, index=False)
df_cell_dist.to_csv(fname_cell_dist)
df_cell_dist_sp.to_csv(fname_cell_dist_sp, index=False)
| 10,211 | 38.890625 | 117 | py |
ngmm_tools | ngmm_tools-master/Analyses/Code_Verification/regression/ds1/main_pystan_model1_NGAWest2CANorth.py | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Wed Jul 14 14:17:52 2021
@author: glavrent
"""
# Working directory and Packages
# ---------------------------
#load libraries
import os
import sys
import numpy as np
import pandas as pd
import time
#user functions
sys.path.insert(0,'../../../Python_lib/regression/pystan/')
from regression_pystan_model1_unbounded_hyp import RunStan
# Define variables
# ---------------------------
#filename suffix
# synds_suffix = '_small_corr_len'
# synds_suffix = '_large_corr_len'
#synthetic datasets directory
ds_dir = '../../../../Data/Verification/synthetic_datasets/ds1'
ds_dir = r'%s%s/'%(ds_dir, synds_suffix)
# dataset info
# ds_fname_main = 'CatalogNGAWest3CA_synthetic_data'
ds_fname_main = 'CatalogNGAWest3CALite_synthetic_data'
ds_id = np.arange(1,6)
#stan model
# sm_fname = '../../../Stan_lib/regression_stan_model1_unbounded_hyp.stan'
# sm_fname = '../../../Stan_lib/regression_stan_model1_unbounded_hyp_chol.stan'
# sm_fname = '../../../Stan_lib/regression_stan_model1_unbounded_hyp_chol_efficient.stan'
# sm_fname = '../../../Stan_lib/regression_stan_model1_unbounded_hyp_chol_efficient2.stan'
#output info
#main output filename
out_fname_main = 'NGAWest2CANorth_syndata'
#main output directory
out_dir_main = '../../../../Data/Verification/regression/ds1/'
#output sub-directory
#pystan2
# out_dir_sub = 'PYSTAN_NGAWest2CANorth'
# out_dir_sub = 'PYSTAN_NGAWest2CANorth_chol'
# out_dir_sub = 'PYSTAN_NGAWest2CANorth_chol_eff'
# out_dir_sub = 'PYSTAN_NGAWest2CANorth_chol_eff2'
#pystan3
# out_dir_sub = 'PYSTAN3_NGAWest2CANorth'
# out_dir_sub = 'PYSTAN3_NGAWest2CANorth_chol'
# out_dir_sub = 'PYSTAN3_NGAWest2CANorth_chol_eff'
# out_dir_sub = 'PYSTAN3_NGAWest2CANorth_chol_eff2'
#stan parameters
runstan_flag = True
# pystan_ver = 2
pystan_ver = 3
res_name = 'tot'
n_iter = 1000
n_chains = 4
adapt_delta = 0.8
max_treedepth = 10
#parallel options
# flag_parallel = True
flag_parallel = False
#output sub-dir with corr with suffix info
out_dir_sub = f'%s%s'%(out_dir_sub, synds_suffix)
# Run stan regression
# ---------------------------
#create datafame with computation time
df_run_info = list()
#iterate over all synthetic datasets
for d_id in ds_id:
print('Synthetic dataset %i fo %i'%(d_id, len(ds_id)))
#run time start
run_t_strt = time.time()
#input flatfile
ds_fname = '%s%s%s_Y%i.csv'%(ds_dir, ds_fname_main, synds_suffix, d_id)
#load flatfile
df_flatfile = pd.read_csv(ds_fname)
#keep only North records of NGAWest2
df_flatfile = df_flatfile.loc[np.logical_and(df_flatfile.dsid==0,
df_flatfile.sreg==1),:]
#output file name and directory
out_fname = '%s%s_Y%i'%(out_fname_main, synds_suffix, d_id)
out_dir = '%s/%s/Y%i/'%(out_dir_main, out_dir_sub, d_id)
#run stan model
RunStan(df_flatfile, sm_fname, out_fname, out_dir, res_name,
runstan_flag=runstan_flag, n_iter=n_iter, n_chains=n_chains,
adapt_delta=adapt_delta, max_treedepth=max_treedepth,
pystan_ver=pystan_ver, pystan_parallel=flag_parallel)
#run time end
run_t_end = time.time()
#compute run time
run_tm = (run_t_end - run_t_strt)/60
#log run time
df_run_info.append(pd.DataFrame({'computer_name':os.uname()[1],'out_name':out_dir_sub,
'ds_id':d_id,'run_time':run_tm}, index=[d_id]))
#write out run info
out_fname = '%s%s/run_info.csv'%(out_dir_main, out_dir_sub)
pd.concat(df_run_info).reset_index(drop=True).to_csv(out_fname, index=False)
| 3,687 | 30.793103 | 90 | py |
ngmm_tools | ngmm_tools-master/Analyses/Code_Verification/regression/ds1/comparison_stan_model1.py | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Thu Aug 12 20:52:09 2021
@author: glavrent
"""
# Working directory and Packages
# ---------------------------
#load packages
import os
import sys
import pathlib
import glob
import re #regular expression package
import pickle
#arithmetic libraries
import numpy as np
#statistics libraries
import pandas as pd
#plot libraries
import matplotlib as mpl
import matplotlib.pyplot as plt
from matplotlib.ticker import AutoLocator as plt_autotick
#user functions
sys.path.insert(0,'../../../Python_lib/regression/')
from pylib_stats import CalcRMS
from pylib_stats import CalcLKDivergece
# Define variables
# ---------------------------
# USER SETS DIRECTORIES AND FILE INFO OF SYNTHETIC DS AND REGRESSION RESULTS
# ++++++++++++++++++++++++++++++++++++++++
#processed dataset
# name_dataset = 'NGAWest2CANorth'
name_dataset = 'NGAWest2CA'
# name_dataset = 'NGAWest3CA'
#correlation info
# 1: Small Correlation Lengths
# 2: Large Correlation Lenghts
corr_id = 1
#package
# 1: Pystan v2
# 2: Pystan v3
# 3: stancmd
pkg_id = 3
#approximation type
# 1: multivariate normal
# 2: cholesky
# 3: cholesky efficient
# 4: cholesky efficient v2
aprox_id = 3
#directories (synthetic dataset)
if corr_id == 1:
dir_syndata = '../../../../Data/Verification/synthetic_datasets/ds1_small_corr_len'
elif corr_id == 2:
dir_syndata = '../../../../Data/Verification/synthetic_datasets/ds1_large_corr_len'
#directories (regression results)
if pkg_id == 1:
dir_results = f'../../../../Data/Verification/regression/ds1/PYSTAN_%s'%name_dataset
elif pkg_id == 2:
dir_results = f'../../../../Data/Verification/regression/ds1/PYSTAN3_%s'%name_dataset
elif pkg_id == 3:
dir_results = f'../../../../Data/Verification/regression/ds1/CMDSTAN_%s'%name_dataset
#prefix for synthetic data and results
prfx_syndata = 'CatalogNGAWest3CALite_synthetic'
# FILE INFO FOR REGRESSION RESULTS
# ++++++++++++++++++++++++++++++++++++++++
#regression results filename prefix
prfx_results = f'%s_syndata'%name_dataset
#output filename sufix (synthetic dataset)
if corr_id == 1: synds_suffix = '_small_corr_len'
elif corr_id == 2: synds_suffix = '_large_corr_len'
#output filename sufix (regression results)
if aprox_id == 1: synds_suffix_stan = synds_suffix
elif aprox_id == 2: synds_suffix_stan = '_chol' + synds_suffix
elif aprox_id == 3: synds_suffix_stan = '_chol_eff' + synds_suffix
elif aprox_id == 4: synds_suffix_stan = '_chol_eff2' + synds_suffix
# dataset info
ds_id = np.arange(1,6)
# USER NEEDS TO SPECIFY HYPERPARAMETERS OF SYNTHETIC DATASET
# ++++++++++++++++++++++++++++++++++++++++
# hyper-parameters
if corr_id == 1:
# small correlation lengths
hyp = {'omega_0': 0.1, 'omega_1e':0.1, 'omega_1as': 0.35, 'omega_1bs': 0.25,
'ell_1e':60, 'ell_1as':30, 'phi_0':0.4, 'tau_0':0.3 }
elif corr_id == 2:
#large correlation lengths
hyp = {'omega_0': 0.1, 'omega_1e':0.2, 'omega_1as': 0.4, 'omega_1bs': 0.3,
'ell_1e':100, 'ell_1as':70, 'phi_0':0.4, 'tau_0':0.3 }
# ++++++++++++++++++++++++++++++++++++++++
#ploting options
flag_report = True
# Compare coefficients
# ---------------------------
#initialize misfit metrics dataframe
df_misfit = pd.DataFrame(index=['Y%i'%d_id for d_id in ds_id])
#iterate over different datasets
for d_id in ds_id:
# Load Data
#--- --- --- --- ---
#file names
#synthetic data
fname_sdata_gmotion = '%s/%s_%s%s_Y%i'%(dir_syndata, prfx_syndata, 'data', synds_suffix, d_id) + '.csv'
#regression results
fname_reg_gmotion = '%s%s/Y%i/%s%s_Y%i_stan_%s'%(dir_results, synds_suffix_stan, d_id, prfx_results, synds_suffix, d_id, 'residuals') + '.csv'
fname_reg_coeff = '%s%s/Y%i/%s%s_Y%i_stan_%s'%(dir_results, synds_suffix_stan, d_id, prfx_results, synds_suffix, d_id, 'coefficients') + '.csv'
#load synthetic results
df_sdata_gmotion = pd.read_csv(fname_sdata_gmotion).set_index('rsn')
#load regression results
df_reg_gmotion = pd.read_csv(fname_reg_gmotion, index_col=0)
df_reg_coeff = pd.read_csv(fname_reg_coeff, index_col=0)
# Processing
#--- --- --- --- ---
#keep only common records from synthetic dataset
df_sdata_gmotion = df_sdata_gmotion.reindex(df_reg_gmotion.index)
#find unique earthqakes and stations
eq_id, eq_idx, eq_nrec = np.unique(df_sdata_gmotion.eqid, return_index=True, return_counts=True)
sta_id, sta_idx, sta_nrec = np.unique(df_sdata_gmotion.ssn, return_index=True, return_counts=True)
# Compute Root Mean Square Error
#--- --- --- --- ---
df_misfit.loc['Y%i'%d_id,'nerg_tot_rms'] = CalcRMS(df_sdata_gmotion.nerg_gm.values, df_reg_gmotion.nerg_mu.values)
df_misfit.loc['Y%i'%d_id,'dc_1e_rms'] = CalcRMS(df_sdata_gmotion['dc_1e'].values[eq_idx], df_reg_coeff['dc_1e_mean'].values[eq_idx])
df_misfit.loc['Y%i'%d_id,'dc_1as_rms'] = CalcRMS(df_sdata_gmotion['dc_1as'].values[sta_idx], df_reg_coeff['dc_1as_mean'].values[sta_idx])
df_misfit.loc['Y%i'%d_id,'dc_1bs_rms'] = CalcRMS(df_sdata_gmotion['dc_1bs'].values[sta_idx], df_reg_coeff['dc_1bs_mean'].values[sta_idx])
# Compute Divergence
#--- --- --- --- ---
df_misfit.loc['Y%i'%d_id,'nerg_tot_KL'] = CalcLKDivergece(df_sdata_gmotion.nerg_gm.values, df_reg_gmotion.nerg_mu.values)
df_misfit.loc['Y%i'%d_id,'dc_1e_KL'] = CalcLKDivergece(df_sdata_gmotion['dc_1e'].values[eq_idx], df_reg_coeff['dc_1e_mean'].values[eq_idx])
df_misfit.loc['Y%i'%d_id,'dc_1as_KL'] = CalcLKDivergece(df_sdata_gmotion['dc_1as'].values[sta_idx], df_reg_coeff['dc_1as_mean'].values[sta_idx])
df_misfit.loc['Y%i'%d_id,'dc_1bs_KL'] = CalcLKDivergece(df_sdata_gmotion['dc_1bs'].values[sta_idx], df_reg_coeff['dc_1bs_mean'].values[sta_idx])
# Output
#--- --- --- --- ---
#figure directory
dir_fig = '%s%s/Y%i/figures_cmp/'%(dir_results,synds_suffix_stan,d_id)
pathlib.Path(dir_fig).mkdir(parents=True, exist_ok=True)
#compare ground motion predictions
#... ... ... ... ... ...
#figure title
fname_fig = 'Y%i_tot_res_scatter'%d_id
#create figure
fig, ax = plt.subplots(figsize = (10,10))
#median
ax.scatter(df_sdata_gmotion.nerg_gm.values, df_reg_gmotion.nerg_mu.values)
ax.axline((0,0), slope=1, color="black", linestyle="--")
#edit figure
if not flag_report: ax.set_title('Comparison total residuals, Y: %i'%d_id, fontsize=30)
ax.set_xlabel('Synthetic dataset', fontsize=35)
ax.set_ylabel('Estimated', fontsize=35)
ax.grid(which='both')
ax.tick_params(axis='x', labelsize=32)
ax.tick_params(axis='y', labelsize=32)
#plot limits
# plt_lim = np.array([ax.get_xlim(), ax.get_ylim()])
# plt_lim = (plt_lim[:,0].min(), plt_lim[:,1].max())
# ax.set_xlim(plt_lim)
# ax.set_ylim(plt_lim)
ax.set_xlim([-2,2])
ax.set_ylim([-2,2])
fig.tight_layout()
fig.savefig( dir_fig + fname_fig + '.png' )
#compare dc_1e
#... ... ... ... ... ...
#figure title
fname_fig = 'Y%i_dc_1e_scatter'%d_id
#create figure
fig, ax = plt.subplots(figsize = (10,10))
#coefficient scatter
ax.scatter(df_sdata_gmotion['dc_1e'].values[eq_idx], df_reg_coeff['dc_1e_mean'].values[eq_idx])
ax.axline((0,0), slope=1, color="black", linestyle="--")
#edit figure
if not flag_report: ax.set_title(r'Comparison $\delta c_{1,e}$, Y: %i'%d_id, fontsize=30)
ax.set_xlabel('Synthetic dataset', fontsize=25)
ax.set_ylabel('Estimated', fontsize=25)
ax.grid(which='both')
ax.tick_params(axis='x', labelsize=22)
ax.tick_params(axis='y', labelsize=22)
#plot limits
# plt_lim = np.array([ax.get_xlim(), ax.get_ylim()])
# plt_lim = (plt_lim[:,0].min(), plt_lim[:,1].max())
# ax.set_xlim(plt_lim)
# ax.set_ylim(plt_lim)
ax.set_xlim([-.4,.4])
ax.set_ylim([-.4,.4])
#save figure
fig.tight_layout()
fig.savefig( dir_fig + fname_fig + '.png' )
#figure title
fname_fig = 'Y%i_dc_1e_accuracy'%d_id
#create figure
fig, ax = plt.subplots(figsize = (10,10))
#coefficient scatter
ax.scatter(df_reg_coeff['dc_1e_sig'].values[eq_idx],
df_sdata_gmotion['dc_1e'].values[eq_idx] - df_reg_coeff['dc_1e_mean'].values[eq_idx])
ax.axline((0,0), slope=0, color="black", linestyle="--")
#edit figure
if not flag_report: ax.set_title(r'Comparison $\delta c_{1,e}$, Y: %i'%d_id, fontsize=30)
ax.set_xlabel('Standard Deviation', fontsize=25)
ax.set_ylabel('Actual - Estimated', fontsize=25)
ax.grid(which='both')
ax.tick_params(axis='x', labelsize=22)
ax.tick_params(axis='y', labelsize=22)
#plot limits
# ax.set_ylim(np.abs(ax.get_ylim()).max()*np.array([-1,1]))
ax.set_xlim([0,.15])
ax.set_ylim([-.4,.4])
#save figure
fig.tight_layout()
fig.savefig( dir_fig + fname_fig + '.png' )
#figure title
fname_fig = 'Y%i_dc_1e_nrec'%d_id
#create figure
fig, ax = plt.subplots(figsize = (10,10))
#coefficient scatter
ax.scatter(eq_nrec,
df_sdata_gmotion['dc_1e'].values[eq_idx] - df_reg_coeff['dc_1e_mean'].values[eq_idx])
ax.axline((0,0), slope=0, color="black", linestyle="--")
#edit figure
if not flag_report: ax.set_title(r'Comparison $\delta c_{1,e}$, Y: %i'%d_id, fontsize=30)
ax.set_xlabel('Number of records', fontsize=25)
ax.set_ylabel('Actual - Estimated', fontsize=25)
ax.grid(which='both')
ax.set_xscale('log')
ax.tick_params(axis='x', labelsize=22)
ax.tick_params(axis='y', labelsize=22)
#plot limits
# ax.set_ylim(np.abs(ax.get_ylim()).max()*np.array([-1,1]))
ax.set_xlim([0.9,1e3])
ax.set_ylim([-.4,.4])
#save figure
fig.tight_layout()
fig.savefig( dir_fig + fname_fig + '.png' )
#compare dc_1as
#... ... ... ... ... ...
#figure title
fname_fig = 'Y%i_dc_1as_scatter'%d_id
#create figure
fig, ax = plt.subplots(figsize = (10,10))
#coefficient scatter
ax.scatter(df_sdata_gmotion['dc_1as'].values[sta_idx], df_reg_coeff['dc_1as_mean'].values[sta_idx])
ax.axline((0,0), slope=1, color="black", linestyle="--")
#edit figure
if not flag_report: ax.set_title(r'Comparison $\delta c_{1a,s}$, Y: %i'%d_id, fontsize=30)
ax.set_xlabel('Synthetic dataset', fontsize=25)
ax.set_ylabel('Estimated', fontsize=25)
ax.grid(which='both')
ax.tick_params(axis='x', labelsize=22)
ax.tick_params(axis='y', labelsize=22)
#plot limits
# plt_lim = np.array([ax.get_xlim(), ax.get_ylim()])
# plt_lim = (plt_lim[:,0].min(), plt_lim[:,1].max())
# ax.set_xlim(plt_lim)
# ax.set_ylim(plt_lim)
ax.set_xlim([-1.5,1.5])
ax.set_ylim([-1.5,1.5])
#save figure
fig.tight_layout()
fig.savefig( dir_fig + fname_fig + '.png' )
#figure title
fname_fig = 'Y%i_dc_1as_accuracy'%d_id
#create figure
fig, ax = plt.subplots(figsize = (10,10))
#accuray
ax.scatter(df_reg_coeff['dc_1as_sig'].values[sta_idx],
df_sdata_gmotion['dc_1as'].values[sta_idx] - df_reg_coeff['dc_1as_mean'].values[sta_idx])
ax.axline((0,0), slope=0, color="black", linestyle="--")
#edit figure
if not flag_report: ax.set_title(r'Comparison $\delta c_{1a,s}$, Y: %i'%d_id, fontsize=30)
ax.set_xlabel('Standard Deviation', fontsize=25)
ax.set_ylabel('Actual - Estimated', fontsize=25)
ax.grid(which='both')
ax.tick_params(axis='x', labelsize=22)
ax.tick_params(axis='y', labelsize=22)
#plot limits
# ax.set_ylim(np.abs(ax.get_ylim()).max()*np.array([-1,1]))
ax.set_xlim([0,.4])
ax.set_ylim([-1.5,1.5])
#save figure
fig.tight_layout()
fig.savefig( dir_fig + fname_fig + '.png' )
#figure title
fname_fig = 'Y%i_dc_1as_nrec'%d_id
#create figure
fig, ax = plt.subplots(figsize = (10,10))
#accuray
ax.scatter(sta_nrec,
df_sdata_gmotion['dc_1as'].values[sta_idx] - df_reg_coeff['dc_1as_mean'].values[sta_idx])
ax.axline((0,0), slope=0, color="black", linestyle="--")
#edit figure
if not flag_report: ax.set_title(r'Comparison $\delta c_{1a,s}$, Y: %i'%d_id, fontsize=30)
ax.set_xlabel('Number of records', fontsize=25)
ax.set_ylabel('Actual - Estimated', fontsize=25)
ax.grid(which='both')
ax.set_xscale('log')
ax.tick_params(axis='x', labelsize=22)
ax.tick_params(axis='y', labelsize=22)
#plot limits
# ax.set_ylim(np.abs(ax.get_ylim()).max()*np.array([-1,1]))
ax.set_xlim([.9,1000])
ax.set_ylim([-1.5,1.5])
#save figure
fig.tight_layout()
fig.savefig( dir_fig + fname_fig + '.png' )
#compare dc_1bs
#... ... ... ... ... ...
#figure title
fname_fig = 'Y%i_dc_1bs_scatter'%d_id
#create figure
fig, ax = plt.subplots(figsize = (10,10))
#coefficient scatter
ax.scatter(df_sdata_gmotion['dc_1bs'].values[sta_idx], df_reg_coeff['dc_1bs_mean'].values[sta_idx])
ax.axline((0,0), slope=1, color="black", linestyle="--")
#edit figure
if not flag_report: ax.set_title(r'Comparison $\delta c_{1b,s}$, Y: %i'%d_id, fontsize=30)
ax.set_xlabel('Synthetic dataset', fontsize=25)
ax.set_ylabel('Estimated', fontsize=25)
ax.grid(which='both')
ax.tick_params(axis='x', labelsize=22)
ax.tick_params(axis='y', labelsize=22)
#plot limits
# plt_lim = np.array([ax.get_xlim(), ax.get_ylim()])
# plt_lim = (plt_lim[:,0].min(), plt_lim[:,1].max())
# ax.set_xlim(plt_lim)
# ax.set_ylim(plt_lim)
ax.set_xlim([-1.5,1.5])
ax.set_ylim([-1.5,1.5])
#save figure
fig.tight_layout()
fig.savefig( dir_fig + fname_fig + '.png' )
#figure title
fname_fig = 'Y%i_dc_1bs_accuracy'%d_id
#create figure
fig, ax = plt.subplots(figsize = (10,10))
#accuray
ax.scatter(df_reg_coeff['dc_1bs_sig'].values[sta_idx],
df_sdata_gmotion['dc_1bs'].values[sta_idx] - df_reg_coeff['dc_1bs_mean'].values[sta_idx])
ax.axline((0,0), slope=0, color="black", linestyle="--")
#edit figure
if not flag_report: ax.set_title(r'Comparison $\delta c_{1b,s}$, Y: %i'%d_id, fontsize=30)
ax.set_xlabel('Standard Deviation', fontsize=25)
ax.set_ylabel('Actual - Estimated', fontsize=25)
ax.grid(which='both')
ax.tick_params(axis='x', labelsize=22)
ax.tick_params(axis='y', labelsize=22)
#plot limits
# ax.set_ylim(np.abs(ax.get_ylim()).max()*np.array([-1,1]))
ax.set_xlim([0,.4])
ax.set_ylim([-1.5,1.5])
#save figure
fig.tight_layout()
fig.savefig( dir_fig + fname_fig + '.png' )
#figure title
fname_fig = 'Y%i_dc_1bs_nrec'%d_id
#create figure
fig, ax = plt.subplots(figsize = (10,10))
#accuray
ax.scatter(sta_nrec,
df_sdata_gmotion['dc_1bs'].values[sta_idx] - df_reg_coeff['dc_1bs_mean'].values[sta_idx])
ax.axline((0,0), slope=0, color="black", linestyle="--")
#edit figure
if not flag_report: ax.set_title(r'Comparison $\delta c_{1b,s}$, Y: %i'%d_id, fontsize=30)
ax.set_xlabel('Number of records', fontsize=25)
ax.set_ylabel('Actual - Estimated', fontsize=25)
ax.grid(which='both')
ax.set_xscale('log')
ax.tick_params(axis='x', labelsize=22)
ax.tick_params(axis='y', labelsize=22)
#plot limits
# ax.set_ylim(np.abs(ax.get_ylim()).max()*np.array([-1,1]))
ax.set_xlim([.9,1000])
ax.set_ylim([-1.5,1.5])
#save figure
fig.tight_layout()
fig.savefig( dir_fig + fname_fig + '.png' )
# Compare Misfit Metrics
# ---------------------------
#summary directory
dir_sum = '%s%s/summary/'%(dir_results,synds_suffix_stan)
pathlib.Path(dir_fig).mkdir(parents=True, exist_ok=True)
#figure directory
dir_fig = '%s/figures/'%(dir_sum)
pathlib.Path(dir_fig).mkdir(parents=True, exist_ok=True)
#save
df_misfit.to_csv(dir_sum + 'misfit_summary.csv')
#RMS misfit
fname_fig = 'misfit_score'
#plot KL divergence
fig, ax = plt.subplots(figsize = (10,10))
ax.plot(ds_id, df_misfit.nerg_tot_rms, linestyle='-', marker='o', linewidth=2, markersize=10, label= 'tot nerg')
ax.plot(ds_id, df_misfit.dc_1e_rms, linestyle='-', marker='o', linewidth=2, markersize=10, label=r'$\delta c_{1,e}$')
ax.plot(ds_id, df_misfit.dc_1as_rms, linestyle='-', marker='o', linewidth=2, markersize=10, label=r'$\delta c_{1a,s}$')
ax.plot(ds_id, df_misfit.dc_1bs_rms, linestyle='-', marker='o', linewidth=2, markersize=10, label=r'$\delta c_{1b,s}$')
#figure properties
ax.set_ylim([0,0.50])
ax.set_xlabel('synthetic dataset', fontsize=25)
ax.set_ylabel('RSME', fontsize=25)
ax.grid(which='both')
ax.set_xticks(ds_id)
ax.set_xticklabels(labels=df_misfit.index)
ax.tick_params(axis='x', labelsize=22)
ax.tick_params(axis='y', labelsize=22)
#legend
ax.legend(loc='upper left', fontsize=25)
#save figure
fig.tight_layout()
fig.savefig( dir_fig + fname_fig + '.png' )
#KL divergence
fname_fig = 'KLdiv_score'
#plot KL divergence
fig, ax = plt.subplots(figsize = (10,10))
ax.plot(ds_id, df_misfit.nerg_tot_KL, linestyle='-', marker='o', linewidth=2, markersize=10, label= 'tot nerg')
ax.plot(ds_id, df_misfit.dc_1e_KL, linestyle='-', marker='o', linewidth=2, markersize=10, label=r'$\delta c_{1,e}$')
ax.plot(ds_id, df_misfit.dc_1as_KL, linestyle='-', marker='o', linewidth=2, markersize=10, label=r'$\delta c_{1a,s}$')
ax.plot(ds_id, df_misfit.dc_1bs_KL, linestyle='-', marker='o', linewidth=2, markersize=10, label=r'$\delta c_{1b,s}$')
#figure properties
ax.set_ylim([0,0.50])
ax.set_xlabel('synthetic dataset', fontsize=25)
ax.set_ylabel('KL divergence', fontsize=25)
ax.grid(which='both')
ax.set_xticks(ds_id)
ax.set_xticklabels(labels=df_misfit.index)
ax.tick_params(axis='x', labelsize=22)
ax.tick_params(axis='y', labelsize=22)
#legend
ax.legend(loc='upper left', fontsize=25)
#save figure
fig.tight_layout()
fig.savefig( dir_fig + fname_fig + '.png' )
# Compare hyper-paramters
# ---------------------------
#iterate over different datasets
df_reg_hyp = list()
df_reg_hyp_post = list()
for d_id in ds_id:
# Load Data
#--- --- --- --- ---
#regression hyperparamters results
fname_reg_hyp = '%s%s/Y%i/%s%s_Y%i_stan_%s'%(dir_results,synds_suffix_stan, d_id,prfx_results, synds_suffix, d_id, 'hyperparameters') + '.csv'
fname_reg_hyp_post = '%s%s/Y%i/%s%s_Y%i_stan_%s'%(dir_results,synds_suffix_stan, d_id,prfx_results, synds_suffix, d_id, 'hyperposterior') + '.csv'
#load regression results
df_reg_hyp.append( pd.read_csv(fname_reg_hyp, index_col=0) )
df_reg_hyp_post.append( pd.read_csv(fname_reg_hyp_post, index_col=0) )
# Omega_1e
#--- --- --- --- ---
#hyper-paramter name
name_hyp = 'omega_1e'
#figure title
fname_fig = 'post_dist_' + name_hyp
#create figure
fig, ax = plt.subplots(figsize = (10,10))
for d_id, df_r_h, df_r_h_p in zip(ds_id, df_reg_hyp, df_reg_hyp_post):
#estimate vertical line height for mean and mode
# ymax_mode = df_r_h_p.loc[:,name_hyp+'_pdf'].max()
# ymax_mean = 1.5*np.ceil(ymax_mode/10)*10
ymax_mode = 40
ymax_mean = 40
#plot posterior dist
pl_hyp = ax.vlines(df_r_h.loc['mean',name_hyp], ymin=0, ymax=ymax_mean, linestyle='-', label='Mean')
ax.vlines(df_r_h.loc['prc_0.50',name_hyp], ymin=0, ymax=ymax_mean, linestyle='--', color=pl_hyp.get_color(), label='Mode')
#plot true value
ymax_hyp = ymax_mean
ax.vlines(hyp[name_hyp], ymin=0, ymax=ymax_hyp, linestyle='-', linewidth=4, color='black', label='True value')
#edit figure
if not flag_report: ax.set_title(r'Comparison $\omega_{1,e}$', fontsize=30)
ax.set_xlabel('$\omega_{1,e}$', fontsize=25)
ax.set_ylabel('probability density function ', fontsize=25)
ax.grid(which='both')
ax.tick_params(axis='x', labelsize=22)
ax.tick_params(axis='y', labelsize=22)
#plot limits
ax.set_xlim([0,0.25])
ax.set_ylim([0,ymax_hyp])
#save figure
fig.tight_layout()
fig.savefig( dir_fig + fname_fig + '.png' )
# Omega_1as
#--- --- --- --- ---
#hyper-paramter name
name_hyp = 'omega_1as'
#figure title
fname_fig = 'post_dist_' + name_hyp
#create figure
fig, ax = plt.subplots(figsize = (10,10))
for d_id, df_r_h, df_r_h_p in zip(ds_id, df_reg_hyp, df_reg_hyp_post):
#estimate vertical line height for mean and mode
# ymax_mode = df_r_h_p.loc[:,name_hyp+'_pdf'].max()
# ymax_mean = 1.5*np.ceil(ymax_mode/10)*10
ymax_mode = 30
ymax_mean = 30
#plot posterior dist
pl_hyp = ax.vlines(df_r_h.loc['mean',name_hyp], ymin=0, ymax=ymax_mean, linestyle='-', label='Mean')
ax.vlines(df_r_h.loc['prc_0.50',name_hyp], ymin=0, ymax=ymax_mode, linestyle='--', color=pl_hyp.get_color(), label='Mode')
#plot true value
ymax_hyp = ymax_mean
ax.vlines(hyp[name_hyp], ymin=0, ymax=ymax_hyp, linestyle='-', linewidth=4, color='black', label='True value')
#edit figure
if not flag_report: ax.set_title(r'Comparison $\omega_{1a,s}$', fontsize=30)
ax.set_xlabel('$\omega_{1a,s}$', fontsize=25)
ax.set_ylabel('probability density function ', fontsize=25)
ax.grid(which='both')
ax.tick_params(axis='x', labelsize=22)
ax.tick_params(axis='y', labelsize=22)
#plot limits
ax.set_xlim([0,0.5])
ax.set_ylim([0,ymax_hyp])
#save figure
fig.tight_layout()
fig.savefig( dir_fig + fname_fig + '.png' )
# Omega_1bs
#--- --- --- --- ---
#hyper-paramter name
name_hyp = 'omega_1bs'
#figure title
fname_fig = 'post_dist_' + name_hyp
#create figure
fig, ax = plt.subplots(figsize = (10,10))
for d_id, df_r_h, df_r_h_p in zip(ds_id, df_reg_hyp, df_reg_hyp_post):
#estimate vertical line height for mean and mode
# ymax_mode = df_r_h_p.loc[:,name_hyp+'_pdf'].max()
# ymax_mean = 1.5*np.ceil(ymax_mode/10)*10
ymax_mode = 60
ymax_mean = 60
#plot posterior dist
pl_hyp = ax.vlines(df_r_h.loc['mean',name_hyp], ymin=0, ymax=ymax_mean, linestyle='-', label='Mean')
ax.vlines(df_r_h.loc['prc_0.50',name_hyp], ymin=0, ymax=ymax_mode, linestyle='--', color=pl_hyp.get_color(), label='Mode')
#plot true value
ymax_hyp = ymax_mean
ax.vlines(hyp[name_hyp], ymin=0, ymax=ymax_hyp, linestyle='-', linewidth=4, color='black', label='True value')
#edit figure
if not flag_report: ax.set_title(r'Comparison $\omega_{1b,s}$', fontsize=30)
ax.set_xlabel('$\omega_{1b,s}$', fontsize=25)
ax.set_ylabel('probability density function ', fontsize=25)
ax.grid(which='both')
ax.tick_params(axis='x', labelsize=22)
ax.tick_params(axis='y', labelsize=22)
#plot limits
ax.set_xlim([0,0.5])
ax.set_ylim([0,ymax_hyp])
#save figure
fig.tight_layout()
fig.savefig( dir_fig + fname_fig + '.png' )
# Ell_1e
#--- --- --- --- ---
#hyper-paramter name
name_hyp = 'ell_1e'
#figure title
fname_fig = 'post_dist_' + name_hyp
#create figure
fig, ax = plt.subplots(figsize = (10,10))
for d_id, df_r_h, df_r_h_p in zip(ds_id, df_reg_hyp, df_reg_hyp_post):
#estimate vertical line height for mean and mode
# ymax_mode = df_r_h_p.loc[:,name_hyp+'_pdf'].max()
# ymax_mean = 1.5*np.ceil(ymax_mode/10)*10
ymax_mode = 0.02
ymax_mean = 0.02
#plot posterior dist
pl_hyp = ax.vlines(df_r_h.loc['mean',name_hyp], ymin=0, ymax=ymax_mean, linestyle='-', label='Mean')
ax.vlines(df_r_h.loc['prc_0.50',name_hyp], ymin=0, ymax=ymax_mode, linestyle='--', color=pl_hyp.get_color(), label='Mode')
#plot true value
ymax_hyp = ymax_mean
ax.vlines(hyp[name_hyp], ymin=0, ymax=ymax_hyp, linestyle='-', linewidth=4, color='black', label='True value')
#edit figure
if not flag_report: ax.set_title(r'Comparison $\ell_{1,e}$', fontsize=30)
ax.set_xlabel('$\ell_{1,e}$', fontsize=25)
ax.set_ylabel('probability density function ', fontsize=25)
ax.grid(which='both')
ax.tick_params(axis='x', labelsize=22)
ax.tick_params(axis='y', labelsize=22)
#plot limits
ax.set_xlim([0,500])
ax.set_ylim([0,ymax_hyp])
#save figure
fig.tight_layout()
fig.savefig( dir_fig + fname_fig + '.png' )
# Ell_1as
#--- --- --- --- ---
#hyper-paramter name
name_hyp = 'ell_1as'
#figure title
fname_fig = 'post_dist_' + name_hyp
#create figure
fig, ax = plt.subplots(figsize = (10,10))
for d_id, df_r_h, df_r_h_p in zip(ds_id, df_reg_hyp, df_reg_hyp_post):
#estimate vertical line height for mean and mode
# ymax_mode = df_r_h_p.loc[:,name_hyp+'_pdf'].max()
# ymax_mean = 1.5*np.ceil(ymax_mode/10)*10
ymax_mode = 0.1
ymax_mean = 0.1
#plot posterior dist
pl_hyp = ax.vlines(df_r_h.loc['mean',name_hyp], ymin=0, ymax=ymax_mean, linestyle='-', label='Mean')
ax.vlines(df_r_h.loc['prc_0.50',name_hyp], ymin=0, ymax=ymax_mode, linestyle='--', color=pl_hyp.get_color(), label='Mode')
#plot true value
ymax_hyp = ymax_mean
ax.vlines(hyp[name_hyp], ymin=0, ymax=ymax_hyp, linestyle='-', linewidth=4, color='black', label='True value')
#edit figure
if not flag_report: ax.set_title(r'Comparison $\ell_{1a,s}$', fontsize=30)
ax.set_xlabel('$\ell_{1a,s}$', fontsize=25)
ax.set_ylabel('probability density function ', fontsize=25)
ax.grid(which='both')
ax.tick_params(axis='x', labelsize=22)
ax.tick_params(axis='y', labelsize=22)
#plot limits
ax.set_xlim([0,150])
ax.set_ylim([0,ymax_hyp])
#save figure
fig.tight_layout()
fig.savefig( dir_fig + fname_fig + '.png' )
# Tau_0
#--- --- --- --- ---
#hyper-paramter name
name_hyp = 'tau_0'
#figure title
fname_fig = 'post_dist_' + name_hyp
#create figure
fig, ax = plt.subplots(figsize = (10,10))
for d_id, df_r_h, df_r_h_p in zip(ds_id, df_reg_hyp, df_reg_hyp_post):
#estimate vertical line height for mean and mode
# ymax_mode = df_r_h_p.loc[:,name_hyp+'_pdf'].max()
# ymax_mean = 1.5*np.ceil(ymax_mode/10)*10
ymax_mode = 60
ymax_mean = 60
#plot posterior dist
pl_hyp = ax.vlines(df_r_h.loc['mean',name_hyp], ymin=0, ymax=ymax_mean, linestyle='-', label='Mean')
ax.vlines(df_r_h.loc['prc_0.50',name_hyp], ymin=0, ymax=ymax_mode, linestyle='--', color=pl_hyp.get_color(), label='Mode')
#plot true value
ymax_hyp = ymax_mean
ax.vlines(hyp[name_hyp], ymin=0, ymax=ymax_hyp, linestyle='-', linewidth=4, color='black', label='True value')
#edit figure
if not flag_report: ax.set_title(r'Comparison $\tau_{0}$', fontsize=30)
ax.set_xlabel(r'$\tau_{0}$', fontsize=25)
ax.set_ylabel(r'probability density function ', fontsize=25)
ax.grid(which='both')
ax.tick_params(axis='x', labelsize=22)
ax.tick_params(axis='y', labelsize=22)
#plot limits
ax.set_xlim([0,0.5])
ax.set_ylim([0,ymax_hyp])
#save figure
fig.tight_layout()
fig.savefig( dir_fig + fname_fig + '.png' )
# Phi_0
#--- --- --- --- ---
#hyper-paramter name
name_hyp = 'phi_0'
#figure title
fname_fig = 'post_dist_' + name_hyp
#create figure
fig, ax = plt.subplots(figsize = (10,10))
for d_id, df_r_h, df_r_h_p in zip(ds_id, df_reg_hyp, df_reg_hyp_post):
#estimate vertical line height for mean and mode
# ymax_mode = df_r_h_p.loc[:,name_hyp+'_pdf'].max()
# ymax_mean = 1.5*np.ceil(ymax_mode/10)*10
ymax_mode = 100
ymax_mean = 100
#plot posterior dist
ax.vlines(df_r_h.loc['mean',name_hyp], ymin=0, ymax=ymax_mean, linestyle='-', label='Mean')
ax.vlines(df_r_h.loc['prc_0.50',name_hyp], ymin=0, ymax=ymax_mode, linestyle='--', color=pl_hyp.get_color(), label='Mode')
#plot true value
ymax_hyp = ymax_mean
ax.vlines(hyp[name_hyp], ymin=0, ymax=ymax_hyp, linestyle='-', linewidth=4, color='black', label='True value')
#edit figure
if not flag_report: ax.set_title(r'Comparison $\phi_{0}$', fontsize=30)
ax.set_xlabel('$\phi_{0}$', fontsize=25)
ax.set_ylabel(r'probability density function ', fontsize=25)
ax.grid(which='both')
ax.tick_params(axis='x', labelsize=22)
ax.tick_params(axis='y', labelsize=22)
#plot limits
ax.set_xlim([0,0.6])
ax.set_ylim([0,ymax_hyp])
#save figure
fig.tight_layout()
fig.savefig( dir_fig + fname_fig + '.png' )
# # Delta c_0
# #--- --- --- --- ---
# #hyper-paramter name
# name_hyp = 'dc_0'
# #figure title
# fname_fig = 'post_dist_' + name_hyp
# #create figure
# fig, ax = plt.subplots(figsize = (10,10))
# for d_id, df_r_h, df_r_h_p in zip(ds_id, df_reg_hyp, df_reg_hyp_post):
# #estimate vertical line height for mean and mode
# ymax_mode = df_r_h_p.loc[:,name_hyp+'_pdf'].max()
# ymax_mean = 1.5*np.ceil(ymax_mode/10)*10
# ymax_mean = 15
# #plot posterior dist
# pl_pdf = ax.plot(df_r_h_p.loc[:,name_hyp], df_r_h_p.loc[:,name_hyp+'_pdf'])
# ax.vlines(df_r_h.loc[name_hyp,'mean'], ymin=0, ymax=ymax_mean, linestyle='-', color=pl_pdf[0].get_color(), label='Mean')
# ax.vlines(df_r_h.loc[name_hyp,'mode'], ymin=0, ymax=ymax_mode, linestyle='--', color=pl_pdf[0].get_color(), label='Mode')
# #plot true value
# ymax_hyp = ymax_mean
# # ax.vlines(hyp[name_hyp], ymin=0, ymax=ymax_hyp, linestyle='-', linewidth=4, color='black', label='True value')
# #edit figure
# ax.set_title(r'Comparison $\delta c_{0}$', fontsize=30)
# ax.set_xlabel('$\delta c_{0}$', fontsize=25)
# ax.set_ylabel('probability density function ', fontsize=25)
# ax.grid(which='both')
# ax.tick_params(axis='x', labelsize=22)
# ax.tick_params(axis='y', labelsize=22)
# #plot limits
# ax.set_xlim([-1,1])
# ax.set_ylim([0,ymax_hyp])
# #save figure
# fig.tight_layout()
# # fig.savefig( dir_fig + fname_fig + '.png' )
| 29,388 | 37.977454 | 153 | py |
ngmm_tools | ngmm_tools-master/Analyses/Code_Verification/regression/ds1/comparison_inla_model1_time.py | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Tue Mar 15 22:38:50 2022
@author: glavrent
"""
# Working directory and Packages
# ---------------------------
#load variables
import os
import sys
import pathlib
#arithmetic libraries
import numpy as np
#statistics libraries
import pandas as pd
#plot libraries
import matplotlib as mpl
import matplotlib.pyplot as plt
from matplotlib.ticker import AutoLocator as plt_autotick
# Define variables
# ---------------------------
#mesh info
mesh_info = ['coarse', 'medium', 'fine']
#dataset name
dataset_name = ['NGAWest2CANorth', 'NGAWest2CA', 'NGAWest3CA']
#correlation info
# 1: Small Correlation Lengths
# 2: Large Correlation Lenghts
corr_id = 1
#correlation name
if corr_id == 1:
synds_name = 'small corr len'
synds_suffix = '_small_corr_len'
elif corr_id == 2:
synds_name = 'large corr len'
synds_suffix = '_large_corr_len'
#directories regressions
dir_reg = '../../../../Data/Verification/regression/ds1/'
#directory output
dir_out = '../../../../Data/Verification/regression/ds1/comparisons/'
# Load Data
# ---------------------------
#initialize dataframe
df_runinfo_all = {};
#iterate over different analyses
for j1, m_i in enumerate(mesh_info):
for j2, d_n in enumerate(dataset_name):
key_runinfo = '%s_%s'%(m_i, d_n)
fname_runinfo = '%s/INLA_%s_%s%s/run_info.csv'%(dir_reg, d_n, m_i, synds_suffix)
#store calc time
df_runinfo_all[key_runinfo] = pd.read_csv(fname_runinfo)
# Comparison Figures
# ---------------------------
pathlib.Path(dir_out).mkdir(parents=True, exist_ok=True)
#line style (iterate with mesh info)
line_style = [':','--','-']
#color map (iterate with dataset)
c_map = plt.get_cmap('Dark2')
#run time figure
fig_fname = 'run_time_inla'
#create figure axes
fig, ax = plt.subplots(figsize = (20,10))
#iterate over different analyses
for j2, d_n in enumerate(dataset_name):
for j1, (m_i, l_s) in enumerate(zip(mesh_info, line_style)):
key_runinfo = '%s_%s'%(m_i, d_n)
#
ds_id = df_runinfo_all[key_runinfo].ds_id
ds_name = ['Y%i'%d_i for d_i in ds_id]
#
run_time = df_runinfo_all[key_runinfo].run_time
ax.plot(ds_id, run_time, linestyle=l_s, marker='o', linewidth=2, markersize=10, color=c_map(j2), label='%s - %s'%(d_n, m_i))
#figure properties
ax.set_ylim([0, max(0.50, max(ax.get_ylim()))])
ax.set_xlabel('synthetic dataset', fontsize=30)
ax.set_ylabel('Run Time (min)', fontsize=30)
ax.grid(which='both')
ax.set_xticks(ds_id)
ax.set_xticklabels(labels=ds_name)
ax.tick_params(axis='x', labelsize=25)
ax.tick_params(axis='y', labelsize=25)
#legend
ax.legend(loc='center left', bbox_to_anchor=(1, 0.5), fontsize=25)
#save figure
fig.tight_layout()
fig.savefig( dir_out + fig_fname + '.png' )
| 2,861 | 25.747664 | 132 | py |
ngmm_tools | ngmm_tools-master/Analyses/Code_Verification/regression/ds1/comparison_stan_inla_model1_misfit.py | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Fri Jun 10 15:40:29 2022
@author: glavrent
"""
# Working directory and Packages
# ---------------------------
#load variables
import os
import sys
import pathlib
#arithmetic libraries
import numpy as np
#statistics libraries
import pandas as pd
#plot libraries
import matplotlib as mpl
import matplotlib.pyplot as plt
from matplotlib.ticker import FormatStrFormatter
#user functions
def PlotRSMCmp(df_list, names_list, c_name, width, fig_fname):
#create figure axes
fig, ax = plt.subplots(figsize = (10,10))
#
x_offset =
#plot rms value
for nm, df_l in zip(names_list, df_list):
ax.bar(np.arange(df_l)-x_offset, df_sum_reg_stan.nerg_tot_rms.values, width=width, label=nm)
#figure properties
ax.set_ylim([0, 0.2])
ax.set_xticks(labels=df_l.ds_name)
ax.set_xlabel('Dataset', fontsize=35)
ax.set_ylabel('RMSE', fontsize=35)
ax.grid(which='both')
ax.tick_params(axis='x', labelsize=32)
ax.tick_params(axis='y', labelsize=32)
ax.yaxis.set_major_formatter(FormatStrFormatter('%.2f'))
#legend
ax.legend(loc='upper left', fontsize=32)
#save figure
fig.tight_layout()
fig.savefig( fig_fname + '.png' )
# Define variables
# ---------------------------
# COMPARISONS
# Different Mesh sizes
# --- --- --- --- ---
cmp_name = 'STAN_INLA_medium'
reg_title = [f'STAN - NGAW2 CA, North', f'STAN - NGAW2 CA', f'STAN - NGAW3* CA',
f'INLA - NGAW2 CA, North', f'INLA - NGAW2 CA', f'INLA - NGAW3* CA' ]
reg_fname = ['CMDSTAN_NGAWest2CANorth_chol_eff_small_corr_len', 'CMDSTAN_NGAWest2CA_chol_eff_small_corr_len', 'CMDSTAN_NGAWest3CA_chol_eff_small_corr_len',
'INLA_NGAWest2CANorth_medium_small_corr_len', 'INLA_NGAWest2CA_medium_small_corr_len', 'INLA_NGAWest3CA_medium_small_corr_len']
ds_name = ['NGAW2\nCA, North','NGAW2\nCA', 'NGAW3\nCA',
'NGAW2\nCA, North','NGAW2\nCA', 'NGAW3*\nCA']
ds_id = np.array([1,2,3,1,2,3])
sftwr_name = 3*['STAN'] + 3*['INLA']
sftwr_id = np.array(3*[1]+3*[2])
#directories regressions
reg_dir = [f'../../../../Data/Verification/regression/ds1/%s/'%r_f for r_f in reg_fname]
#directory output
dir_out = '../../../../Data/Verification/regression/ds1/comparisons/'
# Load Data
# ---------------------------
#intialize main regression summary dataframe
df_sum_reg = pd.DataFrame({'ds_id':ds_id, 'ds_name':ds_name, 'sftwr_id':sftwr_id, 'sftwr_name':sftwr_name})
#initialize misfit dataframe
df_sum_misfit_all = {};
#read misfit info
for k, (r_t, r_d) in enumerate(zip(reg_title, reg_dir)):
#filename misfit info
fname_sum = r_d + 'summary/misfit_summary.csv'
#read KL score for coefficients
df_sum_misfit_all[r_t] = pd.read_csv(fname_sum, index_col=0)
#summarize regression rms
df_sum_reg.loc[k,'nerg_tot_rms'] = df_sum_misfit_all[r_t].nerg_tot_rms.mean()
#initialize run time dataframe
df_runinfo_all = {};
#read run time info
for k, (r_t, r_d) in enumerate(zip(reg_title, reg_dir)):
#filename run time
fname_runinfo = r_d + '/run_info.csv'
#store calc time
df_runinfo_all[r_t] = pd.read_csv(fname_runinfo)
#summarize regression rms
df_sum_reg.loc[k,'run_time'] = df_runinfo_all[r_t].run_time.mean()
#print mean run time
print(f'%s: %.1f min'%( r_t, df_runinfo_all[r_t].run_time.mean() ))
#separate STNA and INLA runs
df_sum_reg_stan = df_sum_reg.loc[df_sum_reg.sftwr_id==1,:]
df_sum_reg_inla = df_sum_reg.loc[df_sum_reg.sftwr_id==2,:]
# Comparison Figures
# ---------------------------
pathlib.Path(dir_out).mkdir(parents=True, exist_ok=True)
# RMSE
# --- --- --- --- ---
#coefficient name
c_name = 'nerg_tot'
#figure name
fig_fname = '%s/%s_%s_RMSE'%(dir_out, cmp_name, c_name)
# #create figure axes
# fig, ax = plt.subplots(figsize = (10,10))
# #plot rms value
# ax.bar(np.array([1,3,5])-0.3, df_sum_reg_stan.nerg_tot_rms.values, width=0.6, label='STAN')
# ax.bar(np.array([1,3,5])+0.3, df_sum_reg_inla.nerg_tot_rms.values, width=0.6, label='INLA')
# #figure properties
# ax.set_ylim([0, 0.2])
# ax.set_xticks([1,3,5], df_sum_reg_stan.ds_name)
# ax.set_xlabel('Dataset', fontsize=35)
# ax.set_ylabel('RMSE', fontsize=35)
# ax.grid(which='both')
# ax.tick_params(axis='x', labelsize=32)
# ax.tick_params(axis='y', labelsize=32)
# ax.yaxis.set_major_formatter(FormatStrFormatter('%.2f'))
# #legend
# ax.legend(loc='upper left', fontsize=32)
# #save figure
# fig.tight_layout()
# fig.savefig( fig_fname + '.png' )
# Run Time
# --- --- --- --- ---
#run time figure
fig_fname = '%s/%s_run_time'%(dir_out, cmp_name)
#create figure axes
fig, ax = plt.subplots(figsize = (10,10))
#plot rms value
ax.bar(np.array([1,3,5])-0.3, df_sum_reg_stan.run_time.values, width=0.6, label='STAN')
ax.bar(np.array([1,3,5])+0.3, df_sum_reg_inla.run_time.values, width=0.6, label='INLA')
#figure properties
# ax.set_ylim([0, 0.2])
ax.set_xticks([1,3,5], df_sum_reg_stan.ds_name)
ax.set_xlabel('Dataset', fontsize=35)
ax.set_ylabel('Run Time (min)', fontsize=35)
ax.grid(which='both')
ax.set_yscale('log')
ax.tick_params(axis='x', labelsize=32)
ax.tick_params(axis='y', labelsize=32)
# ax.yaxis.set_major_formatter(FormatStrFormatter('%.2f'))
#legend
ax.legend(loc='upper left', fontsize=32)
#save figure
fig.tight_layout()
fig.savefig( fig_fname + '.png' )
| 5,403 | 31.95122 | 158 | py |
ngmm_tools | ngmm_tools-master/Analyses/Code_Verification/regression/ds1/comparison_model1_misfit.py | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Tue Mar 15 14:50:27 2022
@author: glavrent
"""
# Working directory and Packages
# ---------------------------
#load variables
import os
import sys
import pathlib
#arithmetic libraries
import numpy as np
#statistics libraries
import pandas as pd
#plot libraries
import matplotlib as mpl
import matplotlib.pyplot as plt
#user functions
def PlotRSMCmp(df_rms_all, c_name, fig_fname):
#create figure axes
fig, ax = plt.subplots(figsize = (10,10))
for k in df_rms_all:
df_rms = df_rms_all[k]
ds_id = np.array(range(len(df_rms)))
ax.plot(ds_id, df_rms.loc[:,c_name+'_rms'], linestyle='-', marker='o', linewidth=2, markersize=10, label=k)
#figure properties
ax.set_ylim([0, max(0.50, max(ax.get_ylim()))])
ax.set_xlabel('synthetic dataset', fontsize=35)
ax.set_ylabel('RMSE', fontsize=35)
ax.grid(which='both')
ax.set_xticks(ds_id)
ax.set_xticklabels(labels=df_rms.index)
ax.tick_params(axis='x', labelsize=32)
ax.tick_params(axis='y', labelsize=32)
#legend
ax.legend(loc='upper left', fontsize=32)
#save figure
fig.tight_layout()
fig.savefig( fig_fname + '.png' )
return fig, ax
def PlotKLCmp(df_KL_all, c_name, fig_fname):
#create figure axes
fig, ax = plt.subplots(figsize = (10,10))
for k in df_KL_all:
df_KL = df_KL_all[k]
ds_id = np.array(range(len(df_KL)))
ax.plot(ds_id, df_KL.loc[:,c_name+'_KL'], linestyle='-', marker='o', linewidth=2, markersize=10, label=k)
#figure properties
ax.set_ylim([0, max(0.50, max(ax.get_ylim()))])
ax.set_xlabel('synthetic dataset', fontsize=35)
ax.set_ylabel('KL divergence', fontsize=35)
ax.grid(which='both')
ax.set_xticks(ds_id)
ax.set_xticklabels(labels=df_KL.index)
ax.tick_params(axis='x', labelsize=32)
ax.tick_params(axis='y', labelsize=32)
#legend
ax.legend(loc='upper left', fontsize=32)
#save figure
fig.tight_layout()
fig.savefig( fig_fname + '.png' )
return fig, ax
# Define variables
# ---------------------------
# COMPARISONS
# Different Packages
# --- --- --- --- ---
cmp_name = 'STAN_pckg_cmp_NGAWest2CANorth'
reg_title = ['PYSTAN2', 'PYSTAN3', 'CMDSTANPY']
reg_fname = ['PYSTAN_NGAWest2CANorth_chol_eff_small_corr_len','PYSTAN3_NGAWest2CANorth_chol_eff_small_corr_len','CMDSTAN_NGAWest2CANorth_chol_eff_small_corr_len']
ylim_time = [0, 700]
# # Different Implementations
# # --- --- --- --- ---
# cmp_name = 'STAN_impl_cmp_NGAWest2CANorth'
# reg_title = ['CMDSTANPY Chol.', 'CMDSTANPY Chol. Eff.']
# reg_fname = ['CMDSTAN_NGAWest2CANorth_chol_small_corr_len','CMDSTAN_NGAWest2CANorth_chol_eff_small_corr_len']
# ylim_time = [0, 700]
# # Different Software
# # --- --- --- --- ---
# cmp_name = 'STAN_vs_INLA_cmp_NGAWest2CANorth'
# reg_title = ['STAN','INLA']
# reg_fname = ['CMDSTAN_NGAWest2CANorth_chol_eff_small_corr_len','INLA_NGAWest2CANorth_coarse_small_corr_len']
# ylim_time = [0, 700]
# Different
# --- --- --- --- ---
# # NGAWest2CANorth
# cmp_name = 'INLA_mesh_cmp_NGAWest2CANorth'
# reg_title = ['INLA coarse mesh', 'INLA medium mesh', 'INLA fine mesh']
# reg_fname = ['INLA_NGAWest2CANorth_coarse_small_corr_len','INLA_NGAWest2CANorth_medium_small_corr_len','INLA_NGAWest2CANorth_fine_small_corr_len']
# ylim_time = [0, 20]
# # NGAWest2CANorth
# cmp_name = 'INLA_mesh_cmp_NGAWest3CA'
# reg_title = ['INLA coarse mesh', 'INLA medium mesh', 'INLA fine mesh']
# reg_fname = ['INLA_NGAWest3CA_coarse_small_corr_len','INLA_NGAWest3CA_medium_small_corr_len','INLA_NGAWest3CA_fine_small_corr_len']
# ylim_time = [0, 100]
#directories regressions
reg_dir = [f'../../../../Data/Verification/regression/ds1/%s/'%r_f for r_f in reg_fname]
#directory output
dir_out = '../../../../Data/Verification/regression/ds1/comparisons/'
# Load Data
# ---------------------------
#initialize misfit dataframe
df_sum_misfit_all = {};
#read misfit info
for k, (r_t, r_d) in enumerate(zip(reg_title, reg_dir)):
#filename misfit info
fname_sum = r_d + 'summary/misfit_summary.csv'
#read KL score for coefficients
df_sum_misfit_all[r_t] = pd.read_csv(fname_sum, index_col=0)
#initialize run time dataframe
df_runinfo_all = {};
#read run time info
for k, (r_t, r_d) in enumerate(zip(reg_title, reg_dir)):
#filename run time
fname_runinfo = r_d + '/run_info.csv'
#store calc time
df_runinfo_all[r_t] = pd.read_csv(fname_runinfo)
# Comparison Figures
# ---------------------------
pathlib.Path(dir_out).mkdir(parents=True, exist_ok=True)
# RMSE divergence
# --- --- --- --- ---
#coefficient name
c_name = 'nerg_tot'
#figure name
fig_fname = '%s/%s_%s_RMSE'%(dir_out, cmp_name, c_name)
#plotting
PlotRSMCmp(df_sum_misfit_all , c_name, fig_fname);
#coefficient name
c_name = 'dc_1e'
#figure name
fig_fname = '%s/%s_%s_RMSE'%(dir_out, cmp_name, c_name)
#plotting
PlotRSMCmp(df_sum_misfit_all , c_name, fig_fname);
#coefficient name
c_name = 'dc_1as'
#figure name
fig_fname = '%s/%s_%s_RMSE'%(dir_out, cmp_name, c_name)
#plotting
PlotRSMCmp(df_sum_misfit_all , c_name, fig_fname);
#coefficient name
c_name = 'dc_1bs'
#figure name
fig_fname = '%s/%s_%s_RMSE'%(dir_out, cmp_name, c_name)
#plotting
PlotRSMCmp(df_sum_misfit_all , c_name, fig_fname);
# KL divergence
# --- --- --- --- ---
#coefficient name
c_name = 'nerg_tot'
#figure name
fig_fname = '%s/%s_%s_KLdiv'%(dir_out, cmp_name, c_name)
#plotting
PlotKLCmp(df_sum_misfit_all , c_name, fig_fname);
#coefficient name
c_name = 'dc_1e'
#figure name
fig_fname = '%s/%s_%s_KLdiv'%(dir_out, cmp_name, c_name)
#plotting
PlotKLCmp(df_sum_misfit_all , c_name, fig_fname);
#coefficient name
c_name = 'dc_1as'
#figure name
fig_fname = '%s/%s_%s_KLdiv'%(dir_out, cmp_name, c_name)
#plotting
PlotKLCmp(df_sum_misfit_all , c_name, fig_fname);
#coefficient name
c_name = 'dc_1bs'
#figure name
fig_fname = '%s/%s_%s_KLdiv'%(dir_out, cmp_name, c_name)
#plotting
PlotKLCmp(df_sum_misfit_all , c_name, fig_fname);
# Run Time
# --- --- --- --- ---
#run time figure
fig_fname = '%s/%s_run_time'%(dir_out, cmp_name)
#create figure axes
fig, ax = plt.subplots(figsize = (10,10))
#iterate over different analyses
for j, k in enumerate(df_runinfo_all):
ds_id = df_runinfo_all[k].ds_id
ds_name = ['Y%i'%d_i for d_i in ds_id]
run_time = df_runinfo_all[k].run_time
ax.plot(ds_id, run_time, marker='o', linewidth=2, markersize=10, label=k)
#figure properties
ax.set_ylim(ylim_time)
ax.set_xlabel('synthetic dataset', fontsize=35)
ax.set_ylabel('Run Time (min)', fontsize=35)
ax.grid(which='both')
ax.set_xticks(ds_id)
ax.set_xticklabels(labels=ds_name)
ax.tick_params(axis='x', labelsize=32)
ax.tick_params(axis='y', labelsize=32)
#legend
ax.legend(loc='lower left', fontsize=32)
# ax.legend(loc='upper left', fontsize=32)
# ax.legend(loc='center left', bbox_to_anchor=(1, 0.5), fontsize=25)
#save figure
fig.tight_layout()
fig.savefig( fig_fname + '.png' )
| 7,051 | 29.79476 | 162 | py |
ngmm_tools | ngmm_tools-master/Analyses/Code_Verification/regression/ds1/comparison_inla_model1.py | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Thu Aug 12 20:52:09 2021
@author: glavrent
"""
# Working directory and Packages
# ---------------------------
#load variables
import os
import sys
import pathlib
import glob
import re #regular expression package
import pickle
#arithmetic libraries
import numpy as np
#statistics libraries
import pandas as pd
#plot libraries
import matplotlib as mpl
import matplotlib.pyplot as plt
from matplotlib.ticker import AutoLocator as plt_autotick
#user functions
sys.path.insert(0,'../../../Python_lib/regression/')
from pylib_stats import CalcRMS
from pylib_stats import CalcLKDivergece
# Define variables
# ---------------------------
# USER SETS DIRECTORIES AND FILE INFO OF SYNTHETIC DS AND REGRESSION RESULTS
# ++++++++++++++++++++++++++++++++++++++++
#processed dataset
# name_dataset = 'NGAWest2CANorth'
# name_dataset = 'NGAWest2CA'
# name_dataset = 'NGAWest3CA'
#correlation info
# 1: Small Correlation Lengths
# 2: Large Correlation Lenghts
# corr_id = 1
#kernel function
# 1: Mattern kernel (alpha=2)
# 2: Negative Exp (alpha=3/2)
# ker_id = 1
#mesh type
# 1: Fine Mesh
# 2: Medium Mesh
# 3: Coarse Mesh
# mesh_id = 3
#directories (synthetic dataset)
if corr_id == 1:
dir_syndata = '../../../../Data/Verification/synthetic_datasets/ds1_small_corr_len'
elif corr_id == 2:
dir_syndata = '../../../../Data/Verification/synthetic_datasets/ds1_large_corr_len'
#directories (regression results)
if mesh_id == 1:
dir_results = f'../../../../Data/Verification/regression/ds1/INLA_%s_fine'%name_dataset
elif mesh_id == 2:
dir_results = f'../../../../Data/Verification/regression/ds1/INLA_%s_medium'%name_dataset
elif mesh_id == 3:
dir_results = f'../../../../Data/Verification/regression/ds1/INLA_%s_coarse'%name_dataset
#prefix for synthetic data and results
prfx_syndata = 'CatalogNGAWest3CALite_synthetic'
#regression results filename prefix
prfx_results = f'%s_syndata'%name_dataset
# dataset info
ds_id = np.arange(1,6)
# ++++++++++++++++++++++++++++++++++++++++
# USER NEEDS TO SPECIFY HYPERPARAMETERS OF SYNTHETIC DATASET
# ++++++++++++++++++++++++++++++++++++++++
# hyper-parameters
if corr_id == 1:
# small correlation lengths
hyp = {'omega_0': 0.1, 'omega_1e':0.1, 'omega_1as': 0.35, 'omega_1bs': 0.25,
'ell_1e':60, 'ell_1as':30, 'phi_0':0.4, 'tau_0':0.3 }
elif corr_id == 2:
#large correlation lengths
hyp = {'omega_0': 0.1, 'omega_1e':0.2, 'omega_1as': 0.4, 'omega_1bs': 0.3,
'ell_1e':100, 'ell_1as':70, 'phi_0':0.4, 'tau_0':0.3 }
# ++++++++++++++++++++++++++++++++++++++++
# FILE INFO FOR REGRESSION RESULTS
# ++++++++++++++++++++++++++++++++++++++++
#output filename sufix
if corr_id == 1:
synds_suffix = '_small_corr_len'
elif corr_id == 2:
synds_suffix = '_large_corr_len'
#kenel info
if ker_id == 1:
ker_suffix = ''
elif ker_id == 2:
ker_suffix = '_nexp'
# ++++++++++++++++++++++++++++++++++++++++
#ploting options
flag_report = True
# Compare coefficients
# ---------------------------
#initialize misfit metrics dataframe
df_misfit = pd.DataFrame(index=['Y%i'%d_id for d_id in ds_id])
#iterate over different datasets
for d_id in ds_id:
# Load Data
#--- --- --- --- ---
#file names
#synthetic data
fname_sdata_gmotion = '%s/%s_%s%s_Y%i'%(dir_syndata, prfx_syndata, 'data', synds_suffix, d_id) + '.csv'
#regression results
fname_reg_gmotion = '%s%s/Y%i/%s%s_Y%i_inla_%s'%(dir_results, ker_suffix+synds_suffix, d_id, prfx_results, synds_suffix, d_id, 'residuals') + '.csv'
fname_reg_coeff = '%s%s/Y%i/%s%s_Y%i_inla_%s'%(dir_results, ker_suffix+synds_suffix, d_id, prfx_results, synds_suffix, d_id, 'coefficients') + '.csv'
#load synthetic results
df_sdata_gmotion = pd.read_csv(fname_sdata_gmotion).set_index('rsn')
#load regression results
df_reg_gmotion = pd.read_csv(fname_reg_gmotion, index_col=0)
df_reg_coeff = pd.read_csv(fname_reg_coeff, index_col=0)
# Processing
#--- --- --- --- ---
#keep only common records from synthetic dataset
df_sdata_gmotion = df_sdata_gmotion.reindex(df_reg_gmotion.index)
#find unique earthqakes and stations
eq_id, eq_idx, eq_nrec = np.unique(df_sdata_gmotion.eqid, return_index=True, return_counts=True)
sta_id, sta_idx, sta_nrec = np.unique(df_sdata_gmotion.ssn, return_index=True, return_counts=True)
# Compute Root Mean Square Error
#--- --- --- --- ---
df_misfit.loc['Y%i'%d_id,'nerg_tot_rms'] = CalcRMS(df_sdata_gmotion.nerg_gm.values, df_reg_gmotion.nerg_mu.values)
df_misfit.loc['Y%i'%d_id,'dc_1e_rms'] = CalcRMS(df_sdata_gmotion['dc_1e'].values[eq_idx], df_reg_coeff['dc_1e_mean'].values[eq_idx])
df_misfit.loc['Y%i'%d_id,'dc_1as_rms'] = CalcRMS(df_sdata_gmotion['dc_1as'].values[sta_idx], df_reg_coeff['dc_1as_mean'].values[sta_idx])
df_misfit.loc['Y%i'%d_id,'dc_1bs_rms'] = CalcRMS(df_sdata_gmotion['dc_1bs'].values[sta_idx], df_reg_coeff['dc_1bs_mean'].values[sta_idx])
# Compute Divergence
#--- --- --- --- ---
df_misfit.loc['Y%i'%d_id,'nerg_tot_KL'] = CalcLKDivergece(df_sdata_gmotion.nerg_gm.values, df_reg_gmotion.nerg_mu.values)
df_misfit.loc['Y%i'%d_id,'dc_1e_KL'] = CalcLKDivergece(df_sdata_gmotion['dc_1e'].values[eq_idx], df_reg_coeff['dc_1e_mean'].values[eq_idx])
df_misfit.loc['Y%i'%d_id,'dc_1as_KL'] = CalcLKDivergece(df_sdata_gmotion['dc_1as'].values[sta_idx], df_reg_coeff['dc_1as_mean'].values[sta_idx])
df_misfit.loc['Y%i'%d_id,'dc_1bs_KL'] = CalcLKDivergece(df_sdata_gmotion['dc_1bs'].values[sta_idx], df_reg_coeff['dc_1bs_mean'].values[sta_idx])
# Output
#--- --- --- --- ---
#figure directory
dir_fig = '%s%s/Y%i/figures_cmp/'%(dir_results,ker_suffix+synds_suffix,d_id)
pathlib.Path(dir_fig).mkdir(parents=True, exist_ok=True)
#compare ground motion predictions
#... ... ... ... ... ...
#figure title
fname_fig = 'Y%i_tot_res_scatter'%d_id
#create figure
fig, ax = plt.subplots(figsize = (10,10))
#median
ax.scatter(df_sdata_gmotion.nerg_gm.values, df_reg_gmotion.nerg_mu.values)
ax.axline((0,0), slope=1, color="black", linestyle="--")
#edit figure
if not flag_report: ax.set_title('Comparison total residuals, Y: %i'%d_id, fontsize=30)
ax.set_xlabel('Synthetic dataset', fontsize=25)
ax.set_ylabel('Estimated', fontsize=25)
ax.grid(which='both')
ax.tick_params(axis='x', labelsize=22)
ax.tick_params(axis='y', labelsize=22)
#plot limits
# plt_lim = np.array([ax.get_xlim(), ax.get_ylim()])
# plt_lim = (plt_lim[:,0].min(), plt_lim[:,1].max())
# ax.set_xlim(plt_lim)
# ax.set_ylim(plt_lim)
ax.set_xlim([-2,2])
ax.set_ylim([-2,2])
fig.tight_layout()
fig.savefig( dir_fig + fname_fig + '.png' )
#compare dc_1e
#... ... ... ... ... ...
#figure title
fname_fig = 'Y%i_dc_1e_scatter'%d_id
#create figure
fig, ax = plt.subplots(figsize = (10,10))
#coefficient scatter
ax.scatter(df_sdata_gmotion['dc_1e'].values[eq_idx], df_reg_coeff['dc_1e_mean'].values[eq_idx])
ax.axline((0,0), slope=1, color="black", linestyle="--")
#edit figure
if not flag_report: ax.set_title(r'Comparison $\delta c_{1,e}$, Y: %i'%d_id, fontsize=30)
ax.set_xlabel('Synthetic dataset', fontsize=25)
ax.set_ylabel('Estimated', fontsize=25)
ax.grid(which='both')
ax.tick_params(axis='x', labelsize=22)
ax.tick_params(axis='y', labelsize=22)
#plot limits
# plt_lim = np.array([ax.get_xlim(), ax.get_ylim()])
# plt_lim = (plt_lim[:,0].min(), plt_lim[:,1].max())
# ax.set_xlim(plt_lim)
# ax.set_ylim(plt_lim)
ax.set_xlim([-.4,.4])
ax.set_ylim([-.4,.4])
#save figure
fig.tight_layout()
fig.savefig( dir_fig + fname_fig + '.png' )
#figure title
fname_fig = 'Y%i_dc_1e_accuracy'%d_id
#create figure
fig, ax = plt.subplots(figsize = (10,10))
#coefficient scatter
ax.scatter(df_reg_coeff['dc_1e_sig'].values[eq_idx],
df_sdata_gmotion['dc_1e'].values[eq_idx] - df_reg_coeff['dc_1e_mean'].values[eq_idx])
ax.axline((0,0), slope=0, color="black", linestyle="--")
#edit figure
if not flag_report: ax.set_title(r'Comparison $\delta c_{1,e}$, Y: %i'%d_id, fontsize=30)
ax.set_xlabel('Standard Deviation', fontsize=25)
ax.set_ylabel('Actual - Estimated', fontsize=25)
ax.grid(which='both')
ax.tick_params(axis='x', labelsize=22)
ax.tick_params(axis='y', labelsize=22)
#plot limits
# ax.set_ylim(np.abs(ax.get_ylim()).max()*np.array([-1,1]))
ax.set_xlim([0,.15])
ax.set_ylim([-.4,.4])
#save figure
fig.tight_layout()
fig.savefig( dir_fig + fname_fig + '.png' )
#figure title
fname_fig = 'Y%i_dc_1e_nrec'%d_id
#create figure
fig, ax = plt.subplots(figsize = (10,10))
#coefficient scatter
ax.scatter(eq_nrec,
df_sdata_gmotion['dc_1e'].values[eq_idx] - df_reg_coeff['dc_1e_mean'].values[eq_idx])
ax.axline((0,0), slope=0, color="black", linestyle="--")
#edit figure
if not flag_report: ax.set_title(r'Comparison $\delta c_{1,e}$, Y: %i'%d_id, fontsize=30)
ax.set_xlabel('Number of records', fontsize=25)
ax.set_ylabel('Actual - Estimated', fontsize=25)
ax.grid(which='both')
ax.set_xscale('log')
ax.tick_params(axis='x', labelsize=22)
ax.tick_params(axis='y', labelsize=22)
#plot limits
# ax.set_ylim(np.abs(ax.get_ylim()).max()*np.array([-1,1]))
ax.set_xlim([0.9,1e3])
ax.set_ylim([-.4,.4])
#save figure
fig.tight_layout()
fig.savefig( dir_fig + fname_fig + '.png' )
#compare dc_1as
#... ... ... ... ... ...
#figure title
fname_fig = 'Y%i_dc_1as_scatter'%d_id
#create figure
fig, ax = plt.subplots(figsize = (10,10))
#coefficient scatter
ax.scatter(df_sdata_gmotion['dc_1as'].values[sta_idx], df_reg_coeff['dc_1as_mean'].values[sta_idx])
ax.axline((0,0), slope=1, color="black", linestyle="--")
#edit figure
if not flag_report: ax.set_title(r'Comparison $\delta c_{1a,s}$, Y: %i'%d_id, fontsize=30)
ax.set_xlabel('Synthetic dataset', fontsize=25)
ax.set_ylabel('Estimated', fontsize=25)
ax.grid(which='both')
ax.tick_params(axis='x', labelsize=22)
ax.tick_params(axis='y', labelsize=22)
#plot limits
# plt_lim = np.array([ax.get_xlim(), ax.get_ylim()])
# plt_lim = (plt_lim[:,0].min(), plt_lim[:,1].max())
# ax.set_xlim(plt_lim)
# ax.set_ylim(plt_lim)
ax.set_xlim([-1.5,1.5])
ax.set_ylim([-1.5,1.5])
#save figure
fig.tight_layout()
fig.savefig( dir_fig + fname_fig + '.png' )
#figure title
fname_fig = 'Y%i_dc_1as_accuracy'%d_id
#create figure
fig, ax = plt.subplots(figsize = (10,10))
#accuray
ax.scatter(df_reg_coeff['dc_1as_sig'].values[sta_idx],
df_sdata_gmotion['dc_1as'].values[sta_idx] - df_reg_coeff['dc_1as_mean'].values[sta_idx])
ax.axline((0,0), slope=0, color="black", linestyle="--")
#edit figure
if not flag_report: ax.set_title(r'Comparison $\delta c_{1a,s}$, Y: %i'%d_id, fontsize=30)
ax.set_xlabel('Standard Deviation', fontsize=25)
ax.set_ylabel('Actual - Estimated', fontsize=25)
ax.grid(which='both')
ax.tick_params(axis='x', labelsize=22)
ax.tick_params(axis='y', labelsize=22)
#plot limits
# ax.set_ylim(np.abs(ax.get_ylim()).max()*np.array([-1,1]))
ax.set_xlim([0,.4])
ax.set_ylim([-1.5,1.5])
#save figure
fig.tight_layout()
fig.savefig( dir_fig + fname_fig + '.png' )
#figure title
fname_fig = 'Y%i_dc_1as_nrec'%d_id
#create figure
fig, ax = plt.subplots(figsize = (10,10))
#accuray
ax.scatter(sta_nrec,
df_sdata_gmotion['dc_1as'].values[sta_idx] - df_reg_coeff['dc_1as_mean'].values[sta_idx])
ax.axline((0,0), slope=0, color="black", linestyle="--")
#edit figure
if not flag_report: ax.set_title(r'Comparison $\delta c_{1a,s}$, Y: %i'%d_id, fontsize=30)
ax.set_xlabel('Number of records', fontsize=25)
ax.set_ylabel('Actual - Estimated', fontsize=25)
ax.grid(which='both')
ax.set_xscale('log')
ax.tick_params(axis='x', labelsize=22)
ax.tick_params(axis='y', labelsize=22)
#plot limits
# ax.set_ylim(np.abs(ax.get_ylim()).max()*np.array([-1,1]))
ax.set_xlim([.9,1000])
ax.set_ylim([-1.5,1.5])
#save figure
fig.tight_layout()
fig.savefig( dir_fig + fname_fig + '.png' )
#compare dc_1bs
#... ... ... ... ... ...
#figure title
fname_fig = 'Y%i_dc_1bs_scatter'%d_id
#create figure
fig, ax = plt.subplots(figsize = (10,10))
#coefficient scatter
ax.scatter(df_sdata_gmotion['dc_1bs'].values[sta_idx], df_reg_coeff['dc_1bs_mean'].values[sta_idx])
ax.axline((0,0), slope=1, color="black", linestyle="--")
#edit figure
if not flag_report: ax.set_title(r'Comparison $\delta c_{1b,s}$, Y: %i'%d_id, fontsize=30)
ax.set_xlabel('Synthetic dataset', fontsize=25)
ax.set_ylabel('Estimated', fontsize=25)
ax.grid(which='both')
ax.tick_params(axis='x', labelsize=22)
ax.tick_params(axis='y', labelsize=22)
#plot limits
# plt_lim = np.array([ax.get_xlim(), ax.get_ylim()])
# plt_lim = (plt_lim[:,0].min(), plt_lim[:,1].max())
# ax.set_xlim(plt_lim)
# ax.set_ylim(plt_lim)
ax.set_xlim([-1.5,1.5])
ax.set_ylim([-1.5,1.5])
#save figure
fig.tight_layout()
fig.savefig( dir_fig + fname_fig + '.png' )
#figure title
fname_fig = 'Y%i_dc_1bs_accuracy'%d_id
#create figure
fig, ax = plt.subplots(figsize = (10,10))
#accuray
ax.scatter(df_reg_coeff['dc_1bs_sig'].values[sta_idx],
df_sdata_gmotion['dc_1bs'].values[sta_idx] - df_reg_coeff['dc_1bs_mean'].values[sta_idx])
ax.axline((0,0), slope=0, color="black", linestyle="--")
#edit figure
if not flag_report: ax.set_title(r'Comparison $\delta c_{1b,s}$, Y: %i'%d_id, fontsize=30)
ax.set_xlabel('Standard Deviation', fontsize=25)
ax.set_ylabel('Actual - Estimated', fontsize=25)
ax.grid(which='both')
ax.tick_params(axis='x', labelsize=22)
ax.tick_params(axis='y', labelsize=22)
#plot limits
# ax.set_ylim(np.abs(ax.get_ylim()).max()*np.array([-1,1]))
ax.set_xlim([0,.4])
ax.set_ylim([-1.5,1.5])
#save figure
fig.tight_layout()
fig.savefig( dir_fig + fname_fig + '.png' )
#figure title
fname_fig = 'Y%i_dc_1bs_nrec'%d_id
#create figure
fig, ax = plt.subplots(figsize = (10,10))
#accuray
ax.scatter(sta_nrec,
df_sdata_gmotion['dc_1bs'].values[sta_idx] - df_reg_coeff['dc_1bs_mean'].values[sta_idx])
ax.axline((0,0), slope=0, color="black", linestyle="--")
#edit figure
if not flag_report: ax.set_title(r'Comparison $\delta c_{1b,s}$, Y: %i'%d_id, fontsize=30)
ax.set_xlabel('Number of records', fontsize=25)
ax.set_ylabel('Actual - Estimated', fontsize=25)
ax.grid(which='both')
ax.set_xscale('log')
ax.tick_params(axis='x', labelsize=22)
ax.tick_params(axis='y', labelsize=22)
#plot limits
# ax.set_ylim(np.abs(ax.get_ylim()).max()*np.array([-1,1]))
ax.set_xlim([.9,1000])
ax.set_ylim([-1.5,1.5])
#save figure
fig.tight_layout()
fig.savefig( dir_fig + fname_fig + '.png' )
# Compare Misfit Metrics
# ---------------------------
#summary directory
dir_sum = '%s%s/summary/'%(dir_results,ker_suffix+synds_suffix)
pathlib.Path(dir_fig).mkdir(parents=True, exist_ok=True)
#figure directory
dir_fig = '%s/figures/'%(dir_sum)
pathlib.Path(dir_fig).mkdir(parents=True, exist_ok=True)
#save
df_misfit.to_csv(dir_sum + 'misfit_summary.csv')
#RMS misfit
fname_fig = 'misfit_score'
#plot KL divergence
fig, ax = plt.subplots(figsize = (10,10))
ax.plot(ds_id, df_misfit.nerg_tot_rms, linestyle='-', marker='o', linewidth=2, markersize=10, label= 'tot nerg')
ax.plot(ds_id, df_misfit.dc_1e_rms, linestyle='-', marker='o', linewidth=2, markersize=10, label=r'$\delta c_{1,e}$')
ax.plot(ds_id, df_misfit.dc_1as_rms, linestyle='-', marker='o', linewidth=2, markersize=10, label=r'$\delta c_{1a,s}$')
ax.plot(ds_id, df_misfit.dc_1bs_rms, linestyle='-', marker='o', linewidth=2, markersize=10, label=r'$\delta c_{1b,s}$')
#figure properties
ax.set_ylim([0,0.50])
ax.set_xlabel('synthetic dataset', fontsize=25)
ax.set_ylabel('RSME', fontsize=25)
ax.grid(which='both')
ax.set_xticks(ds_id)
ax.set_xticklabels(labels=df_misfit.index)
ax.tick_params(axis='x', labelsize=22)
ax.tick_params(axis='y', labelsize=22)
#legend
ax.legend(loc='upper left', fontsize=25)
#save figure
fig.tight_layout()
fig.savefig( dir_fig + fname_fig + '.png' )
#KL divergence
fname_fig = 'KLdiv_score'
#plot KL divergence
fig, ax = plt.subplots(figsize = (10,10))
ax.plot(ds_id, df_misfit.nerg_tot_KL, linestyle='-', marker='o', linewidth=2, markersize=10, label= 'tot nerg')
ax.plot(ds_id, df_misfit.dc_1e_KL, linestyle='-', marker='o', linewidth=2, markersize=10, label=r'$\delta c_{1,e}$')
ax.plot(ds_id, df_misfit.dc_1as_KL, linestyle='-', marker='o', linewidth=2, markersize=10, label=r'$\delta c_{1a,s}$')
ax.plot(ds_id, df_misfit.dc_1bs_KL, linestyle='-', marker='o', linewidth=2, markersize=10, label=r'$\delta c_{1b,s}$')
#figure properties
ax.set_ylim([0,0.50])
ax.set_xlabel('synthetic dataset', fontsize=25)
ax.set_ylabel('KL divergence', fontsize=25)
ax.grid(which='both')
ax.set_xticks(ds_id)
ax.set_xticklabels(labels=df_misfit.index)
ax.tick_params(axis='x', labelsize=22)
ax.tick_params(axis='y', labelsize=22)
#legend
ax.legend(loc='upper left', fontsize=25)
#save figure
fig.tight_layout()
fig.savefig( dir_fig + fname_fig + '.png' )
# Compare hyper-paramters
# ---------------------------
#iterate over different datasets
df_reg_hyp = list()
df_reg_hyp_post = list()
for d_id in ds_id:
# Load Data
#--- --- --- --- ---
#regression hyperparamters results
fname_reg_hyp = '%s%s/Y%i/%s%s_Y%i_inla_%s'%(dir_results,synds_suffix, d_id,prfx_results, synds_suffix, d_id, 'hyperparameters') + '.csv'
fname_reg_hyp_post = '%s%s/Y%i/%s%s_Y%i_inla_%s'%(dir_results,synds_suffix, d_id,prfx_results, synds_suffix, d_id, 'hyperposterior') + '.csv'
#load regression results
df_reg_hyp.append( pd.read_csv(fname_reg_hyp, index_col=0) )
df_reg_hyp_post.append( pd.read_csv(fname_reg_hyp_post, index_col=0) )
# Omega_1e
#--- --- --- --- ---
#hyper-paramter name
name_hyp = 'omega_1e'
#figure title
fname_fig = 'post_dist_' + name_hyp
#create figure
fig, ax = plt.subplots(figsize = (10,10))
for d_id, df_r_h, df_r_h_p in zip(ds_id, df_reg_hyp, df_reg_hyp_post):
#estimate vertical line height for mean and mode
ymax_mode = df_r_h_p.loc[:,name_hyp+'_pdf'].max()
# ymax_mean = 1.5*np.ceil(ymax_mode/10)*10
# ymax_mode = 40
ymax_mean = 40
#plot posterior dist
pl_pdf = ax.plot(df_r_h_p.loc[:,name_hyp], df_r_h_p.loc[:,name_hyp+'_pdf'])
ax.vlines(df_r_h.loc[name_hyp,'mean'], ymin=0, ymax=ymax_mean, linestyle='-', color=pl_pdf[0].get_color(), label='Mean')
ax.vlines(df_r_h.loc[name_hyp,'mode'], ymin=0, ymax=ymax_mode, linestyle='--', color=pl_pdf[0].get_color(), label='Mode')
#plot true value
ymax_hyp = ymax_mean
ax.vlines(hyp[name_hyp], ymin=0, ymax=ymax_hyp, linestyle='-', linewidth=4, color='black', label='True value')
#edit figure
if not flag_report: ax.set_title(r'Comparison $\omega_{1,e}$', fontsize=30)
ax.set_xlabel('$\omega_{1,e}$', fontsize=25)
ax.set_ylabel('probability density function ', fontsize=25)
ax.grid(which='both')
ax.tick_params(axis='x', labelsize=22)
ax.tick_params(axis='y', labelsize=22)
#plot limits
ax.set_xlim([0,0.25])
ax.set_ylim([0,ymax_hyp])
#save figure
fig.tight_layout()
fig.savefig( dir_fig + fname_fig + '.png' )
# Omega_1as
#--- --- --- --- ---
#hyper-paramter name
name_hyp = 'omega_1as'
#figure title
fname_fig = 'post_dist_' + name_hyp
#create figure
fig, ax = plt.subplots(figsize = (10,10))
for d_id, df_r_h, df_r_h_p in zip(ds_id, df_reg_hyp, df_reg_hyp_post):
#estimate vertical line height for mean and mode
ymax_mode = df_r_h_p.loc[:,name_hyp+'_pdf'].max()
# ymax_mean = 1.5*np.ceil(ymax_mode/10)*10
# ymax_mode = 30
ymax_mean = 30
#plot posterior dist
pl_pdf = ax.plot(df_r_h_p.loc[:,name_hyp], df_r_h_p.loc[:,name_hyp+'_pdf'])
ax.vlines(df_r_h.loc[name_hyp,'mean'], ymin=0, ymax=ymax_mean, linestyle='-', color=pl_pdf[0].get_color(), label='Mean')
ax.vlines(df_r_h.loc[name_hyp,'mode'], ymin=0, ymax=ymax_mode, linestyle='--', color=pl_pdf[0].get_color(), label='Mode')
#plot true value
ymax_hyp = ymax_mean
ax.vlines(hyp[name_hyp], ymin=0, ymax=ymax_hyp, linestyle='-', linewidth=4, color='black', label='True value')
#edit figure
if not flag_report: ax.set_title(r'Comparison $\omega_{1a,s}$', fontsize=30)
ax.set_xlabel('$\omega_{1a,s}$', fontsize=25)
ax.set_ylabel('probability density function ', fontsize=25)
ax.grid(which='both')
ax.tick_params(axis='x', labelsize=22)
ax.tick_params(axis='y', labelsize=22)
#plot limits
ax.set_xlim([0,0.5])
ax.set_ylim([0,ymax_hyp])
#save figure
fig.tight_layout()
fig.savefig( dir_fig + fname_fig + '.png' )
# Omega_1bs
#--- --- --- --- ---
#hyper-paramter name
name_hyp = 'omega_1bs'
#figure title
fname_fig = 'post_dist_' + name_hyp
#create figure
fig, ax = plt.subplots(figsize = (10,10))
for d_id, df_r_h, df_r_h_p in zip(ds_id, df_reg_hyp, df_reg_hyp_post):
#estimate vertical line height for mean and mode
ymax_mode = df_r_h_p.loc[:,name_hyp+'_pdf'].max()
# ymax_mean = 1.5*np.ceil(ymax_mode/10)*10
ymax_mode = 60
# ymax_mean = 60
#plot posterior dist
pl_pdf = ax.plot(df_r_h_p.loc[:,name_hyp], df_r_h_p.loc[:,name_hyp+'_pdf'])
ax.vlines(df_r_h.loc[name_hyp,'mean'], ymin=0, ymax=ymax_mean, linestyle='-', color=pl_pdf[0].get_color(), label='Mean')
ax.vlines(df_r_h.loc[name_hyp,'mode'], ymin=0, ymax=ymax_mode, linestyle='--', color=pl_pdf[0].get_color(), label='Mode')
#plot true value
ymax_hyp = ymax_mean
ax.vlines(hyp[name_hyp], ymin=0, ymax=ymax_hyp, linestyle='-', linewidth=4, color='black', label='True value')
#edit figure
if not flag_report: ax.set_title(r'Comparison $\omega_{1b,s}$', fontsize=30)
ax.set_xlabel('$\omega_{1b,s}$', fontsize=25)
ax.set_ylabel('probability density function ', fontsize=25)
ax.grid(which='both')
ax.tick_params(axis='x', labelsize=22)
ax.tick_params(axis='y', labelsize=22)
#plot limits
ax.set_xlim([0,0.5])
ax.set_ylim([0,ymax_hyp])
#save figure
fig.tight_layout()
fig.savefig( dir_fig + fname_fig + '.png' )
# Ell_1e
#--- --- --- --- ---
#hyper-paramter name
name_hyp = 'ell_1e'
#figure title
fname_fig = 'post_dist_' + name_hyp
#create figure
fig, ax = plt.subplots(figsize = (10,10))
for d_id, df_r_h, df_r_h_p in zip(ds_id, df_reg_hyp, df_reg_hyp_post):
#estimate vertical line height for mean and mode
ymax_mode = df_r_h_p.loc[:,name_hyp+'_pdf'].max()
# ymax_mean = 1.5*np.ceil(ymax_mode/10)*10
ymax_mode = 0.02
ymax_mean = 0.02
#plot posterior dist
pl_pdf = ax.plot(df_r_h_p.loc[:,name_hyp], df_r_h_p.loc[:,name_hyp+'_pdf'])
ax.vlines(df_r_h.loc[name_hyp,'0.5quant'], ymin=0, ymax=ymax_mean, linestyle='-', color=pl_pdf[0].get_color(), label='Mean')
# ax.vlines(df_r_h.loc[name_hyp,'mean'], ymin=0, ymax=ymax_mean, linestyle='-', color=pl_pdf[0].get_color(), label='Mean')
ax.vlines(df_r_h.loc[name_hyp,'mode'], ymin=0, ymax=ymax_mode, linestyle='--', color=pl_pdf[0].get_color(), label='Mode')
#plot true value
ymax_hyp = ymax_mean
ax.vlines(hyp[name_hyp], ymin=0, ymax=ymax_hyp, linestyle='-', linewidth=4, color='black', label='True value')
#edit figure
if not flag_report: ax.set_title(r'Comparison $\ell_{1,e}$', fontsize=30)
ax.set_xlabel('$\ell_{1,e}$', fontsize=25)
ax.set_ylabel('probability density function ', fontsize=25)
ax.grid(which='both')
ax.tick_params(axis='x', labelsize=22)
ax.tick_params(axis='y', labelsize=22)
#plot limits
ax.set_xlim([0,500])
ax.set_ylim([0,ymax_hyp])
#save figure
fig.tight_layout()
fig.savefig( dir_fig + fname_fig + '.png' )
# Ell_1as
#--- --- --- --- ---
#hyper-paramter name
name_hyp = 'ell_1as'
#figure title
fname_fig = 'post_dist_' + name_hyp
#create figure
fig, ax = plt.subplots(figsize = (10,10))
for d_id, df_r_h, df_r_h_p in zip(ds_id, df_reg_hyp, df_reg_hyp_post):
#estimate vertical line height for mean and mode
ymax_mode = df_r_h_p.loc[:,name_hyp+'_pdf'].max()
# ymax_mean = 1.5*np.ceil(ymax_mode/10)*10
# ymax_mode = 0.1
ymax_mean = 0.1
#plot posterior dist
pl_pdf = ax.plot(df_r_h_p.loc[:,name_hyp], df_r_h_p.loc[:,name_hyp+'_pdf'])
ax.vlines(df_r_h.loc[name_hyp,'mean'], ymin=0, ymax=ymax_mean, linestyle='-', color=pl_pdf[0].get_color(), label='Mean')
# ax.vlines(df_r_h.loc[name_hyp,'mean'], ymin=0, ymax=ymax_mean, linestyle='-', color=pl_pdf[0].get_color(), label='Mean')
ax.vlines(df_r_h.loc[name_hyp,'mode'], ymin=0, ymax=ymax_mode, linestyle='--', color=pl_pdf[0].get_color(), label='Mode')
#plot true value
ymax_hyp = ymax_mean
ax.vlines(hyp[name_hyp], ymin=0, ymax=ymax_hyp, linestyle='-', linewidth=4, color='black', label='True value')
#edit figure
if not flag_report: ax.set_title(r'Comparison $\ell_{1a,s}$', fontsize=30)
ax.set_xlabel('$\ell_{1a,s}$', fontsize=25)
ax.set_ylabel('probability density function ', fontsize=25)
ax.grid(which='both')
ax.tick_params(axis='x', labelsize=22)
ax.tick_params(axis='y', labelsize=22)
#plot limits
ax.set_xlim([0,150])
ax.set_ylim([0,ymax_hyp])
#save figure
fig.tight_layout()
fig.savefig( dir_fig + fname_fig + '.png' )
# Tau_0
#--- --- --- --- ---
#hyper-paramter name
name_hyp = 'tau_0'
#figure title
fname_fig = 'post_dist_' + name_hyp
#create figure
fig, ax = plt.subplots(figsize = (10,10))
for d_id, df_r_h, df_r_h_p in zip(ds_id, df_reg_hyp, df_reg_hyp_post):
#estimate vertical line height for mean and mode
ymax_mode = df_r_h_p.loc[:,name_hyp+'_pdf'].max()
# ymax_mean = 1.5*np.ceil(ymax_mode/10)*10
# ymax_mode = 60
ymax_mean = 60
#plot posterior dist
pl_pdf = ax.plot(df_r_h_p.loc[:,name_hyp], df_r_h_p.loc[:,name_hyp+'_pdf'])
ax.vlines(df_r_h.loc[name_hyp,'mean'], ymin=0, ymax=ymax_mean, linestyle='-', color=pl_pdf[0].get_color(), label='Mean')
# ax.vlines(df_r_h.loc[name_hyp,'mean'], ymin=0, ymax=ymax_mean, linestyle='-', color=pl_pdf[0].get_color(), label='Mean')
ax.vlines(df_r_h.loc[name_hyp,'mode'], ymin=0, ymax=ymax_mode, linestyle='--', color=pl_pdf[0].get_color(), label='Mode')
#plot true value
ymax_hyp = ymax_mean
ax.vlines(hyp[name_hyp], ymin=0, ymax=ymax_hyp, linestyle='-', linewidth=4, color='black', label='True value')
#edit figure
if not flag_report: ax.set_title(r'Comparison $\tau_{0}$', fontsize=30)
ax.set_xlabel(r'$\tau_{0}$', fontsize=25)
ax.set_ylabel(r'probability density function ', fontsize=25)
ax.grid(which='both')
ax.tick_params(axis='x', labelsize=22)
ax.tick_params(axis='y', labelsize=22)
#plot limits
ax.set_xlim([0,0.5])
ax.set_ylim([0,ymax_hyp])
#save figure
fig.tight_layout()
fig.savefig( dir_fig + fname_fig + '.png' )
# Phi_0
#--- --- --- --- ---
#hyper-paramter name
name_hyp = 'phi_0'
#figure title
fname_fig = 'post_dist_' + name_hyp
#create figure
fig, ax = plt.subplots(figsize = (10,10))
for d_id, df_r_h, df_r_h_p in zip(ds_id, df_reg_hyp, df_reg_hyp_post):
#estimate vertical line height for mean and mode
ymax_mode = df_r_h_p.loc[:,name_hyp+'_pdf'].max()
# ymax_mean = 1.5*np.ceil(ymax_mode/10)*10
# ymax_mode = 100
ymax_mean = 100
#plot posterior dist
pl_pdf = ax.plot(df_r_h_p.loc[:,name_hyp], df_r_h_p.loc[:,name_hyp+'_pdf'])
ax.vlines(df_r_h.loc[name_hyp,'mean'], ymin=0, ymax=ymax_mean, linestyle='-', color=pl_pdf[0].get_color(), label='Mean')
ax.vlines(df_r_h.loc[name_hyp,'mode'], ymin=0, ymax=ymax_mode, linestyle='--', color=pl_pdf[0].get_color(), label='Mode')
#plot true value
ymax_hyp = ymax_mean
ax.vlines(hyp[name_hyp], ymin=0, ymax=ymax_hyp, linestyle='-', linewidth=4, color='black', label='True value')
#edit figure
if not flag_report: ax.set_title(r'Comparison $\phi_{0}$', fontsize=30)
ax.set_xlabel('$\phi_{0}$', fontsize=25)
ax.set_ylabel(r'probability density function ', fontsize=25)
ax.grid(which='both')
ax.tick_params(axis='x', labelsize=22)
ax.tick_params(axis='y', labelsize=22)
#plot limits
ax.set_xlim([0,0.6])
ax.set_ylim([0,ymax_hyp])
#save figure
fig.tight_layout()
fig.savefig( dir_fig + fname_fig + '.png' )
# # Delta c_0
# #--- --- --- --- ---
# #hyper-paramter name
# name_hyp = 'dc_0'
# #figure title
# fname_fig = 'post_dist_' + name_hyp
# #create figure
# fig, ax = plt.subplots(figsize = (10,10))
# for d_id, df_r_h, df_r_h_p in zip(ds_id, df_reg_hyp, df_reg_hyp_post):
# #estimate vertical line height for mean and mode
# ymax_mode = df_r_h_p.loc[:,name_hyp+'_pdf'].max()
# ymax_mean = 1.5*np.ceil(ymax_mode/10)*10
# ymax_mean = 15
# #plot posterior dist
# pl_pdf = ax.plot(df_r_h_p.loc[:,name_hyp], df_r_h_p.loc[:,name_hyp+'_pdf'])
# ax.vlines(df_r_h.loc[name_hyp,'mean'], ymin=0, ymax=ymax_mean, linestyle='-', color=pl_pdf[0].get_color(), label='Mean')
# ax.vlines(df_r_h.loc[name_hyp,'mode'], ymin=0, ymax=ymax_mode, linestyle='--', color=pl_pdf[0].get_color(), label='Mode')
# #plot true value
# ymax_hyp = ymax_mean
# # ax.vlines(hyp[name_hyp], ymin=0, ymax=ymax_hyp, linestyle='-', linewidth=4, color='black', label='True value')
# #edit figure
# ax.set_title(r'Comparison $\delta c_{0}$', fontsize=30)
# ax.set_xlabel('$\delta c_{0}$', fontsize=25)
# ax.set_ylabel('probability density function ', fontsize=25)
# ax.grid(which='both')
# ax.tick_params(axis='x', labelsize=22)
# ax.tick_params(axis='y', labelsize=22)
# #plot limits
# ax.set_xlim([-1,1])
# ax.set_ylim([0,ymax_hyp])
# #save figure
# fig.tight_layout()
# # fig.savefig( dir_fig + fname_fig + '.png' )
| 30,341 | 38.71466 | 155 | py |
ngmm_tools | ngmm_tools-master/Analyses/Code_Verification/regression/ds1/main_cmdstan_model1_NGAWest3CA.py | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Wed Jul 14 14:17:52 2021
@author: glavrent
"""
# Working directory and Packages
# ---------------------------
#load libraries
import os
import sys
import numpy as np
import pandas as pd
import time
#user functions
sys.path.insert(0,'../../../Python_lib/regression/cmdstan/')
from regression_cmdstan_model1_unbounded_hyp import RunStan
# Define variables
# ---------------------------
#filename suffix
# synds_suffix = '_small_corr_len'
# synds_suffix = '_large_corr_len'
#synthetic datasets directory
ds_dir = '../../../../Data/Verification/synthetic_datasets/ds1'
ds_dir = r'%s%s/'%(ds_dir, synds_suffix)
# dataset info
# ds_fname_main = 'CatalogNGAWest3CA_synthetic_data'
ds_fname_main = 'CatalogNGAWest3CALite_synthetic_data'
ds_id = np.arange(1,6)
#stan model
# sm_fname = '../../../Stan_lib/regression_stan_model1_unbounded_hyp.stan'
# sm_fname = '../../../Stan_lib/regression_stan_model1_unbounded_hyp_chol.stan'
# sm_fname = '../../../Stan_lib/regression_stan_model1_unbounded_hyp_chol_efficient.stan'
# sm_fname = '../../../Stan_lib/regression_stan_model1_unbounded_hyp_chol_efficient2.stan'
#output info
#main output filename
out_fname_main = 'NGAWest3CA_syndata'
#main output directory
out_dir_main = '../../../../Data/Verification/regression/ds1/'
#output sub-directory
# out_dir_sub = 'CMDSTAN_NGAWest3CA'
# out_dir_sub = 'CMDSTAN_NGAWest3CA_chol'
# out_dir_sub = 'CMDSTAN_NGAWest3CA_chol_eff'
# out_dir_sub = 'CMDSTAN_NGAWest3CA_chol_eff2'
#stan parameters
res_name='tot'
n_iter_warmup = 500
n_iter_sampling = 500
n_chains = 4
adapt_delta = 0.8
max_treedepth = 10
#parallel options
stan_parallel=False
#output sub-dir with corr with suffix info
out_dir_sub = f'%s%s'%(out_dir_sub, synds_suffix)
# Run stan regression
# ---------------------------
#create datafame with computation time
df_run_info = list()
#iterate over all synthetic datasets
for d_id in ds_id:
print('Synthetic dataset %i fo %i'%(d_id, len(ds_id)))
#run time start
run_t_strt = time.time()
#input flatfile
ds_fname = '%s%s%s_Y%i.csv'%(ds_dir, ds_fname_main, synds_suffix, d_id)
#load flatfile
df_flatfile = pd.read_csv(ds_fname)
#output file name and directory
out_fname = '%s%s_Y%i'%(out_fname_main, synds_suffix, d_id)
out_dir = '%s/%s/Y%i/'%(out_dir_main, out_dir_sub, d_id)
#run stan model
RunStan(df_flatfile, sm_fname,
out_fname, out_dir, res_name,
n_iter_warmup=n_iter_warmup, n_iter_sampling=n_iter_sampling, n_chains=n_chains,
adapt_delta=adapt_delta, max_treedepth=max_treedepth,
stan_parallel=stan_parallel)
#run time end
run_t_end = time.time()
#compute run time
run_tm = (run_t_end - run_t_strt)/60
#log run time
df_run_info.append(pd.DataFrame({'computer_name':os.uname()[1],'out_name':out_dir_sub,
'ds_id':d_id,'run_time':run_tm}, index=[d_id]))
#write out run info
out_fname = '%s%s/run_info.csv'%(out_dir_main, out_dir_sub)
pd.concat(df_run_info).reset_index(drop=True).to_csv(out_fname, index=False)
| 3,227 | 29.742857 | 92 | py |
ngmm_tools | ngmm_tools-master/Analyses/Code_Verification/regression/ds1/comparison_inla_model1_misfit_mesh.py | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Tue Mar 15 14:50:27 2022
@author: glavrent
"""
# Working directory and Packages
# ---------------------------
#load variables
import os
import sys
import pathlib
#arithmetic libraries
import numpy as np
#statistics libraries
import pandas as pd
#plot libraries
import matplotlib as mpl
import matplotlib.pyplot as plt
#user functions
def PlotRSMCmp(df_rms_all, c_name, fig_fname):
#create figure axes
fig, ax = plt.subplots(figsize = (10,10))
ltype_array = ['-','--',':']
for j, k in enumerate(df_rms_all):
df_rms = df_rms_all[k]
ds_id = np.array(range(len(df_rms)))
#plot info
lcol = mpl.cm.get_cmap('tab10')( np.floor_divide(j,3) )
ltype = ltype_array[ np.mod(j,3) ]
ax.plot(ds_id, df_rms.loc[:,c_name+'_rms'], marker='o', linewidth=2, markersize=10, label=k, linestyle=ltype, color=lcol)
#figure properties
ax.set_ylim([0, max(0.50, max(ax.get_ylim()))])
ax.set_xlabel('synthetic dataset', fontsize=35)
ax.set_ylabel('RMSE', fontsize=35)
ax.grid(which='both')
ax.set_xticks(ds_id)
ax.set_xticklabels(labels=df_rms.index)
ax.tick_params(axis='x', labelsize=32)
ax.tick_params(axis='y', labelsize=32)
#legend
# ax.legend(loc='upper left', fontsize=32)
#save figure
fig.tight_layout()
fig.savefig( fig_fname + '.png' )
return fig, ax
def PlotKLCmp(df_KL_all, c_name, fig_fname):
#create figure axes
fig, ax = plt.subplots(figsize = (10,10))
for k in df_KL_all:
df_KL = df_KL_all[k]
ds_id = np.array(range(len(df_KL)))
ax.plot(ds_id, df_KL.loc[:,c_name+'_KL'], linestyle='-', marker='o', linewidth=2, markersize=10, label=k)
#figure properties
ax.set_ylim([0, max(0.50, max(ax.get_ylim()))])
ax.set_xlabel('synthetic dataset', fontsize=35)
ax.set_ylabel('KL divergence', fontsize=35)
ax.grid(which='both')
ax.set_xticks(ds_id)
ax.set_xticklabels(labels=df_KL.index)
ax.tick_params(axis='x', labelsize=32)
ax.tick_params(axis='y', labelsize=32)
#legend
# ax.legend(loc='upper left', fontsize=32)
# ax.legend(loc='center left', bbox_to_anchor=(1, 0.5), fontsize=25)
#save figure
fig.tight_layout()
fig.savefig( fig_fname + '.png' )
return fig, ax
# Define variables
# ---------------------------
# COMPARISONS
# Different Mesh sizes
# --- --- --- --- ---
cmp_name = 'INLA_mesh'
reg_title = [f'NGAW3* CA\nfine', f'NGAW3* CA \nmedium', f'NGAW3 CA \ncoarse',
f'NGAW2 CA \nfine', f'NGAW2 CA \nmedium', f'NGAW2 CA \ncoarse',
f'NGAW2 CA, North\nfine', f'NGAW2 CA, North\nmedium', f'NGAW2 CA, North\ncoarse']
reg_fname = ['INLA_NGAWest3CA_fine_small_corr_len', 'INLA_NGAWest3CA_medium_small_corr_len', 'INLA_NGAWest3CA_coarse_small_corr_len',
'INLA_NGAWest2CA_fine_small_corr_len', 'INLA_NGAWest2CA_medium_small_corr_len', 'INLA_NGAWest2CA_coarse_small_corr_len',
'INLA_NGAWest2CANorth_fine_small_corr_len','INLA_NGAWest2CANorth_medium_small_corr_len','INLA_NGAWest2CANorth_coarse_small_corr_len']
ylim_time = [0, 50]
# # Different Implementations
# # --- --- --- --- ---
# cmp_name = 'STAN_impl_cmp_NGAWest2CANorth'
# reg_title = ['CMDSTANPY Chol.', 'CMDSTANPY Chol. Eff.']
# reg_fname = ['CMDSTAN_NGAWest2CANorth_chol_small_corr_len','CMDSTAN_NGAWest2CANorth_chol_eff_small_corr_len']
# ylim_time = [0, 700]
# # Different Software
# # --- --- --- --- ---
# cmp_name = 'STAN_vs_INLA_cmp_NGAWest2CANorth'
# reg_title = ['STAN','INLA']
# reg_fname = ['CMDSTAN_NGAWest2CANorth_chol_eff_small_corr_len','INLA_NGAWest2CANorth_coarse_small_corr_len']
# ylim_time = [0, 700]
# Different
# --- --- --- --- ---
# # NGAWest2CANorth
# cmp_name = 'INLA_mesh_cmp_NGAWest2CANorth'
# reg_title = ['INLA coarse mesh', 'INLA medium mesh', 'INLA fine mesh']
# reg_fname = ['INLA_NGAWest2CANorth_coarse_small_corr_len','INLA_NGAWest2CANorth_medium_small_corr_len','INLA_NGAWest2CANorth_fine_small_corr_len']
# ylim_time = [0, 20]
# # NGAWest2CANorth
# cmp_name = 'INLA_mesh_cmp_NGAWest3CA'
# reg_title = ['INLA coarse mesh', 'INLA medium mesh', 'INLA fine mesh']
# reg_fname = ['INLA_NGAWest3CA_coarse_small_corr_len','INLA_NGAWest3CA_medium_small_corr_len','INLA_NGAWest3CA_fine_small_corr_len']
# ylim_time = [0, 100]
#directories regressions
reg_dir = [f'../../../../Data/Verification/regression/ds1/%s/'%r_f for r_f in reg_fname]
#directory output
dir_out = '../../../../Data/Verification/regression/ds1/comparisons/'
# Load Data
# ---------------------------
#initialize misfit dataframe
df_sum_misfit_all = {};
#read misfit info
for k, (r_t, r_d) in enumerate(zip(reg_title, reg_dir)):
#filename misfit info
fname_sum = r_d + 'summary/misfit_summary.csv'
#read KL score for coefficients
df_sum_misfit_all[r_t] = pd.read_csv(fname_sum, index_col=0)
#initialize run time dataframe
df_runinfo_all = {};
#read run time info
for k, (r_t, r_d) in enumerate(zip(reg_title, reg_dir)):
#filename run time
fname_runinfo = r_d + '/run_info.csv'
#store calc time
df_runinfo_all[r_t] = pd.read_csv(fname_runinfo)
#
print(f'%s: %.1f min'%( r_t, df_runinfo_all[r_t].run_time.mean() ))
# Comparison Figures
# ---------------------------
pathlib.Path(dir_out).mkdir(parents=True, exist_ok=True)
# RMSE divergence
# --- --- --- --- ---
#coefficient name
c_name = 'nerg_tot'
#figure name
fig_fname = '%s/%s_%s_RMSE'%(dir_out, cmp_name, c_name)
#plotting
PlotRSMCmp(df_sum_misfit_all , c_name, fig_fname);
#coefficient name
c_name = 'dc_1e'
#figure name
fig_fname = '%s/%s_%s_RMSE'%(dir_out, cmp_name, c_name)
#plotting
PlotRSMCmp(df_sum_misfit_all , c_name, fig_fname);
#coefficient name
c_name = 'dc_1as'
#figure name
fig_fname = '%s/%s_%s_RMSE'%(dir_out, cmp_name, c_name)
#plotting
PlotRSMCmp(df_sum_misfit_all , c_name, fig_fname);
#coefficient name
c_name = 'dc_1bs'
#figure name
fig_fname = '%s/%s_%s_RMSE'%(dir_out, cmp_name, c_name)
#plotting
PlotRSMCmp(df_sum_misfit_all , c_name, fig_fname);
# KL divergence
# --- --- --- --- ---
#coefficient name
c_name = 'nerg_tot'
#figure name
fig_fname = '%s/%s_%s_KLdiv'%(dir_out, cmp_name, c_name)
#plotting
PlotKLCmp(df_sum_misfit_all , c_name, fig_fname);
#coefficient name
c_name = 'dc_1e'
#figure name
fig_fname = '%s/%s_%s_KLdiv'%(dir_out, cmp_name, c_name)
#plotting
PlotKLCmp(df_sum_misfit_all , c_name, fig_fname);
#coefficient name
c_name = 'dc_1as'
#figure name
fig_fname = '%s/%s_%s_KLdiv'%(dir_out, cmp_name, c_name)
#plotting
PlotKLCmp(df_sum_misfit_all , c_name, fig_fname);
#coefficient name
c_name = 'dc_1bs'
#figure name
fig_fname = '%s/%s_%s_KLdiv'%(dir_out, cmp_name, c_name)
#plotting
PlotKLCmp(df_sum_misfit_all , c_name, fig_fname);
# Run Time
# --- --- --- --- ---
#run time figure
fig_fname = '%s/%s_run_time'%(dir_out, cmp_name)
#create figure axes
# fig, ax = plt.subplots(figsize = (10,10))
fig, ax = plt.subplots(figsize = (14,10))
ltype_array = ['-','--',':']
#iterate over different analyses
for j, k in enumerate(df_runinfo_all):
ds_id = df_runinfo_all[k].ds_id
ds_name = ['Y%i'%d_i for d_i in ds_id]
run_time = df_runinfo_all[k].run_time
lcol = mpl.cm.get_cmap('tab10')( np.floor_divide(j,3) )
ltype = ltype_array[ np.mod(j,3) ]
ax.plot(ds_id, run_time, marker='o', linewidth=2, markersize=10, label=k, linestyle=ltype, color=lcol)
#figure properties
ax.set_ylim(ylim_time)
ax.set_xlabel('synthetic dataset', fontsize=35)
ax.set_ylabel('Run Time (min)', fontsize=35)
ax.grid(which='both')
ax.set_xticks(ds_id)
ax.set_xticklabels(labels=ds_name)
ax.tick_params(axis='x', labelsize=32)
ax.tick_params(axis='y', labelsize=32)
#legend
# ax.legend(loc='lower left', fontsize=32)
# ax.legend(loc='upper left', fontsize=32)
ax.legend(loc='center left', bbox_to_anchor=(1, 0.5), fontsize=25)
#save figure
fig.tight_layout()
fig.savefig( fig_fname + '.png' )
| 8,074 | 32.094262 | 148 | py |
ngmm_tools | ngmm_tools-master/Analyses/Code_Verification/regression/ds1/comparison_stan_model1_misfit.py | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Tue Mar 15 14:50:27 2022
@author: glavrent
"""
# Working directory and Packages
# ---------------------------
#change working directory
import os
os.chdir('/mnt/halcloud_nfs/glavrent/Research/Nonerg_GMM_methodology/Analyses/Code_Verification/regressions/ds1')
#load variables
import os
import sys
import pathlib
#arithmetic libraries
import numpy as np
#statistics libraries
import pandas as pd
#plot libraries
import matplotlib as mpl
import matplotlib.pyplot as plt
#user functions
def PlotRSMCmp(df_rms_all, c_name, fig_fname):
#create figure axes
fig, ax = plt.subplots(figsize = (10,10))
for k in df_rms_all:
df_rms = df_rms_all[k]
ds_id = np.array(range(len(df_rms)))
ax.plot(ds_id, df_rms.loc[:,c_name+'_rms'], linestyle='-', marker='o', linewidth=2, markersize=10, label=k)
#figure properties
ax.set_ylim([0, max(0.50, max(ax.get_ylim()))])
ax.set_xlabel('synthetic dataset', fontsize=35)
ax.set_ylabel('RMSE', fontsize=35)
ax.grid(which='both')
ax.set_xticks(ds_id)
ax.set_xticklabels(labels=df_rms.index)
ax.tick_params(axis='x', labelsize=32)
ax.tick_params(axis='y', labelsize=32)
#legend
ax.legend(loc='upper left', fontsize=32)
#save figure
fig.tight_layout()
fig.savefig( fig_fname + '.png' )
return fig, ax
def PlotKLCmp(df_KL_all, c_name, fig_fname):
#create figure axes
fig, ax = plt.subplots(figsize = (10,10))
for k in df_KL_all:
df_KL = df_KL_all[k]
ds_id = np.array(range(len(df_KL)))
ax.plot(ds_id, df_KL.loc[:,c_name+'_KL'], linestyle='-', marker='o', linewidth=2, markersize=10, label=k)
#figure properties
ax.set_ylim([0, max(0.50, max(ax.get_ylim()))])
ax.set_xlabel('synthetic dataset', fontsize=35)
ax.set_ylabel('KL divergence', fontsize=35)
ax.grid(which='both')
ax.set_xticks(ds_id)
ax.set_xticklabels(labels=df_KL.index)
ax.tick_params(axis='x', labelsize=32)
ax.tick_params(axis='y', labelsize=32)
#legend
ax.legend(loc='upper left', fontsize=32)
#save figure
fig.tight_layout()
fig.savefig( fig_fname + '.png' )
return fig, ax
# Define variables
# ---------------------------
# COMPARISONS
# # Different Packages
# # --- --- --- --- ---
# cmp_name = 'STAN_pckg_cmp_NGAWest2CANorth'
# reg_title = ['PYSTAN2', 'PYSTAN3', 'CMDSTANPY']
# reg_fname = ['PYSTAN_NGAWest2CANorth_chol_eff_small_corr_len','PYSTAN3_NGAWest2CANorth_chol_eff_small_corr_len','CMDSTAN_NGAWest2CANorth_chol_eff_small_corr_len']
# ylim_time = [0, 700]
# Different Implementations
# --- --- --- --- ---
cmp_name = 'STAN_impl_cmp_NGAWest2CANorth'
reg_title = ['CMDSTANPY Chol.', 'CMDSTANPY Chol. Eff.']
reg_fname = ['CMDSTAN_NGAWest2CANorth_chol_small_corr_len','CMDSTAN_NGAWest2CANorth_chol_eff_small_corr_len']
# reg_fname = ['PYSTAN_NGAWest2CANorth_chol_small_corr_len','PYSTAN_NGAWest2CANorth_chol_eff_small_corr_len']
ylim_time = [0, 700]
#directories regressions
reg_dir = [f'../../../../Data/Verification/regression/ds1/%s/'%r_f for r_f in reg_fname]
#directory output
dir_out = '../../../../Data/Verification/regression/ds1/comparisons/'
# Load Data
# ---------------------------
#initialize misfit dataframe
df_sum_misfit_all = {};
#read misfit info
for k, (r_t, r_d) in enumerate(zip(reg_title, reg_dir)):
#filename misfit info
fname_sum = r_d + 'summary/misfit_summary.csv'
#read KL score for coefficients
df_sum_misfit_all[r_t] = pd.read_csv(fname_sum, index_col=0)
#initialize run time dataframe
df_runinfo_all = {};
#read run time info
for k, (r_t, r_d) in enumerate(zip(reg_title, reg_dir)):
#filename run time
fname_runinfo = r_d + '/run_info.csv'
#store calc time
df_runinfo_all[r_t] = pd.read_csv(fname_runinfo)
# Comparison Figures
# ---------------------------
pathlib.Path(dir_out).mkdir(parents=True, exist_ok=True)
# RMSE divergence
# --- --- --- --- ---
#coefficient name
c_name = 'nerg_tot'
#figure name
fig_fname = '%s/%s_%s_RMSE'%(dir_out, cmp_name, c_name)
#plotting
PlotRSMCmp(df_sum_misfit_all , c_name, fig_fname);
#coefficient name
c_name = 'dc_1e'
#figure name
fig_fname = '%s/%s_%s_RMSE'%(dir_out, cmp_name, c_name)
#plotting
PlotRSMCmp(df_sum_misfit_all , c_name, fig_fname);
#coefficient name
c_name = 'dc_1as'
#figure name
fig_fname = '%s/%s_%s_RMSE'%(dir_out, cmp_name, c_name)
#plotting
PlotRSMCmp(df_sum_misfit_all , c_name, fig_fname);
#coefficient name
c_name = 'dc_1bs'
#figure name
fig_fname = '%s/%s_%s_RMSE'%(dir_out, cmp_name, c_name)
#plotting
PlotRSMCmp(df_sum_misfit_all , c_name, fig_fname);
# KL divergence
# --- --- --- --- ---
#coefficient name
c_name = 'nerg_tot'
#figure name
fig_fname = '%s/%s_%s_KLdiv'%(dir_out, cmp_name, c_name)
#plotting
PlotKLCmp(df_sum_misfit_all , c_name, fig_fname);
#coefficient name
c_name = 'dc_1e'
#figure name
fig_fname = '%s/%s_%s_KLdiv'%(dir_out, cmp_name, c_name)
#plotting
PlotKLCmp(df_sum_misfit_all , c_name, fig_fname);
#coefficient name
c_name = 'dc_1as'
#figure name
fig_fname = '%s/%s_%s_KLdiv'%(dir_out, cmp_name, c_name)
#plotting
PlotKLCmp(df_sum_misfit_all , c_name, fig_fname);
#coefficient name
c_name = 'dc_1bs'
#figure name
fig_fname = '%s/%s_%s_KLdiv'%(dir_out, cmp_name, c_name)
#plotting
PlotKLCmp(df_sum_misfit_all , c_name, fig_fname);
# RMSE divergence
# --- --- --- --- ---
#run time figure
fig_fname = '%s/%s_run_time'%(dir_out, cmp_name)
#create figure axes
fig, ax = plt.subplots(figsize = (10,10))
#iterate over different analyses
for j, k in enumerate(df_runinfo_all):
ds_id = df_runinfo_all[k].ds_id
ds_name = ['Y%i'%d_i for d_i in ds_id]
run_time = df_runinfo_all[k].run_time
ax.plot(ds_id, run_time, marker='o', linewidth=2, markersize=10, label=k)
#figure properties
ax.set_ylim(ylim_time)
ax.set_xlabel('synthetic dataset', fontsize=35)
ax.set_ylabel('Run Time (min)', fontsize=35)
ax.grid(which='both')
ax.set_xticks(ds_id)
ax.set_xticklabels(labels=ds_name)
ax.tick_params(axis='x', labelsize=32)
ax.tick_params(axis='y', labelsize=32)
#legend
ax.legend(loc='lower left', fontsize=32)
# ax.legend(loc='center left', bbox_to_anchor=(1, 0.5), fontsize=25)
#save figure
fig.tight_layout()
fig.savefig( fig_fname + '.png' ) | 6,363 | 28.738318 | 164 | py |
ngmm_tools | ngmm_tools-master/Analyses/Code_Verification/regression/ds1/main_pystan_model1_NGAWest2CA.py | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Wed Jul 14 14:17:52 2021
@author: glavrent
"""
# Working directory and Packages
# ---------------------------
#load libraries
import os
import sys
import numpy as np
import pandas as pd
import time
#user functions
sys.path.insert(0,'../../../Python_lib/regression/pystan/')
from regression_pystan_model1_unbounded_hyp import RunStan
# Define variables
# ---------------------------
#filename suffix
# synds_suffix = '_small_corr_len'
# synds_suffix = '_large_corr_len'
#synthetic datasets directory
ds_dir = '../../../../Data/Verification/synthetic_datasets/ds1'
ds_dir = r'%s%s/'%(ds_dir, synds_suffix)
# dataset info
# ds_fname_main = 'CatalogNGAWest3CA_synthetic_data'
ds_fname_main = 'CatalogNGAWest3CALite_synthetic_data'
ds_id = np.arange(1,6)
#stan model
# sm_fname = '../../../Stan_lib/regression_stan_model1_unbounded_hyp.stan'
# sm_fname = '../../../Stan_lib/regression_stan_model1_unbounded_hyp_chol.stan'
# sm_fname = '../../../Stan_lib/regression_stan_model1_unbounded_hyp_chol_efficient.stan'
# sm_fname = '../../../Stan_lib/regression_stan_model1_unbounded_hyp_chol_efficient2.stan'
#output info
#main output filename
out_fname_main = 'NGAWest2CA_syndata'
#main output directory
out_dir_main = '../../../../Data/Verification/regression/ds1/'
#output sub-directory
#python 2
# out_dir_sub = 'PYSTAN_NGAWest2CA'
# out_dir_sub = 'PYSTAN_NGAWest2CA_chol'
# out_dir_sub = 'PYSTAN_NGAWest2CA_chol_eff'
# out_dir_sub = 'PYSTAN_NGAWest2CA_chol_eff2'
#python 3
# out_dir_sub = 'PYSTAN3_NGAWest2CA'
# out_dir_sub = 'PYSTAN3_NGAWest2CA_chol'
# out_dir_sub = 'PYSTAN3_NGAWest2CA_chol_eff'
# out_dir_sub = 'PYSTAN3_NGAWest2CA_chol_eff2'
#stan parameters
runstan_flag = True
#pystan_ver = 2
pystan_ver = 3
res_name = 'tot'
n_iter = 1000
n_chains = 4
adapt_delta = 0.8
max_treedepth = 10
#parallel options
# flag_parallel = True
flag_parallel = False
#output sub-dir with corr with suffix info
out_dir_sub = f'%s%s'%(out_dir_sub, synds_suffix)
# Run stan regression
# ---------------------------
#create datafame with computation time
df_run_info = list()
#iterate over all synthetic datasets
for d_id in ds_id:
print('Synthetic dataset %i fo %i'%(d_id, len(ds_id)))
#run time start
run_t_strt = time.time()
#input flatfile
ds_fname = '%s%s%s_Y%i.csv'%(ds_dir, ds_fname_main, synds_suffix, d_id)
#load flatfile
df_flatfile = pd.read_csv(ds_fname)
#keep only NGAWest2 records
df_flatfile = df_flatfile.loc[df_flatfile.dsid==0,:]
#output file name and directory
out_fname = '%s%s_Y%i'%(out_fname_main, synds_suffix, d_id)
out_dir = '%s/%s/Y%i/'%(out_dir_main, out_dir_sub, d_id)
#run stan model
RunStan(df_flatfile, sm_fname, out_fname, out_dir, res_name,
runstan_flag=runstan_flag, n_iter=n_iter, n_chains=n_chains,
adapt_delta=adapt_delta, max_treedepth=max_treedepth,
pystan_ver=pystan_ver, pystan_parallel=flag_parallel)
#run time end
run_t_end = time.time()
#compute run time
run_tm = (run_t_end - run_t_strt)/60
#log run time
df_run_info.append(pd.DataFrame({'computer_name':os.uname()[1],'out_name':out_dir_sub,
'ds_id':d_id,'run_time':run_tm}, index=[d_id]))
#write out run info
out_fname = '%s%s/run_info.csv'%(out_dir_main, out_dir_sub)
pd.concat(df_run_info).reset_index(drop=True).to_csv(out_fname, index=False)
| 3,544 | 29.826087 | 90 | py |
ngmm_tools | ngmm_tools-master/Analyses/Code_Verification/regression/ds1/main_cmdstan_model1_NGAWest2CANorth.py | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Wed Jul 14 14:17:52 2021
@author: glavrent
"""
# Working directory and Packages
# ---------------------------
#load libraries
import os
import sys
import numpy as np
import pandas as pd
import time
#user functions
sys.path.insert(0,'../../../Python_lib/regression/cmdstan/')
from regression_cmdstan_model1_unbounded_hyp import RunStan
# Define variables
# ---------------------------
#filename suffix
# synds_suffix = '_small_corr_len'
# synds_suffix = '_large_corr_len'
#synthetic datasets directory
ds_dir = '../../../../Data/Verification/synthetic_datasets/ds1'
ds_dir = r'%s%s/'%(ds_dir, synds_suffix)
# dataset info
# ds_fname_main = 'CatalogNGAWest3CA_synthetic_data'
ds_fname_main = 'CatalogNGAWest3CALite_synthetic_data'
ds_id = np.arange(1,6)
#stan model
# sm_fname = '../../../Stan_lib/regression_stan_model1_unbounded_hyp.stan'
# sm_fname = '../../../Stan_lib/regression_stan_model1_unbounded_hyp_chol.stan'
# sm_fname = '../../../Stan_lib/regression_stan_model1_unbounded_hyp_chol_efficient.stan'
# sm_fname = '../../../Stan_lib/regression_stan_model1_unbounded_hyp_chol_efficient2.stan'
#output info
#main output filename
out_fname_main = 'NGAWest2CANorth_syndata'
#main output directory
out_dir_main = '../../../../Data/Verification/regression/ds1/'
#output sub-directory
# out_dir_sub = 'CMDSTAN_NGAWest2CANorth'
# out_dir_sub = 'CMDSTAN_NGAWest2CANorth_chol'
# out_dir_sub = 'CMDSTAN_NGAWest2CANorth_chol_eff'
# out_dir_sub = 'CMDSTAN_NGAWest2CANorth_chol_eff2'
#stan parameters
res_name='tot'
n_iter_warmup = 500
n_iter_sampling = 500
n_chains = 4
adapt_delta = 0.8
max_treedepth = 10
#parallel options
stan_parallel=False
#output sub-dir with corr with suffix info
out_dir_sub = f'%s%s'%(out_dir_sub, synds_suffix)
# Run stan regression
# ---------------------------
#create datafame with computation time
df_run_info = list()
#iterate over all synthetic datasets
for d_id in ds_id:
print('Synthetic dataset %i fo %i'%(d_id, len(ds_id)))
#run time start
run_t_strt = time.time()
#input flatfile
ds_fname = '%s%s%s_Y%i.csv'%(ds_dir, ds_fname_main, synds_suffix, d_id)
#load flatfile
df_flatfile = pd.read_csv(ds_fname)
#keep only North records of NGAWest2
df_flatfile = df_flatfile.loc[np.logical_and(df_flatfile.dsid==0,
df_flatfile.sreg==1),:]
#output file name and directory
out_fname = '%s%s_Y%i'%(out_fname_main, synds_suffix, d_id)
out_dir = '%s/%s/Y%i/'%(out_dir_main, out_dir_sub, d_id)
#run stan model
RunStan(df_flatfile, sm_fname,
out_fname, out_dir, res_name,
n_iter_warmup=n_iter_warmup, n_iter_sampling=n_iter_sampling, n_chains=n_chains,
adapt_delta=adapt_delta, max_treedepth=max_treedepth,
stan_parallel=stan_parallel)
#run time end
run_t_end = time.time()
#compute run time
run_tm = (run_t_end - run_t_strt)/60
#log run time
df_run_info.append(pd.DataFrame({'computer_name':os.uname()[1],'out_name':out_dir_sub,
'ds_id':d_id,'run_time':run_tm}, index=[d_id]))
#write out run info
out_fname = '%s%s/run_info.csv'%(out_dir_main, out_dir_sub)
pd.concat(df_run_info).reset_index(drop=True).to_csv(out_fname, index=False)
| 3,436 | 30.824074 | 92 | py |
ngmm_tools | ngmm_tools-master/Analyses/Code_Verification/regression/ds1/main_cmdstan_model1_NGAWest2CA.py | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Wed Jul 14 14:17:52 2021
@author: glavrent
"""
# Working directory and Packages
# ---------------------------
#load libraries
import os
import sys
import numpy as np
import pandas as pd
import time
#user functions
sys.path.insert(0,'../../../Python_lib/regression/cmdstan/')
from regression_cmdstan_model1_unbounded_hyp import RunStan
# Define variables
# ---------------------------
#filename suffix
# synds_suffix = '_small_corr_len'
# synds_suffix = '_large_corr_len'
#synthetic datasets directory
ds_dir = '../../../../Data/Verification/synthetic_datasets/ds1'
ds_dir = r'%s%s/'%(ds_dir, synds_suffix)
# dataset info
# ds_fname_main = 'CatalogNGAWest3CA_synthetic_data'
ds_fname_main = 'CatalogNGAWest3CALite_synthetic_data'
ds_id = np.arange(1,6)
#stan model
# sm_fname = '../../../Stan_lib/regression_stan_model1_unbounded_hyp.stan'
# sm_fname = '../../../Stan_lib/regression_stan_model1_unbounded_hyp_chol.stan'
# sm_fname = '../../../Stan_lib/regression_stan_model1_unbounded_hyp_chol_efficient.stan'
# sm_fname = '../../../Stan_lib/regression_stan_model1_unbounded_hyp_chol_efficient2.stan'
#output info
#main output filename
out_fname_main = 'NGAWest2CA_syndata'
#main output directory
out_dir_main = '../../../../Data/Verification/regression/ds1/'
#output sub-directory
# out_dir_sub = 'CMDSTAN_NGAWest2CA'
# out_dir_sub = 'CMDSTAN_NGAWest2CA_chol'
# out_dir_sub = 'CMDSTAN_NGAWest2CA_chol_eff'
# out_dir_sub = 'CMDSTAN_NGAWest2CA_chol_eff2'
#stan parameters
res_name='tot'
n_iter_warmup = 500
n_iter_sampling = 500
n_chains = 4
adapt_delta = 0.8
max_treedepth = 10
#parallel options
stan_parallel=False
#output sub-dir with corr with suffix info
out_dir_sub = f'%s%s'%(out_dir_sub, synds_suffix)
# Run stan regression
# ---------------------------
#create datafame with computation time
df_run_info = list()
#iterate over all synthetic datasets
for d_id in ds_id:
print('Synthetic dataset %i fo %i'%(d_id, len(ds_id)))
#run time start
run_t_strt = time.time()
#input flatfile
ds_fname = '%s%s%s_Y%i.csv'%(ds_dir, ds_fname_main, synds_suffix, d_id)
#load flatfile
df_flatfile = pd.read_csv(ds_fname)
#keep only NGAWest2 records
df_flatfile = df_flatfile.loc[df_flatfile.dsid==0,:]
#output file name and directory
out_fname = '%s%s_Y%i'%(out_fname_main, synds_suffix, d_id)
out_dir = '%s/%s/Y%i/'%(out_dir_main, out_dir_sub, d_id)
#run stan model
RunStan(df_flatfile, sm_fname,
out_fname, out_dir, res_name,
n_iter_warmup=n_iter_warmup, n_iter_sampling=n_iter_sampling, n_chains=n_chains,
adapt_delta=adapt_delta, max_treedepth=max_treedepth,
stan_parallel=stan_parallel)
#run time end
run_t_end = time.time()
#compute run time
run_tm = (run_t_end - run_t_strt)/60
#log run time
df_run_info.append(pd.DataFrame({'computer_name':os.uname()[1],'out_name':out_dir_sub,
'ds_id':d_id,'run_time':run_tm}, index=[d_id]))
#write out run info
out_fname = '%s%s/run_info.csv'%(out_dir_main, out_dir_sub)
pd.concat(df_run_info).reset_index(drop=True).to_csv(out_fname, index=False)
| 3,316 | 30 | 92 | py |
ngmm_tools | ngmm_tools-master/Analyses/Code_Verification/regression/ds1/main_pystan_model1_NGAWest3CA.py | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Wed Jul 14 14:17:52 2021
@author: glavrent
"""
# Working directory and Packages
# ---------------------------
#load libraries
import os
import sys
import numpy as np
import pandas as pd
import time
#user functions
sys.path.insert(0,'../../../Python_lib/regression/pystan/')
from regression_pystan_model1_unbounded_hyp import RunStan
# Define variables
# ---------------------------
#filename suffix
# synds_suffix = '_small_corr_len'
# synds_suffix = '_large_corr_len'
#synthetic datasets directory
ds_dir = '../../../../Data/Verification/synthetic_datasets/ds1'
ds_dir = r'%s%s/'%(ds_dir, synds_suffix)
# dataset info
#ds_fname_main = 'CatalogNGAWest3CA_synthetic_data'
ds_fname_main = 'CatalogNGAWest3CALite_synthetic_data'
ds_id = np.arange(1,6)
#stan model
# sm_fname = '../../../Stan_lib/regression_stan_model1_unbounded_hyp.stan'
# sm_fname = '../../../Stan_lib/regression_stan_model1_unbounded_hyp_chol.stan'
# sm_fname = '../../../Stan_lib/regression_stan_model1_unbounded_hyp_chol_efficient.stan'
# sm_fname = '../../../Stan_lib/regression_stan_model1_unbounded_hyp_chol_efficient2.stan'
#output info
#main output filename
out_fname_main = 'NGAWest3CA_syndata'
#main output directory
out_dir_main = '../../../../Data/Verification/regression/ds1/'
#output sub-directory
# out_dir_sub = 'PYSTAN_NGAWest3CA'
# out_dir_sub = 'PYSTAN_NGAWest3CA_chol'
# out_dir_sub = 'PYSTAN_NGAWest3CA_chol_eff'
# out_dir_sub = 'PYSTAN_NGAWest3CA_chol_eff2'
#stan parameters
runstan_flag = True
# pystan_ver = 2
pystan_ver = 3
res_name = 'tot'
n_iter = 1000
n_chains = 4
adapt_delta = 0.8
max_treedepth = 10
#parallel options
# flag_parallel = True
flag_parallel = False
#output sub-dir with corr with suffix info
out_dir_sub = f'%s%s'%(out_dir_sub, synds_suffix)
# Run stan regression
# ---------------------------
#create datafame with computation time
df_run_info = list()
#iterate over all synthetic datasets
for d_id in ds_id:
print('Synthetic dataset %i fo %i'%(d_id, len(ds_id)))
#run time start
run_t_strt = time.time()
#input flatfile
ds_fname = '%s%s%s_Y%i.csv'%(ds_dir, ds_fname_main, synds_suffix, d_id)
#load flatfile
df_flatfile = pd.read_csv(ds_fname)
#output file name and directory
out_fname = '%s%s_Y%i'%(out_fname_main, synds_suffix, d_id)
out_dir = '%s/%s/Y%i/'%(out_dir_main, out_dir_sub, d_id)
#run stan model
RunStan(df_flatfile, sm_fname, out_fname, out_dir, res_name,
runstan_flag=runstan_flag, n_iter=n_iter, n_chains=n_chains,
adapt_delta=adapt_delta, max_treedepth=max_treedepth,
pystan_ver=pystan_ver, pystan_parallel=flag_parallel)
#run time end
run_t_end = time.time()
#compute run time
run_tm = (run_t_end - run_t_strt)/60
#log run time
df_run_info.append(pd.DataFrame({'computer_name':os.uname()[1],'out_name':out_dir_sub,
'ds_id':d_id,'run_time':run_tm}, index=[d_id]))
#write out run info
out_fname = '%s%s/run_info.csv'%(out_dir_main, out_dir_sub)
pd.concat(df_run_info).reset_index(drop=True).to_csv(out_fname, index=False)
| 3,244 | 29.327103 | 90 | py |
ngmm_tools | ngmm_tools-master/Analyses/Code_Verification/regression/ds1/comparison_inla_model1_misfit.py | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Tue Mar 15 14:50:27 2022
@author: glavrent
"""
# Working directory and Packages
# ---------------------------
#change working directory
import os
os.chdir('/mnt/halcloud_nfs/glavrent/Research/Nonerg_GMM_methodology/Analyses/Code_Verification/regressions/ds1')
#load variables
import os
import sys
import pathlib
#arithmetic libraries
import numpy as np
#statistics libraries
import pandas as pd
#plot libraries
import matplotlib as mpl
import matplotlib.pyplot as plt
#user functions
def PlotRSMCmp(df_KL_all, c_name, fig_fname):
#create figure axes
fig, ax = plt.subplots(figsize = (10,10))
for m_i in df_KL_all:
df_KL = df_KL_all[m_i]
ds_id = np.array(range(len(df_KL)))
ax.plot(ds_id, df_KL.loc[:,c_name], linestyle='-', marker='o', linewidth=2, markersize=10, label=m_i)
#figure properties
ax.set_ylim([0, max(0.50, max(ax.get_ylim()))])
ax.set_xlabel('synthetic dataset', fontsize=30)
ax.set_ylabel('RMSE divergence', fontsize=30)
ax.grid(which='both')
ax.set_xticks(ds_id)
ax.set_xticklabels(labels=df_KL.index)
ax.tick_params(axis='x', labelsize=30)
ax.tick_params(axis='y', labelsize=30)
#legend
ax.legend(loc='upper left', fontsize=30)
#save figure
fig.tight_layout()
fig.savefig( fig_fname + '.png' )
return fig, ax
def PlotKLCmp(df_KL_all, c_name, fig_fname):
#create figure axes
fig, ax = plt.subplots(figsize = (10,10))
for m_i in df_KL_all:
df_KL = df_KL_all[m_i]
ds_id = np.array(range(len(df_KL)))
ax.plot(ds_id, df_KL.loc[:,c_name], linestyle='-', marker='o', linewidth=2, markersize=10, label=m_i)
#figure properties
ax.set_ylim([0, max(0.50, max(ax.get_ylim()))])
ax.set_xlabel('synthetic dataset', fontsize=30)
ax.set_ylabel('KL divergence', fontsize=30)
ax.grid(which='both')
ax.set_xticks(ds_id)
ax.set_xticklabels(labels=df_KL.index)
ax.tick_params(axis='x', labelsize=25)
ax.tick_params(axis='y', labelsize=25)
#legend
ax.legend(loc='upper left', fontsize=25)
#save figure
fig.tight_layout()
fig.savefig( fig_fname + '.png' )
return fig, ax
# Define variables
# ---------------------------
#comparisons
name_reg = ['PYSTAN_NGAWest2CANorth_chol_eff_small_corr_len','PYSTAN3_NGAWest2CANorth_chol_eff_small_corr_len',]
#dataset info
ds_id = 1
#correlation info
# 1: Small Correlation Lengths
# 2: Large Correlation Lenghts
corr_id = 1
#packages comparison
packg_info = ['PYSTAN', 'PYSTAN3', 'fine']
#correlation name
if corr_id == 1: synds_suffix = '_small_corr_len'
elif corr_id == 2: synds_suffix = '_large_corr_len'
#dataset name
if ds_id == 1: name_dataset = 'NGAWest2CANorth'
elif ds_id == 2: name_dataset = 'NGAWest2CA'
elif ds_id == 3: name_dataset = 'NGAWest3CA'
#directories regressions
dir_reg = [f'../../../../Data/Verification/regression/ds1/%s_%s_%s%s/'%(name_dataset, m_info, synds_suffix) for m_info in mesh_info]
#directory output
dir_out = '../../../../Data/Verification/regression/ds1/comparisons/'
# Load Data
# ---------------------------
#initialize dataframe
df_KL_all = {};
#read KL scores
for k, (d_r, m_i) in enumerate(zip(dir_reg, mesh_info)):
#filename KL score
fname_KL = d_r + 'summary/coeffs_KL_divergence.csv'
#read KL score for coefficients
df_KL_all[m_i] = pd.read_csv(fname_KL, index_col=0)
# Comparison Figures
# ---------------------------
pathlib.Path(dir_out).mkdir(parents=True, exist_ok=True)
#coefficient name
c_name = 'nerg_tot'
#figure name
fig_fname = '%s/%s%s_KLdiv_%s'%(dir_out, name_dataset, synds_suffix, c_name)
#plotting
PlotKLCmp(df_KL_all, c_name, fig_fname);
#coefficient name
c_name = 'dc_1e'
#figure name
fig_fname = '%s/%s%s_KLdiv_%s'%(dir_out, name_dataset, synds_suffix, c_name)
#plotting
PlotKLCmp(df_KL_all, c_name, fig_fname);
#coefficient name
c_name = 'dc_1as'
#figure name
fig_fname = '%s/%s%s_KLdiv_%s'%(dir_out, name_dataset, synds_suffix, c_name)
#plotting
PlotKLCmp(df_KL_all, c_name, fig_fname);
#coefficient name
c_name = 'dc_1bs'
#figure name
fig_fname = '%s/%s%s_KLdiv_%s'%(dir_out, name_dataset, synds_suffix, c_name)
#plotting
PlotKLCmp(df_KL_all, c_name, fig_fname);
| 4,303 | 26.767742 | 132 | py |
ngmm_tools | ngmm_tools-master/Analyses/Code_Verification/regression/ds2/main_pystan_model2_corr_cells_NGAWest2CANorth_sparse.py | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Wed Jul 14 14:17:52 2021
@author: glavrent
"""
# Working directory and Packages
# ---------------------------
#load libraries
import os
import sys
import numpy as np
import pandas as pd
import time
#user functions
sys.path.insert(0,'../../../Python_lib/regression/pystan/')
from regression_pystan_model2_corr_cells_sparse_unbounded_hyp import RunStan
# Define variables
# ---------------------------
#filename suffix
# synds_suffix = '_small_corr_len'
# synds_suffix = '_large_corr_len'
#synthetic datasets directory
ds_dir = '../../../../Data/Verification/synthetic_datasets/ds2'
ds_dir = r'%s%s/'%(ds_dir, synds_suffix)
# dataset info
#ds_fname_main = 'CatalogNGAWest3CA_synthetic_data'
ds_fname_main = 'CatalogNGAWest3CALite_synthetic_data'
ds_id = np.arange(1,6)
#cell specific anelastic attenuation
ds_fname_cellinfo = 'CatalogNGAWest3CALite_cellinfo'
ds_fname_celldist = 'CatalogNGAWest3CALite_distancematrix'
#stan model
sm_fname = '../../../Stan_lib/regression_stan_model2_corr_cells_sparse_unbounded_hyp_chol_efficient.stan'
#output info
#main output filename
out_fname_main = 'NGAWest2CANorth_syndata'
#main output directory
out_dir_main = '../../../../Data/Verification/regression/ds2/'
#output sub-directory
out_dir_sub = 'PYSTAN_NGAWest2CANorth_corr_cells_chol_eff_sp'
#stan parameters
runstan_flag = True
# pystan_ver = 2
pystan_ver = 3
res_name = 'tot'
n_iter = 1000
n_chains = 4
adapt_delta = 0.8
max_treedepth = 10
#ergodic coefficients
c_a_erg=0.0
#parallel options
# flag_parallel = True
flag_parallel = False
#output sub-dir with corr with suffix info
out_dir_sub = f'%s%s'%(out_dir_sub, synds_suffix)
#load cell dataframes
cellinfo_fname = '%s%s.csv'%(ds_dir, ds_fname_cellinfo)
celldist_fname = '%s%s.csv'%(ds_dir, ds_fname_celldist)
df_cellinfo = pd.read_csv(cellinfo_fname)
df_celldist = pd.read_csv(celldist_fname)
# Run stan regression
# ---------------------------
#create datafame with computation time
df_run_info = list()
#iterate over all synthetic datasets
for d_id in ds_id:
print('Synthetic dataset %i fo %i'%(d_id, len(ds_id)))
#run time start
run_t_strt = time.time()
#input flatfile
ds_fname = '%s%s%s_Y%i.csv'%(ds_dir, ds_fname_main, synds_suffix, d_id)
#load flatfile
df_flatfile = pd.read_csv(ds_fname)
#keep only North records of NGAWest2
df_flatfile = df_flatfile.loc[np.logical_and(df_flatfile.dsid==0,
df_flatfile.sreg==1),:]
#output file name and directory
out_fname = '%s%s_Y%i'%(out_fname_main, synds_suffix, d_id)
out_dir = '%s/%s/Y%i/'%(out_dir_main, out_dir_sub, d_id)
#run stan model
RunStan(df_flatfile, df_cellinfo, df_celldist, sm_fname,
out_fname, out_dir, res_name, c_a_erg=c_a_erg,
runstan_flag=runstan_flag, n_iter=n_iter, n_chains=n_chains,
adapt_delta=adapt_delta, max_treedepth=max_treedepth,
pystan_ver=pystan_ver, pystan_parallel=flag_parallel)
#run time end
run_t_end = time.time()
#compute run time
run_tm = (run_t_end - run_t_strt)/60
#log run time
df_run_info.append(pd.DataFrame({'computer_name':os.uname()[1],'out_name':out_dir_sub,
'ds_id':d_id,'run_time':run_tm}, index=[d_id]))
#write out run info
out_fname = '%s%s/run_info.csv'%(out_dir_main, out_dir_sub)
pd.concat(df_run_info).reset_index(drop=True).to_csv(out_fname, index=False)
| 3,573 | 29.547009 | 105 | py |
ngmm_tools | ngmm_tools-master/Analyses/Code_Verification/regression/ds2/main_pystan_model2_corr_cells_NGAWest2CANorth.py | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Wed Jul 14 14:17:52 2021
@author: glavrent
"""
# Working directory and Packages
# ---------------------------
#load libraries
import os
import sys
import numpy as np
import pandas as pd
import time
#user functions
sys.path.insert(0,'../../../Python_lib/regression/pystan/')
from regression_pystan_model2_corr_cells_unbounded_hyp import RunStan
# from regression_pystan_model2_corr_cells_sparse_unbounded_hyp import RunStan
# Define variables
# ---------------------------
#filename suffix
# synds_suffix = '_small_corr_len'
# synds_suffix = '_large_corr_len'
#synthetic datasets directory
ds_dir = '../../../../Data/Verification/synthetic_datasets/ds2'
ds_dir = r'%s%s/'%(ds_dir, synds_suffix)
# dataset info
#ds_fname_main = 'CatalogNGAWest3CA_synthetic_data'
ds_fname_main = 'CatalogNGAWest3CALite_synthetic_data'
ds_id = np.arange(1,6)
#cell specific anelastic attenuation
ds_fname_cellinfo = 'CatalogNGAWest3CALite_cellinfo'
ds_fname_celldist = 'CatalogNGAWest3CALite_distancematrix'
#stan model
# sm_fname = '../../../Stan_lib/regression_stan_model2_corr_cells_unbounded_hyp.stan'
# sm_fname = '../../../Stan_lib/regression_stan_model2_corr_cells_unbounded_hyp_chol.stan'
# sm_fname = '../../../Stan_lib/regression_stan_model2_corr_cells_unbounded_hyp_chol_efficient.stan'
# sm_fname = '../../../Stan_lib/regression_stan_model2_corr_cells_unbounded_hyp_chol_efficient2.stan'
# sm_fname = '../../../Stan_lib/regression_stan_model2_corr_cells_sparse_unbounded_hyp_chol_efficient.stan'
#output info
#main output filename
out_fname_main = 'NGAWest2CANorth_syndata'
#main output directory
out_dir_main = '../../../../Data/Verification/regression/ds2/'
#output sub-directory
#pystan 2
# out_dir_sub = 'PYSTAN_NGAWest2CANorth_corr_cells'
# out_dir_sub = 'PYSTAN_NGAWest2CANorth_corr_cells_chol'
# out_dir_sub = 'PYSTAN_NGAWest2CANorth_corr_cells_chol_eff'
# out_dir_sub = 'PYSTAN_NGAWest2CANorth_corr_cells_chol_eff2'
# out_dir_sub = 'PYSTAN_NGAWest2CANorth_corr_cells_chol_eff_sp'
#pystan 3
# out_dir_sub = 'PYSTAN3_NGAWest2CANorth_corr_cells'
# out_dir_sub = 'PYSTAN3_NGAWest2CANorth_corr_cells_chol'
# out_dir_sub = 'PYSTAN3_NGAWest2CANorth_corr_cells_chol_eff'
# out_dir_sub = 'PYSTAN3_NGAWest2CANorth_corr_cells_chol_eff2'
# out_dir_sub = 'PYSTAN3_NGAWest2CANorth_corr_cells_chol_eff_sp'
#stan parameters
runstan_flag = True
# pystan_ver = 2
pystan_ver = 3
res_name = 'tot'
n_iter = 1000
n_chains = 4
adapt_delta = 0.8
max_treedepth = 10
#ergodic coefficients
c_a_erg=0.0
#parallel options
# flag_parallel = True
flag_parallel = False
#output sub-dir with corr with suffix info
out_dir_sub = f'%s%s'%(out_dir_sub, synds_suffix)
#load cell dataframes
cellinfo_fname = '%s%s.csv'%(ds_dir, ds_fname_cellinfo)
celldist_fname = '%s%s.csv'%(ds_dir, ds_fname_celldist)
df_cellinfo = pd.read_csv(cellinfo_fname)
df_celldist = pd.read_csv(celldist_fname)
# Run stan regression
# ---------------------------
#create datafame with computation time
df_run_info = list()
#iterate over all synthetic datasets
for d_id in ds_id:
print('Synthetic dataset %i fo %i'%(d_id, len(ds_id)))
#run time start
run_t_strt = time.time()
#input flatfile
ds_fname = '%s%s%s_Y%i.csv'%(ds_dir, ds_fname_main, synds_suffix, d_id)
#load flatfile
df_flatfile = pd.read_csv(ds_fname)
#keep only North records of NGAWest2
df_flatfile = df_flatfile.loc[np.logical_and(df_flatfile.dsid==0,
df_flatfile.sreg==1),:]
#output file name and directory
out_fname = '%s%s_Y%i'%(out_fname_main, synds_suffix, d_id)
out_dir = '%s/%s/Y%i/'%(out_dir_main, out_dir_sub, d_id)
#run stan model
RunStan(df_flatfile, df_cellinfo, df_celldist, sm_fname,
out_fname, out_dir, res_name, c_a_erg=c_a_erg,
runstan_flag=runstan_flag, n_iter=n_iter, n_chains=n_chains,
adapt_delta=adapt_delta, max_treedepth=max_treedepth,
pystan_ver=pystan_ver, pystan_parallel=flag_parallel)
#run time end
run_t_end = time.time()
#compute run time
run_tm = (run_t_end - run_t_strt)/60
#log run time
df_run_info.append(pd.DataFrame({'computer_name':os.uname()[1],'out_name':out_dir_sub,
'ds_id':d_id,'run_time':run_tm}, index=[d_id]))
#write out run info
out_fname = '%s%s/run_info.csv'%(out_dir_main, out_dir_sub)
pd.concat(df_run_info).reset_index(drop=True).to_csv(out_fname, index=False)
| 4,608 | 33.916667 | 107 | py |
ngmm_tools | ngmm_tools-master/Analyses/Code_Verification/regression/ds2/comparison_stan_model2_corr_cells.py | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Thu Aug 12 10:26:06 2021
@author: glavrent
"""
# Working directory and Packages
# ---------------------------
#load packages
import os
import sys
import pathlib
import glob
import re #regular expression package
import pickle
#arithmetic libraries
import numpy as np
#statistics libraries
import pandas as pd
#plot libraries
import matplotlib as mpl
import matplotlib.pyplot as plt
from matplotlib.ticker import AutoLocator as plt_autotick
#user functions
sys.path.insert(0,'../../../Python_lib/regression/')
from pylib_stats import CalcRMS
from pylib_stats import CalcLKDivergece
# Define variables
# ---------------------------
# USER SETS DIRECTORIES AND FILE INFO OF SYNTHETIC DS AND REGRESSION RESULTS
# ++++++++++++++++++++++++++++++++++++++++
#processed dataset
# name_dataset = 'NGAWest2CANorth'
name_dataset = 'NGAWest2CA'
# name_dataset = 'NGAWest3CA'
#correlation info
# 1: Small Correlation Lengths
# 2: Large Correlation Lenghts
corr_id = 1
#package
# 1: Pystan v2
# 2: Pystan v3
# 3: stancmd
pkg_id = 3
#approximation type
# 1: multivariate normal
# 2: cholesky
# 3: cholesky efficient
# 4: cholesky efficient v2
# 5: cholesky efficient, sparse cells
aprox_id = 5
#directories (synthetic dataset)
if corr_id == 1:
dir_syndata = '../../../../Data/Verification/synthetic_datasets/ds2_small_corr_len'
elif corr_id == 2:
dir_syndata = '../../../../Data/Verification/synthetic_datasets/ds2_large_corr_len'
#cell info
fname_cellinfo = dir_syndata + '/' + 'CatalogNGAWest3CALite_cellinfo.csv'
fname_distmat = dir_syndata + '/' + 'CatalogNGAWest3CALite_distancematrix.csv'
#directories (regression results)
if pkg_id == 1:
dir_results = f'../../../../Data/Verification/regression/ds2/PYSTAN_%s'%name_dataset
elif pkg_id == 2:
dir_results = f'../../../../Data/Verification/regression/ds2/PYSTAN3_%s'%name_dataset
elif pkg_id == 3:
dir_results = f'../../../../Data/Verification/regression/ds2/CMDSTAN_%s'%name_dataset
#directories (regression results)
if pkg_id == 1:
dir_results = f'../../../../Data/Verification/regression_old/ds2/PYSTAN_%s'%name_dataset
elif pkg_id == 2:
dir_results = f'../../../../Data/Verification/regression_old/ds2/PYSTAN3_%s'%name_dataset
elif pkg_id == 3:
dir_results = f'../../../../Data/Verification/regression_old/ds2/CMDSTAN_%s'%name_dataset
#prefix for synthetic data and results
prfx_syndata = 'CatalogNGAWest3CALite_synthetic'
#regression results filename prefix
prfx_results = f'%s_syndata'%name_dataset
# FILE INFO FOR REGRESSION RESULTS
# ++++++++++++++++++++++++++++++++++++++++
#output filename sufix (synthetic dataset)
if corr_id == 1: synds_suffix = '_small_corr_len'
elif corr_id == 2: synds_suffix = '_large_corr_len'
#output filename sufix (regression results)
if aprox_id == 1: synds_suffix_stan = '_corr_cells' + synds_suffix
elif aprox_id == 2: synds_suffix_stan = '_corr_cells' + '_chol' + synds_suffix
elif aprox_id == 3: synds_suffix_stan = '_corr_cells' + '_chol_eff' + synds_suffix
elif aprox_id == 4: synds_suffix_stan = '_corr_cells' + '_chol_eff2' + synds_suffix
elif aprox_id == 5: synds_suffix_stan = '_corr_cells' + '_chol_eff_sp' + synds_suffix
# dataset info
ds_id = np.arange(1,6)
# ++++++++++++++++++++++++++++++++++++++++
# USER NEEDS TO SPECIFY HYPERPARAMETERS OF SYNTHETIC DATASET
# ++++++++++++++++++++++++++++++++++++++++
# hyper-parameters
if corr_id == 1:
# small correlation lengths
hyp = {'omega_0': 0.1, 'omega_1e':0.1, 'omega_1as': 0.35, 'omega_1bs': 0.25,
'ell_1e':60, 'ell_1as':30,
'c_cap_erg': -0.011,
'omega_cap_mu': 0.005, 'omega_ca1p':0.004, 'omega_ca2p':0.002,
'ell_ca1p': 75,
'phi_0':0.4, 'tau_0':0.3 }
elif corr_id == 2:
#large correlation lengths
hyp = {'omega_0': 0.1, 'omega_1e':0.2, 'omega_1as': 0.4, 'omega_1bs': 0.3,
'ell_1e':100, 'ell_1as':70,
'c_cap_erg': -0.02,
'omega_cap_mu': 0.008, 'omega_ca1p':0.005, 'omega_ca2p':0.003,
'ell_ca1p': 120,
'phi_0':0.4, 'tau_0':0.3}
# ++++++++++++++++++++++++++++++++++++++++
#ploting options
flag_report = True
# Compare results
# ---------------------------
#load cell data
df_cellinfo = pd.read_csv(fname_cellinfo).set_index('cellid')
df_distmat = pd.read_csv(fname_distmat).set_index('rsn')
#initialize misfit metrics dataframe
df_misfit = pd.DataFrame(index=['Y%i'%d_id for d_id in ds_id])
#iterate over different datasets
for d_id in ds_id:
# Load Data
#--- --- --- --- ---
#file names
#synthetic data
fname_sdata_gmotion = '%s/%s_%s%s_Y%i'%(dir_syndata, prfx_syndata, 'data', synds_suffix, d_id) + '.csv'
fname_sdata_atten = '%s/%s_%s%s_Y%i'%(dir_syndata, prfx_syndata, 'atten', synds_suffix, d_id) + '.csv'
#regression results
fname_reg_gmotion = '%s%s/Y%i/%s%s_Y%i_stan_%s'%(dir_results, synds_suffix_stan, d_id, prfx_results, synds_suffix, d_id, 'residuals') + '.csv'
fname_reg_coeff = '%s%s/Y%i/%s%s_Y%i_stan_%s'%(dir_results, synds_suffix_stan, d_id, prfx_results, synds_suffix, d_id, 'coefficients') + '.csv'
fname_reg_atten = '%s%s/Y%i/%s%s_Y%i_stan_%s'%(dir_results, synds_suffix_stan, d_id, prfx_results, synds_suffix, d_id, 'catten') + '.csv'
#load synthetic results
df_sdata_gmotion = pd.read_csv(fname_sdata_gmotion).set_index('rsn')
df_sdata_atten = pd.read_csv(fname_sdata_atten).set_index('cellid')
#load regression results
df_reg_gmotion = pd.read_csv(fname_reg_gmotion, index_col=0)
df_reg_coeff = pd.read_csv(fname_reg_coeff, index_col=0)
df_reg_atten = pd.read_csv(fname_reg_atten, index_col=0)
# Processing
#--- --- --- --- ---
#keep only relevant columns from synthetic dataset
df_sdata_gmotion = df_sdata_gmotion.reindex(df_reg_gmotion.index)
df_sdata_atten = df_sdata_atten.reindex(df_reg_atten.index)
#distance matrix for records of interest
df_dmat = df_distmat.reindex(df_sdata_gmotion.index)
#find unique earthqakes and stations
eq_id, eq_idx, eq_nrec = np.unique(df_sdata_gmotion.eqid, return_index=True, return_counts=True)
sta_id, sta_idx, sta_nrec = np.unique(df_sdata_gmotion.ssn, return_index=True, return_counts=True)
#number of paths per cell
cell_npath = np.sum(df_dmat.loc[:,df_reg_atten.cellname] > 0, axis=0)
# Compute Root Mean Square Error
#--- --- --- --- ---
df_misfit.loc['Y%i'%d_id,'nerg_tot_rms'] = CalcRMS(df_sdata_gmotion.nerg_gm.values, df_reg_gmotion.nerg_mu.values)
df_misfit.loc['Y%i'%d_id,'dc_1e_rms'] = CalcRMS(df_sdata_gmotion['dc_1e'].values[eq_idx], df_reg_coeff['dc_1e_mean'].values[eq_idx])
df_misfit.loc['Y%i'%d_id,'dc_1as_rms'] = CalcRMS(df_sdata_gmotion['dc_1as'].values[sta_idx], df_reg_coeff['dc_1as_mean'].values[sta_idx])
df_misfit.loc['Y%i'%d_id,'dc_1bs_rms'] = CalcRMS(df_sdata_gmotion['dc_1bs'].values[sta_idx], df_reg_coeff['dc_1bs_mean'].values[sta_idx])
df_misfit.loc['Y%i'%d_id,'c_cap_rms'] = CalcRMS(df_sdata_atten['c_cap'].values, df_reg_atten['c_cap_mean'].values)
# Compute Divergence
#--- --- --- --- ---
df_misfit.loc['Y%i'%d_id,'nerg_tot_KL'] = CalcLKDivergece(df_sdata_gmotion.nerg_gm.values, df_reg_gmotion.nerg_mu.values)
df_misfit.loc['Y%i'%d_id,'dc_1e_KL'] = CalcLKDivergece(df_sdata_gmotion['dc_1e'].values[eq_idx], df_reg_coeff['dc_1e_mean'].values[eq_idx])
df_misfit.loc['Y%i'%d_id,'dc_1as_KL'] = CalcLKDivergece(df_sdata_gmotion['dc_1as'].values[sta_idx], df_reg_coeff['dc_1as_mean'].values[sta_idx])
df_misfit.loc['Y%i'%d_id,'dc_1bs_KL'] = CalcLKDivergece(df_sdata_gmotion['dc_1bs'].values[sta_idx], df_reg_coeff['dc_1bs_mean'].values[sta_idx])
df_misfit.loc['Y%i'%d_id,'c_cap_KL'] = CalcLKDivergece(df_sdata_atten['c_cap'].values, df_reg_atten['c_cap_mean'].values)
# Output
#--- --- --- --- ---
#figure directory
dir_fig = '%s%s/Y%i/figures_cmp/'%(dir_results,synds_suffix_stan,d_id)
pathlib.Path(dir_fig).mkdir(parents=True, exist_ok=True)
#compare ground motion predictions
#... ... ... ... ... ...
#figure title
fname_fig = 'Y%i_scatter_tot_res'%d_id
#create figure
fig, ax = plt.subplots(figsize = (10,10))
#median
ax.scatter(df_sdata_gmotion.nerg_gm.values, df_reg_gmotion.nerg_mu.values)
ax.axline((0,0), slope=1, color="black", linestyle="--")
#edit figure
if not flag_report: ax.set_title('Comparison total residuals, Y: %i'%d_id, fontsize=30)
ax.set_xlabel('Synthetic dataset', fontsize=35)
ax.set_ylabel('Estimated', fontsize=35)
ax.grid(which='both')
ax.tick_params(axis='x', labelsize=32)
ax.tick_params(axis='y', labelsize=32)
#plot limits
# plt_lim = np.array([ax.get_xlim(), ax.get_ylim()])
# plt_lim = (plt_lim[:,0].min(), plt_lim[:,1].max())
# ax.set_xlim(plt_lim)
# ax.set_ylim(plt_lim)
ax.set_xlim([-10,2])
ax.set_ylim([-10,2])
fig.tight_layout()
fig.savefig( dir_fig + fname_fig + '.png' )
#compare dc_1e
#... ... ... ... ... ...
#figure title
fname_fig = 'Y%i_dc_1e_scatter'%d_id
#create figure
fig, ax = plt.subplots(figsize = (10,10))
#coefficient scatter
ax.scatter(df_sdata_gmotion['dc_1e'].values[eq_idx], df_reg_coeff['dc_1e_mean'].values[eq_idx])
ax.axline((0,0), slope=1, color="black", linestyle="--")
#edit figure
if not flag_report: ax.set_title(r'Comparison $\delta c_{1,E}$, Y: %i'%d_id, fontsize=30)
ax.set_xlabel('Synthetic dataset', fontsize=25)
ax.set_ylabel('Estimated', fontsize=25)
ax.grid(which='both')
ax.tick_params(axis='x', labelsize=22)
ax.tick_params(axis='y', labelsize=22)
#plot limits
# plt_lim = np.array([ax.get_xlim(), ax.get_ylim()])
# plt_lim = (plt_lim[:,0].min(), plt_lim[:,1].max())
# ax.set_xlim(plt_lim)
# ax.set_ylim(plt_lim)
ax.set_xlim([-.4,.4])
ax.set_ylim([-.4,.4])
#save figure
fig.tight_layout()
fig.savefig( dir_fig + fname_fig + '.png' )
#figure title
fname_fig = 'Y%i_dc_1e_accuracy'%d_id
#create figure
fig, ax = plt.subplots(figsize = (10,10))
#coefficient scatter
ax.scatter(df_reg_coeff['dc_1e_sig'].values[eq_idx],
df_sdata_gmotion['dc_1e'].values[eq_idx] - df_reg_coeff['dc_1e_mean'].values[eq_idx])
ax.axline((0,0), slope=0, color="black", linestyle="--")
#edit figure
if not flag_report: ax.set_title(r'Comparison $\delta c_{1,E}$, Y: %i'%d_id, fontsize=30)
ax.set_xlabel('Standard Deviation', fontsize=25)
ax.set_ylabel('Actual - Estimated', fontsize=25)
ax.grid(which='both')
ax.tick_params(axis='x', labelsize=22)
ax.tick_params(axis='y', labelsize=22)
#plot limits
# ax.set_ylim(np.abs(ax.get_ylim()).max()*np.array([-1,1]))
ax.set_xlim([0,.15])
ax.set_ylim([-.4,.4])
#save figure
fig.tight_layout()
fig.savefig( dir_fig + fname_fig + '.png' )
#figure title
fname_fig = 'Y%i_dc_1e_nrec'%d_id
#create figure
fig, ax = plt.subplots(figsize = (10,10))
#coefficient scatter
ax.scatter(eq_nrec,
df_sdata_gmotion['dc_1e'].values[eq_idx] - df_reg_coeff['dc_1e_mean'].values[eq_idx])
ax.axline((0,0), slope=0, color="black", linestyle="--")
#edit figure
if not flag_report: ax.set_title(r'Comparison $\delta c_{1,E}$, Y: %i'%d_id, fontsize=30)
ax.set_xlabel('Number of records', fontsize=25)
ax.set_ylabel('Actual - Estimated', fontsize=25)
ax.grid(which='both')
ax.set_xscale('log')
ax.tick_params(axis='x', labelsize=22)
ax.tick_params(axis='y', labelsize=22)
#plot limits
# ax.set_ylim(np.abs(ax.get_ylim()).max()*np.array([-1,1]))
ax.set_xlim([0.9,1e3])
ax.set_ylim([-.4,.4])
#save figure
fig.tight_layout()
fig.savefig( dir_fig + fname_fig + '.png' )
#compare dc_1as
#... ... ... ... ... ...
#figure title
fname_fig = 'Y%i_dc_1as_scatter'%d_id
#create figure
fig, ax = plt.subplots(figsize = (10,10))
#coefficient scatter
ax.scatter(df_sdata_gmotion['dc_1as'].values[sta_idx], df_reg_coeff['dc_1as_mean'].values[sta_idx])
ax.axline((0,0), slope=1, color="black", linestyle="--")
#edit figure
if not flag_report: ax.set_title(r'Comparison $\delta c_{1a,S}$, Y: %i'%d_id, fontsize=30)
ax.set_xlabel('Synthetic dataset', fontsize=25)
ax.set_ylabel('Estimated', fontsize=25)
ax.grid(which='both')
ax.tick_params(axis='x', labelsize=22)
ax.tick_params(axis='y', labelsize=22)
#plot limits
# plt_lim = np.array([ax.get_xlim(), ax.get_ylim()])
# plt_lim = (plt_lim[:,0].min(), plt_lim[:,1].max())
# ax.set_xlim(plt_lim)
# ax.set_ylim(plt_lim)
ax.set_xlim([-1.5,1.5])
ax.set_ylim([-1.5,1.5])
#save figure
fig.tight_layout()
fig.savefig( dir_fig + fname_fig + '.png' )
#figure title
fname_fig = 'Y%i_dc_1as_accuracy'%d_id
#create figure
fig, ax = plt.subplots(figsize = (10,10))
#accuray
ax.scatter(df_reg_coeff['dc_1as_sig'].values[sta_idx],
df_sdata_gmotion['dc_1as'].values[sta_idx] - df_reg_coeff['dc_1as_mean'].values[sta_idx])
ax.axline((0,0), slope=0, color="black", linestyle="--")
#edit figure
if not flag_report: ax.set_title(r'Comparison $\delta c_{1a,S}$, Y: %i'%d_id, fontsize=30)
ax.set_xlabel('Standard Deviation', fontsize=25)
ax.set_ylabel('Actual - Estimated', fontsize=25)
ax.grid(which='both')
ax.tick_params(axis='x', labelsize=22)
ax.tick_params(axis='y', labelsize=22)
#plot limits
# ax.set_ylim(np.abs(ax.get_ylim()).max()*np.array([-1,1]))
ax.set_xlim([0,.4])
ax.set_ylim([-1.5,1.5])
#save figure
fig.tight_layout()
fig.savefig( dir_fig + fname_fig + '.png' )
#figure title
fname_fig = 'Y%i_dc_1as_nrec'%d_id
#create figure
fig, ax = plt.subplots(figsize = (10,10))
#accuray
ax.scatter(sta_nrec,
df_sdata_gmotion['dc_1as'].values[sta_idx] - df_reg_coeff['dc_1as_mean'].values[sta_idx])
ax.axline((0,0), slope=0, color="black", linestyle="--")
#edit figure
if not flag_report: ax.set_title(r'Comparison $\delta c_{1a,S}$, Y: %i'%d_id, fontsize=30)
ax.set_xlabel('Number of records', fontsize=25)
ax.set_ylabel('Actual - Estimated', fontsize=25)
ax.grid(which='both')
ax.set_xscale('log')
ax.tick_params(axis='x', labelsize=22)
ax.tick_params(axis='y', labelsize=22)
#plot limits
# ax.set_ylim(np.abs(ax.get_ylim()).max()*np.array([-1,1]))
ax.set_xlim([.9,1000])
ax.set_ylim([-1.5,1.5])
#save figure
fig.tight_layout()
fig.savefig( dir_fig + fname_fig + '.png' )
#compare dc_1bs
#... ... ... ... ... ...
#figure title
fname_fig = 'Y%i_dc_1bs_scatter'%d_id
#create figure
fig, ax = plt.subplots(figsize = (10,10))
#coefficient scatter
ax.scatter(df_sdata_gmotion['dc_1bs'].values[sta_idx], df_reg_coeff['dc_1bs_mean'].values[sta_idx])
ax.axline((0,0), slope=1, color="black", linestyle="--")
#edit figure
if not flag_report: ax.set_title(r'Comparison $\delta c_{1b,S}$, Y: %i'%d_id, fontsize=30)
ax.set_xlabel('Synthetic dataset', fontsize=25)
ax.set_ylabel('Estimated', fontsize=25)
ax.grid(which='both')
ax.tick_params(axis='x', labelsize=22)
ax.tick_params(axis='y', labelsize=22)
#plot limits
# plt_lim = np.array([ax.get_xlim(), ax.get_ylim()])
# plt_lim = (plt_lim[:,0].min(), plt_lim[:,1].max())
# ax.set_xlim(plt_lim)
# ax.set_ylim(plt_lim)
ax.set_xlim([-1.5,1.5])
ax.set_ylim([-1.5,1.5])
#save figure
fig.tight_layout()
fig.savefig( dir_fig + fname_fig + '.png' )
#figure title
fname_fig = 'Y%i_dc_1bs_accuracy'%d_id
#create figure
fig, ax = plt.subplots(figsize = (10,10))
#accuray
ax.scatter(df_reg_coeff['dc_1bs_sig'].values[sta_idx],
df_sdata_gmotion['dc_1bs'].values[sta_idx] - df_reg_coeff['dc_1bs_mean'].values[sta_idx])
ax.axline((0,0), slope=0, color="black", linestyle="--")
#edit figure
if not flag_report: ax.set_title(r'Comparison $\delta c_{1b,S}$, Y: %i'%d_id, fontsize=30)
ax.set_xlabel('Standard Deviation', fontsize=25)
ax.set_ylabel('Actual - Estimated', fontsize=25)
ax.grid(which='both')
ax.tick_params(axis='x', labelsize=22)
ax.tick_params(axis='y', labelsize=22)
#plot limits
# ax.set_ylim(np.abs(ax.get_ylim()).max()*np.array([-1,1]))
ax.set_xlim([0,.4])
ax.set_ylim([-1.5,1.5])
#save figure
fig.tight_layout()
fig.savefig( dir_fig + fname_fig + '.png' )
#figure title
fname_fig = 'Y%i_dc_1bs_nrec'%d_id
#create figure
fig, ax = plt.subplots(figsize = (10,10))
#accuray
ax.scatter(sta_nrec,
df_sdata_gmotion['dc_1bs'].values[sta_idx] - df_reg_coeff['dc_1bs_mean'].values[sta_idx])
ax.axline((0,0), slope=0, color="black", linestyle="--")
#edit figure
if not flag_report: ax.set_title(r'Comparison $\delta c_{1b,S}$, Y: %i'%d_id, fontsize=30)
ax.set_xlabel('Number of records', fontsize=25)
ax.set_ylabel('Actual - Estimated', fontsize=25)
ax.grid(which='both')
ax.set_xscale('log')
ax.tick_params(axis='x', labelsize=22)
ax.tick_params(axis='y', labelsize=22)
#plot limits
# ax.set_ylim(np.abs(ax.get_ylim()).max()*np.array([-1,1]))
ax.set_xlim([.9,1000])
ax.set_ylim([-1.5,1.5])
#save figure
fig.tight_layout()
fig.savefig( dir_fig + fname_fig + '.png' )
#compare c_cap
#... ... ... ... ... ...
#figure title
fname_fig = 'Y%i_c_cap_scatter'%d_id
#create figure
fig, ax = plt.subplots(figsize = (10,10))
#coefficient scatter
ax.scatter(df_sdata_atten['c_cap'].values, df_reg_atten['c_cap_mean'].values)
ax.axline((0,0), slope=1, color="black", linestyle="--")
#edit figure
if not flag_report: ax.set_title(r'Comparison $c_{ca,P}$, Y: %i'%d_id, fontsize=30)
ax.set_xlabel('Synthetic dataset', fontsize=25)
ax.set_ylabel('Estimated', fontsize=25)
ax.grid(which='both')
ax.tick_params(axis='x', labelsize=22)
ax.tick_params(axis='y', labelsize=22)
#plot limits
# plt_lim = np.array([ax.get_xlim(), ax.get_ylim()])
# plt_lim = (plt_lim[:,0].min(), plt_lim[:,1].max())
# ax.set_xlim(plt_lim)
# ax.set_ylim(plt_lim)
ax.set_xlim([-0.05,0.02])
ax.set_ylim([-0.05,0.02])
#save figure
fig.tight_layout()
fig.savefig( dir_fig + fname_fig + '.png' )
#figure title
fname_fig = 'Y%i_c_cap_accuracy'%d_id
#create figure
fig, ax = plt.subplots(figsize = (10,10))
#coefficient scatter
ax.scatter(df_reg_atten['c_cap_sig'],
df_sdata_atten['c_cap'].values - df_reg_atten['c_cap_mean'].values)
ax.axline((0,0), slope=0, color="black", linestyle="--")
#edit figure
if not flag_report: ax.set_title(r'Comparison $c_{ca,P}$, Y: %i'%d_id, fontsize=30)
ax.set_xlabel('Standard Deviation', fontsize=25)
ax.set_ylabel('Actual - Estimated', fontsize=25)
ax.grid(which='both')
ax.tick_params(axis='x', labelsize=22)
ax.tick_params(axis='y', labelsize=22)
#plot limits
# ax.set_ylim(np.abs(ax.get_ylim()).max()*np.array([-1,1]))
ax.set_xlim([0.00,0.03])
ax.set_ylim([-0.04,0.04])
#save figure
fig.tight_layout()
fig.savefig( dir_fig + fname_fig + '.png' )
#figure title
fname_fig = 'Y%i_c_cap_npath'%d_id
#create figure
fig, ax = plt.subplots(figsize = (10,10))
#coefficient scatter
ax.scatter(cell_npath,
df_sdata_atten['c_cap'].values - df_reg_atten['c_cap_mean'].values)
ax.axline((0,0), slope=0, color="black", linestyle="--")
#edit figure
if not flag_report: ax.set_title(r'Comparison $c_{ca,P}$, Y: %i'%d_id, fontsize=30)
ax.set_xlabel('Number of paths', fontsize=25)
ax.set_ylabel('Actual - Estimated', fontsize=25)
ax.grid(which='both')
ax.set_xscale('log')
ax.tick_params(axis='x', labelsize=22)
ax.tick_params(axis='y', labelsize=22)
#plot limits
# ax.set_ylim(np.abs(ax.get_ylim()).max()*np.array([-1,1]))
ax.set_xlim([.9,5e4])
ax.set_ylim([-0.04,0.04])
#save figure
fig.tight_layout()
fig.savefig( dir_fig + fname_fig + '.png' )
# Compare Misfit Metrics
# ---------------------------
#summary directory
dir_sum = '%s%s/summary/'%(dir_results,synds_suffix_stan)
pathlib.Path(dir_fig).mkdir(parents=True, exist_ok=True)
#figure directory
dir_fig = '%s/figures/'%(dir_sum)
pathlib.Path(dir_fig).mkdir(parents=True, exist_ok=True)
#save
df_misfit.to_csv(dir_sum + 'misfit_summary.csv')
#RMS misfit
fname_fig = 'misfit_score'
#plot KL divergence
fig, ax = plt.subplots(figsize = (10,10))
ax.plot(ds_id, df_misfit.nerg_tot_rms, linestyle='-', marker='o', linewidth=2, markersize=10, label= 'tot nerg')
ax.plot(ds_id, df_misfit.dc_1e_rms, linestyle='-', marker='o', linewidth=2, markersize=10, label=r'$\delta c_{1,E}$')
ax.plot(ds_id, df_misfit.dc_1as_rms, linestyle='-', marker='o', linewidth=2, markersize=10, label=r'$\delta c_{1a,S}$')
ax.plot(ds_id, df_misfit.dc_1bs_rms, linestyle='-', marker='o', linewidth=2, markersize=10, label=r'$\delta c_{1b,S}$')
ax.plot(ds_id, df_misfit.c_cap_rms, linestyle='-', marker='o', linewidth=2, markersize=10, label=r'$c_{ca,P}$')
#figure properties
ax.set_ylim([0,0.50])
ax.set_xlabel('synthetic dataset', fontsize=25)
ax.set_ylabel('RSME', fontsize=25)
ax.grid(which='both')
ax.set_xticks(ds_id)
ax.set_xticklabels(labels=df_misfit.index)
ax.tick_params(axis='x', labelsize=22)
ax.tick_params(axis='y', labelsize=22)
#legend
ax.legend(loc='upper left', fontsize=25)
#save figure
fig.tight_layout()
fig.savefig( dir_fig + fname_fig + '.png' )
#KL divergence
fname_fig = 'KLdiv_score'
#plot KL divergence
fig, ax = plt.subplots(figsize = (10,10))
ax.plot(ds_id, df_misfit.nerg_tot_KL, linestyle='-', marker='o', linewidth=2, markersize=10, label= 'tot nerg')
ax.plot(ds_id, df_misfit.dc_1e_KL, linestyle='-', marker='o', linewidth=2, markersize=10, label=r'$\delta c_{1,E}$')
ax.plot(ds_id, df_misfit.dc_1as_KL, linestyle='-', marker='o', linewidth=2, markersize=10, label=r'$\delta c_{1a,S}$')
ax.plot(ds_id, df_misfit.dc_1bs_KL, linestyle='-', marker='o', linewidth=2, markersize=10, label=r'$\delta c_{1b,S}$')
ax.plot(ds_id, df_misfit.c_cap_KL, linestyle='-', marker='o', linewidth=2, markersize=10, label=r'$c_{ca,P}$')
#figure properties
ax.set_ylim([0,0.50])
ax.set_xlabel('synthetic dataset', fontsize=25)
ax.set_ylabel('KL divergence', fontsize=25)
ax.grid(which='both')
ax.set_xticks(ds_id)
ax.set_xticklabels(labels=df_misfit.index)
ax.tick_params(axis='x', labelsize=22)
ax.tick_params(axis='y', labelsize=22)
#legend
ax.legend(loc='upper left', fontsize=25)
#save figure
fig.tight_layout()
fig.savefig( dir_fig + fname_fig + '.png' )
# Compare hyper-paramters
# ---------------------------
#iterate over different datasets
df_reg_hyp = list()
df_reg_hyp_post = list()
for d_id in ds_id:
# Load Data
#--- --- --- --- ---
#regression hyperparamters results
fname_reg_hyp = '%s%s/Y%i/%s%s_Y%i_stan_%s'%(dir_results,synds_suffix_stan, d_id,prfx_results, synds_suffix, d_id, 'hyperparameters') + '.csv'
fname_reg_hyp_post = '%s%s/Y%i/%s%s_Y%i_stan_%s'%(dir_results,synds_suffix_stan, d_id,prfx_results, synds_suffix, d_id, 'hyperposterior') + '.csv'
#load regression results
df_reg_hyp.append( pd.read_csv(fname_reg_hyp, index_col=0) )
df_reg_hyp_post.append( pd.read_csv(fname_reg_hyp_post, index_col=0) )
# Omega_1e
#--- --- --- --- ---
#hyper-paramter name
name_hyp = 'omega_1e'
#figure title
fname_fig = 'post_dist_' + name_hyp
#create figure
fig, ax = plt.subplots(figsize = (10,10))
for d_id, df_r_h, df_r_h_p in zip(ds_id, df_reg_hyp, df_reg_hyp_post):
#estimate vertical line height for mean and mode
ymax_mode = 40
ymax_mean = 40
#plot posterior dist
pl_hyp = ax.vlines(df_r_h.loc['mean',name_hyp], ymin=0, ymax=ymax_mean, linestyle='-', label='Mean')
ax.vlines(df_r_h.loc['prc_0.50',name_hyp], ymin=0, ymax=ymax_mode, linestyle='--', color=pl_hyp.get_color(), label='Mode')
#plot true value
ymax_hyp = ymax_mean
ax.vlines(hyp[name_hyp], ymin=0, ymax=ymax_hyp, linestyle='-', linewidth=4, color='black', label='True value')
#edit figure
if not flag_report: ax.set_title(r'Comparison $\omega_{1,E}$', fontsize=30)
ax.set_xlabel('$\omega_{1,E}$', fontsize=25)
ax.set_ylabel('probability density function ', fontsize=25)
ax.grid(which='both')
ax.tick_params(axis='x', labelsize=22)
ax.tick_params(axis='y', labelsize=22)
#plot limits
ax.set_xlim([0,0.25])
ax.set_ylim([0,ymax_hyp])
#save figure
fig.tight_layout()
fig.savefig( dir_fig + fname_fig + '.png' )
# Omega_1as
#--- --- --- --- ---
#hyper-paramter name
name_hyp = 'omega_1as'
#figure title
fname_fig = 'post_dist_' + name_hyp
#create figure
fig, ax = plt.subplots(figsize = (10,10))
for d_id, df_r_h, df_r_h_p in zip(ds_id, df_reg_hyp, df_reg_hyp_post):
#estimate vertical line height for mean and mode
ymax_mode = 30
ymax_mean = 30
#plot posterior dist
pl_hyp = ax.vlines(df_r_h.loc['mean',name_hyp], ymin=0, ymax=ymax_mean, linestyle='-', label='Mean')
ax.vlines(df_r_h.loc['prc_0.50',name_hyp], ymin=0, ymax=ymax_mode, linestyle='--', color=pl_hyp.get_color(), label='Mode')
#plot true value
ymax_hyp = ymax_mean
ax.vlines(hyp[name_hyp], ymin=0, ymax=ymax_hyp, linestyle='-', linewidth=4, color='black', label='True value')
#edit figure
if not flag_report: ax.set_title(r'Comparison $\omega_{1a,S}$', fontsize=30)
ax.set_xlabel('$\omega_{1a,S}$', fontsize=25)
ax.set_ylabel('probability density function ', fontsize=25)
ax.grid(which='both')
ax.tick_params(axis='x', labelsize=22)
ax.tick_params(axis='y', labelsize=22)
#plot limits
ax.set_xlim([0,0.5])
ax.set_ylim([0,ymax_hyp])
#save figure
fig.tight_layout()
fig.savefig( dir_fig + fname_fig + '.png' )
# Omega_1bs
#--- --- --- --- ---
#hyper-paramter name
name_hyp = 'omega_1bs'
#figure title
fname_fig = 'post_dist_' + name_hyp
#create figure
fig, ax = plt.subplots(figsize = (10,10))
for d_id, df_r_h, df_r_h_p in zip(ds_id, df_reg_hyp, df_reg_hyp_post):
#estimate vertical line height for mean and mode
ymax_mode = 60
ymax_mean = 60
#plot posterior dist
pl_hyp = ax.vlines(df_r_h.loc['mean',name_hyp], ymin=0, ymax=ymax_mean, linestyle='-', label='Mean')
ax.vlines(df_r_h.loc['prc_0.50',name_hyp], ymin=0, ymax=ymax_mode, linestyle='--', color=pl_hyp.get_color(), label='Mode')
#plot true value
ymax_hyp = ymax_mean
ax.vlines(hyp[name_hyp], ymin=0, ymax=ymax_hyp, linestyle='-', linewidth=4, color='black', label='True value')
#edit figure
if not flag_report: ax.set_title(r'Comparison $\omega_{1b,S}$', fontsize=30)
ax.set_xlabel('$\omega_{1b,S}$', fontsize=25)
ax.set_ylabel('probability density function ', fontsize=25)
ax.grid(which='both')
ax.tick_params(axis='x', labelsize=22)
ax.tick_params(axis='y', labelsize=22)
#plot limits
ax.set_xlim([0,0.5])
ax.set_ylim([0,ymax_hyp])
#save figure
fig.tight_layout()
fig.savefig( dir_fig + fname_fig + '.png' )
# Ell_1e
#--- --- --- --- ---
#hyper-paramter name
name_hyp = 'ell_1e'
#figure title
fname_fig = 'post_dist_' + name_hyp
#create figure
fig, ax = plt.subplots(figsize = (10,10))
for d_id, df_r_h, df_r_h_p in zip(ds_id, df_reg_hyp, df_reg_hyp_post):
#estimate vertical line height for mean and mode
ymax_mode = 0.02
ymax_mean = 0.02
#plot posterior dist
pl_hyp = ax.vlines(df_r_h.loc['mean',name_hyp], ymin=0, ymax=ymax_mean, linestyle='-', label='Mean')
ax.vlines(df_r_h.loc['prc_0.50',name_hyp], ymin=0, ymax=ymax_mode, linestyle='--', color=pl_hyp.get_color(), label='Mode')
#plot true value
ymax_hyp = ymax_mean
ax.vlines(hyp[name_hyp], ymin=0, ymax=ymax_hyp, linestyle='-', linewidth=4, color='black', label='True value')
#edit figure
if not flag_report: ax.set_title(r'Comparison $\ell_{1,E}$', fontsize=30)
ax.set_xlabel('$\ell_{1,E}$', fontsize=25)
ax.set_ylabel('probability density function ', fontsize=25)
ax.grid(which='both')
ax.tick_params(axis='x', labelsize=22)
ax.tick_params(axis='y', labelsize=22)
#plot limits
ax.set_xlim([0,500])
ax.set_ylim([0,ymax_hyp])
#save figure
fig.tight_layout()
fig.savefig( dir_fig + fname_fig + '.png' )
# Ell_1as
#--- --- --- --- ---
#hyper-paramter name
name_hyp = 'ell_1as'
#figure title
fname_fig = 'post_dist_' + name_hyp
#create figure
fig, ax = plt.subplots(figsize = (10,10))
for d_id, df_r_h, df_r_h_p in zip(ds_id, df_reg_hyp, df_reg_hyp_post):
#estimate vertical line height for mean and mode
ymax_mode = 0.1
ymax_mean = 0.1
#plot posterior dist
pl_hyp = ax.vlines(df_r_h.loc['mean',name_hyp], ymin=0, ymax=ymax_mean, linestyle='-', label='Mean')
ax.vlines(df_r_h.loc['prc_0.50',name_hyp], ymin=0, ymax=ymax_mode, linestyle='--', color=pl_hyp.get_color(), label='Mode')
#plot true value
ymax_hyp = ymax_mean
ax.vlines(hyp[name_hyp], ymin=0, ymax=ymax_hyp, linestyle='-', linewidth=4, color='black', label='True value')
#edit figure
if not flag_report: ax.set_title(r'Comparison $\ell_{1a,S}$', fontsize=30)
ax.set_xlabel('$\ell_{1a,S}$', fontsize=25)
ax.set_ylabel('probability density function ', fontsize=25)
ax.grid(which='both')
ax.tick_params(axis='x', labelsize=22)
ax.tick_params(axis='y', labelsize=22)
#plot limits
ax.set_xlim([0,150])
ax.set_ylim([0,ymax_hyp])
#save figure
fig.tight_layout()
fig.savefig( dir_fig + fname_fig + '.png' )
# Tau_0
#--- --- --- --- ---
#hyper-paramter name
name_hyp = 'tau_0'
#figure title
fname_fig = 'post_dist_' + name_hyp
#create figure
fig, ax = plt.subplots(figsize = (10,10))
for d_id, df_r_h, df_r_h_p in zip(ds_id, df_reg_hyp, df_reg_hyp_post):
#estimate vertical line height for mean and mode
ymax_mode = 150
ymax_mean = 150
#plot posterior dist
pl_hyp = ax.vlines(df_r_h.loc['mean',name_hyp], ymin=0, ymax=ymax_mean, linestyle='-', label='Mean')
ax.vlines(df_r_h.loc['prc_0.50',name_hyp], ymin=0, ymax=ymax_mode, linestyle='--', color=pl_hyp.get_color(), label='Mode')
#plot true value
ymax_hyp = ymax_mean
ax.vlines(hyp[name_hyp], ymin=0, ymax=ymax_hyp, linestyle='-', linewidth=4, color='black', label='True value')
#edit figure
if not flag_report: ax.set_title(r'Comparison $\tau_{0}$', fontsize=30)
ax.set_xlabel(r'$\tau_{0}$', fontsize=25)
ax.set_ylabel(r'probability density function ', fontsize=25)
ax.grid(which='both')
ax.tick_params(axis='x', labelsize=22)
ax.tick_params(axis='y', labelsize=22)
#plot limits
ax.set_xlim([0,0.5])
ax.set_ylim([0,ymax_hyp])
#save figure
fig.tight_layout()
fig.savefig( dir_fig + fname_fig + '.png' )
# Phi_0
#--- --- --- --- ---
#hyper-paramter name
name_hyp = 'phi_0'
#figure title
fname_fig = 'post_dist_' + name_hyp
#create figure
fig, ax = plt.subplots(figsize = (10,10))
for d_id, df_r_h, df_r_h_p in zip(ds_id, df_reg_hyp, df_reg_hyp_post):
#estimate vertical line height for mean and mode
ymax_mode = 1000
ymax_mean = 1000
#plot posterior dist
pl_hyp = ax.vlines(df_r_h.loc['mean',name_hyp], ymin=0, ymax=ymax_mean, linestyle='-', label='Mean')
ax.vlines(df_r_h.loc['prc_0.50',name_hyp], ymin=0, ymax=ymax_mode, linestyle='--', color=pl_hyp.get_color(), label='Mode')
#plot true value
ymax_hyp = ymax_mean
ax.vlines(hyp[name_hyp], ymin=0, ymax=ymax_hyp, linestyle='-', linewidth=4, color='black', label='True value')
#edit figure
if not flag_report: ax.set_title(r'Comparison $\phi_{0}$', fontsize=30)
ax.set_xlabel('$\phi_{0}$', fontsize=25)
ax.set_ylabel(r'probability density function ', fontsize=25)
ax.grid(which='both')
ax.tick_params(axis='x', labelsize=22)
ax.tick_params(axis='y', labelsize=22)
#plot limits
ax.set_xlim([0,0.6])
ax.set_ylim([0,ymax_hyp])
#save figure
fig.tight_layout()
fig.savefig( dir_fig + fname_fig + '.png' )
# Omega_ca1
#--- --- --- --- ---
#hyper-paramter name
name_hyp = 'omega_ca1p'
#figure title
fname_fig = 'post_dist_' + name_hyp
#create figure
fig, ax = plt.subplots(figsize = (10,10))
for d_id, df_r_h, df_r_h_p in zip(ds_id, df_reg_hyp, df_reg_hyp_post):
#estimate vertical line height for mean and mode
ymax_mode = 1500
ymax_mean = 1500
#plot posterior dist
pl_hyp = ax.vlines(df_r_h.loc['mean',name_hyp], ymin=0, ymax=ymax_mean, linestyle='-', label='Mean')
ax.vlines(df_r_h.loc['prc_0.50',name_hyp], ymin=0, ymax=ymax_mode, linestyle='--', color=pl_hyp.get_color(), label='Mode')
#plot true value
ymax_hyp = ymax_mean
ax.vlines(hyp['omega_ca2p'], ymin=0, ymax=ymax_hyp, linestyle='-', linewidth=4, color='black', label='True value')
#edit figure
if not flag_report: ax.set_title(r'Comparison $\omega_{ca1,P}$', fontsize=30)
ax.set_xlabel('$\omega_{ca1,P}$', fontsize=25)
ax.set_ylabel('probability density function ', fontsize=25)
ax.grid(which='both')
ax.tick_params(axis='x', labelsize=22)
ax.tick_params(axis='y', labelsize=22)
#plot limits
ax.set_xlim([0,0.05])
ax.set_ylim([0,ymax_hyp])
#save figure
fig.tight_layout()
fig.savefig( dir_fig + fname_fig + '.png' )
# Omega_ca2
#--- --- --- --- ---
#hyper-paramter name
name_hyp = 'omega_ca2p'
#figure title
fname_fig = 'post_dist_' + name_hyp
#create figure
fig, ax = plt.subplots(figsize = (10,10))
for d_id, df_r_h, df_r_h_p in zip(ds_id, df_reg_hyp, df_reg_hyp_post):
#estimate vertical line height for mean and mode
ymax_mode = 1500
ymax_mean = 1500
#plot posterior dist
pl_hyp = ax.vlines(df_r_h.loc['mean',name_hyp], ymin=0, ymax=ymax_mean, linestyle='-', label='Mean')
ax.vlines(df_r_h.loc['prc_0.50',name_hyp], ymin=0, ymax=ymax_mode, linestyle='--', color=pl_hyp.get_color(), label='Mode')
#plot true value
ymax_hyp = ymax_mean
ax.vlines(hyp['omega_ca2p'], ymin=0, ymax=ymax_hyp, linestyle='-', linewidth=4, color='black', label='True value')
#edit figure
if not flag_report: ax.set_title(r'Comparison $\omega_{ca2,P}$', fontsize=30)
ax.set_xlabel('$\omega_{ca2,P}$', fontsize=25)
ax.set_ylabel('probability density function ', fontsize=25)
ax.grid(which='both')
ax.tick_params(axis='x', labelsize=22)
ax.tick_params(axis='y', labelsize=22)
#plot limits
ax.set_xlim([0,0.05])
ax.set_ylim([0,ymax_hyp])
#save figure
fig.tight_layout()
fig.savefig( dir_fig + fname_fig + '.png' )
# Ell_ca1p
#--- --- --- --- ---
#hyper-paramter name
name_hyp = 'ell_ca1p'
#figure title
fname_fig = 'post_dist_' + name_hyp
#create figure
fig, ax = plt.subplots(figsize = (10,10))
for d_id, df_r_h, df_r_h_p in zip(ds_id, df_reg_hyp, df_reg_hyp_post):
#estimate vertical line height for mean and mode
ymax_mode = 0.02
ymax_mean = 0.02
#plot posterior dist
pl_hyp = ax.vlines(df_r_h.loc['mean',name_hyp], ymin=0, ymax=ymax_mean, linestyle='-', label='Mean')
ax.vlines(df_r_h.loc['prc_0.50',name_hyp], ymin=0, ymax=ymax_mode, linestyle='--', color=pl_hyp.get_color(), label='Mode')
#plot true value
ymax_hyp = ymax_mean
ax.vlines(hyp[name_hyp], ymin=0, ymax=ymax_hyp, linestyle='-', linewidth=4, color='black', label='True value')
#edit figure
if not flag_report: ax.set_title(r'Comparison $\ell_{ca1,P}$', fontsize=30)
ax.set_xlabel('$\ell_{ca1,P}$', fontsize=25)
ax.set_ylabel('probability density function ', fontsize=25)
ax.grid(which='both')
ax.tick_params(axis='x', labelsize=22)
ax.tick_params(axis='y', labelsize=22)
#plot limits
ax.set_xlim([0,500])
ax.set_ylim([0,ymax_hyp])
#save figure
fig.tight_layout()
fig.savefig( dir_fig + fname_fig + '.png' )
# # Delta c_0
# #--- --- --- --- ---
# #hyper-paramter name
# name_hyp = 'dc_0'
# #figure title
# fname_fig = 'post_dist_' + name_hyp
# #create figure
# fig, ax = plt.subplots(figsize = (10,10))
# for d_id, df_r_h, df_r_h_p in zip(ds_id, df_reg_hyp, df_reg_hyp_post):
# #estimate vertical line height for mean and mode
# ymax_mode = df_r_h_p.loc[:,name_hyp+'_pdf'].max()
# ymax_mean = 1.5*np.ceil(ymax_mode/10)*10
# ymax_mean = 15
# #plot posterior dist
# pl_pdf = ax.plot(df_r_h_p.loc[:,name_hyp], df_r_h_p.loc[:,name_hyp+'_pdf'])
# ax.vlines(df_r_h.loc[name_hyp,'mean'], ymin=0, ymax=ymax_mean, linestyle='-', color=pl_pdf[0].get_color(), label='Mean')
# ax.vlines(df_r_h.loc[name_hyp,'mode'], ymin=0, ymax=ymax_mode, linestyle='--', color=pl_pdf[0].get_color(), label='Mode')
# #plot true value
# ymax_hyp = ymax_mean
# # ax.vlines(hyp[name_hyp], ymin=0, ymax=ymax_hyp, linestyle='-', linewidth=4, color='black', label='True value')
# #edit figure
# ax.set_title(r'Comparison $\delta c_{0}$', fontsize=30)
# ax.set_xlabel('$\delta c_{0}$', fontsize=25)
# ax.set_ylabel('probability density function ', fontsize=25)
# ax.grid(which='both')
# ax.tick_params(axis='x', labelsize=22)
# ax.tick_params(axis='y', labelsize=22)
# #plot limits
# ax.set_xlim([-1,1])
# ax.set_ylim([0,ymax_hyp])
# #save figure
# fig.tight_layout()
# # fig.savefig( dir_fig + fname_fig + '.png' )
| 37,471 | 38.320042 | 153 | py |
ngmm_tools | ngmm_tools-master/Analyses/Code_Verification/regression/ds2/main_pystan_model2_corr_cells_NGAWest2CA.py | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Wed Jul 14 14:17:52 2021
@author: glavrent
"""
# Working directory and Packages
# ---------------------------
#load libraries
import os
import sys
import numpy as np
import pandas as pd
import time
#user functions
sys.path.insert(0,'../../../Python_lib/regression/pystan/')
from regression_pystan_model2_corr_cells_unbounded_hyp import RunStan
# from regression_pystan_model2_corr_cells_sparse_unbounded_hyp import RunStan
# Define variables
# ---------------------------
#filename suffix
# synds_suffix = '_small_corr_len'
# synds_suffix = '_large_corr_len'
#synthetic datasets directory
ds_dir = '../../../../Data/Verification/synthetic_datasets/ds2'
ds_dir = r'%s%s/'%(ds_dir, synds_suffix)
# dataset info
#ds_fname_main = 'CatalogNGAWest3CA_synthetic_data'
ds_fname_main = 'CatalogNGAWest3CALite_synthetic_data'
ds_id = np.arange(1,6)
#cell specific anelastic attenuation
ds_fname_cellinfo = 'CatalogNGAWest3CALite_cellinfo'
ds_fname_celldist = 'CatalogNGAWest3CALite_distancematrix'
#stan model
# sm_fname = '../../../Stan_lib/regression_stan_model2_corr_cells_unbounded_hyp.stan'
# sm_fname = '../../../Stan_lib/regression_stan_model2_corr_cells_unbounded_hyp_chol.stan'
# sm_fname = '../../../Stan_lib/regression_stan_model2_corr_cells_unbounded_hyp_chol_efficient.stan'
# sm_fname = '../../../Stan_lib/regression_stan_model2_corr_cells_unbounded_hyp_chol_efficient2.stan'
# sm_fname = '../../../Stan_lib/regression_stan_model2_corr_cells_sparse_unbounded_hyp_chol_efficient2.stan'
#output info
#main output filename
out_fname_main = 'NGAWest2CA_syndata'
#main output directory
out_dir_main = '../../../../Data/Verification/regression/ds2/'
#output sub-directory
#python 2
# out_dir_sub = 'PYSTAN_NGAWest2CA_corr_cells'
# out_dir_sub = 'PYSTAN_NGAWest2CA_corr_cells_chol'
# out_dir_sub = 'PYSTAN_NGAWest2CA_corr_cells_chol_eff'
# out_dir_sub = 'PYSTAN_NGAWest2CA_corr_cells_chol_eff2'
#python 3
# out_dir_sub = 'PYSTAN3_NGAWest2CA_corr_cells'
# out_dir_sub = 'PYSTAN3_NGAWest2CA_corr_cells_chol'
# out_dir_sub = 'PYSTAN3_NGAWest2CA_corr_cells_chol_eff'
# out_dir_sub = 'PYSTAN3_NGAWest2CA_corr_cells_chol_eff2'
# out_dir_sub = 'PYSTAN3_NGAWest2CA_corr_cells_chol_eff_sp'
#stan parameters
runstan_flag = True
pystan_ver = 2
# pystan_ver = 3
res_name = 'tot'
n_iter = 1000
n_chains = 4
adapt_delta = 0.8 #0.9
max_treedepth = 10
#ergodic coefficients
c_a_erg=0.0
#parallel options
# flag_parallel = True
flag_parallel = False
#output sub-dir with corr with suffix info
out_dir_sub = f'%s%s'%(out_dir_sub, synds_suffix)
#load cell dataframes
cellinfo_fname = '%s%s.csv'%(ds_dir, ds_fname_cellinfo)
celldist_fname = '%s%s.csv'%(ds_dir, ds_fname_celldist)
df_cellinfo = pd.read_csv(cellinfo_fname)
df_celldist = pd.read_csv(celldist_fname)
# Run stan regression
# ---------------------------
#create datafame with computation time
df_run_info = list()
#iterate over all synthetic datasets
for d_id in ds_id:
print('Synthetic dataset %i fo %i'%(d_id, len(ds_id)))
#run time start
run_t_strt = time.time()
#input flatfile
ds_fname = '%s%s%s_Y%i.csv'%(ds_dir, ds_fname_main, synds_suffix, d_id)
#load flatfile
df_flatfile = pd.read_csv(ds_fname)
#keep only NGAWest2 records
df_flatfile = df_flatfile.loc[df_flatfile.dsid==0,:]
#output file name and directory
out_fname = '%s%s_Y%i'%(out_fname_main, synds_suffix, d_id)
out_dir = '%s/%s/Y%i/'%(out_dir_main, out_dir_sub, d_id)
#run stan model
RunStan(df_flatfile, df_cellinfo, df_celldist, sm_fname,
out_fname, out_dir, res_name, c_a_erg=c_a_erg,
runstan_flag=runstan_flag, n_iter=n_iter, n_chains=n_chains,
adapt_delta=adapt_delta, max_treedepth=max_treedepth,
pystan_ver=pystan_ver, pystan_parallel=flag_parallel)
#run time end
run_t_end = time.time()
#compute run time
run_tm = (run_t_end - run_t_strt)/60
#log run time
df_run_info.append(pd.DataFrame({'computer_name':os.uname()[1],'out_name':out_dir_sub,
'ds_id':d_id,'run_time':run_tm}, index=[d_id]))
#write out run info
out_fname = '%s%s/run_info.csv'%(out_dir_main, out_dir_sub)
pd.concat(df_run_info).reset_index(drop=True).to_csv(out_fname, index=False)
| 4,412 | 32.687023 | 108 | py |
ngmm_tools | ngmm_tools-master/Analyses/Code_Verification/regression/ds2/comparison_model2_misfit_stan_sparse.py | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Tue Mar 15 14:50:27 2022
@author: glavrent
"""
# Working directory and Packages
# ---------------------------
#load variables
import os
import sys
import pathlib
#arithmetic libraries
import numpy as np
#statistics libraries
import pandas as pd
#plot libraries
import matplotlib as mpl
import matplotlib.pyplot as plt
#user functions
def PlotRSMCmp(df_rms_all, c_name, fig_fname):
#create figure axes
fig, ax = plt.subplots(figsize = (10,10))
for j, k in enumerate(df_rms_all):
df_rms = df_rms_all[k]
ds_id = np.array(range(len(df_rms)))
lcol = mpl.cm.get_cmap('tab10')(0) if j in [0,2] else mpl.cm.get_cmap('tab10')(1)
ltype = '-' if j in [0,1] else '--'
ax.plot(ds_id, df_rms.loc[:,c_name+'_rms'], marker='o', linewidth=2, markersize=10, label=k, linestyle=ltype, color=lcol)
#figure properties
ax.set_ylim([0, max(0.50, max(ax.get_ylim()))])
ax.set_xlabel('synthetic dataset', fontsize=35)
ax.set_ylabel('RMSE', fontsize=35)
ax.grid(which='both')
ax.set_xticks(ds_id)
ax.set_xticklabels(labels=df_rms.index)
ax.tick_params(axis='x', labelsize=32)
ax.tick_params(axis='y', labelsize=32)
#legend
ax.legend(loc='upper left', fontsize=32)
#save figure
fig.tight_layout()
fig.savefig( fig_fname + '.png' )
return fig, ax
def PlotKLCmp(df_KL_all, c_name, fig_fname):
#create figure axes
fig, ax = plt.subplots(figsize = (10,10))
for k in df_KL_all:
df_KL = df_KL_all[k]
ds_id = np.array(range(len(df_KL)))
ax.plot(ds_id, df_KL.loc[:,c_name+'_KL'], linestyle='-', marker='o', linewidth=2, markersize=10, label=k)
#figure properties
ax.set_ylim([0, max(0.50, max(ax.get_ylim()))])
ax.set_xlabel('synthetic dataset', fontsize=35)
ax.set_ylabel('KL divergence', fontsize=35)
ax.grid(which='both')
ax.set_xticks(ds_id)
ax.set_xticklabels(labels=df_KL.index)
ax.tick_params(axis='x', labelsize=32)
ax.tick_params(axis='y', labelsize=32)
#legend
ax.legend(loc='upper left', fontsize=32)
#save figure
fig.tight_layout()
fig.savefig( fig_fname + '.png' )
return fig, ax
# Define variables
# ---------------------------
# # Sparse Distance Matrix
# # --- --- --- --- ---
# # NGAWest 2 CA North
# cmp_name = 'STAN_sparse_cmp_NGAWest2CA'
# reg_title = ['STAN','STAN w/ sp dist matrix']
# reg_fname = ['CMDSTAN_NGAWest2CANorth_corr_cells_chol_eff_small_corr_len','CMDSTAN_NGAWest2CANorth_corr_cells_chol_eff_sp_small_corr_len']
# ylim_time = [0, 800]
# NGAWest 2 CA
cmp_name = 'STAN_sparse_cmp_NGAWest2CA'
reg_title = ['STAN','STAN w/ sp dist matrix']
reg_fname = ['CMDSTAN_NGAWest2CA_corr_cells_chol_eff_small_corr_len','CMDSTAN_NGAWest2CA_corr_cells_chol_eff_sp_small_corr_len']
ylim_time = [0, 7000]
# NGAWest 2 CA & NGAWest 2 CA North
cmp_name = 'STAN_sparse_cmp_NGAWest2CA_'
reg_title = ['STAN - NGAW2 CA','STAN - NGAW2 CA North',
'STAN - NGAW2 CA\nw/ sp dist matrix',f'STAN NGAW2 CA North\nw/ sp dist matrix, ']
reg_fname = ['CMDSTAN_NGAWest2CA_corr_cells_chol_eff_small_corr_len', 'CMDSTAN_NGAWest2CANorth_corr_cells_chol_eff_small_corr_len',
'CMDSTAN_NGAWest2CA_corr_cells_chol_eff_sp_small_corr_len', 'CMDSTAN_NGAWest2CANorth_corr_cells_chol_eff_sp_small_corr_len']
ylim_time = [0, 7000]
# # Different Software
# # --- --- --- --- ---
# cmp_name = 'STAN_vs_INLA_cmp_NGAWest2CANorth'
# reg_title = ['STAN corr. cells','STAN uncorr. cells','INLA uncorr. cells']
# reg_fname = ['CMDSTAN_NGAWest2CANorth_corr_cells_chol_eff_small_corr_len','CMDSTAN_NGAWest2CANorth_corr_cells_chol_eff_small_corr_len',
# 'INLA_NGAWest2CANorth_uncorr_cells_coarse_small_corr_len']
# reg_fname = ['PYSTAN_NGAWest2CANorth_corr_cells_chol_eff_small_corr_len','PYSTAN_NGAWest2CANorth_uncorr_cells_chol_eff_small_corr_len',
# 'INLA_NGAWest2CANorth_uncorr_cells_coarse_small_corr_len']
# ylim_time = [0, 800]
#directories regressions
reg_dir = [f'../../../../Data/Verification/regression/ds2/%s/'%r_f for r_f in reg_fname]
#directory output
dir_out = '../../../../Data/Verification/regression/ds2/comparisons/'
# Load Data
# ---------------------------
#initialize misfit dataframe
df_sum_misfit_all = {};
#read misfit info
for k, (r_t, r_d) in enumerate(zip(reg_title, reg_dir)):
#filename misfit info
fname_sum = r_d + 'summary/misfit_summary.csv'
#read KL score for coefficients
df_sum_misfit_all[r_t] = pd.read_csv(fname_sum, index_col=0)
#initialize run time dataframe
df_runinfo_all = {};
#read run time info
for k, (r_t, r_d) in enumerate(zip(reg_title, reg_dir)):
#filename run time
fname_runinfo = r_d + '/run_info.csv'
#store calc time
df_runinfo_all[r_t] = pd.read_csv(fname_runinfo)
# Comparison Figures
# ---------------------------
pathlib.Path(dir_out).mkdir(parents=True, exist_ok=True)
# RMSE divergence
# --- --- --- --- ---
#coefficient name
c_name = 'nerg_tot'
#figure name
fig_fname = '%s/%s_%s_RMSE'%(dir_out, cmp_name, c_name)
#plotting
PlotRSMCmp(df_sum_misfit_all , c_name, fig_fname);
#coefficient name
c_name = 'dc_1e'
#figure name
fig_fname = '%s/%s_%s_RMSE'%(dir_out, cmp_name, c_name)
#plotting
PlotRSMCmp(df_sum_misfit_all , c_name, fig_fname);
#coefficient name
c_name = 'dc_1as'
#figure name
fig_fname = '%s/%s_%s_RMSE'%(dir_out, cmp_name, c_name)
#plotting
PlotRSMCmp(df_sum_misfit_all , c_name, fig_fname);
#coefficient name
c_name = 'dc_1bs'
#figure name
fig_fname = '%s/%s_%s_RMSE'%(dir_out, cmp_name, c_name)
#plotting
PlotRSMCmp(df_sum_misfit_all , c_name, fig_fname);
# KL divergence
# --- --- --- --- ---
#coefficient name
c_name = 'nerg_tot'
#figure name
fig_fname = '%s/%s_%s_KLdiv'%(dir_out, cmp_name, c_name)
#plotting
PlotKLCmp(df_sum_misfit_all , c_name, fig_fname);
#coefficient name
c_name = 'dc_1e'
#figure name
fig_fname = '%s/%s_%s_KLdiv'%(dir_out, cmp_name, c_name)
#plotting
PlotKLCmp(df_sum_misfit_all , c_name, fig_fname);
#coefficient name
c_name = 'dc_1as'
#figure name
fig_fname = '%s/%s_%s_KLdiv'%(dir_out, cmp_name, c_name)
#plotting
PlotKLCmp(df_sum_misfit_all , c_name, fig_fname);
#coefficient name
c_name = 'dc_1bs'
#figure name
fig_fname = '%s/%s_%s_KLdiv'%(dir_out, cmp_name, c_name)
#plotting
PlotKLCmp(df_sum_misfit_all , c_name, fig_fname);
# Run Time
# --- --- --- --- ---
#run time figure
fig_fname = '%s/%s_run_time'%(dir_out, cmp_name)
#create figure axes
fig, ax = plt.subplots(figsize = (10,10))
#iterate over different analyses
for j, k in enumerate(df_runinfo_all):
ds_id = df_runinfo_all[k].ds_id
ds_name = ['Y%i'%d_i for d_i in ds_id]
run_time = df_runinfo_all[k].run_time
#
lcol = mpl.cm.get_cmap('tab10')(0) if j in [0,2] else mpl.cm.get_cmap('tab10')(1)
ltype = '-' if j in [0,1] else '--'
ax.plot(ds_id, run_time, marker='o', linewidth=2, markersize=10, label=k, linestyle=ltype, color=lcol)
#figure properties
ax.set_ylim(ylim_time)
ax.set_xlabel('synthetic dataset', fontsize=35)
ax.set_ylabel('Run Time (min)', fontsize=35)
ax.grid(which='both')
ax.set_xticks(ds_id)
ax.set_xticklabels(labels=ds_name)
ax.tick_params(axis='x', labelsize=32)
ax.tick_params(axis='y', labelsize=32)
#legend
# ax.legend(loc='lower left', fontsize=32)
# ax.legend(loc='upper left', fontsize=32)
# ax.legend(loc='center left', bbox_to_anchor=(1, 0.5), fontsize=25)
#save figure
fig.tight_layout()
fig.savefig( fig_fname + '.png' )
| 7,569 | 31.48927 | 140 | py |
ngmm_tools | ngmm_tools-master/Analyses/Code_Verification/regression/ds2/main_pystan_model2_uncorr_cells_NGAWest2CANorth.py | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Wed Jul 14 14:17:52 2021
@author: glavrent
"""
# Working directory and Packages
# ---------------------------
#load libraries
import os
import sys
import numpy as np
import pandas as pd
import time
#user functions
sys.path.insert(0,'../../../Python_lib/regression/pystan/')
from regression_pystan_model2_uncorr_cells_unbounded_hyp import RunStan
# Define variables
# ---------------------------
#filename suffix
# synds_suffix = '_small_corr_len'
# synds_suffix = '_large_corr_len'
#synthetic datasets directory
ds_dir = '../../../../Data/Verification/synthetic_datasets/ds2'
ds_dir = r'%s%s/'%(ds_dir, synds_suffix)
# dataset info
#ds_fname_main = 'CatalogNGAWest3CA_synthetic_data'
ds_fname_main = 'CatalogNGAWest3CALite_synthetic_data'
ds_id = np.arange(1,6)
#cell specific anelastic attenuation
ds_fname_cellinfo = 'CatalogNGAWest3CALite_cellinfo'
ds_fname_celldist = 'CatalogNGAWest3CALite_distancematrix'
#stan model
# sm_fname = '../../../Stan_lib/regression_stan_model2_uncorr_cells_unbounded_hyp.stan'
# sm_fname = '../../../Stan_lib/regression_stan_model2_uncorr_cells_unbounded_hyp_chol.stan'
# sm_fname = '../../../Stan_lib/regression_stan_model2_uncorr_cells_unbounded_hyp_chol_efficient.stan'
# sm_fname = '../../../Stan_lib/regression_stan_model2_uncorr_cells_unbounded_hyp_chol_efficient2.stan'
#output info
#main output filename
out_fname_main = 'NGAWest2CANorth_syndata'
#main output directory
out_dir_main = '../../../../Data/Verification/regression/ds2/'
#output sub-directory
#pystan2
# out_dir_sub = 'PYSTAN_NGAWest2CANorth_uncorr_cells'
# out_dir_sub = 'PYSTAN_NGAWest2CANorth_uncorr_cells_chol'
# out_dir_sub = 'PYSTAN_NGAWest2CANorth_uncorr_cells_chol_eff'
# out_dir_sub = 'PYSTAN_NGAWest2CANorth_uncorr_cells_chol_eff2'
#pystan3
# out_dir_sub = 'PYSTAN3_NGAWest2CANorth_uncorr_cells'
# out_dir_sub = 'PYSTAN3_NGAWest2CANorth_uncorr_cells_chol'
# out_dir_sub = 'PYSTAN3_NGAWest2CANorth_uncorr_cells_chol_eff'
# out_dir_sub = 'PYSTAN3_NGAWest2CANorth_uncorr_cells_chol_eff2'
#stan parameters
runstan_flag = True
# pystan_ver = 2
pystan_ver = 3
res_name = 'tot'
n_iter = 1000
n_chains = 4
adapt_delta = 0.8
max_treedepth = 10
#ergodic coefficients
c_a_erg=0.0
#parallel options
# flag_parallel = True
flag_parallel = False
#output sub-dir with corr with suffix info
out_dir_sub = f'%s%s'%(out_dir_sub, synds_suffix)
#load cell dataframes
cellinfo_fname = '%s%s.csv'%(ds_dir, ds_fname_cellinfo)
celldist_fname = '%s%s.csv'%(ds_dir, ds_fname_celldist)
df_cellinfo = pd.read_csv(cellinfo_fname)
df_celldist = pd.read_csv(celldist_fname)
# Run stan regression
# ---------------------------
#create datafame with computation time
df_run_info = list()
#iterate over all synthetic datasets
for d_id in ds_id:
print('Synthetic dataset %i fo %i'%(d_id, len(ds_id)))
#run time start
run_t_strt = time.time()
#input flatfile
ds_fname = '%s%s%s_Y%i.csv'%(ds_dir, ds_fname_main, synds_suffix, d_id)
#load flatfile
df_flatfile = pd.read_csv(ds_fname)
#keep only North records of NGAWest2
df_flatfile = df_flatfile.loc[np.logical_and(df_flatfile.dsid==0,
df_flatfile.sreg==1),:]
#output file name and directory
out_fname = '%s%s_Y%i'%(out_fname_main, synds_suffix, d_id)
out_dir = '%s/%s/Y%i/'%(out_dir_main, out_dir_sub, d_id)
#run stan model
RunStan(df_flatfile, df_cellinfo, df_celldist, sm_fname,
out_fname, out_dir, res_name, c_a_erg=c_a_erg,
runstan_flag=runstan_flag, n_iter=n_iter, n_chains=n_chains,
adapt_delta=adapt_delta, max_treedepth=max_treedepth,
pystan_ver=pystan_ver, pystan_parallel=flag_parallel)
#run time end
run_t_end = time.time()
#compute run time
run_tm = (run_t_end - run_t_strt)/60
#log run time
df_run_info.append(pd.DataFrame({'computer_name':os.uname()[1],'out_name':out_dir_sub,
'ds_id':d_id,'run_time':run_tm}, index=[d_id]))
#write out run info
out_fname = '%s%s/run_info.csv'%(out_dir_main, out_dir_sub)
pd.concat(df_run_info).reset_index(drop=True).to_csv(out_fname, index=False)
| 4,311 | 32.169231 | 103 | py |
ngmm_tools | ngmm_tools-master/Analyses/Code_Verification/regression/ds2/main_pystan_model2_corr_cells_NGAWest2CA_sparse.py | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Wed Jul 14 14:17:52 2021
@author: glavrent
"""
# Working directory and Packages
# ---------------------------
#load libraries
import os
import sys
import numpy as np
import pandas as pd
import time
#user functions
sys.path.insert(0,'../../../Python_lib/regression/pystan/')
from regression_pystan_model2_corr_cells_sparse_unbounded_hyp import RunStan
# Define variables
# ---------------------------
#filename suffix
# synds_suffix = '_small_corr_len'
# synds_suffix = '_large_corr_len'
#synthetic datasets directory
ds_dir = '../../../../Data/Verification/synthetic_datasets/ds2'
ds_dir = r'%s%s/'%(ds_dir, synds_suffix)
# dataset info
#ds_fname_main = 'CatalogNGAWest3CA_synthetic_data'
ds_fname_main = 'CatalogNGAWest3CALite_synthetic_data'
ds_id = np.arange(1,6)
#cell specific anelastic attenuation
ds_fname_cellinfo = 'CatalogNGAWest3CALite_cellinfo'
ds_fname_celldist = 'CatalogNGAWest3CALite_distancematrix'
#stan model
sm_fname = '../../../Stan_lib/regression_stan_model2_corr_cells_sparse_unbounded_hyp_chol_efficient.stan'
#output info
#main output filename
out_fname_main = 'NGAWest2CA_syndata'
#main output directory
out_dir_main = '../../../../Data/Verification/regression/ds2/'
#output sub-directory
# out_dir_sub = 'PYSTAN_NGAWest2CA_corr_cells_chol_eff_sp'
out_dir_sub = 'PYSTAN3_NGAWest2CA_corr_cells_chol_eff_sp'
#stan parameters
runstan_flag = True
# pystan_ver = 2
pystan_ver = 3
res_name = 'tot'
n_iter = 1000
n_chains = 4
adapt_delta = 0.8 #0.9
max_treedepth = 10
#ergodic coefficients
c_a_erg=0.0
#parallel options
# flag_parallel = True
flag_parallel = False
#output sub-dir with corr with suffix info
out_dir_sub = f'%s%s'%(out_dir_sub, synds_suffix)
#load cell dataframes
cellinfo_fname = '%s%s.csv'%(ds_dir, ds_fname_cellinfo)
celldist_fname = '%s%s.csv'%(ds_dir, ds_fname_celldist)
df_cellinfo = pd.read_csv(cellinfo_fname)
df_celldist = pd.read_csv(celldist_fname)
# Run stan regression
# ---------------------------
#create datafame with computation time
df_run_info = list()
#iterate over all synthetic datasets
for d_id in ds_id:
print('Synthetic dataset %i fo %i'%(d_id, len(ds_id)))
#run time start
run_t_strt = time.time()
#input flatfile
ds_fname = '%s%s%s_Y%i.csv'%(ds_dir, ds_fname_main, synds_suffix, d_id)
#load flatfile
df_flatfile = pd.read_csv(ds_fname)
#keep only NGAWest2 records
df_flatfile = df_flatfile.loc[df_flatfile.dsid==0,:]
#output file name and directory
out_fname = '%s%s_Y%i'%(out_fname_main, synds_suffix, d_id)
out_dir = '%s/%s/Y%i/'%(out_dir_main, out_dir_sub, d_id)
#run stan model
RunStan(df_flatfile, df_cellinfo, df_celldist, sm_fname,
out_fname, out_dir, res_name, c_a_erg=c_a_erg,
runstan_flag=runstan_flag, n_iter=n_iter, n_chains=n_chains,
adapt_delta=adapt_delta, max_treedepth=max_treedepth,
pystan_ver=pystan_ver, pystan_parallel=flag_parallel)
#run time end
run_t_end = time.time()
#compute run time
run_tm = (run_t_end - run_t_strt)/60
#log run time
df_run_info.append(pd.DataFrame({'computer_name':os.uname()[1],'out_name':out_dir_sub,
'ds_id':d_id,'run_time':run_tm}, index=[d_id]))
#write out run info
out_fname = '%s%s/run_info.csv'%(out_dir_main, out_dir_sub)
pd.concat(df_run_info).reset_index(drop=True).to_csv(out_fname, index=False)
| 3,545 | 29.307692 | 105 | py |
ngmm_tools | ngmm_tools-master/Analyses/Code_Verification/regression/ds2/comparison_inla_model2_uncorr_cells.py | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Thu Aug 12 10:26:06 2021
@author: glavrent
"""
# Working directory and Packages
# ---------------------------
#load packages
import os
import sys
import pathlib
import glob
import re #regular expression package
import pickle
#arithmetic libraries
import numpy as np
#statistics libraries
import pandas as pd
#plot libraries
import matplotlib as mpl
import matplotlib.pyplot as plt
from matplotlib.ticker import AutoLocator as plt_autotick
#user functions
sys.path.insert(0,'../../../Python_lib/regression/')
from pylib_stats import CalcRMS
from pylib_stats import CalcLKDivergece
# Define variables
# ---------------------------
# USER SETS DIRECTORIES AND FILE INFO OF SYNTHETIC DS AND REGRESSION RESULTS
# ++++++++++++++++++++++++++++++++++++++++
#processed dataset
# name_dataset = 'NGAWest2CANorth'
# name_dataset = 'NGAWest2CA'
# name_dataset = 'NGAWest3CA'
#correlation info
# 1: Small Correlation Lengths
# 2: Large Correlation Lenghts
corr_id = 1
#kernel function
# 1: Mattern kernel (alpha=2)
# 2: Negative Exp (alpha=3/2)
ker_id = 1
#mesh type
# 1: Fine Mesh
# 2: Medium Mesh
# 3: Coarse Mesh
mesh_id = 1
#directories (synthetic dataset)
if corr_id == 1:
dir_syndata = '../../../../Data/Verification/synthetic_datasets/ds2_small_corr_len'
elif corr_id == 2:
dir_syndata = '../../../../Data/Verification/synthetic_datasets/ds2_large_corr_len'
#directories (regression results)
if mesh_id == 1:
dir_results = f'../../../../Data/Verification/regression/ds2/INLA_%s_uncorr_cells_fine'%name_dataset
elif mesh_id == 2:
dir_results = f'../../../../Data/Verification/regression/ds2/INLA_%s_uncorr_cells_medium'%name_dataset
elif mesh_id == 3:
dir_results = f'../../../../Data/Verification/regression/ds2/INLA_%s_uncorr_cells_coarse'%name_dataset
#cell info
fname_cellinfo = dir_syndata + '/' + 'CatalogNGAWest3CALite_cellinfo.csv'
fname_distmat = dir_syndata + '/' + 'CatalogNGAWest3CALite_distancematrix.csv'
#prefix for synthetic data and results
prfx_syndata = 'CatalogNGAWest3CALite_synthetic'
#regression results filename prefix
prfx_results = f'%s_syndata'%name_dataset
# dataset info
ds_id = np.arange(1,6)
# ++++++++++++++++++++++++++++++++++++++++
# USER NEEDS TO SPECIFY HYPERPARAMETERS OF SYNTHETIC DATASET
# ++++++++++++++++++++++++++++++++++++++++
# hyper-parameters
if corr_id == 1:
# small correlation lengths
hyp = {'omega_0': 0.1, 'omega_1e':0.1, 'omega_1as': 0.35, 'omega_1bs': 0.25,
'ell_1e':60, 'ell_1as':30,
'c_cap_erg': -0.011,
'omega_cap_mu': 0.005, 'omega_ca1p':0.004, 'omega_ca2p':0.002,
'ell_ca1p': 75,
'phi_0':0.4, 'tau_0':0.3 }
elif corr_id == 2:
#large correlation lengths
hyp = {'omega_0': 0.1, 'omega_1e':0.2, 'omega_1as': 0.4, 'omega_1bs': 0.3,
'ell_1e':100, 'ell_1as':70,
'c_cap_erg': -0.02,
'omega_cap_mu': 0.008, 'omega_ca1p':0.005, 'omega_ca2p':0.003,
'ell_ca1p': 120,
'phi_0':0.4, 'tau_0':0.3}
# ++++++++++++++++++++++++++++++++++++++++
# FILE INFO FOR REGRESSION RESULTS
# ++++++++++++++++++++++++++++++++++++++++
#output filename sufix
if corr_id == 1:
synds_suffix = '_small_corr_len'
elif corr_id == 2:
synds_suffix = '_large_corr_len'
#kenel info
if ker_id == 1:
ker_suffix = ''
elif ker_id == 2:
ker_suffix = '_nexp'
# ++++++++++++++++++++++++++++++++++++++++
#ploting options
flag_report = True
# Compare results
# ---------------------------
#load cell data
df_cellinfo = pd.read_csv(fname_cellinfo).set_index('cellid')
df_distmat = pd.read_csv(fname_distmat).set_index('rsn')
#initialize misfit metrics dataframe
df_misfit = pd.DataFrame(index=['Y%i'%d_id for d_id in ds_id])
#iterate over different datasets
for d_id in ds_id:
# Load Data
#--- --- --- --- ---
#file names
#synthetic data
fname_sdata_gmotion = '%s/%s_%s%s_Y%i'%(dir_syndata, prfx_syndata, 'data', synds_suffix, d_id) + '.csv'
fname_sdata_atten = '%s/%s_%s%s_Y%i'%(dir_syndata, prfx_syndata, 'atten', synds_suffix, d_id) + '.csv'
#regression results
fname_reg_gmotion = '%s%s/Y%i/%s%s_Y%i_inla_%s'%(dir_results, ker_suffix+synds_suffix, d_id, prfx_results, synds_suffix, d_id, 'residuals') + '.csv'
fname_reg_coeff = '%s%s/Y%i/%s%s_Y%i_inla_%s'%(dir_results, ker_suffix+synds_suffix, d_id, prfx_results, synds_suffix, d_id, 'coefficients') + '.csv'
fname_reg_atten = '%s%s/Y%i/%s%s_Y%i_inla_%s'%(dir_results, ker_suffix+synds_suffix, d_id, prfx_results, synds_suffix, d_id, 'catten') + '.csv'
#load synthetic results
df_sdata_gmotion = pd.read_csv(fname_sdata_gmotion).set_index('rsn')
df_sdata_atten = pd.read_csv(fname_sdata_atten).set_index('cellid')
#load regression results
df_reg_gmotion = pd.read_csv(fname_reg_gmotion).set_index('rsn')
df_reg_coeff = pd.read_csv(fname_reg_coeff).set_index('rsn')
df_reg_atten = pd.read_csv(fname_reg_atten).set_index('cellid')
# Processing
#--- --- --- --- ---
#keep only relevant columns from synthetic dataset
df_sdata_gmotion = df_sdata_gmotion.reindex(df_reg_gmotion.index)
df_sdata_atten = df_sdata_atten.reindex(df_reg_atten.index)
#distance matrix for records of interest
df_dmat = df_distmat.reindex(df_sdata_gmotion.index)
#find unique earthqakes and stations
eq_id, eq_idx, eq_nrec = np.unique(df_sdata_gmotion.eqid, return_index=True, return_counts=True)
sta_id, sta_idx, sta_nrec = np.unique(df_sdata_gmotion.ssn, return_index=True, return_counts=True)
#number of paths per cell
cell_npath = np.sum(df_dmat.loc[:,df_reg_atten.cellname] > 0, axis=0)
# Compute Root Mean Square Error
#--- --- --- --- ---
df_misfit.loc['Y%i'%d_id,'nerg_tot_rms'] = CalcRMS(df_sdata_gmotion.nerg_gm.values, df_reg_gmotion.nerg_mu.values)
df_misfit.loc['Y%i'%d_id,'dc_1e_rms'] = CalcRMS(df_sdata_gmotion['dc_1e'].values[eq_idx], df_reg_coeff['dc_1e_mean'].values[eq_idx])
df_misfit.loc['Y%i'%d_id,'dc_1as_rms'] = CalcRMS(df_sdata_gmotion['dc_1as'].values[sta_idx], df_reg_coeff['dc_1as_mean'].values[sta_idx])
df_misfit.loc['Y%i'%d_id,'dc_1bs_rms'] = CalcRMS(df_sdata_gmotion['dc_1bs'].values[sta_idx], df_reg_coeff['dc_1bs_mean'].values[sta_idx])
df_misfit.loc['Y%i'%d_id,'c_cap_rms'] = CalcRMS(df_sdata_atten['c_cap'].values, df_reg_atten['c_cap_mean'].values)
# Compute Divergence
#--- --- --- --- ---
df_misfit.loc['Y%i'%d_id,'nerg_tot_KL'] = CalcLKDivergece(df_sdata_gmotion.nerg_gm.values, df_reg_gmotion.nerg_mu.values)
df_misfit.loc['Y%i'%d_id,'dc_1e_KL'] = CalcLKDivergece(df_sdata_gmotion['dc_1e'].values[eq_idx], df_reg_coeff['dc_1e_mean'].values[eq_idx])
df_misfit.loc['Y%i'%d_id,'dc_1as_KL'] = CalcLKDivergece(df_sdata_gmotion['dc_1as'].values[sta_idx], df_reg_coeff['dc_1as_mean'].values[sta_idx])
df_misfit.loc['Y%i'%d_id,'dc_1bs_KL'] = CalcLKDivergece(df_sdata_gmotion['dc_1bs'].values[sta_idx], df_reg_coeff['dc_1bs_mean'].values[sta_idx])
df_misfit.loc['Y%i'%d_id,'c_cap_KL'] = CalcLKDivergece(df_sdata_atten['c_cap'].values, df_reg_atten['c_cap_mean'].values)
# Output
#--- --- --- --- ---
#figure directory
dir_fig = '%s%s/Y%i/figures_cmp/'%(dir_results, ker_suffix+synds_suffix, d_id)
pathlib.Path(dir_fig).mkdir(parents=True, exist_ok=True)
#compare ground motion predictions
#... ... ... ... ... ...
#figure title
fname_fig = 'Y%i_scatter_tot_res'%d_id
#create figure
fig, ax = plt.subplots(figsize = (10,10))
#median
ax.scatter(df_sdata_gmotion.nerg_gm.values, df_reg_gmotion.nerg_mu.values)
ax.axline((0,0), slope=1, color="black", linestyle="--")
#edit figure
if not flag_report: ax.set_title('Comparison total residuals, Y: %i'%d_id, fontsize=30)
ax.set_xlabel('Synthetic dataset', fontsize=35)
ax.set_ylabel('Estimated', fontsize=35)
ax.grid(which='both')
ax.tick_params(axis='x', labelsize=32)
ax.tick_params(axis='y', labelsize=32)
#plot limits
# plt_lim = np.array([ax.get_xlim(), ax.get_ylim()])
# plt_lim = (plt_lim[:,0].min(), plt_lim[:,1].max())
# ax.set_xlim(plt_lim)
# ax.set_ylim(plt_lim)
ax.set_xlim([-10,2])
ax.set_ylim([-10,2])
fig.tight_layout()
fig.savefig( dir_fig + fname_fig + '.png' )
#compare dc_1e
#... ... ... ... ... ...
#figure title
fname_fig = 'Y%i_dc_1e_scatter'%d_id
#create figure
fig, ax = plt.subplots(figsize = (10,10))
#coefficient scatter
ax.scatter(df_sdata_gmotion['dc_1e'].values[eq_idx], df_reg_coeff['dc_1e_mean'].values[eq_idx])
ax.axline((0,0), slope=1, color="black", linestyle="--")
#edit figure
if not flag_report: ax.set_title(r'Comparison $\delta c_{1,E}$, Y: %i'%d_id, fontsize=30)
ax.set_xlabel('Synthetic dataset', fontsize=25)
ax.set_ylabel('Estimated', fontsize=25)
ax.grid(which='both')
ax.tick_params(axis='x', labelsize=22)
ax.tick_params(axis='y', labelsize=22)
#plot limits
# plt_lim = np.array([ax.get_xlim(), ax.get_ylim()])
# plt_lim = (plt_lim[:,0].min(), plt_lim[:,1].max())
# ax.set_xlim(plt_lim)
# ax.set_ylim(plt_lim)
ax.set_xlim([-2,2])
ax.set_ylim([-2,2])
#save figure
fig.tight_layout()
fig.savefig( dir_fig + fname_fig + '.png' )
#figure title
fname_fig = 'Y%i_dc_1e_accuracy'%d_id
#create figure
fig, ax = plt.subplots(figsize = (10,10))
#coefficient scatter
ax.scatter(df_reg_coeff['dc_1e_sig'].values[eq_idx],
df_sdata_gmotion['dc_1e'].values[eq_idx] - df_reg_coeff['dc_1e_mean'].values[eq_idx])
ax.axline((0,0), slope=0, color="black", linestyle="--")
#edit figure
if not flag_report: ax.set_title(r'Comparison $\delta c_{1,E}$, Y: %i'%d_id, fontsize=30)
ax.set_xlabel('Standard Deviation', fontsize=25)
ax.set_ylabel('Actual - Estimated', fontsize=25)
ax.grid(which='both')
ax.tick_params(axis='x', labelsize=22)
ax.tick_params(axis='y', labelsize=22)
#plot limits
# ax.set_ylim(np.abs(ax.get_ylim()).max()*np.array([-1,1]))
ax.set_xlim([0,.5])
ax.set_ylim([-2,2])
#save figure
fig.tight_layout()
fig.savefig( dir_fig + fname_fig + '.png' )
#figure title
fname_fig = 'Y%i_dc_1e_nrec'%d_id
#create figure
fig, ax = plt.subplots(figsize = (10,10))
#coefficient scatter
ax.scatter(eq_nrec,
df_sdata_gmotion['dc_1e'].values[eq_idx] - df_reg_coeff['dc_1e_mean'].values[eq_idx])
ax.axline((0,0), slope=0, color="black", linestyle="--")
#edit figure
if not flag_report: ax.set_title(r'Comparison $\delta c_{1,E}$, Y: %i'%d_id, fontsize=30)
ax.set_xlabel('Number of records', fontsize=25)
ax.set_ylabel('Actual - Estimated', fontsize=25)
ax.grid(which='both')
ax.set_xscale('log')
ax.tick_params(axis='x', labelsize=22)
ax.tick_params(axis='y', labelsize=22)
#plot limits
# ax.set_ylim(np.abs(ax.get_ylim()).max()*np.array([-1,1]))
ax.set_xlim([0.9,1e3])
ax.set_ylim([-2,2])
#save figure
fig.tight_layout()
fig.savefig( dir_fig + fname_fig + '.png' )
#compare dc_1as
#... ... ... ... ... ...
#figure title
fname_fig = 'Y%i_dc_1as_scatter'%d_id
#create figure
fig, ax = plt.subplots(figsize = (10,10))
#coefficient scatter
ax.scatter(df_sdata_gmotion['dc_1as'].values[sta_idx], df_reg_coeff['dc_1as_mean'].values[sta_idx])
ax.axline((0,0), slope=1, color="black", linestyle="--")
#edit figure
if not flag_report: ax.set_title(r'Comparison $\delta c_{1a,S}$, Y: %i'%d_id, fontsize=30)
ax.set_xlabel('Synthetic dataset', fontsize=25)
ax.set_ylabel('Estimated', fontsize=25)
ax.grid(which='both')
ax.tick_params(axis='x', labelsize=22)
ax.tick_params(axis='y', labelsize=22)
#plot limits
# plt_lim = np.array([ax.get_xlim(), ax.get_ylim()])
# plt_lim = (plt_lim[:,0].min(), plt_lim[:,1].max())
# ax.set_xlim(plt_lim)
# ax.set_ylim(plt_lim)
ax.set_xlim([-2,2])
ax.set_ylim([-2,2])
#save figure
fig.tight_layout()
fig.savefig( dir_fig + fname_fig + '.png' )
#figure title
fname_fig = 'Y%i_dc_1as_accuracy'%d_id
#create figure
fig, ax = plt.subplots(figsize = (10,10))
#accuray
ax.scatter(df_reg_coeff['dc_1as_sig'].values[sta_idx],
df_sdata_gmotion['dc_1as'].values[sta_idx] - df_reg_coeff['dc_1as_mean'].values[sta_idx])
ax.axline((0,0), slope=0, color="black", linestyle="--")
#edit figure
if not flag_report: ax.set_title(r'Comparison $\delta c_{1a,S}$, Y: %i'%d_id, fontsize=30)
ax.set_xlabel('Standard Deviation', fontsize=25)
ax.set_ylabel('Actual - Estimated', fontsize=25)
ax.grid(which='both')
ax.tick_params(axis='x', labelsize=22)
ax.tick_params(axis='y', labelsize=22)
#plot limits
# ax.set_ylim(np.abs(ax.get_ylim()).max()*np.array([-1,1]))
ax.set_xlim([0,.5])
ax.set_ylim([-2,2])
#save figure
fig.tight_layout()
fig.savefig( dir_fig + fname_fig + '.png' )
#figure title
fname_fig = 'Y%i_dc_1as_nrec'%d_id
#create figure
fig, ax = plt.subplots(figsize = (10,10))
#accuray
ax.scatter(sta_nrec,
df_sdata_gmotion['dc_1as'].values[sta_idx] - df_reg_coeff['dc_1as_mean'].values[sta_idx])
ax.axline((0,0), slope=0, color="black", linestyle="--")
#edit figure
if not flag_report: ax.set_title(r'Comparison $\delta c_{1a,S}$, Y: %i'%d_id, fontsize=30)
ax.set_xlabel('Number of records', fontsize=25)
ax.set_ylabel('Actual - Estimated', fontsize=25)
ax.grid(which='both')
ax.set_xscale('log')
ax.tick_params(axis='x', labelsize=22)
ax.tick_params(axis='y', labelsize=22)
#plot limits
# ax.set_ylim(np.abs(ax.get_ylim()).max()*np.array([-1,1]))
ax.set_xlim([.9,1000])
ax.set_ylim([-2,2])
#save figure
fig.tight_layout()
fig.savefig( dir_fig + fname_fig + '.png' )
#compare dc_1bs
#... ... ... ... ... ...
#figure title
fname_fig = 'Y%i_dc_1bs_scatter'%d_id
#create figure
fig, ax = plt.subplots(figsize = (10,10))
#coefficient scatter
ax.scatter(df_sdata_gmotion['dc_1bs'].values[sta_idx], df_reg_coeff['dc_1bs_mean'].values[sta_idx])
ax.axline((0,0), slope=1, color="black", linestyle="--")
#edit figure
if not flag_report: ax.set_title(r'Comparison $\delta c_{1b,S}$, Y: %i'%d_id, fontsize=30)
ax.set_xlabel('Synthetic dataset', fontsize=25)
ax.set_ylabel('Estimated', fontsize=25)
ax.grid(which='both')
ax.tick_params(axis='x', labelsize=22)
ax.tick_params(axis='y', labelsize=22)
#plot limits
# plt_lim = np.array([ax.get_xlim(), ax.get_ylim()])
# plt_lim = (plt_lim[:,0].min(), plt_lim[:,1].max())
# ax.set_xlim(plt_lim)
# ax.set_ylim(plt_lim)
ax.set_xlim([-2,2])
ax.set_ylim([-2,2])
#save figure
fig.tight_layout()
fig.savefig( dir_fig + fname_fig + '.png' )
#figure title
fname_fig = 'Y%i_dc_1bs_accuracy'%d_id
#create figure
fig, ax = plt.subplots(figsize = (10,10))
#accuray
ax.scatter(df_reg_coeff['dc_1bs_sig'].values[sta_idx],
df_sdata_gmotion['dc_1bs'].values[sta_idx] - df_reg_coeff['dc_1bs_mean'].values[sta_idx])
ax.axline((0,0), slope=0, color="black", linestyle="--")
#edit figure
if not flag_report: ax.set_title(r'Comparison $\delta c_{1b,S}$, Y: %i'%d_id, fontsize=30)
ax.set_xlabel('Standard Deviation', fontsize=25)
ax.set_ylabel('Actual - Estimated', fontsize=25)
ax.grid(which='both')
ax.tick_params(axis='x', labelsize=22)
ax.tick_params(axis='y', labelsize=22)
#plot limits
# ax.set_ylim(np.abs(ax.get_ylim()).max()*np.array([-1,1]))
ax.set_xlim([0,.4])
ax.set_ylim([-2,2])
#save figure
fig.tight_layout()
fig.savefig( dir_fig + fname_fig + '.png' )
#figure title
fname_fig = 'Y%i_dc_1bs_nrec'%d_id
#create figure
fig, ax = plt.subplots(figsize = (10,10))
#accuray
ax.scatter(sta_nrec,
df_sdata_gmotion['dc_1bs'].values[sta_idx] - df_reg_coeff['dc_1bs_mean'].values[sta_idx])
ax.axline((0,0), slope=0, color="black", linestyle="--")
#edit figure
if not flag_report: ax.set_title(r'Comparison $\delta c_{1b,S}$, Y: %i'%d_id, fontsize=30)
ax.set_xlabel('Number of records', fontsize=25)
ax.set_ylabel('Actual - Estimated', fontsize=25)
ax.grid(which='both')
ax.set_xscale('log')
ax.tick_params(axis='x', labelsize=22)
ax.tick_params(axis='y', labelsize=22)
#plot limits
# ax.set_ylim(np.abs(ax.get_ylim()).max()*np.array([-1,1]))
ax.set_xlim([.9,1000])
ax.set_ylim([-2,2])
#save figure
fig.tight_layout()
fig.savefig( dir_fig + fname_fig + '.png' )
#compare c_cap
#... ... ... ... ... ...
#figure title
fname_fig = 'Y%i_c_cap_scatter'%d_id
#create figure
fig, ax = plt.subplots(figsize = (10,10))
#coefficient scatter
ax.scatter(df_sdata_atten['c_cap'].values, df_reg_atten['c_cap_mean'].values)
ax.axline((0,0), slope=1, color="black", linestyle="--")
#edit figure
if not flag_report: ax.set_title(r'Comparison $c_{ca,P}$, Y: %i'%d_id, fontsize=30)
ax.set_xlabel('Synthetic dataset', fontsize=25)
ax.set_ylabel('Estimated', fontsize=25)
ax.grid(which='both')
ax.tick_params(axis='x', labelsize=22)
ax.tick_params(axis='y', labelsize=22)
#plot limits
# plt_lim = np.array([ax.get_xlim(), ax.get_ylim()])
# plt_lim = (plt_lim[:,0].min(), plt_lim[:,1].max())
# ax.set_xlim(plt_lim)
# ax.set_ylim(plt_lim)
ax.set_xlim([-0.05,0.02])
ax.set_ylim([-0.05,0.02])
#save figure
fig.tight_layout()
fig.savefig( dir_fig + fname_fig + '.png' )
#figure title
fname_fig = 'Y%i_c_cap_accuracy'%d_id
#create figure
fig, ax = plt.subplots(figsize = (10,10))
#coefficient scatter
ax.scatter(df_reg_atten['c_cap_sig'],
df_sdata_atten['c_cap'].values - df_reg_atten['c_cap_mean'].values)
ax.axline((0,0), slope=0, color="black", linestyle="--")
#edit figure
if not flag_report: ax.set_title(r'Comparison $c_{ca,P}$, Y: %i'%d_id, fontsize=30)
ax.set_xlabel('Standard Deviation', fontsize=25)
ax.set_ylabel('Actual - Estimated', fontsize=25)
ax.grid(which='both')
ax.tick_params(axis='x', labelsize=22)
ax.tick_params(axis='y', labelsize=22)
#plot limits
# ax.set_ylim(np.abs(ax.get_ylim()).max()*np.array([-1,1]))
ax.set_xlim([0.00,0.03])
ax.set_ylim([-0.04,0.04])
#save figure
fig.tight_layout()
fig.savefig( dir_fig + fname_fig + '.png' )
#figure title
fname_fig = 'Y%i_c_cap_npath'%d_id
#create figure
fig, ax = plt.subplots(figsize = (10,10))
#coefficient scatter
ax.scatter(cell_npath,
df_sdata_atten['c_cap'].values - df_reg_atten['c_cap_mean'].values)
ax.axline((0,0), slope=0, color="black", linestyle="--")
#edit figure
if not flag_report: ax.set_title(r'Comparison $c_{ca,P}$, Y: %i'%d_id, fontsize=30)
ax.set_xlabel('Number of paths', fontsize=25)
ax.set_ylabel('Actual - Estimated', fontsize=25)
ax.grid(which='both')
ax.set_xscale('log')
ax.tick_params(axis='x', labelsize=22)
ax.tick_params(axis='y', labelsize=22)
#plot limits
# ax.set_ylim(np.abs(ax.get_ylim()).max()*np.array([-1,1]))
ax.set_xlim([.9,5e4])
ax.set_ylim([-0.04,0.04])
#save figure
fig.tight_layout()
fig.savefig( dir_fig + fname_fig + '.png' )
# Compare Misfit Metrics
# ---------------------------
#summary directory
dir_sum = '%s%s/summary/'%(dir_results,ker_suffix+synds_suffix)
pathlib.Path(dir_fig).mkdir(parents=True, exist_ok=True)
#figure directory
dir_fig = '%s/figures/'%(dir_sum)
pathlib.Path(dir_fig).mkdir(parents=True, exist_ok=True)
#save
df_misfit.to_csv(dir_sum + 'misfit_summary.csv')
#RMS misfit
fname_fig = 'misfit_score'
#plot KL divergence
fig, ax = plt.subplots(figsize = (10,10))
ax.plot(ds_id, df_misfit.nerg_tot_rms, linestyle='-', marker='o', linewidth=2, markersize=10, label= 'tot nerg')
ax.plot(ds_id, df_misfit.dc_1e_rms, linestyle='-', marker='o', linewidth=2, markersize=10, label=r'$\delta c_{1,E}$')
ax.plot(ds_id, df_misfit.dc_1as_rms, linestyle='-', marker='o', linewidth=2, markersize=10, label=r'$\delta c_{1a,S}$')
ax.plot(ds_id, df_misfit.dc_1bs_rms, linestyle='-', marker='o', linewidth=2, markersize=10, label=r'$\delta c_{1b,S}$')
ax.plot(ds_id, df_misfit.c_cap_rms, linestyle='-', marker='o', linewidth=2, markersize=10, label=r'$c_{ca,P}$')
#figure properties
ax.set_ylim([0,0.50])
ax.set_xlabel('synthetic dataset', fontsize=25)
ax.set_ylabel('RSME', fontsize=25)
ax.grid(which='both')
ax.set_xticks(ds_id)
ax.set_xticklabels(labels=df_misfit.index)
ax.tick_params(axis='x', labelsize=22)
ax.tick_params(axis='y', labelsize=22)
#legend
ax.legend(loc='upper left', fontsize=25)
#save figure
fig.tight_layout()
fig.savefig( dir_fig + fname_fig + '.png' )
#KL divergence
fname_fig = 'KLdiv_score'
#plot KL divergence
fig, ax = plt.subplots(figsize = (10,10))
ax.plot(ds_id, df_misfit.nerg_tot_KL, linestyle='-', marker='o', linewidth=2, markersize=10, label= 'tot nerg')
ax.plot(ds_id, df_misfit.dc_1e_KL, linestyle='-', marker='o', linewidth=2, markersize=10, label=r'$\delta c_{1,E}$')
ax.plot(ds_id, df_misfit.dc_1as_KL, linestyle='-', marker='o', linewidth=2, markersize=10, label=r'$\delta c_{1a,S}$')
ax.plot(ds_id, df_misfit.dc_1bs_KL, linestyle='-', marker='o', linewidth=2, markersize=10, label=r'$\delta c_{1b,S}$')
ax.plot(ds_id, df_misfit.c_cap_KL, linestyle='-', marker='o', linewidth=2, markersize=10, label=r'$c_{ca,P}$')
#figure properties
ax.set_ylim([0,0.50])
ax.set_xlabel('synthetic dataset', fontsize=25)
ax.set_ylabel('KL divergence', fontsize=25)
ax.grid(which='both')
ax.set_xticks(ds_id)
ax.set_xticklabels(labels=df_misfit.index)
ax.tick_params(axis='x', labelsize=22)
ax.tick_params(axis='y', labelsize=22)
#legend
ax.legend(loc='upper left', fontsize=25)
#save figure
fig.tight_layout()
fig.savefig( dir_fig + fname_fig + '.png' )
# Compare hyper-paramters
# ---------------------------
#iterate over different datasets
df_reg_hyp = list()
df_reg_hyp_post = list()
for d_id in ds_id:
# Load Data
#--- --- --- --- ---
#regression hyperparamters results
fname_reg_hyp = '%s%s/Y%i/%s%s_Y%i_inla_%s'%(dir_results, ker_suffix+synds_suffix, d_id,prfx_results, synds_suffix, d_id, 'hyperparameters') + '.csv'
fname_reg_hyp_post = '%s%s/Y%i/%s%s_Y%i_inla_%s'%(dir_results, ker_suffix+synds_suffix, d_id,prfx_results, synds_suffix, d_id, 'hyperposterior') + '.csv'
#load regression results
df_reg_hyp.append( pd.read_csv(fname_reg_hyp, index_col=0) )
df_reg_hyp_post.append( pd.read_csv(fname_reg_hyp_post, index_col=0) )
# Omega_1e
#--- --- --- --- ---
#hyper-paramter name
name_hyp = 'omega_1e'
#figure title
fname_fig = 'post_dist_' + name_hyp
#create figure
fig, ax = plt.subplots(figsize = (10,10))
for d_id, df_r_h, df_r_h_p in zip(ds_id, df_reg_hyp, df_reg_hyp_post):
#estimate vertical line height for mean and mode
ymax_mode = df_r_h_p.loc[:,name_hyp+'_pdf'].max()
# ymax_mean = 1.5*np.ceil(ymax_mode/10)*10
ymax_mean = 40
#plot posterior dist
pl_pdf = ax.plot(df_r_h_p.loc[:,name_hyp], df_r_h_p.loc[:,name_hyp+'_pdf'])
ax.vlines(df_r_h.loc[name_hyp,'mean'], ymin=0, ymax=ymax_mean, linestyle='-', color=pl_pdf[0].get_color(), label='Mean')
ax.vlines(df_r_h.loc[name_hyp,'mode'], ymin=0, ymax=ymax_mode, linestyle='--', color=pl_pdf[0].get_color(), label='Mode')
#plot true value
ymax_hyp = ymax_mean
ax.vlines(hyp[name_hyp], ymin=0, ymax=ymax_hyp, linestyle='-', linewidth=4, color='black', label='True value')
#edit figure
if not flag_report: ax.set_title(r'Comparison $\omega_{1,E}$', fontsize=30)
ax.set_xlabel('$\omega_{1,e}$', fontsize=25)
ax.set_ylabel('probability density function ', fontsize=25)
ax.grid(which='both')
ax.tick_params(axis='x', labelsize=22)
ax.tick_params(axis='y', labelsize=22)
#plot limits
ax.set_xlim([0,0.25])
ax.set_ylim([0,ymax_hyp])
#save figure
fig.tight_layout()
fig.savefig( dir_fig + fname_fig + '.png' )
# Omega_1as
#--- --- --- --- ---
#hyper-paramter name
name_hyp = 'omega_1as'
#figure title
fname_fig = 'post_dist_' + name_hyp
#create figure
fig, ax = plt.subplots(figsize = (10,10))
for d_id, df_r_h, df_r_h_p in zip(ds_id, df_reg_hyp, df_reg_hyp_post):
#estimate vertical line height for mean and mode
ymax_mode = df_r_h_p.loc[:,name_hyp+'_pdf'].max()
# ymax_mean = 1.5*np.ceil(ymax_mode/10)*10
ymax_mean = 30
#plot posterior dist
pl_pdf = ax.plot(df_r_h_p.loc[:,name_hyp], df_r_h_p.loc[:,name_hyp+'_pdf'])
ax.vlines(df_r_h.loc[name_hyp,'mean'], ymin=0, ymax=ymax_mean, linestyle='-', color=pl_pdf[0].get_color(), label='Mean')
ax.vlines(df_r_h.loc[name_hyp,'mode'], ymin=0, ymax=ymax_mode, linestyle='--', color=pl_pdf[0].get_color(), label='Mode')
#plot true value
ymax_hyp = ymax_mean
ax.vlines(hyp[name_hyp], ymin=0, ymax=ymax_hyp, linestyle='-', linewidth=4, color='black', label='True value')
#edit figure
if not flag_report: ax.set_title(r'Comparison $\omega_{1a,S}$', fontsize=30)
ax.set_xlabel('$\omega_{1a,s}$', fontsize=25)
ax.set_ylabel('probability density function ', fontsize=25)
ax.grid(which='both')
ax.tick_params(axis='x', labelsize=22)
ax.tick_params(axis='y', labelsize=22)
#plot limits
ax.set_xlim([0,0.5])
ax.set_ylim([0,ymax_hyp])
#save figure
fig.tight_layout()
fig.savefig( dir_fig + fname_fig + '.png' )
# Omega_1bs
#--- --- --- --- ---
#hyper-paramter name
name_hyp = 'omega_1bs'
#figure title
fname_fig = 'post_dist_' + name_hyp
#create figure
fig, ax = plt.subplots(figsize = (10,10))
for d_id, df_r_h, df_r_h_p in zip(ds_id, df_reg_hyp, df_reg_hyp_post):
#estimate vertical line height for mean and mode
ymax_mode = df_r_h_p.loc[:,name_hyp+'_pdf'].max()
# ymax_mean = 1.5*np.ceil(ymax_mode/10)*10
ymax_mean = 60
#plot posterior dist
pl_pdf = ax.plot(df_r_h_p.loc[:,name_hyp], df_r_h_p.loc[:,name_hyp+'_pdf'])
ax.vlines(df_r_h.loc[name_hyp,'mean'], ymin=0, ymax=ymax_mean, linestyle='-', color=pl_pdf[0].get_color(), label='Mean')
ax.vlines(df_r_h.loc[name_hyp,'mode'], ymin=0, ymax=ymax_mode, linestyle='--', color=pl_pdf[0].get_color(), label='Mode')
#plot true value
ymax_hyp = ymax_mean
ax.vlines(hyp[name_hyp], ymin=0, ymax=ymax_hyp, linestyle='-', linewidth=4, color='black', label='True value')
#edit figure
if not flag_report: ax.set_title(r'Comparison $\omega_{1b,S}$', fontsize=30)
ax.set_xlabel('$\omega_{1b,s}$', fontsize=25)
ax.set_ylabel('probability density function ', fontsize=25)
ax.grid(which='both')
ax.tick_params(axis='x', labelsize=22)
ax.tick_params(axis='y', labelsize=22)
#plot limits
ax.set_xlim([0,0.5])
ax.set_ylim([0,ymax_hyp])
#save figure
fig.tight_layout()
fig.savefig( dir_fig + fname_fig + '.png' )
# Ell_1e
#--- --- --- --- ---
#hyper-paramter name
name_hyp = 'ell_1e'
#figure title
fname_fig = 'post_dist_' + name_hyp
#create figure
fig, ax = plt.subplots(figsize = (10,10))
for d_id, df_r_h, df_r_h_p in zip(ds_id, df_reg_hyp, df_reg_hyp_post):
#estimate vertical line height for mean and mode
ymax_mode = df_r_h_p.loc[:,name_hyp+'_pdf'].max()
# ymax_mean = 1.5*np.ceil(ymax_mode/10)*10
ymax_mean = 0.02
#plot posterior dist
pl_pdf = ax.plot(df_r_h_p.loc[:,name_hyp], df_r_h_p.loc[:,name_hyp+'_pdf'])
ax.vlines(df_r_h.loc[name_hyp,'mean'], ymin=0, ymax=ymax_mean, linestyle='-', color=pl_pdf[0].get_color(), label='Mean')
ax.vlines(df_r_h.loc[name_hyp,'mode'], ymin=0, ymax=ymax_mode, linestyle='--', color=pl_pdf[0].get_color(), label='Mode')
#plot true value
ymax_hyp = ymax_mean
ax.vlines(hyp[name_hyp], ymin=0, ymax=ymax_hyp, linestyle='-', linewidth=4, color='black', label='True value')
#edit figure
if not flag_report: ax.set_title(r'Comparison $\ell_{1,E}$', fontsize=30)
ax.set_xlabel('$\ell_{1,e}$', fontsize=25)
ax.set_ylabel('probability density function ', fontsize=25)
ax.grid(which='both')
ax.tick_params(axis='x', labelsize=22)
ax.tick_params(axis='y', labelsize=22)
#plot limits
ax.set_xlim([0,500])
ax.set_ylim([0,ymax_hyp])
#save figure
fig.tight_layout()
fig.savefig( dir_fig + fname_fig + '.png' )
# Ell_1as
#--- --- --- --- ---
#hyper-paramter name
name_hyp = 'ell_1as'
#figure title
fname_fig = 'post_dist_' + name_hyp
#create figure
fig, ax = plt.subplots(figsize = (10,10))
for d_id, df_r_h, df_r_h_p in zip(ds_id, df_reg_hyp, df_reg_hyp_post):
#estimate vertical line height for mean and mode
ymax_mode = df_r_h_p.loc[:,name_hyp+'_pdf'].max()
# ymax_mean = 1.5*np.ceil(ymax_mode/10)*10
ymax_mean = 0.1
#plot posterior dist
pl_pdf = ax.plot(df_r_h_p.loc[:,name_hyp], df_r_h_p.loc[:,name_hyp+'_pdf'])
ax.vlines(df_r_h.loc[name_hyp,'mean'], ymin=0, ymax=ymax_mean, linestyle='-', color=pl_pdf[0].get_color(), label='Mean')
ax.vlines(df_r_h.loc[name_hyp,'mode'], ymin=0, ymax=ymax_mode, linestyle='--', color=pl_pdf[0].get_color(), label='Mode')
#plot true value
ymax_hyp = ymax_mean
ax.vlines(hyp[name_hyp], ymin=0, ymax=ymax_hyp, linestyle='-', linewidth=4, color='black', label='True value')
#edit figure
if not flag_report: ax.set_title(r'Comparison $\ell_{1a,S}$', fontsize=30)
ax.set_xlabel('$\ell_{1a,s}$', fontsize=25)
ax.set_ylabel('probability density function ', fontsize=25)
ax.grid(which='both')
ax.tick_params(axis='x', labelsize=22)
ax.tick_params(axis='y', labelsize=22)
#plot limits
ax.set_xlim([0,150])
ax.set_ylim([0,ymax_hyp])
#save figure
fig.tight_layout()
fig.savefig( dir_fig + fname_fig + '.png' )
# Tau_0
#--- --- --- --- ---
#hyper-paramter name
name_hyp = 'tau_0'
#figure title
fname_fig = 'post_dist_' + name_hyp
#create figure
fig, ax = plt.subplots(figsize = (10,10))
for d_id, df_r_h, df_r_h_p in zip(ds_id, df_reg_hyp, df_reg_hyp_post):
#estimate vertical line height for mean and mode
ymax_mode = df_r_h_p.loc[:,name_hyp+'_pdf'].max()
# ymax_mean = 1.5*np.ceil(ymax_mode/10)*10
ymax_mean = 60
#plot posterior dist
pl_pdf = ax.plot(df_r_h_p.loc[:,name_hyp], df_r_h_p.loc[:,name_hyp+'_pdf'])
ax.vlines(df_r_h.loc[name_hyp,'mean'], ymin=0, ymax=ymax_mean, linestyle='-', color=pl_pdf[0].get_color(), label='Mean')
ax.vlines(df_r_h.loc[name_hyp,'mode'], ymin=0, ymax=ymax_mode, linestyle='--', color=pl_pdf[0].get_color(), label='Mode')
#plot true value
ymax_hyp = ymax_mean
ax.vlines(hyp[name_hyp], ymin=0, ymax=ymax_hyp, linestyle='-', linewidth=4, color='black', label='True value')
#edit figure
if not flag_report: ax.set_title(r'Comparison $\tau_{0}$', fontsize=30)
ax.set_xlabel(r'$\tau_{0}$', fontsize=25)
ax.set_ylabel(r'probability density function ', fontsize=25)
ax.grid(which='both')
ax.tick_params(axis='x', labelsize=22)
ax.tick_params(axis='y', labelsize=22)
#plot limits
ax.set_xlim([0,0.5])
ax.set_ylim([0,ymax_hyp])
#save figure
fig.tight_layout()
fig.savefig( dir_fig + fname_fig + '.png' )
# Phi_0
#--- --- --- --- ---
#hyper-paramter name
name_hyp = 'phi_0'
#figure title
fname_fig = 'post_dist_' + name_hyp
#create figure
fig, ax = plt.subplots(figsize = (10,10))
for d_id, df_r_h, df_r_h_p in zip(ds_id, df_reg_hyp, df_reg_hyp_post):
#estimate vertical line height for mean and mode
ymax_mode = df_r_h_p.loc[:,name_hyp+'_pdf'].max()
# ymax_mean = 1.5*np.ceil(ymax_mode/10)*10
ymax_mean = 100
#plot posterior dist
pl_pdf = ax.plot(df_r_h_p.loc[:,name_hyp], df_r_h_p.loc[:,name_hyp+'_pdf'])
ax.vlines(df_r_h.loc[name_hyp,'mean'], ymin=0, ymax=ymax_mean, linestyle='-', color=pl_pdf[0].get_color(), label='Mean')
ax.vlines(df_r_h.loc[name_hyp,'mode'], ymin=0, ymax=ymax_mode, linestyle='--', color=pl_pdf[0].get_color(), label='Mode')
#plot true value
ymax_hyp = ymax_mean
ax.vlines(hyp[name_hyp], ymin=0, ymax=ymax_hyp, linestyle='-', linewidth=4, color='black', label='True value')
#edit figure
if not flag_report: ax.set_title(r'Comparison $\phi_{0}$', fontsize=30)
ax.set_xlabel('$\phi_{0}$', fontsize=25)
ax.set_ylabel(r'probability density function ', fontsize=25)
ax.grid(which='both')
ax.tick_params(axis='x', labelsize=22)
ax.tick_params(axis='y', labelsize=22)
#plot limits
ax.set_xlim([0,0.6])
ax.set_ylim([0,ymax_hyp])
#save figure
fig.tight_layout()
fig.savefig( dir_fig + fname_fig + '.png' )
# Omega_ca
#--- --- --- --- ---
#hyper-paramter name
name_hyp = 'omega_cap'
#figure title
fname_fig = 'post_dist_' + name_hyp
#create figure
fig, ax = plt.subplots(figsize = (10,10))
for d_id, df_r_h, df_r_h_p in zip(ds_id, df_reg_hyp, df_reg_hyp_post):
#estimate vertical line height for mean and mode
ymax_mode = df_r_h_p.loc[:,name_hyp+'_pdf'].max()
# ymax_mean = 1.5*np.ceil(ymax_mode/10)*10
ymax_mean = 1500
#plot posterior dist
pl_pdf = ax.plot(df_r_h_p.loc[:,name_hyp], df_r_h_p.loc[:,name_hyp+'_pdf'])
ax.vlines(df_r_h.loc[name_hyp,'mean'], ymin=0, ymax=ymax_mean, linestyle='-', color=pl_pdf[0].get_color(), label='Mean')
ax.vlines(df_r_h.loc[name_hyp,'mode'], ymin=0, ymax=ymax_mode, linestyle='--', color=pl_pdf[0].get_color(), label='Mode')
#plot true value
ymax_hyp = ymax_mean
ax.vlines(np.sqrt(hyp['omega_ca1p']**2+hyp['omega_ca2p']**2), ymin=0, ymax=ymax_hyp, linestyle='-', linewidth=4, color='black', label='True value')
#edit figure
if not flag_report: ax.set_title(r'Comparison $\omega_{ca,P}$', fontsize=30)
ax.set_xlabel('$\omega_{ca,p}$', fontsize=25)
ax.set_ylabel('probability density function ', fontsize=25)
ax.grid(which='both')
ax.tick_params(axis='x', labelsize=22)
ax.tick_params(axis='y', labelsize=22)
#plot limits
ax.set_xlim([0,0.05])
ax.set_ylim([0,ymax_hyp])
#save figure
fig.tight_layout()
fig.savefig( dir_fig + fname_fig + '.png' )
# # Delta c_0
# #--- --- --- --- ---
# #hyper-paramter name
# name_hyp = 'dc_0'
# #figure title
# fname_fig = 'post_dist_' + name_hyp
# #create figure
# fig, ax = plt.subplots(figsize = (10,10))
# for d_id, df_r_h, df_r_h_p in zip(ds_id, df_reg_hyp, df_reg_hyp_post):
# #estimate vertical line height for mean and mode
# ymax_mode = df_r_h_p.loc[:,name_hyp+'_pdf'].max()
# ymax_mean = 1.5*np.ceil(ymax_mode/10)*10
# ymax_mean = 15
# #plot posterior dist
# pl_pdf = ax.plot(df_r_h_p.loc[:,name_hyp], df_r_h_p.loc[:,name_hyp+'_pdf'])
# ax.vlines(df_r_h.loc[name_hyp,'mean'], ymin=0, ymax=ymax_mean, linestyle='-', color=pl_pdf[0].get_color(), label='Mean')
# ax.vlines(df_r_h.loc[name_hyp,'mode'], ymin=0, ymax=ymax_mode, linestyle='--', color=pl_pdf[0].get_color(), label='Mode')
# #plot true value
# ymax_hyp = ymax_mean
# # ax.vlines(hyp[name_hyp], ymin=0, ymax=ymax_hyp, linestyle='-', linewidth=4, color='black', label='True value')
# #edit figure
# ax.set_title(r'Comparison $\delta c_{0}$', fontsize=30)
# ax.set_xlabel('$\delta c_{0}$', fontsize=25)
# ax.set_ylabel('probability density function ', fontsize=25)
# ax.grid(which='both')
# ax.tick_params(axis='x', labelsize=22)
# ax.tick_params(axis='y', labelsize=22)
# #plot limits
# ax.set_xlim([-1,1])
# ax.set_ylim([0,ymax_hyp])
# #save figure
# fig.tight_layout()
# # fig.savefig( dir_fig + fname_fig + '.png' )
| 35,819 | 39.022346 | 160 | py |
ngmm_tools | ngmm_tools-master/Analyses/Code_Verification/regression/ds2/main_pystan_model2_uncorr_cells_NGAWest3CA.py | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Wed Jul 14 14:17:52 2021
@author: glavrent
"""
# Working directory and Packages
# ---------------------------
#load libraries
import os
import sys
import numpy as np
import pandas as pd
import time
#user functions
sys.path.insert(0,'../../../Python_lib/regression/pystan/')
from regression_pystan_model2_uncorr_cells_unbounded_hyp import RunStan
# Define variables
# ---------------------------
#filename suffix
# synds_suffix = '_small_corr_len'
# synds_suffix = '_large_corr_len'
#synthetic datasets directory
ds_dir = '../../../../Data/Verification/synthetic_datasets/ds2'
ds_dir = r'%s%s/'%(ds_dir, synds_suffix)
# dataset info
#ds_fname_main = 'CatalogNGAWest3CA_synthetic_data'
ds_fname_main = 'CatalogNGAWest3CALite_synthetic_data'
ds_id = np.arange(1,6)
#cell specific anelastic attenuation
ds_fname_cellinfo = 'CatalogNGAWest3CALite_cellinfo'
ds_fname_celldist = 'CatalogNGAWest3CALite_distancematrix'
#stan model
# sm_fname = '../../../Stan_lib/regression_stan_model2_uncorr_cells_unbounded_hyp.stan'
# sm_fname = '../../../Stan_lib/regression_stan_model2_uncorr_cells_unbounded_hyp_chol.stan'
# sm_fname = '../../../Stan_lib/regression_stan_model2_uncorr_cells_unbounded_hyp_chol_efficient.stan'
# sm_fname = '../../../Stan_lib/regression_stan_model2_uncorr_cells_unbounded_hyp_chol_efficient2.stan'
#output info
#main output filename
out_fname_main = 'NGAWest3CA_syndata'
#main output directory
out_dir_main = '../../../../Data/Verification/regression/ds2/'
#output sub-directory
# out_dir_sub = 'PYSTAN_NGAWest3CA_uncorr_cells'
# out_dir_sub = 'PYSTAN_NGAWest3CA_uncorr_cells_chol'
# out_dir_sub = 'PYSTAN_NGAWest3CA_uncorr_cells_chol_eff'
# out_dir_sub = 'PYSTAN_NGAWest3CA_uncorr_cells_chol_eff2'
#stan parameters
runstan_flag = True
# pystan_ver = 2
pystan_ver = 3
res_name = 'tot'
n_iter = 1000
n_chains = 4
adapt_delta = 0.8
max_treedepth = 10
#ergodic coefficients
c_a_erg=0.0
#parallel options
# flag_parallel = True
flag_parallel = False
#output sub-dir with corr with suffix info
out_dir_sub = f'%s%s'%(out_dir_sub, synds_suffix)
#load cell dataframes
cellinfo_fname = '%s%s.csv'%(ds_dir, ds_fname_cellinfo)
celldist_fname = '%s%s.csv'%(ds_dir, ds_fname_celldist)
df_cellinfo = pd.read_csv(cellinfo_fname)
df_celldist = pd.read_csv(celldist_fname)
# Run stan regression
# ---------------------------
#create datafame with computation time
df_run_info = list()
#iterate over all synthetic datasets
for d_id in ds_id:
print('Synthetic dataset %i fo %i'%(d_id, len(ds_id)))
#run time start
run_t_strt = time.time()
#input flatfile
ds_fname = '%s%s%s_Y%i.csv'%(ds_dir, ds_fname_main, synds_suffix, d_id)
#load flatfile
df_flatfile = pd.read_csv(ds_fname)
#output file name and directory
out_fname = '%s%s_Y%i'%(out_fname_main, synds_suffix, d_id)
out_dir = '%s/%s/Y%i/'%(out_dir_main, out_dir_sub, d_id)
#run stan model
RunStan(df_flatfile, df_cellinfo, df_celldist, sm_fname,
out_fname, out_dir, res_name, c_a_erg=c_a_erg,
runstan_flag=runstan_flag, n_iter=n_iter, n_chains=n_chains,
adapt_delta=adapt_delta, max_treedepth=max_treedepth,
pystan_ver=pystan_ver, pystan_parallel=flag_parallel)
#run time end
run_t_end = time.time()
#compute run time
run_tm = (run_t_end - run_t_strt)/60
#log run time
df_run_info.append(pd.DataFrame({'computer_name':os.uname()[1],'out_name':out_dir_sub,
'ds_id':d_id,'run_time':run_tm}, index=[d_id]))
#write out run info
out_fname = '%s%s/run_info.csv'%(out_dir_main, out_dir_sub)
pd.concat(df_run_info).reset_index(drop=True).to_csv(out_fname, index=False)
| 3,831 | 30.933333 | 103 | py |
ngmm_tools | ngmm_tools-master/Analyses/Code_Verification/regression/ds2/main_cmdstan_model2_uncorr_cells_NGAWest2CANorth.py | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Wed Dec 29 15:16:15 2021
@author: glavrent
"""
# Working directory and Packages
# ---------------------------
#load libraries
import os
import sys
import numpy as np
import pandas as pd
import time
#user functions
sys.path.insert(0,'../../../Python_lib/regression/cmdstan/')
# from regression_cmdstan_model2_uncorr_cells_unbounded_hyp import RunStan
from regression_cmdstan_model2_uncorr_cells_sparse_unbounded_hyp import RunStan
# Define variables
# ---------------------------
#filename suffix
# synds_suffix = '_small_corr_len'
# synds_suffix = '_large_corr_len'
#synthetic datasets directory
ds_dir = '../../../../Data/Verification/synthetic_datasets/ds2'
ds_dir = r'%s%s/'%(ds_dir, synds_suffix)
# dataset info
#ds_fname_main = 'CatalogNGAWest3CA_synthetic_data'
ds_fname_main = 'CatalogNGAWest3CALite_synthetic_data'
ds_id = np.arange(1,6)
#cell specific anelastic attenuation
ds_fname_cellinfo = 'CatalogNGAWest3CALite_cellinfo'
ds_fname_celldist = 'CatalogNGAWest3CALite_distancematrix'
#stan model
# sm_fname = '../../../Stan_lib/regression_stan_model2_uncorr_cells_unbounded_hyp.stan'
# sm_fname = '../../../Stan_lib/regression_stan_model2_uncorr_cells_unbounded_hyp_chol.stan'
# sm_fname = '../../../Stan_lib/regression_stan_model2_uncorr_cells_unbounded_hyp_chol_efficient.stan'
# sm_fname = '../../../Stan_lib/regression_stan_model2_uncorr_cells_unbounded_hyp_chol_efficient2.stan'
# sm_fname = '../../../Stan_lib/regression_stan_model2_uncorr_cells_sparse_unbounded_hyp_chol_efficient.stan'
#output info
#main output filename
out_fname_main = 'NGAWest2CANorth_syndata'
#main output directory
out_dir_main = '../../../../Data/Verification/regression/ds2/'
#output sub-directory
# out_dir_sub = 'CMDSTAN_NGAWest2CANorth_uncorr_cells'
# out_dir_sub = 'CMDSTAN_NGAWest2CANorth_uncorr_cells_chol'
# out_dir_sub = 'CMDSTAN_NGAWest2CANorth_uncorr_cells_chol_eff'
# out_dir_sub = 'CMDSTAN_NGAWest2CANorth_uncorr_cells_chol_eff2'
# out_dir_sub = 'CMDSTAN_NGAWest2CANorth_uncorr_cells_chol_eff_sp'
#stan parameters
res_name = 'tot'
n_iter_warmup = 500
n_iter_sampling = 500
n_chains = 4
adapt_delta = 0.8
max_treedepth = 10
#ergodic coefficients
c_a_erg=0.0
#parallel options
# flag_parallel = True
flag_parallel = False
#output sub-dir with corr with suffix info
out_dir_sub = f'%s%s'%(out_dir_sub, synds_suffix)
#load cell dataframes
cellinfo_fname = '%s%s.csv'%(ds_dir, ds_fname_cellinfo)
celldist_fname = '%s%s.csv'%(ds_dir, ds_fname_celldist)
df_cellinfo = pd.read_csv(cellinfo_fname)
df_celldist = pd.read_csv(celldist_fname)
# Run stan regression
# ---------------------------
#create datafame with computation time
df_run_info = list()
#iterate over all synthetic datasets
for d_id in ds_id:
print('Synthetic dataset %i fo %i'%(d_id, len(ds_id)))
#run time start
run_t_strt = time.time()
#input flatfile
ds_fname = '%s%s%s_Y%i.csv'%(ds_dir, ds_fname_main, synds_suffix, d_id)
#load flatfile
df_flatfile = pd.read_csv(ds_fname)
#keep only North records of NGAWest2
df_flatfile = df_flatfile.loc[np.logical_and(df_flatfile.dsid==0,
df_flatfile.sreg==1),:]
#output file name and directory
out_fname = '%s%s_Y%i'%(out_fname_main, synds_suffix, d_id)
out_dir = '%s/%s/Y%i/'%(out_dir_main, out_dir_sub, d_id)
#run stan model
RunStan(df_flatfile, df_cellinfo, df_celldist, sm_fname,
out_fname, out_dir, res_name, c_a_erg=c_a_erg,
n_iter_warmup=n_iter_warmup, n_iter_sampling=n_iter_sampling, n_chains=n_chains,
adapt_delta=adapt_delta, max_treedepth=max_treedepth,
stan_parallel=flag_parallel)
#run time end
run_t_end = time.time()
#compute run time
run_tm = (run_t_end - run_t_strt)/60
#log run time
df_run_info.append(pd.DataFrame({'computer_name':os.uname()[1],'out_name':out_dir_sub,
'ds_id':d_id,'run_time':run_tm}, index=[d_id]))
#write out run info
out_fname = '%s%s/run_info.csv'%(out_dir_main, out_dir_sub)
pd.concat(df_run_info).reset_index(drop=True).to_csv(out_fname, index=False)
| 4,298 | 33.669355 | 109 | py |
ngmm_tools | ngmm_tools-master/Analyses/Code_Verification/regression/ds2/main_cmdstan_model2_uncorr_cells_NGAWest2CA.py | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Wed Dec 29 15:16:15 2021
@author: glavrent
"""
# Working directory and Packages
# ---------------------------
#load libraries
import os
import sys
import numpy as np
import pandas as pd
import time
#user functions
sys.path.insert(0,'../../../Python_lib/regression/cmdstan/')
# from regression_cmdstan_model2_uncorr_cells_unbounded_hyp import RunStan
from regression_cmdstan_model2_uncorr_cells_sparse_unbounded_hyp import RunStan
# Define variables
# ---------------------------
#filename suffix
# synds_suffix = '_small_corr_len'
# synds_suffix = '_large_corr_len'
#synthetic datasets directory
ds_dir = '../../../../Data/Verification/synthetic_datasets/ds2'
ds_dir = r'%s%s/'%(ds_dir, synds_suffix)
# dataset info
#ds_fname_main = 'CatalogNGAWest3CA_synthetic_data'
ds_fname_main = 'CatalogNGAWest3CALite_synthetic_data'
ds_id = np.arange(1,6)
#cell specific anelastic attenuation
ds_fname_cellinfo = 'CatalogNGAWest3CALite_cellinfo'
ds_fname_celldist = 'CatalogNGAWest3CALite_distancematrix'
#stan model
# sm_fname = '../../../Stan_lib/regression_stan_model2_uncorr_cells_unbounded_hyp.stan'
# sm_fname = '../../../Stan_lib/regression_stan_model2_uncorr_cells_unbounded_hyp_chol.stan'
# sm_fname = '../../../Stan_lib/regression_stan_model2_uncorr_cells_unbounded_hyp_chol_efficient.stan'
# sm_fname = '../../../Stan_lib/regression_stan_model2_uncorr_cells_unbounded_hyp_chol_efficient2.stan'
# sm_fname = '../../../Stan_lib/regression_stan_model2_uncorr_cells_sparse_unbounded_hyp_chol_efficient.stan'
#output info
#main output filename
out_fname_main = 'NGAWest2CA_syndata'
#main output directory
out_dir_main = '../../../../Data/Verification/regression/ds2/'
#output sub-directory
# out_dir_sub = 'CMDSTAN_NGAWest2CA_uncorr_cells'
# out_dir_sub = 'CMDSTAN_NGAWest2CA_uncorr_cells_chol'
# out_dir_sub = 'CMDSTAN_NGAWest2CA_uncorr_cells_chol_eff'
# out_dir_sub = 'CMDSTAN_NGAWest2CA_uncorr_cells_chol_eff2'
# out_dir_sub = 'CMDSTAN_NGAWest2CA_uncorr_cells_chol_eff_sp'
#stan parameters
res_name = 'tot'
n_iter_warmup = 500
n_iter_sampling = 500
n_chains = 4
adapt_delta = 0.8
max_treedepth = 10
#ergodic coefficients
c_a_erg=0.0
#parallel options
# flag_parallel = True
flag_parallel = False
#output sub-dir with corr with suffix info
out_dir_sub = f'%s%s'%(out_dir_sub, synds_suffix)
#load cell dataframes
cellinfo_fname = '%s%s.csv'%(ds_dir, ds_fname_cellinfo)
celldist_fname = '%s%s.csv'%(ds_dir, ds_fname_celldist)
df_cellinfo = pd.read_csv(cellinfo_fname)
df_celldist = pd.read_csv(celldist_fname)
# Run stan regression
# ---------------------------
#create datafame with computation time
df_run_info = list()
#iterate over all synthetic datasets
for d_id in ds_id:
print('Synthetic dataset %i fo %i'%(d_id, len(ds_id)))
#run time start
run_t_strt = time.time()
#input flatfile
ds_fname = '%s%s%s_Y%i.csv'%(ds_dir, ds_fname_main, synds_suffix, d_id)
#load flatfile
df_flatfile = pd.read_csv(ds_fname)
#keep only NGAWest2 records
df_flatfile = df_flatfile.loc[df_flatfile.dsid==0,:]
#output file name and directory
out_fname = '%s%s_Y%i'%(out_fname_main, synds_suffix, d_id)
out_dir = '%s/%s/Y%i/'%(out_dir_main, out_dir_sub, d_id)
#run stan model
RunStan(df_flatfile, df_cellinfo, df_celldist, sm_fname,
out_fname, out_dir, res_name, c_a_erg=c_a_erg,
n_iter_warmup=n_iter_warmup, n_iter_sampling=n_iter_sampling, n_chains=n_chains,
adapt_delta=adapt_delta, max_treedepth=max_treedepth,
stan_parallel=flag_parallel)
#run time end
run_t_end = time.time()
#compute run time
run_tm = (run_t_end - run_t_strt)/60
#log run time
df_run_info.append(pd.DataFrame({'computer_name':os.uname()[1],'out_name':out_dir_sub,
'ds_id':d_id,'run_time':run_tm}, index=[d_id]))
#write out run info
out_fname = '%s%s/run_info.csv'%(out_dir_main, out_dir_sub)
pd.concat(df_run_info).reset_index(drop=True).to_csv(out_fname, index=False)
| 4,177 | 32.96748 | 109 | py |
ngmm_tools | ngmm_tools-master/Analyses/Code_Verification/regression/ds2/main_cmdstan_model2_corr_cells_NGAWest3CA.py | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Wed Dec 29 15:16:15 2021
@author: glavrent
"""
# Working directory and Packages
# ---------------------------
#load libraries
import os
import sys
import numpy as np
import pandas as pd
import time
#user functions
sys.path.insert(0,'../../../Python_lib/regression/cmdstan/')
# from regression_cmdstan_model2_corr_cells_unbounded_hyp import RunStan
from regression_cmdstan_model2_corr_cells_sparse_unbounded_hyp import RunStan
# Define variables
# ---------------------------
#filename suffix
# synds_suffix = '_small_corr_len'
# synds_suffix = '_large_corr_len'
#synthetic datasets directory
ds_dir = '../../../../Data/Verification/synthetic_datasets/ds2'
ds_dir = r'%s%s/'%(ds_dir, synds_suffix)
# dataset info
#ds_fname_main = 'CatalogNGAWest3CA_synthetic_data'
ds_fname_main = 'CatalogNGAWest3CALite_synthetic_data'
ds_id = np.arange(1,6)
#cell specific anelastic attenuation
ds_fname_cellinfo = 'CatalogNGAWest3CALite_cellinfo'
ds_fname_celldist = 'CatalogNGAWest3CALite_distancematrix'
#stan model
# sm_fname = '../../../Stan_lib/regression_stan_model2_corr_cells_unbounded_hyp.stan'
# sm_fname = '../../../Stan_lib/regression_stan_model2_corr_cells_unbounded_hyp_chol.stan'
# sm_fname = '../../../Stan_lib/regression_stan_model2_corr_cells_unbounded_hyp_chol_efficient.stan'
# sm_fname = '../../../Stan_lib/regression_stan_model2_corr_cells_unbounded_hyp_chol_efficient2.stan'
# sm_fname = '../../../Stan_lib/regression_stan_model2_corr_cells_sparse_unbounded_hyp_chol_efficient.stan'
#output info
#main output filename
out_fname_main = 'NGAWest3CA_syndata'
#main output directory
out_dir_main = '../../../../Data/Verification/regression/ds2/'
#output sub-directory
# out_dir_sub = 'CMDSTAN_NGAWest3CA_corr_cells'
# out_dir_sub = 'CMDSTAN_NGAWest3CA_corr_cells_chol'
# out_dir_sub = 'CMDSTAN_NGAWest3CA_corr_cells_chol_efficient'
# out_dir_sub = 'CMDSTAN_NGAWest3CA_corr_cells_chol_efficient2'
# out_dir_sub = 'CMDSTAN_NGAWest3CA_corr_cells_chol_efficient_sp'
#stan parameters
res_name = 'tot'
n_iter_warmup = 500
n_iter_sampling = 500
n_chains = 4
adapt_delta = 0.8
max_treedepth = 10
#ergodic coefficients
c_a_erg=0.0
#parallel options
# flag_parallel = True
flag_parallel = False
#output sub-dir with corr with suffix info
out_dir_sub = f'%s%s'%(out_dir_sub, synds_suffix)
#load cell dataframes
cellinfo_fname = '%s%s.csv'%(ds_dir, ds_fname_cellinfo)
celldist_fname = '%s%s.csv'%(ds_dir, ds_fname_celldist)
df_cellinfo = pd.read_csv(cellinfo_fname)
df_celldist = pd.read_csv(celldist_fname)
# Run stan regression
# ---------------------------
#create datafame with computation time
df_run_info = list()
#iterate over all synthetic datasets
for d_id in ds_id:
print('Synthetic dataset %i fo %i'%(d_id, len(ds_id)))
#run time start
run_t_strt = time.time()
#input flatfile
ds_fname = '%s%s%s_Y%i.csv'%(ds_dir, ds_fname_main, synds_suffix, d_id)
#load flatfile
df_flatfile = pd.read_csv(ds_fname)
#output file name and directory
out_fname = '%s%s_Y%i'%(out_fname_main, synds_suffix, d_id)
out_dir = '%s/%s/Y%i/'%(out_dir_main, out_dir_sub, d_id)
#run stan model
RunStan(df_flatfile, df_cellinfo, df_celldist, sm_fname,
out_fname, out_dir, res_name, c_a_erg=c_a_erg,
n_iter_warmup=n_iter_warmup, n_iter_sampling=n_iter_sampling, n_chains=n_chains,
adapt_delta=adapt_delta, max_treedepth=max_treedepth,
stan_parallel=flag_parallel)
#run time end
run_t_end = time.time()
#compute run time
run_tm = (run_t_end - run_t_strt)/60
#log run time
df_run_info.append(pd.DataFrame({'computer_name':os.uname()[1],'out_name':out_dir_sub,
'ds_id':d_id,'run_time':run_tm}, index=[d_id]))
#write out run info
out_fname = '%s%s/run_info.csv'%(out_dir_main, out_dir_sub)
pd.concat(df_run_info).reset_index(drop=True).to_csv(out_fname, index=False)
| 4,078 | 32.710744 | 107 | py |
ngmm_tools | ngmm_tools-master/Analyses/Code_Verification/regression/ds2/main_cmdstan_model2_corr_cells_NGAWest2CANorth.py | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Wed Dec 29 15:16:15 2021
@author: glavrent
"""
# Working directory and Packages
# ---------------------------
#load libraries
import os
import sys
import numpy as np
import pandas as pd
import time
#user functions
sys.path.insert(0,'../../../Python_lib/regression/cmdstan/')
# from regression_cmdstan_model2_corr_cells_unbounded_hyp import RunStan
from regression_cmdstan_model2_corr_cells_sparse_unbounded_hyp import RunStan
# Define variables
# ---------------------------
#filename suffix
# synds_suffix = '_small_corr_len'
# synds_suffix = '_large_corr_len'
#synthetic datasets directory
ds_dir = '../../../../Data/Verification/synthetic_datasets/ds2'
ds_dir = r'%s%s/'%(ds_dir, synds_suffix)
# dataset info
#ds_fname_main = 'CatalogNGAWest3CA_synthetic_data'
ds_fname_main = 'CatalogNGAWest3CALite_synthetic_data'
ds_id = np.arange(1,6)
#cell specific anelastic attenuation
ds_fname_cellinfo = 'CatalogNGAWest3CALite_cellinfo'
ds_fname_celldist = 'CatalogNGAWest3CALite_distancematrix'
#stan model
# sm_fname = '../../../Stan_lib/regression_stan_model2_corr_cells_unbounded_hyp.stan'
# sm_fname = '../../../Stan_lib/regression_stan_model2_corr_cells_unbounded_hyp_chol.stan'
# sm_fname = '../../../Stan_lib/regression_stan_model2_corr_cells_unbounded_hyp_chol_efficient.stan'
# sm_fname = '../../../Stan_lib/regression_stan_model2_corr_cells_unbounded_hyp_chol_efficient2.stan'
# sm_fname = '../../../Stan_lib/regression_stan_model2_corr_cells_sparse_unbounded_hyp_chol_efficient.stan'
#output info
#main output filename
out_fname_main = 'NGAWest2CANorth_syndata'
#main output directory
out_dir_main = '../../../../Data/Verification/regression/ds2/'
#output sub-directory
# out_dir_sub = 'CMDSTAN_NGAWest2CANorth_corr_cells'
# out_dir_sub = 'CMDSTAN_NGAWest2CANorth_corr_cells_chol'
# out_dir_sub = 'CMDSTAN_NGAWest2CANorth_corr_cells_chol_eff'
# out_dir_sub = 'CMDSTAN_NGAWest2CANorth_corr_cells_chol_eff2'
# out_dir_sub = 'CMDSTAN_NGAWest2CANorth_corr_cells_chol_eff_sp'
#stan parameters
res_name = 'tot'
n_iter_warmup = 500
n_iter_sampling = 500
n_chains = 4
adapt_delta = 0.8
max_treedepth = 10
#ergodic coefficients
c_a_erg=0.0
#parallel options
# flag_parallel = True
flag_parallel = False
#output sub-dir with corr with suffix info
out_dir_sub = f'%s%s'%(out_dir_sub, synds_suffix)
#load cell dataframes
cellinfo_fname = '%s%s.csv'%(ds_dir, ds_fname_cellinfo)
celldist_fname = '%s%s.csv'%(ds_dir, ds_fname_celldist)
df_cellinfo = pd.read_csv(cellinfo_fname)
df_celldist = pd.read_csv(celldist_fname)
# Run stan regression
# ---------------------------
#create datafame with computation time
df_run_info = list()
#iterate over all synthetic datasets
for d_id in ds_id:
print('Synthetic dataset %i fo %i'%(d_id, len(ds_id)))
#run time start
run_t_strt = time.time()
#input flatfile
ds_fname = '%s%s%s_Y%i.csv'%(ds_dir, ds_fname_main, synds_suffix, d_id)
#load flatfile
df_flatfile = pd.read_csv(ds_fname)
#keep only North records of NGAWest2
df_flatfile = df_flatfile.loc[np.logical_and(df_flatfile.dsid==0,
df_flatfile.sreg==1),:]
#output file name and directory
out_fname = '%s%s_Y%i'%(out_fname_main, synds_suffix, d_id)
out_dir = '%s/%s/Y%i/'%(out_dir_main, out_dir_sub, d_id)
#run stan model
RunStan(df_flatfile, df_cellinfo, df_celldist, sm_fname,
out_fname, out_dir, res_name, c_a_erg=c_a_erg,
n_iter_warmup=n_iter_warmup, n_iter_sampling=n_iter_sampling, n_chains=n_chains,
adapt_delta=adapt_delta, max_treedepth=max_treedepth,
stan_parallel=flag_parallel)
#run time end
run_t_end = time.time()
#compute run time
run_tm = (run_t_end - run_t_strt)/60
#log run time
df_run_info.append(pd.DataFrame({'computer_name':os.uname()[1],'out_name':out_dir_sub,
'ds_id':d_id,'run_time':run_tm}, index=[d_id]))
#write out run info
out_fname = '%s%s/run_info.csv'%(out_dir_main, out_dir_sub)
pd.concat(df_run_info).reset_index(drop=True).to_csv(out_fname, index=False)
| 4,274 | 33.475806 | 107 | py |
ngmm_tools | ngmm_tools-master/Analyses/Code_Verification/regression/ds2/main_pystan_model2_uncorr_cells_NGAWest2CA.py | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Wed Jul 14 14:17:52 2021
@author: glavrent
"""
# Working directory and Packages
# ---------------------------
#load libraries
import os
import sys
import numpy as np
import pandas as pd
import time
#user functions
sys.path.insert(0,'../../../Python_lib/regression/pystan/')
from regression_pystan_model2_uncorr_cells_unbounded_hyp import RunStan
# Define variables
# ---------------------------
#filename suffix
# synds_suffix = '_small_corr_len'
# synds_suffix = '_large_corr_len'
#synthetic datasets directory
ds_dir = '../../../../Data/Verification/synthetic_datasets/ds2'
ds_dir = r'%s%s/'%(ds_dir, synds_suffix)
# dataset info
#ds_fname_main = 'CatalogNGAWest3CA_synthetic_data'
ds_fname_main = 'CatalogNGAWest3CALite_synthetic_data'
ds_id = np.arange(1,6)
#cell specific anelastic attenuation
ds_fname_cellinfo = 'CatalogNGAWest3CALite_cellinfo'
ds_fname_celldist = 'CatalogNGAWest3CALite_distancematrix'
#stan model
# sm_fname = '../../../Stan_lib/regression_stan_model2_uncorr_cells_unbounded_hyp.stan'
# sm_fname = '../../../Stan_lib/regression_stan_model2_uncorr_cells_unbounded_hyp_chol.stan'
# sm_fname = '../../../Stan_lib/regression_stan_model2_uncorr_cells_unbounded_hyp_chol_efficient.stan'
# sm_fname = '../../../Stan_lib/regression_stan_model2_uncorr_cells_unbounded_hyp_chol_efficient2.stan'
#output info
#main output filename
out_fname_main = 'NGAWest2CA_syndata'
#main output directory
out_dir_main = '../../../../Data/Verification/regression/ds2/'
#output sub-directory
#pystan 2
# out_dir_sub = 'PYSTAN_NGAWest2CA_uncorr_cells'
# out_dir_sub = 'PYSTAN_NGAWest2CA_uncorr_cells_chol'
# out_dir_sub = 'PYSTAN_NGAWest2CA_uncorr_cells_chol_eff'
# out_dir_sub = 'PYSTAN_NGAWest2CA_uncorr_cells_chol_eff2'
#pystan 3
# out_dir_sub = 'PYSTAN3_NGAWest2CA_uncorr_cells'
# out_dir_sub = 'PYSTAN3_NGAWest2CA_uncorr_cells_chol'
# out_dir_sub = 'PYSTAN3_NGAWest2CA_uncorr_cells_chol_eff'
# out_dir_sub = 'PYSTAN3_NGAWest2CA_uncorr_cells_chol_eff2'
#stan parameters
runstan_flag = True
# pystan_ver = 2
pystan_ver = 3
res_name = 'tot'
n_iter = 1000
n_chains = 4
adapt_delta = 0.8
max_treedepth = 10
#ergodic coefficients
c_a_erg=0.0
#parallel options
# flag_parallel = True
flag_parallel = False
#output sub-dir with corr with suffix info
out_dir_sub = f'%s%s'%(out_dir_sub, synds_suffix)
#load cell dataframes
cellinfo_fname = '%s%s.csv'%(ds_dir, ds_fname_cellinfo)
celldist_fname = '%s%s.csv'%(ds_dir, ds_fname_celldist)
df_cellinfo = pd.read_csv(cellinfo_fname)
df_celldist = pd.read_csv(celldist_fname)
# Run stan regression
# ---------------------------
#create datafame with computation time
df_run_info = list()
#iterate over all synthetic datasets
for d_id in ds_id:
print('Synthetic dataset %i fo %i'%(d_id, len(ds_id)))
#run time start
run_t_strt = time.time()
#input flatfile
ds_fname = '%s%s%s_Y%i.csv'%(ds_dir, ds_fname_main, synds_suffix, d_id)
#load flatfile
df_flatfile = pd.read_csv(ds_fname)
#keep only NGAWest2 records
df_flatfile = df_flatfile.loc[df_flatfile.dsid==0,:]
#output file name and directory
out_fname = '%s%s_Y%i'%(out_fname_main, synds_suffix, d_id)
out_dir = '%s/%s/Y%i/'%(out_dir_main, out_dir_sub, d_id)
#run stan model
RunStan(df_flatfile, df_cellinfo, df_celldist, sm_fname,
out_fname, out_dir, res_name, c_a_erg=c_a_erg,
runstan_flag=runstan_flag, n_iter=n_iter, n_chains=n_chains,
adapt_delta=adapt_delta, max_treedepth=max_treedepth,
pystan_ver=pystan_ver, pystan_parallel=flag_parallel)
#run time end
run_t_end = time.time()
#compute run time
run_tm = (run_t_end - run_t_strt)/60
#log run time
df_run_info.append(pd.DataFrame({'computer_name':os.uname()[1],'out_name':out_dir_sub,
'ds_id':d_id,'run_time':run_tm}, index=[d_id]))
#write out run info
out_fname = '%s%s/run_info.csv'%(out_dir_main, out_dir_sub)
pd.concat(df_run_info).reset_index(drop=True).to_csv(out_fname, index=False)
| 4,181 | 31.418605 | 103 | py |
ngmm_tools | ngmm_tools-master/Analyses/Code_Verification/regression/ds2/main_cmdstan_model2_uncorr_cells_NGAWest3CA.py | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Wed Dec 29 15:16:15 2021
@author: glavrent
"""
# Working directory and Packages
# ---------------------------
#load libraries
import os
import sys
import numpy as np
import pandas as pd
import time
#user functions
sys.path.insert(0,'../../../Python_lib/regression/cmdstan/')
# from regression_cmdstan_model2_uncorr_cells_unbounded_hyp import RunStan
from regression_cmdstan_model2_uncorr_cells_sparse_unbounded_hyp import RunStan
# Define variables
# ---------------------------
#filename suffix
# synds_suffix = '_small_corr_len'
# synds_suffix = '_large_corr_len'
#synthetic datasets directory
ds_dir = '../../../../Data/Verification/synthetic_datasets/ds2'
ds_dir = r'%s%s/'%(ds_dir, synds_suffix)
# dataset info
#ds_fname_main = 'CatalogNGAWest3CA_synthetic_data'
ds_fname_main = 'CatalogNGAWest3CALite_synthetic_data'
ds_id = np.arange(1,6)
#cell specific anelastic attenuation
ds_fname_cellinfo = 'CatalogNGAWest3CALite_cellinfo'
ds_fname_celldist = 'CatalogNGAWest3CALite_distancematrix'
#stan model
# sm_fname = '../../../Stan_lib/regression_stan_model2_uncorr_cells_unbounded_hyp.stan'
# sm_fname = '../../../Stan_lib/regression_stan_model2_uncorr_cells_unbounded_hyp_chol.stan'
# sm_fname = '../../../Stan_lib/regression_stan_model2_uncorr_cells_unbounded_hyp_chol_efficient.stan'
# sm_fname = '../../../Stan_lib/regression_stan_model2_uncorr_cells_unbounded_hyp_chol_efficient2.stan'
# sm_fname = '../../../Stan_lib/regression_stan_model2_uncorr_cells_sparse_unbounded_hyp_chol_efficient.stan'
#output info
#main output filename
out_fname_main = 'NGAWest3CA_syndata'
#main output directory
out_dir_main = '../../../../Data/Verification/regression/ds2/'
#output sub-directory
# out_dir_sub = 'CMDSTAN_NGAWest3CA_uncorr_cells'
# out_dir_sub = 'CMDSTAN_NGAWest3CA_uncorr_cells_chol'
# out_dir_sub = 'CMDSTAN_NGAWest3CA_uncorr_cells_chol_eff'
# out_dir_sub = 'CMDSTAN_NGAWest3CA_uncorr_cells_chol_eff2'
# out_dir_sub = 'CMDSTAN_NGAWest3CA_uncorr_cells_chol_eff_sp'
#stan parameters
res_name = 'tot'
n_iter_warmup = 500
n_iter_sampling = 500
n_chains = 4
adapt_delta = 0.8
max_treedepth = 10
#ergodic coefficients
c_a_erg=0.0
#parallel options
# flag_parallel = True
flag_parallel = False
#output sub-dir with corr with suffix info
out_dir_sub = f'%s%s'%(out_dir_sub, synds_suffix)
#load cell dataframes
cellinfo_fname = '%s%s.csv'%(ds_dir, ds_fname_cellinfo)
celldist_fname = '%s%s.csv'%(ds_dir, ds_fname_celldist)
df_cellinfo = pd.read_csv(cellinfo_fname)
df_celldist = pd.read_csv(celldist_fname)
# Run stan regression
# ---------------------------
#create datafame with computation time
df_run_info = list()
#iterate over all synthetic datasets
for d_id in ds_id:
print('Synthetic dataset %i fo %i'%(d_id, len(ds_id)))
#run time start
run_t_strt = time.time()
#input flatfile
ds_fname = '%s%s%s_Y%i.csv'%(ds_dir, ds_fname_main, synds_suffix, d_id)
#load flatfile
df_flatfile = pd.read_csv(ds_fname)
#output file name and directory
out_fname = '%s%s_Y%i'%(out_fname_main, synds_suffix, d_id)
out_dir = '%s/%s/Y%i/'%(out_dir_main, out_dir_sub, d_id)
#run stan model
RunStan(df_flatfile, df_cellinfo, df_celldist, sm_fname,
out_fname, out_dir, res_name, c_a_erg=c_a_erg,
n_iter_warmup=n_iter_warmup, n_iter_sampling=n_iter_sampling, n_chains=n_chains,
adapt_delta=adapt_delta, max_treedepth=max_treedepth,
stan_parallel=flag_parallel)
#run time end
run_t_end = time.time()
#compute run time
run_tm = (run_t_end - run_t_strt)/60
#log run time
df_run_info.append(pd.DataFrame({'computer_name':os.uname()[1],'out_name':out_dir_sub,
'ds_id':d_id,'run_time':run_tm}, index=[d_id]))
#write out run info
out_fname = '%s%s/run_info.csv'%(out_dir_main, out_dir_sub)
pd.concat(df_run_info).reset_index(drop=True).to_csv(out_fname, index=False)
| 4,084 | 32.760331 | 109 | py |
ngmm_tools | ngmm_tools-master/Analyses/Code_Verification/regression/ds2/main_pystan_model2_corr_cells_NGAWest3CA_sparse.py | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Wed Jul 14 14:17:52 2021
@author: glavrent
"""
# Working directory and Packages
# ---------------------------
#load libraries
import os
import sys
import numpy as np
import pandas as pd
import time
#user functions
sys.path.insert(0,'../../../Python_lib/regression/pystan/')
from regression_pystan_model2_corr_cells_sparse_unbounded_hyp import RunStan
# Define variables
# ---------------------------
#filename suffix
# synds_suffix = '_small_corr_len'
# synds_suffix = '_large_corr_len'
#synthetic datasets directory
ds_dir = '../../../../Data/Verification/synthetic_datasets/ds2'
ds_dir = r'%s%s/'%(ds_dir, synds_suffix)
# dataset info
#ds_fname_main = 'CatalogNGAWest3CA_synthetic_data'
ds_fname_main = 'CatalogNGAWest3CALite_synthetic_data'
ds_id = np.arange(1,6)
#cell specific anelastic attenuation
ds_fname_cellinfo = 'CatalogNGAWest3CALite_cellinfo'
ds_fname_celldist = 'CatalogNGAWest3CALite_distancematrix'
#stan model
sm_fname = '../../../Stan_lib/regression_stan_model2_corr_cells_sparse_unbounded_hyp_chol_efficient.stan'
#output info
#main output filename
out_fname_main = 'NGAWest3CA_syndata'
#main output directory
out_dir_main = '../../../../Data/Verification/regression/ds2/'
#output sub-directory
out_dir_sub = 'PYSTAN_NGAWest3CA_corr_cells_chol_eff_sp'
#stan parameters
runstan_flag = True
pystan_ver = 2
# pystan_ver = 3
res_name = 'tot'
n_iter = 1000
n_chains = 4
adapt_delta = 0.8
max_treedepth = 10
#ergodic coefficients
c_a_erg=0.0
#parallel options
# flag_parallel = True
flag_parallel = False
#output sub-dir with corr with suffix info
out_dir_sub = f'%s%s'%(out_dir_sub, synds_suffix)
#load cell dataframes
cellinfo_fname = '%s%s.csv'%(ds_dir, ds_fname_cellinfo)
celldist_fname = '%s%s.csv'%(ds_dir, ds_fname_celldist)
df_cellinfo = pd.read_csv(cellinfo_fname)
df_celldist = pd.read_csv(celldist_fname)
# Run stan regression
# ---------------------------
#create datafame with computation time
df_run_info = list()
#iterate over all synthetic datasets
for d_id in ds_id:
print('Synthetic dataset %i fo %i'%(d_id, len(ds_id)))
#run time start
run_t_strt = time.time()
#input flatfile
ds_fname = '%s%s%s_Y%i.csv'%(ds_dir, ds_fname_main, synds_suffix, d_id)
#load flatfile
df_flatfile = pd.read_csv(ds_fname)
#output file name and directory
out_fname = '%s%s_Y%i'%(out_fname_main, synds_suffix, d_id)
out_dir = '%s/%s/Y%i/'%(out_dir_main, out_dir_sub, d_id)
#run stan model
RunStan(df_flatfile, df_cellinfo, df_celldist, sm_fname,
out_fname, out_dir, res_name, c_a_erg=c_a_erg,
runstan_flag=runstan_flag, n_iter=n_iter, n_chains=n_chains,
adapt_delta=adapt_delta, max_treedepth=max_treedepth,
pystan_ver=pystan_ver, pystan_parallel=flag_parallel)
#run time end
run_t_end = time.time()
#compute run time
run_tm = (run_t_end - run_t_strt)/60
#log run time
df_run_info.append(pd.DataFrame({'computer_name':os.uname()[1],'out_name':out_dir_sub,
'ds_id':d_id,'run_time':run_tm}, index=[d_id]))
#write out run info
out_fname = '%s%s/run_info.csv'%(out_dir_main, out_dir_sub)
pd.concat(df_run_info).reset_index(drop=True).to_csv(out_fname, index=False)
| 3,379 | 28.911504 | 105 | py |
ngmm_tools | ngmm_tools-master/Analyses/Code_Verification/regression/ds2/comparison_stan_model2_uncorr_cells.py | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Thu Aug 12 10:26:06 2021
@author: glavrent
"""
# Working directory and Packages
# ---------------------------
#load packages
import os
import sys
import pathlib
import glob
import re #regular expression package
import pickle
#arithmetic libraries
import numpy as np
#statistics libraries
import pandas as pd
#plot libraries
import matplotlib as mpl
import matplotlib.pyplot as plt
from matplotlib.ticker import AutoLocator as plt_autotick
#user functions
sys.path.insert(0,'../../../Python_lib/regression/')
from pylib_stats import CalcRMS
from pylib_stats import CalcLKDivergece
# Define variables
# ---------------------------
# USER SETS DIRECTORIES AND FILE INFO OF SYNTHETIC DS AND REGRESSION RESULTS
# ++++++++++++++++++++++++++++++++++++++++
#processed dataset
# name_dataset = 'NGAWest2CANorth'
name_dataset = 'NGAWest2CA'
# name_dataset = 'NGAWest3CA'
#correlation info
# 1: Small Correlation Lengths
# 2: Large Correlation Lenghts
corr_id = 1
#package
# 1: Pystan v2
# 2: Pystan v3
# 3: stancmd
pkg_id = 1
#approximation type
# 1: multivariate normal
# 2: cholesky
# 3: cholesky efficient
# 4: cholesky efficient v2
# 5: cholesky efficient, sparse cells
aprox_id = 3
#directories (synthetic dataset)
if corr_id == 1:
dir_syndata = '../../../../Data/Verification/synthetic_datasets/ds2_small_corr_len'
elif corr_id == 2:
dir_syndata = '../../../../Data/Verification/synthetic_datasets/ds2_large_corr_len'
#cell info
fname_cellinfo = dir_syndata + '/' + 'CatalogNGAWest3CALite_cellinfo.csv'
fname_distmat = dir_syndata + '/' + 'CatalogNGAWest3CALite_distancematrix.csv'
#directories (regression results)
if pkg_id == 1:
dir_results = f'../../../../Data/Verification/regression/ds2/PYSTAN_%s'%name_dataset
elif pkg_id == 2:
dir_results = f'../../../../Data/Verification/regression/ds2/PYSTAN3_%s'%name_dataset
elif pkg_id == 3:
dir_results = f'../../../../Data/Verification/regression/ds2/CMDSTAN_%s'%name_dataset
# #directories (regression results)
# if pkg_id == 1:
# dir_results = f'../../../../Data/Verification/regression_old/ds2/PYSTAN_%s'%name_dataset
# elif pkg_id == 2:
# dir_results = f'../../../../Data/Verification/regression_old/ds2/PYSTAN3_%s'%name_dataset
# elif pkg_id == 3:
# dir_results = f'../../../../Data/Verification/regression_old/ds2/CMDSTAN_%s'%name_dataset
#prefix for synthetic data and results
prfx_syndata = 'CatalogNGAWest3CALite_synthetic'
#regression results filename prefix
prfx_results = f'%s_syndata'%name_dataset
# FILE INFO FOR REGRESSION RESULTS
# ++++++++++++++++++++++++++++++++++++++++
#output filename sufix (synthetic dataset)
if corr_id == 1: synds_suffix = '_small_corr_len'
elif corr_id == 2: synds_suffix = '_large_corr_len'
#output filename sufix (regression results)
if aprox_id == 1: synds_suffix_stan = '_corr_cells' + synds_suffix
elif aprox_id == 2: synds_suffix_stan = '_corr_cells' + '_chol' + synds_suffix
elif aprox_id == 3: synds_suffix_stan = '_corr_cells' + '_chol_eff' + synds_suffix
elif aprox_id == 4: synds_suffix_stan = '_corr_cells' + '_chol_eff2' + synds_suffix
elif aprox_id == 5: synds_suffix_stan = '_corr_cells' + '_chol_eff_sp' + synds_suffix
# FILE INFO FOR REGRESSION RESULTS
# ++++++++++++++++++++++++++++++++++++++++
#output filename sufix (synthetic dataset)
if corr_id == 1: synds_suffix = '_small_corr_len'
elif corr_id == 2: synds_suffix = '_large_corr_len'
#output filename sufix (regression results)
if aprox_id == 1: synds_suffix_stan = '_uncorr_cells' + synds_suffix
elif aprox_id == 2: synds_suffix_stan = '_uncorr_cells' + '_chol' + synds_suffix
elif aprox_id == 3: synds_suffix_stan = '_uncorr_cells' + '_chol_eff' + synds_suffix
elif aprox_id == 4: synds_suffix_stan = '_uncorr_cells' + '_chol_eff2' + synds_suffix
elif aprox_id == 5: synds_suffix_stan = '_uncorr_cells' + '_chol_eff_sp' + synds_suffix
# dataset info
ds_id = np.arange(1,6)
# ++++++++++++++++++++++++++++++++++++++++
# USER NEEDS TO SPECIFY HYPERPARAMETERS OF SYNTHETIC DATASET
# ++++++++++++++++++++++++++++++++++++++++
# hyper-parameters
if corr_id == 1:
# small correlation lengths
hyp = {'omega_0': 0.1, 'omega_1e':0.1, 'omega_1as': 0.35, 'omega_1bs': 0.25,
'ell_1e':60, 'ell_1as':30,
'c_cap_erg': -0.011,
'omega_cap_mu': 0.005, 'omega_ca1p':0.004, 'omega_ca2p':0.002,
'ell_ca1p': 75,
'phi_0':0.4, 'tau_0':0.3 }
elif corr_id == 2:
#large correlation lengths
hyp = {'omega_0': 0.1, 'omega_1e':0.2, 'omega_1as': 0.4, 'omega_1bs': 0.3,
'ell_1e':100, 'ell_1as':70,
'c_cap_erg': -0.02,
'omega_cap_mu': 0.008, 'omega_ca1p':0.005, 'omega_ca2p':0.003,
'ell_ca1p': 120,
'phi_0':0.4, 'tau_0':0.3}
# ++++++++++++++++++++++++++++++++++++++++
#ploting options
flag_report = True
# Compare results
# ---------------------------
#load cell data
df_cellinfo = pd.read_csv(fname_cellinfo).set_index('cellid')
df_distmat = pd.read_csv(fname_distmat).set_index('rsn')
#initialize misfit metrics dataframe
df_misfit = pd.DataFrame(index=['Y%i'%d_id for d_id in ds_id])
#iterate over different datasets
for d_id in ds_id:
# Load Data
#--- --- --- --- ---
#file names
#synthetic data
fname_sdata_gmotion = '%s/%s_%s%s_Y%i'%(dir_syndata, prfx_syndata, 'data', synds_suffix, d_id) + '.csv'
fname_sdata_atten = '%s/%s_%s%s_Y%i'%(dir_syndata, prfx_syndata, 'atten', synds_suffix, d_id) + '.csv'
#regression results
fname_reg_gmotion = '%s%s/Y%i/%s%s_Y%i_stan_%s'%(dir_results, synds_suffix_stan, d_id, prfx_results, synds_suffix, d_id, 'residuals') + '.csv'
fname_reg_coeff = '%s%s/Y%i/%s%s_Y%i_stan_%s'%(dir_results, synds_suffix_stan, d_id, prfx_results, synds_suffix, d_id, 'coefficients') + '.csv'
fname_reg_atten = '%s%s/Y%i/%s%s_Y%i_stan_%s'%(dir_results, synds_suffix_stan, d_id, prfx_results, synds_suffix, d_id, 'catten') + '.csv'
#load synthetic results
df_sdata_gmotion = pd.read_csv(fname_sdata_gmotion).set_index('rsn')
df_sdata_atten = pd.read_csv(fname_sdata_atten).set_index('cellid')
#load regression results
df_reg_gmotion = pd.read_csv(fname_reg_gmotion, index_col=0)
df_reg_coeff = pd.read_csv(fname_reg_coeff, index_col=0)
df_reg_atten = pd.read_csv(fname_reg_atten, index_col=0)
# Processing
#--- --- --- --- ---
#keep only relevant columns from synthetic dataset
df_sdata_gmotion = df_sdata_gmotion.reindex(df_reg_gmotion.index)
df_sdata_atten = df_sdata_atten.reindex(df_reg_atten.index)
#distance matrix for records of interest
df_dmat = df_distmat.reindex(df_sdata_gmotion.index)
#find unique earthqakes and stations
eq_id, eq_idx, eq_nrec = np.unique(df_sdata_gmotion.eqid, return_index=True, return_counts=True)
sta_id, sta_idx, sta_nrec = np.unique(df_sdata_gmotion.ssn, return_index=True, return_counts=True)
#number of paths per cell
cell_npath = np.sum(df_dmat.loc[:,df_reg_atten.cellname] > 0, axis=0)
# Compute Root Mean Square Error
#--- --- --- --- ---
df_misfit.loc['Y%i'%d_id,'nerg_tot_rms'] = CalcRMS(df_sdata_gmotion.nerg_gm.values, df_reg_gmotion.nerg_mu.values)
df_misfit.loc['Y%i'%d_id,'dc_1e_rms'] = CalcRMS(df_sdata_gmotion['dc_1e'].values[eq_idx], df_reg_coeff['dc_1e_mean'].values[eq_idx])
df_misfit.loc['Y%i'%d_id,'dc_1as_rms'] = CalcRMS(df_sdata_gmotion['dc_1as'].values[sta_idx], df_reg_coeff['dc_1as_mean'].values[sta_idx])
df_misfit.loc['Y%i'%d_id,'dc_1bs_rms'] = CalcRMS(df_sdata_gmotion['dc_1bs'].values[sta_idx], df_reg_coeff['dc_1bs_mean'].values[sta_idx])
df_misfit.loc['Y%i'%d_id,'c_cap_rms'] = CalcRMS(df_sdata_atten['c_cap'].values, df_reg_atten['c_cap_mean'].values)
# Compute Divergence
#--- --- --- --- ---
df_misfit.loc['Y%i'%d_id,'nerg_tot_KL'] = CalcLKDivergece(df_sdata_gmotion.nerg_gm.values, df_reg_gmotion.nerg_mu.values)
df_misfit.loc['Y%i'%d_id,'dc_1e_KL'] = CalcLKDivergece(df_sdata_gmotion['dc_1e'].values[eq_idx], df_reg_coeff['dc_1e_mean'].values[eq_idx])
df_misfit.loc['Y%i'%d_id,'dc_1as_KL'] = CalcLKDivergece(df_sdata_gmotion['dc_1as'].values[sta_idx], df_reg_coeff['dc_1as_mean'].values[sta_idx])
df_misfit.loc['Y%i'%d_id,'dc_1bs_KL'] = CalcLKDivergece(df_sdata_gmotion['dc_1bs'].values[sta_idx], df_reg_coeff['dc_1bs_mean'].values[sta_idx])
df_misfit.loc['Y%i'%d_id,'c_cap_KL'] = CalcLKDivergece(df_sdata_atten['c_cap'].values, df_reg_atten['c_cap_mean'].values)
# Output
#--- --- --- --- ---
#figure directory
dir_fig = '%s%s/Y%i/figures_cmp/'%(dir_results,synds_suffix_stan,d_id)
pathlib.Path(dir_fig).mkdir(parents=True, exist_ok=True)
#compare ground motion predictions
#... ... ... ... ... ...
#figure title
fname_fig = 'Y%i_scatter_tot_res'%d_id
#create figure
fig, ax = plt.subplots(figsize = (10,10))
#median
ax.scatter(df_sdata_gmotion.nerg_gm.values, df_reg_gmotion.nerg_mu.values)
ax.axline((0,0), slope=1, color="black", linestyle="--")
#edit figure
if not flag_report: ax.set_title('Comparison total residuals, Y: %i'%d_id, fontsize=30)
ax.set_xlabel('Synthetic dataset', fontsize=35)
ax.set_ylabel('Estimated', fontsize=35)
ax.grid(which='both')
ax.tick_params(axis='x', labelsize=32)
ax.tick_params(axis='y', labelsize=32)
#plot limits
# plt_lim = np.array([ax.get_xlim(), ax.get_ylim()])
# plt_lim = (plt_lim[:,0].min(), plt_lim[:,1].max())
# ax.set_xlim(plt_lim)
# ax.set_ylim(plt_lim)
ax.set_xlim([-10,2])
ax.set_ylim([-10,2])
fig.tight_layout()
fig.savefig( dir_fig + fname_fig + '.png' )
#compare dc_1e
#... ... ... ... ... ...
#figure title
fname_fig = 'Y%i_dc_1e_scatter'%d_id
#create figure
fig, ax = plt.subplots(figsize = (10,10))
#coefficient scatter
ax.scatter(df_sdata_gmotion['dc_1e'].values[eq_idx], df_reg_coeff['dc_1e_mean'].values[eq_idx])
ax.axline((0,0), slope=1, color="black", linestyle="--")
#edit figure
if not flag_report: ax.set_title(r'Comparison $\delta c_{1,E}$, Y: %i'%d_id, fontsize=30)
ax.set_xlabel('Synthetic dataset', fontsize=25)
ax.set_ylabel('Estimated', fontsize=25)
ax.grid(which='both')
ax.tick_params(axis='x', labelsize=22)
ax.tick_params(axis='y', labelsize=22)
#plot limits
# plt_lim = np.array([ax.get_xlim(), ax.get_ylim()])
# plt_lim = (plt_lim[:,0].min(), plt_lim[:,1].max())
# ax.set_xlim(plt_lim)
# ax.set_ylim(plt_lim)
ax.set_xlim([-.4,.4])
ax.set_ylim([-.4,.4])
#save figure
fig.tight_layout()
fig.savefig( dir_fig + fname_fig + '.png' )
#figure title
fname_fig = 'Y%i_dc_1e_accuracy'%d_id
#create figure
fig, ax = plt.subplots(figsize = (10,10))
#coefficient scatter
ax.scatter(df_reg_coeff['dc_1e_sig'].values[eq_idx],
df_sdata_gmotion['dc_1e'].values[eq_idx] - df_reg_coeff['dc_1e_mean'].values[eq_idx])
ax.axline((0,0), slope=0, color="black", linestyle="--")
#edit figure
if not flag_report: ax.set_title(r'Comparison $\delta c_{1,E}$, Y: %i'%d_id, fontsize=30)
ax.set_xlabel('Standard Deviation', fontsize=25)
ax.set_ylabel('Actual - Estimated', fontsize=25)
ax.grid(which='both')
ax.tick_params(axis='x', labelsize=22)
ax.tick_params(axis='y', labelsize=22)
#plot limits
# ax.set_ylim(np.abs(ax.get_ylim()).max()*np.array([-1,1]))
ax.set_xlim([0,.15])
ax.set_ylim([-.4,.4])
#save figure
fig.tight_layout()
fig.savefig( dir_fig + fname_fig + '.png' )
#figure title
fname_fig = 'Y%i_dc_1e_nrec'%d_id
#create figure
fig, ax = plt.subplots(figsize = (10,10))
#coefficient scatter
ax.scatter(eq_nrec,
df_sdata_gmotion['dc_1e'].values[eq_idx] - df_reg_coeff['dc_1e_mean'].values[eq_idx])
ax.axline((0,0), slope=0, color="black", linestyle="--")
#edit figure
if not flag_report: ax.set_title(r'Comparison $\delta c_{1,E}$, Y: %i'%d_id, fontsize=30)
ax.set_xlabel('Number of records', fontsize=25)
ax.set_ylabel('Actual - Estimated', fontsize=25)
ax.grid(which='both')
ax.set_xscale('log')
ax.tick_params(axis='x', labelsize=22)
ax.tick_params(axis='y', labelsize=22)
#plot limits
# ax.set_ylim(np.abs(ax.get_ylim()).max()*np.array([-1,1]))
ax.set_xlim([0.9,1e3])
ax.set_ylim([-.4,.4])
#save figure
fig.tight_layout()
fig.savefig( dir_fig + fname_fig + '.png' )
#compare dc_1as
#... ... ... ... ... ...
#figure title
fname_fig = 'Y%i_dc_1as_scatter'%d_id
#create figure
fig, ax = plt.subplots(figsize = (10,10))
#coefficient scatter
ax.scatter(df_sdata_gmotion['dc_1as'].values[sta_idx], df_reg_coeff['dc_1as_mean'].values[sta_idx])
ax.axline((0,0), slope=1, color="black", linestyle="--")
#edit figure
if not flag_report: ax.set_title(r'Comparison $\delta c_{1a,S}$, Y: %i'%d_id, fontsize=30)
ax.set_xlabel('Synthetic dataset', fontsize=25)
ax.set_ylabel('Estimated', fontsize=25)
ax.grid(which='both')
ax.tick_params(axis='x', labelsize=22)
ax.tick_params(axis='y', labelsize=22)
#plot limits
# plt_lim = np.array([ax.get_xlim(), ax.get_ylim()])
# plt_lim = (plt_lim[:,0].min(), plt_lim[:,1].max())
# ax.set_xlim(plt_lim)
# ax.set_ylim(plt_lim)
ax.set_xlim([-1.5,1.5])
ax.set_ylim([-1.5,1.5])
#save figure
fig.tight_layout()
fig.savefig( dir_fig + fname_fig + '.png' )
#figure title
fname_fig = 'Y%i_dc_1as_accuracy'%d_id
#create figure
fig, ax = plt.subplots(figsize = (10,10))
#accuray
ax.scatter(df_reg_coeff['dc_1as_sig'].values[sta_idx],
df_sdata_gmotion['dc_1as'].values[sta_idx] - df_reg_coeff['dc_1as_mean'].values[sta_idx])
ax.axline((0,0), slope=0, color="black", linestyle="--")
#edit figure
if not flag_report: ax.set_title(r'Comparison $\delta c_{1a,S}$, Y: %i'%d_id, fontsize=30)
ax.set_xlabel('Standard Deviation', fontsize=25)
ax.set_ylabel('Actual - Estimated', fontsize=25)
ax.grid(which='both')
ax.tick_params(axis='x', labelsize=22)
ax.tick_params(axis='y', labelsize=22)
#plot limits
# ax.set_ylim(np.abs(ax.get_ylim()).max()*np.array([-1,1]))
ax.set_xlim([0,.4])
ax.set_ylim([-1.5,1.5])
#save figure
fig.tight_layout()
fig.savefig( dir_fig + fname_fig + '.png' )
#figure title
fname_fig = 'Y%i_dc_1as_nrec'%d_id
#create figure
fig, ax = plt.subplots(figsize = (10,10))
#accuray
ax.scatter(sta_nrec,
df_sdata_gmotion['dc_1as'].values[sta_idx] - df_reg_coeff['dc_1as_mean'].values[sta_idx])
ax.axline((0,0), slope=0, color="black", linestyle="--")
#edit figure
if not flag_report: ax.set_title(r'Comparison $\delta c_{1a,S}$, Y: %i'%d_id, fontsize=30)
ax.set_xlabel('Number of records', fontsize=25)
ax.set_ylabel('Actual - Estimated', fontsize=25)
ax.grid(which='both')
ax.set_xscale('log')
ax.tick_params(axis='x', labelsize=22)
ax.tick_params(axis='y', labelsize=22)
#plot limits
# ax.set_ylim(np.abs(ax.get_ylim()).max()*np.array([-1,1]))
ax.set_xlim([.9,1000])
ax.set_ylim([-1.5,1.5])
#save figure
fig.tight_layout()
fig.savefig( dir_fig + fname_fig + '.png' )
#compare dc_1bs
#... ... ... ... ... ...
#figure title
fname_fig = 'Y%i_dc_1bs_scatter'%d_id
#create figure
fig, ax = plt.subplots(figsize = (10,10))
#coefficient scatter
ax.scatter(df_sdata_gmotion['dc_1bs'].values[sta_idx], df_reg_coeff['dc_1bs_mean'].values[sta_idx])
ax.axline((0,0), slope=1, color="black", linestyle="--")
#edit figure
if not flag_report: ax.set_title(r'Comparison $\delta c_{1b,S}$, Y: %i'%d_id, fontsize=30)
ax.set_xlabel('Synthetic dataset', fontsize=25)
ax.set_ylabel('Estimated', fontsize=25)
ax.grid(which='both')
ax.tick_params(axis='x', labelsize=22)
ax.tick_params(axis='y', labelsize=22)
#plot limits
# plt_lim = np.array([ax.get_xlim(), ax.get_ylim()])
# plt_lim = (plt_lim[:,0].min(), plt_lim[:,1].max())
# ax.set_xlim(plt_lim)
# ax.set_ylim(plt_lim)
ax.set_xlim([-1.5,1.5])
ax.set_ylim([-1.5,1.5])
#save figure
fig.tight_layout()
fig.savefig( dir_fig + fname_fig + '.png' )
#figure title
fname_fig = 'Y%i_dc_1bs_accuracy'%d_id
#create figure
fig, ax = plt.subplots(figsize = (10,10))
#accuray
ax.scatter(df_reg_coeff['dc_1bs_sig'].values[sta_idx],
df_sdata_gmotion['dc_1bs'].values[sta_idx] - df_reg_coeff['dc_1bs_mean'].values[sta_idx])
ax.axline((0,0), slope=0, color="black", linestyle="--")
#edit figure
if not flag_report: ax.set_title(r'Comparison $\delta c_{1b,S}$, Y: %i'%d_id, fontsize=30)
ax.set_xlabel('Standard Deviation', fontsize=25)
ax.set_ylabel('Actual - Estimated', fontsize=25)
ax.grid(which='both')
ax.tick_params(axis='x', labelsize=22)
ax.tick_params(axis='y', labelsize=22)
#plot limits
# ax.set_ylim(np.abs(ax.get_ylim()).max()*np.array([-1,1]))
ax.set_xlim([0,.4])
ax.set_ylim([-1.5,1.5])
#save figure
fig.tight_layout()
fig.savefig( dir_fig + fname_fig + '.png' )
#figure title
fname_fig = 'Y%i_dc_1bs_nrec'%d_id
#create figure
fig, ax = plt.subplots(figsize = (10,10))
#accuray
ax.scatter(sta_nrec,
df_sdata_gmotion['dc_1bs'].values[sta_idx] - df_reg_coeff['dc_1bs_mean'].values[sta_idx])
ax.axline((0,0), slope=0, color="black", linestyle="--")
#edit figure
if not flag_report: ax.set_title(r'Comparison $\delta c_{1b,S}$, Y: %i'%d_id, fontsize=30)
ax.set_xlabel('Number of records', fontsize=25)
ax.set_ylabel('Actual - Estimated', fontsize=25)
ax.grid(which='both')
ax.set_xscale('log')
ax.tick_params(axis='x', labelsize=22)
ax.tick_params(axis='y', labelsize=22)
#plot limits
# ax.set_ylim(np.abs(ax.get_ylim()).max()*np.array([-1,1]))
ax.set_xlim([.9,1000])
ax.set_ylim([-1.5,1.5])
#save figure
fig.tight_layout()
fig.savefig( dir_fig + fname_fig + '.png' )
#compare c_cap
#... ... ... ... ... ...
#figure title
fname_fig = 'Y%i_c_cap_scatter'%d_id
#create figure
fig, ax = plt.subplots(figsize = (10,10))
#coefficient scatter
ax.scatter(df_sdata_atten['c_cap'].values, df_reg_atten['c_cap_mean'].values)
ax.axline((0,0), slope=1, color="black", linestyle="--")
#edit figure
if not flag_report: ax.set_title(r'Comparison $c_{ca,P}$, Y: %i'%d_id, fontsize=30)
ax.set_xlabel('Synthetic dataset', fontsize=25)
ax.set_ylabel('Estimated', fontsize=25)
ax.grid(which='both')
ax.tick_params(axis='x', labelsize=22)
ax.tick_params(axis='y', labelsize=22)
#plot limits
# plt_lim = np.array([ax.get_xlim(), ax.get_ylim()])
# plt_lim = (plt_lim[:,0].min(), plt_lim[:,1].max())
# ax.set_xlim(plt_lim)
# ax.set_ylim(plt_lim)
ax.set_xlim([-0.05,0.02])
ax.set_ylim([-0.05,0.02])
#save figure
fig.tight_layout()
fig.savefig( dir_fig + fname_fig + '.png' )
#figure title
fname_fig = 'Y%i_c_cap_accuracy'%d_id
#create figure
fig, ax = plt.subplots(figsize = (10,10))
#coefficient scatter
ax.scatter(df_reg_atten['c_cap_sig'],
df_sdata_atten['c_cap'].values - df_reg_atten['c_cap_mean'].values)
ax.axline((0,0), slope=0, color="black", linestyle="--")
#edit figure
if not flag_report: ax.set_title(r'Comparison $c_{ca,P}$, Y: %i'%d_id, fontsize=30)
ax.set_xlabel('Standard Deviation', fontsize=25)
ax.set_ylabel('Actual - Estimated', fontsize=25)
ax.grid(which='both')
ax.tick_params(axis='x', labelsize=22)
ax.tick_params(axis='y', labelsize=22)
#plot limits
# ax.set_ylim(np.abs(ax.get_ylim()).max()*np.array([-1,1]))
ax.set_xlim([0.00,0.03])
ax.set_ylim([-0.04,0.04])
#save figure
fig.tight_layout()
fig.savefig( dir_fig + fname_fig + '.png' )
#figure title
fname_fig = 'Y%i_c_cap_npath'%d_id
#create figure
fig, ax = plt.subplots(figsize = (10,10))
#coefficient scatter
ax.scatter(cell_npath,
df_sdata_atten['c_cap'].values - df_reg_atten['c_cap_mean'].values)
ax.axline((0,0), slope=0, color="black", linestyle="--")
#edit figure
if not flag_report: ax.set_title(r'Comparison $c_{ca,P}$, Y: %i'%d_id, fontsize=30)
ax.set_xlabel('Number of paths', fontsize=25)
ax.set_ylabel('Actual - Estimated', fontsize=25)
ax.grid(which='both')
ax.set_xscale('log')
ax.tick_params(axis='x', labelsize=22)
ax.tick_params(axis='y', labelsize=22)
#plot limits
# ax.set_ylim(np.abs(ax.get_ylim()).max()*np.array([-1,1]))
ax.set_xlim([.9,5e4])
ax.set_ylim([-0.04,0.04])
#save figure
fig.tight_layout()
fig.savefig( dir_fig + fname_fig + '.png' )
# Compare Misfit Metrics
# ---------------------------
#summary directory
dir_sum = '%s%s/summary/'%(dir_results,synds_suffix_stan)
pathlib.Path(dir_fig).mkdir(parents=True, exist_ok=True)
#figure directory
dir_fig = '%s/figures/'%(dir_sum)
pathlib.Path(dir_fig).mkdir(parents=True, exist_ok=True)
#save
df_misfit.to_csv(dir_sum + 'misfit_summary.csv')
#RMS misfit
fname_fig = 'misfit_score'
#plot KL divergence
fig, ax = plt.subplots(figsize = (10,10))
ax.plot(ds_id, df_misfit.nerg_tot_rms, linestyle='-', marker='o', linewidth=2, markersize=10, label= 'tot nerg')
ax.plot(ds_id, df_misfit.dc_1e_rms, linestyle='-', marker='o', linewidth=2, markersize=10, label=r'$\delta c_{1,E}$')
ax.plot(ds_id, df_misfit.dc_1as_rms, linestyle='-', marker='o', linewidth=2, markersize=10, label=r'$\delta c_{1a,S}$')
ax.plot(ds_id, df_misfit.dc_1bs_rms, linestyle='-', marker='o', linewidth=2, markersize=10, label=r'$\delta c_{1b,S}$')
ax.plot(ds_id, df_misfit.c_cap_rms, linestyle='-', marker='o', linewidth=2, markersize=10, label=r'$c_{ca,P}$')
#figure properties
ax.set_ylim([0,0.50])
ax.set_xlabel('synthetic dataset', fontsize=25)
ax.set_ylabel('RSME', fontsize=25)
ax.grid(which='both')
ax.set_xticks(ds_id)
ax.set_xticklabels(labels=df_misfit.index)
ax.tick_params(axis='x', labelsize=22)
ax.tick_params(axis='y', labelsize=22)
#legend
ax.legend(loc='upper left', fontsize=25)
#save figure
fig.tight_layout()
fig.savefig( dir_fig + fname_fig + '.png' )
#KL divergence
fname_fig = 'KLdiv_score'
#plot KL divergence
fig, ax = plt.subplots(figsize = (10,10))
ax.plot(ds_id, df_misfit.nerg_tot_KL, linestyle='-', marker='o', linewidth=2, markersize=10, label= 'tot nerg')
ax.plot(ds_id, df_misfit.dc_1e_KL, linestyle='-', marker='o', linewidth=2, markersize=10, label=r'$\delta c_{1,E}$')
ax.plot(ds_id, df_misfit.dc_1as_KL, linestyle='-', marker='o', linewidth=2, markersize=10, label=r'$\delta c_{1a,S}$')
ax.plot(ds_id, df_misfit.dc_1bs_KL, linestyle='-', marker='o', linewidth=2, markersize=10, label=r'$\delta c_{1b,S}$')
ax.plot(ds_id, df_misfit.c_cap_KL, linestyle='-', marker='o', linewidth=2, markersize=10, label=r'$c_{ca,P}$')
#figure properties
ax.set_ylim([0,0.50])
ax.set_xlabel('synthetic dataset', fontsize=25)
ax.set_ylabel('KL divergence', fontsize=25)
ax.grid(which='both')
ax.set_xticks(ds_id)
ax.set_xticklabels(labels=df_misfit.index)
ax.tick_params(axis='x', labelsize=22)
ax.tick_params(axis='y', labelsize=22)
#legend
ax.legend(loc='upper left', fontsize=25)
#save figure
fig.tight_layout()
fig.savefig( dir_fig + fname_fig + '.png' )
# Compare hyper-paramters
# ---------------------------
#iterate over different datasets
df_reg_hyp = list()
df_reg_hyp_post = list()
for d_id in ds_id:
# Load Data
#--- --- --- --- ---
#regression hyperparamters results
fname_reg_hyp = '%s%s/Y%i/%s%s_Y%i_stan_%s'%(dir_results,synds_suffix_stan, d_id,prfx_results, synds_suffix, d_id, 'hyperparameters') + '.csv'
fname_reg_hyp_post = '%s%s/Y%i/%s%s_Y%i_stan_%s'%(dir_results,synds_suffix_stan, d_id,prfx_results, synds_suffix, d_id, 'hyperposterior') + '.csv'
#load regression results
df_reg_hyp.append( pd.read_csv(fname_reg_hyp, index_col=0) )
df_reg_hyp_post.append( pd.read_csv(fname_reg_hyp_post, index_col=0) )
# Omega_1e
#--- --- --- --- ---
#hyper-paramter name
name_hyp = 'omega_1e'
#figure title
fname_fig = 'post_dist_' + name_hyp
#create figure
fig, ax = plt.subplots(figsize = (10,10))
for d_id, df_r_h, df_r_h_p in zip(ds_id, df_reg_hyp, df_reg_hyp_post):
#estimate vertical line height for mean and mode
ymax_mode = 40
ymax_mean = 40
#plot posterior dist
pl_hyp = ax.vlines(df_r_h.loc['mean',name_hyp], ymin=0, ymax=ymax_mean, linestyle='-', label='Mean')
ax.vlines(df_r_h.loc['prc_0.50',name_hyp], ymin=0, ymax=ymax_mode, linestyle='--', color=pl_hyp.get_color(), label='Mode')
#plot true value
ymax_hyp = ymax_mean
ax.vlines(hyp[name_hyp], ymin=0, ymax=ymax_hyp, linestyle='-', linewidth=4, color='black', label='True value')
#edit figure
if not flag_report: ax.set_title(r'Comparison $\omega_{1,E}$', fontsize=30)
ax.set_xlabel('$\omega_{1,E}$', fontsize=25)
ax.set_ylabel('probability density function ', fontsize=25)
ax.grid(which='both')
ax.tick_params(axis='x', labelsize=22)
ax.tick_params(axis='y', labelsize=22)
#plot limits
ax.set_xlim([0,0.25])
ax.set_ylim([0,ymax_hyp])
#save figure
fig.tight_layout()
fig.savefig( dir_fig + fname_fig + '.png' )
# Omega_1as
#--- --- --- --- ---
#hyper-paramter name
name_hyp = 'omega_1as'
#figure title
fname_fig = 'post_dist_' + name_hyp
#create figure
fig, ax = plt.subplots(figsize = (10,10))
for d_id, df_r_h, df_r_h_p in zip(ds_id, df_reg_hyp, df_reg_hyp_post):
#estimate vertical line height for mean and mode
ymax_mode = 30
ymax_mean = 30
#plot posterior dist
pl_hyp = ax.vlines(df_r_h.loc['mean',name_hyp], ymin=0, ymax=ymax_mean, linestyle='-', label='Mean')
ax.vlines(df_r_h.loc['prc_0.50',name_hyp], ymin=0, ymax=ymax_mode, linestyle='--', color=pl_hyp.get_color(), label='Mode')
#plot true value
ymax_hyp = ymax_mean
ax.vlines(hyp[name_hyp], ymin=0, ymax=ymax_hyp, linestyle='-', linewidth=4, color='black', label='True value')
#edit figure
if not flag_report: ax.set_title(r'Comparison $\omega_{1a,S}$', fontsize=30)
ax.set_xlabel('$\omega_{1a,S}$', fontsize=25)
ax.set_ylabel('probability density function ', fontsize=25)
ax.grid(which='both')
ax.tick_params(axis='x', labelsize=22)
ax.tick_params(axis='y', labelsize=22)
#plot limits
ax.set_xlim([0,0.5])
ax.set_ylim([0,ymax_hyp])
#save figure
fig.tight_layout()
fig.savefig( dir_fig + fname_fig + '.png' )
# Omega_1bs
#--- --- --- --- ---
#hyper-paramter name
name_hyp = 'omega_1bs'
#figure title
fname_fig = 'post_dist_' + name_hyp
#create figure
fig, ax = plt.subplots(figsize = (10,10))
for d_id, df_r_h, df_r_h_p in zip(ds_id, df_reg_hyp, df_reg_hyp_post):
#estimate vertical line height for mean and mode
ymax_mode = 60
ymax_mean = 60
#plot posterior dist
pl_hyp = ax.vlines(df_r_h.loc['mean',name_hyp], ymin=0, ymax=ymax_mean, linestyle='-', label='Mean')
ax.vlines(df_r_h.loc['prc_0.50',name_hyp], ymin=0, ymax=ymax_mode, linestyle='--', color=pl_hyp.get_color(), label='Mode')
#plot true value
ymax_hyp = ymax_mean
ax.vlines(hyp[name_hyp], ymin=0, ymax=ymax_hyp, linestyle='-', linewidth=4, color='black', label='True value')
#edit figure
if not flag_report: ax.set_title(r'Comparison $\omega_{1b,S}$', fontsize=30)
ax.set_xlabel('$\omega_{1b,S}$', fontsize=25)
ax.set_ylabel('probability density function ', fontsize=25)
ax.grid(which='both')
ax.tick_params(axis='x', labelsize=22)
ax.tick_params(axis='y', labelsize=22)
#plot limits
ax.set_xlim([0,0.5])
ax.set_ylim([0,ymax_hyp])
#save figure
fig.tight_layout()
fig.savefig( dir_fig + fname_fig + '.png' )
# Ell_1e
#--- --- --- --- ---
#hyper-paramter name
name_hyp = 'ell_1e'
#figure title
fname_fig = 'post_dist_' + name_hyp
#create figure
fig, ax = plt.subplots(figsize = (10,10))
for d_id, df_r_h, df_r_h_p in zip(ds_id, df_reg_hyp, df_reg_hyp_post):
#estimate vertical line height for mean and mode
ymax_mode = 0.02
ymax_mean = 0.02
#plot posterior dist
pl_hyp = ax.vlines(df_r_h.loc['mean',name_hyp], ymin=0, ymax=ymax_mean, linestyle='-', label='Mean')
ax.vlines(df_r_h.loc['prc_0.50',name_hyp], ymin=0, ymax=ymax_mode, linestyle='--', color=pl_hyp.get_color(), label='Mode')
#plot true value
ymax_hyp = ymax_mean
ax.vlines(hyp[name_hyp], ymin=0, ymax=ymax_hyp, linestyle='-', linewidth=4, color='black', label='True value')
#edit figure
if not flag_report: ax.set_title(r'Comparison $\ell_{1,E}$', fontsize=30)
ax.set_xlabel('$\ell_{1,E}$', fontsize=25)
ax.set_ylabel('probability density function ', fontsize=25)
ax.grid(which='both')
ax.tick_params(axis='x', labelsize=22)
ax.tick_params(axis='y', labelsize=22)
#plot limits
ax.set_xlim([0,500])
ax.set_ylim([0,ymax_hyp])
#save figure
fig.tight_layout()
fig.savefig( dir_fig + fname_fig + '.png' )
# Ell_1as
#--- --- --- --- ---
#hyper-paramter name
name_hyp = 'ell_1as'
#figure title
fname_fig = 'post_dist_' + name_hyp
#create figure
fig, ax = plt.subplots(figsize = (10,10))
for d_id, df_r_h, df_r_h_p in zip(ds_id, df_reg_hyp, df_reg_hyp_post):
#estimate vertical line height for mean and mode
ymax_mode = 0.1
ymax_mean = 0.1
#plot posterior dist
pl_hyp = ax.vlines(df_r_h.loc['mean',name_hyp], ymin=0, ymax=ymax_mean, linestyle='-', label='Mean')
ax.vlines(df_r_h.loc['prc_0.50',name_hyp], ymin=0, ymax=ymax_mode, linestyle='--', color=pl_hyp.get_color(), label='Mode')
#plot true value
ymax_hyp = ymax_mean
ax.vlines(hyp[name_hyp], ymin=0, ymax=ymax_hyp, linestyle='-', linewidth=4, color='black', label='True value')
#edit figure
if not flag_report: ax.set_title(r'Comparison $\ell_{1a,S}$', fontsize=30)
ax.set_xlabel('$\ell_{1a,S}$', fontsize=25)
ax.set_ylabel('probability density function ', fontsize=25)
ax.grid(which='both')
ax.tick_params(axis='x', labelsize=22)
ax.tick_params(axis='y', labelsize=22)
#plot limits
ax.set_xlim([0,150])
ax.set_ylim([0,ymax_hyp])
#save figure
fig.tight_layout()
fig.savefig( dir_fig + fname_fig + '.png' )
# Tau_0
#--- --- --- --- ---
#hyper-paramter name
name_hyp = 'tau_0'
#figure title
fname_fig = 'post_dist_' + name_hyp
#create figure
fig, ax = plt.subplots(figsize = (10,10))
for d_id, df_r_h, df_r_h_p in zip(ds_id, df_reg_hyp, df_reg_hyp_post):
#estimate vertical line height for mean and mode
ymax_mode = 150
ymax_mean = 150
#plot posterior dist
pl_hyp = ax.vlines(df_r_h.loc['mean',name_hyp], ymin=0, ymax=ymax_mean, linestyle='-', label='Mean')
ax.vlines(df_r_h.loc['prc_0.50',name_hyp], ymin=0, ymax=ymax_mode, linestyle='--', color=pl_hyp.get_color(), label='Mode')
#plot true value
ymax_hyp = ymax_mean
ax.vlines(hyp[name_hyp], ymin=0, ymax=ymax_hyp, linestyle='-', linewidth=4, color='black', label='True value')
#edit figure
if not flag_report: ax.set_title(r'Comparison $\tau_{0}$', fontsize=30)
ax.set_xlabel(r'$\tau_{0}$', fontsize=25)
ax.set_ylabel(r'probability density function ', fontsize=25)
ax.grid(which='both')
ax.tick_params(axis='x', labelsize=22)
ax.tick_params(axis='y', labelsize=22)
#plot limits
ax.set_xlim([0,0.5])
ax.set_ylim([0,ymax_hyp])
#save figure
fig.tight_layout()
fig.savefig( dir_fig + fname_fig + '.png' )
# Phi_0
#--- --- --- --- ---
#hyper-paramter name
name_hyp = 'phi_0'
#figure title
fname_fig = 'post_dist_' + name_hyp
#create figure
fig, ax = plt.subplots(figsize = (10,10))
for d_id, df_r_h, df_r_h_p in zip(ds_id, df_reg_hyp, df_reg_hyp_post):
#estimate vertical line height for mean and mode
ymax_mode = 1000
ymax_mean = 1000
#plot posterior dist
pl_hyp = ax.vlines(df_r_h.loc['mean',name_hyp], ymin=0, ymax=ymax_mean, linestyle='-', label='Mean')
ax.vlines(df_r_h.loc['prc_0.50',name_hyp], ymin=0, ymax=ymax_mode, linestyle='--', color=pl_hyp.get_color(), label='Mode')
#plot true value
ymax_hyp = ymax_mean
ax.vlines(hyp[name_hyp], ymin=0, ymax=ymax_hyp, linestyle='-', linewidth=4, color='black', label='True value')
#edit figure
if not flag_report: ax.set_title(r'Comparison $\phi_{0}$', fontsize=30)
ax.set_xlabel('$\phi_{0}$', fontsize=25)
ax.set_ylabel(r'probability density function ', fontsize=25)
ax.grid(which='both')
ax.tick_params(axis='x', labelsize=22)
ax.tick_params(axis='y', labelsize=22)
#plot limits
ax.set_xlim([0,0.6])
ax.set_ylim([0,ymax_hyp])
#save figure
fig.tight_layout()
fig.savefig( dir_fig + fname_fig + '.png' )
# Omega_ca
#--- --- --- --- ---
#hyper-paramter name
name_hyp = 'omega_cap'
#figure title
fname_fig = 'post_dist_' + name_hyp
#create figure
fig, ax = plt.subplots(figsize = (10,10))
for d_id, df_r_h, df_r_h_p in zip(ds_id, df_reg_hyp, df_reg_hyp_post):
#estimate vertical line height for mean and mode
ymax_mode = 1500
ymax_mean = 1500
#plot posterior dist
pl_hyp = ax.vlines(df_r_h.loc['mean',name_hyp], ymin=0, ymax=ymax_mean, linestyle='-', label='Mean')
ax.vlines(df_r_h.loc['prc_0.50',name_hyp], ymin=0, ymax=ymax_mode, linestyle='--', color=pl_hyp.get_color(), label='Mode')
#plot true value
ymax_hyp = ymax_mean
ax.vlines(np.sqrt(hyp['omega_ca1p']**2+hyp['omega_ca2p']**2), ymin=0, ymax=ymax_hyp, linestyle='-', linewidth=4, color='black', label='True value')
#edit figure
if not flag_report: ax.set_title(r'Comparison $\omega_{ca,P}$', fontsize=30)
ax.set_xlabel('$\omega_{ca,P}$', fontsize=25)
ax.set_ylabel('probability density function ', fontsize=25)
ax.grid(which='both')
ax.tick_params(axis='x', labelsize=22)
ax.tick_params(axis='y', labelsize=22)
#plot limits
ax.set_xlim([0,0.05])
ax.set_ylim([0,ymax_hyp])
#save figure
fig.tight_layout()
fig.savefig( dir_fig + fname_fig + '.png' )
# # Delta c_0
# #--- --- --- --- ---
# #hyper-paramter name
# name_hyp = 'dc_0'
# #figure title
# fname_fig = 'post_dist_' + name_hyp
# #create figure
# fig, ax = plt.subplots(figsize = (10,10))
# for d_id, df_r_h, df_r_h_p in zip(ds_id, df_reg_hyp, df_reg_hyp_post):
# #estimate vertical line height for mean and mode
# ymax_mode = df_r_h_p.loc[:,name_hyp+'_pdf'].max()
# ymax_mean = 1.5*np.ceil(ymax_mode/10)*10
# ymax_mean = 15
# #plot posterior dist
# pl_pdf = ax.plot(df_r_h_p.loc[:,name_hyp], df_r_h_p.loc[:,name_hyp+'_pdf'])
# ax.vlines(df_r_h.loc[name_hyp,'mean'], ymin=0, ymax=ymax_mean, linestyle='-', color=pl_pdf[0].get_color(), label='Mean')
# ax.vlines(df_r_h.loc[name_hyp,'mode'], ymin=0, ymax=ymax_mode, linestyle='--', color=pl_pdf[0].get_color(), label='Mode')
# #plot true value
# ymax_hyp = ymax_mean
# # ax.vlines(hyp[name_hyp], ymin=0, ymax=ymax_hyp, linestyle='-', linewidth=4, color='black', label='True value')
# #edit figure
# ax.set_title(r'Comparison $\delta c_{0}$', fontsize=30)
# ax.set_xlabel('$\delta c_{0}$', fontsize=25)
# ax.set_ylabel('probability density function ', fontsize=25)
# ax.grid(which='both')
# ax.tick_params(axis='x', labelsize=22)
# ax.tick_params(axis='y', labelsize=22)
# #plot limits
# ax.set_xlim([-1,1])
# ax.set_ylim([0,ymax_hyp])
# #save figure
# fig.tight_layout()
# # fig.savefig( dir_fig + fname_fig + '.png' )
| 35,753 | 38.682575 | 153 | py |
ngmm_tools | ngmm_tools-master/Analyses/Code_Verification/regression/ds2/comparison_model2_misfit.py | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Tue Mar 15 14:50:27 2022
@author: glavrent
"""
# Working directory and Packages
# ---------------------------
#load variables
import os
import sys
import pathlib
#arithmetic libraries
import numpy as np
#statistics libraries
import pandas as pd
#plot libraries
import matplotlib as mpl
import matplotlib.pyplot as plt
#user functions
def PlotRSMCmp(df_rms_all, c_name, fig_fname):
#create figure axes
fig, ax = plt.subplots(figsize = (10,10))
for k in df_rms_all:
df_rms = df_rms_all[k]
ds_id = np.array(range(len(df_rms)))
ax.plot(ds_id, df_rms.loc[:,c_name+'_rms'], linestyle='-', marker='o', linewidth=2, markersize=10, label=k)
#figure properties
ax.set_ylim([0, max(0.50, max(ax.get_ylim()))])
ax.set_xlabel('synthetic dataset', fontsize=35)
ax.set_ylabel('RMSE', fontsize=35)
ax.grid(which='both')
ax.set_xticks(ds_id)
ax.set_xticklabels(labels=df_rms.index)
ax.tick_params(axis='x', labelsize=32)
ax.tick_params(axis='y', labelsize=32)
#legend
ax.legend(loc='upper left', fontsize=32)
#save figure
fig.tight_layout()
fig.savefig( fig_fname + '.png' )
return fig, ax
def PlotKLCmp(df_KL_all, c_name, fig_fname):
#create figure axes
fig, ax = plt.subplots(figsize = (10,10))
for k in df_KL_all:
df_KL = df_KL_all[k]
ds_id = np.array(range(len(df_KL)))
ax.plot(ds_id, df_KL.loc[:,c_name+'_KL'], linestyle='-', marker='o', linewidth=2, markersize=10, label=k)
#figure properties
ax.set_ylim([0, max(0.50, max(ax.get_ylim()))])
ax.set_xlabel('synthetic dataset', fontsize=35)
ax.set_ylabel('KL divergence', fontsize=35)
ax.grid(which='both')
ax.set_xticks(ds_id)
ax.set_xticklabels(labels=df_KL.index)
ax.tick_params(axis='x', labelsize=32)
ax.tick_params(axis='y', labelsize=32)
#legend
ax.legend(loc='upper left', fontsize=32)
#save figure
fig.tight_layout()
fig.savefig( fig_fname + '.png' )
return fig, ax
# Define variables
# ---------------------------
# # Sparse Distance Matrix
# # --- --- --- --- ---
# # NGAWest 2 CA North
# cmp_name = 'STAN_sparse_cmp_NGAWest2CA'
# reg_title = ['STAN','STAN w/ sp dist matrix']
# reg_fname = ['CMDSTAN_NGAWest2CANorth_corr_cells_chol_eff_small_corr_len','CMDSTAN_NGAWest2CANorth_corr_cells_chol_eff_sp_small_corr_len']
# ylim_time = [0, 800]
# NGAWest 2 CA
cmp_name = 'STAN_sparse_cmp_NGAWest2CA'
reg_title = ['STAN','STAN w/ sp dist matrix']
reg_fname = ['CMDSTAN_NGAWest2CA_corr_cells_chol_eff_small_corr_len','CMDSTAN_NGAWest2CA_corr_cells_chol_eff_sp_small_corr_len']
ylim_time = [0, 7000]
# # Different Software
# # --- --- --- --- ---
# cmp_name = 'STAN_vs_INLA_cmp_NGAWest2CANorth'
# reg_title = ['STAN corr. cells','STAN uncorr. cells','INLA uncorr. cells']
# reg_fname = ['CMDSTAN_NGAWest2CANorth_corr_cells_chol_eff_small_corr_len','CMDSTAN_NGAWest2CANorth_corr_cells_chol_eff_small_corr_len',
# 'INLA_NGAWest2CANorth_uncorr_cells_coarse_small_corr_len']
# reg_fname = ['PYSTAN_NGAWest2CANorth_corr_cells_chol_eff_small_corr_len','PYSTAN_NGAWest2CANorth_uncorr_cells_chol_eff_small_corr_len',
# 'INLA_NGAWest2CANorth_uncorr_cells_coarse_small_corr_len']
# ylim_time = [0, 800]
#directories regressions
# reg_dir = [f'../../../../Data/Verification/regression/ds2/%s/'%r_f for r_f in reg_fname]
reg_dir = [f'../../../../Data/Verification/regression_old/ds2/%s/'%r_f for r_f in reg_fname]
#directory output
# dir_out = '../../../../Data/Verification/regression/ds2/comparisons/'
dir_out = '../../../../Data/Verification/regression_old/ds2/comparisons/'
# Load Data
# ---------------------------
#initialize misfit dataframe
df_sum_misfit_all = {};
#read misfit info
for k, (r_t, r_d) in enumerate(zip(reg_title, reg_dir)):
#filename misfit info
fname_sum = r_d + 'summary/misfit_summary.csv'
#read KL score for coefficients
df_sum_misfit_all[r_t] = pd.read_csv(fname_sum, index_col=0)
#initialize run time dataframe
df_runinfo_all = {};
#read run time info
for k, (r_t, r_d) in enumerate(zip(reg_title, reg_dir)):
#filename run time
fname_runinfo = r_d + '/run_info.csv'
#store calc time
df_runinfo_all[r_t] = pd.read_csv(fname_runinfo)
# Comparison Figures
# ---------------------------
pathlib.Path(dir_out).mkdir(parents=True, exist_ok=True)
# RMSE divergence
# --- --- --- --- ---
#coefficient name
c_name = 'nerg_tot'
#figure name
fig_fname = '%s/%s_%s_RMSE'%(dir_out, cmp_name, c_name)
#plotting
PlotRSMCmp(df_sum_misfit_all , c_name, fig_fname);
#coefficient name
c_name = 'dc_1e'
#figure name
fig_fname = '%s/%s_%s_RMSE'%(dir_out, cmp_name, c_name)
#plotting
PlotRSMCmp(df_sum_misfit_all , c_name, fig_fname);
#coefficient name
c_name = 'dc_1as'
#figure name
fig_fname = '%s/%s_%s_RMSE'%(dir_out, cmp_name, c_name)
#plotting
PlotRSMCmp(df_sum_misfit_all , c_name, fig_fname);
#coefficient name
c_name = 'dc_1bs'
#figure name
fig_fname = '%s/%s_%s_RMSE'%(dir_out, cmp_name, c_name)
#plotting
PlotRSMCmp(df_sum_misfit_all , c_name, fig_fname);
# KL divergence
# --- --- --- --- ---
#coefficient name
c_name = 'nerg_tot'
#figure name
fig_fname = '%s/%s_%s_KLdiv'%(dir_out, cmp_name, c_name)
#plotting
PlotKLCmp(df_sum_misfit_all , c_name, fig_fname);
#coefficient name
c_name = 'dc_1e'
#figure name
fig_fname = '%s/%s_%s_KLdiv'%(dir_out, cmp_name, c_name)
#plotting
PlotKLCmp(df_sum_misfit_all , c_name, fig_fname);
#coefficient name
c_name = 'dc_1as'
#figure name
fig_fname = '%s/%s_%s_KLdiv'%(dir_out, cmp_name, c_name)
#plotting
PlotKLCmp(df_sum_misfit_all , c_name, fig_fname);
#coefficient name
c_name = 'dc_1bs'
#figure name
fig_fname = '%s/%s_%s_KLdiv'%(dir_out, cmp_name, c_name)
#plotting
PlotKLCmp(df_sum_misfit_all , c_name, fig_fname);
# Run Time
# --- --- --- --- ---
#run time figure
fig_fname = '%s/%s_run_time'%(dir_out, cmp_name)
#create figure axes
fig, ax = plt.subplots(figsize = (10,10))
#iterate over different analyses
for j, k in enumerate(df_runinfo_all):
ds_id = df_runinfo_all[k].ds_id
ds_name = ['Y%i'%d_i for d_i in ds_id]
run_time = df_runinfo_all[k].run_time
ax.plot(ds_id, run_time, marker='o', linewidth=2, markersize=10, label=k)
#figure properties
ax.set_ylim(ylim_time)
ax.set_xlabel('synthetic dataset', fontsize=35)
ax.set_ylabel('Run Time (min)', fontsize=35)
ax.grid(which='both')
ax.set_xticks(ds_id)
ax.set_xticklabels(labels=ds_name)
ax.tick_params(axis='x', labelsize=32)
ax.tick_params(axis='y', labelsize=32)
#legend
ax.legend(loc='lower left', fontsize=32)
# ax.legend(loc='upper left', fontsize=32)
# ax.legend(loc='center left', bbox_to_anchor=(1, 0.5), fontsize=25)
#save figure
fig.tight_layout()
fig.savefig( fig_fname + '.png' ) | 6,888 | 29.892377 | 140 | py |
ngmm_tools | ngmm_tools-master/Analyses/Code_Verification/regression/ds2/main_cmdstan_model2_corr_cells_NGAWest2CA.py | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Wed Dec 29 15:16:15 2021
@author: glavrent
"""
# Working directory and Packages
# ---------------------------
#load libraries
import os
import sys
import numpy as np
import pandas as pd
import time
#user functions
sys.path.insert(0,'../../../Python_lib/regression/cmdstan/')
# from regression_cmdstan_model2_corr_cells_unbounded_hyp import RunStan
from regression_cmdstan_model2_corr_cells_sparse_unbounded_hyp import RunStan
# Define variables
# ---------------------------
#filename suffix
# synds_suffix = '_small_corr_len'
# synds_suffix = '_large_corr_len'
#synthetic datasets directory
ds_dir = '../../../../Data/Validation/synthetic_datasets/ds2'
ds_dir = r'%s%s/'%(ds_dir, synds_suffix)
# dataset info
#ds_fname_main = 'CatalogNGAWest3CA_synthetic_data'
ds_fname_main = 'CatalogNGAWest3CALite_synthetic_data'
ds_id = np.arange(1,6)
#cell specific anelastic attenuation
ds_fname_cellinfo = 'CatalogNGAWest3CALite_cellinfo'
ds_fname_celldist = 'CatalogNGAWest3CALite_distancematrix'
#stan model
# sm_fname = '../../../Stan_lib/regression_stan_model2_corr_cells_unbounded_hyp.stan'
# sm_fname = '../../../Stan_lib/regression_stan_model2_corr_cells_unbounded_hyp_chol.stan'
# sm_fname = '../../../Stan_lib/regression_stan_model2_corr_cells_unbounded_hyp_chol_efficient.stan'
# sm_fname = '../../../Stan_lib/regression_stan_model2_corr_cells_unbounded_hyp_chol_efficient2.stan'
# sm_fname = '../../../Stan_lib/regression_stan_model2_corr_cells_sparse_unbounded_hyp_chol_efficient.stan'
#output info
#main output filename
out_fname_main = 'NGAWest2CA_syndata'
#main output directory
out_dir_main = '../../../../Data/Validation/regression/ds2/'
#output sub-directory
# out_dir_sub = 'CMDSTAN_NGAWest2CA_corr_cells'
# out_dir_sub = 'CMDSTAN_NGAWest2CA_corr_cells_chol'
# out_dir_sub = 'CMDSTAN_NGAWest2CA_corr_cells_chol_efficient'
# out_dir_sub = 'CMDSTAN_NGAWest2CA_corr_cells_chol_efficient2'
# out_dir_sub = 'CMDSTAN_NGAWest2CA_corr_cells_chol_efficient_sp'
#stan parameters
res_name = 'tot'
n_iter_warmup = 500
n_iter_sampling = 500
n_chains = 4
adapt_delta = 0.8
max_treedepth = 10
#ergodic coefficients
c_a_erg=0.0
#parallel options
# flag_parallel = True
flag_parallel = False
#output sub-dir with corr with suffix info
out_dir_sub = f'%s%s'%(out_dir_sub, synds_suffix)
#load cell dataframes
cellinfo_fname = '%s%s.csv'%(ds_dir, ds_fname_cellinfo)
celldist_fname = '%s%s.csv'%(ds_dir, ds_fname_celldist)
df_cellinfo = pd.read_csv(cellinfo_fname)
df_celldist = pd.read_csv(celldist_fname)
# Run stan regression
# ---------------------------
#create datafame with computation time
df_run_info = list()
#iterate over all synthetic datasets
for d_id in ds_id:
print('Synthetic dataset %i fo %i'%(d_id, len(ds_id)))
#run time start
run_t_strt = time.time()
#input flatfile
ds_fname = '%s%s%s_Y%i.csv'%(ds_dir, ds_fname_main, synds_suffix, d_id)
#load flatfile
df_flatfile = pd.read_csv(ds_fname)
#keep only NGAWest2 records
df_flatfile = df_flatfile.loc[df_flatfile.dsid==0,:]
#output file name and directory
out_fname = '%s%s_Y%i'%(out_fname_main, synds_suffix, d_id)
out_dir = '%s/%s/Y%i/'%(out_dir_main, out_dir_sub, d_id)
#run stan model
RunStan(df_flatfile, df_cellinfo, df_celldist, sm_fname,
out_fname, out_dir, res_name, c_a_erg=c_a_erg,
n_iter_warmup=n_iter_warmup, n_iter_sampling=n_iter_sampling, n_chains=n_chains,
adapt_delta=adapt_delta, max_treedepth=max_treedepth,
stan_parallel=flag_parallel)
#run time end
run_t_end = time.time()
#compute run time
run_tm = (run_t_end - run_t_strt)/60
#log run time
df_run_info.append(pd.DataFrame({'computer_name':os.uname()[1],'out_name':out_dir_sub,
'ds_id':d_id,'run_time':run_tm}, index=[d_id]))
#write out run info
out_fname = '%s%s/run_info.csv'%(out_dir_main, out_dir_sub)
pd.concat(df_run_info).reset_index(drop=True).to_csv(out_fname, index=False)
| 4,163 | 32.853659 | 107 | py |
ngmm_tools | ngmm_tools-master/Analyses/Code_Verification/regression/ds2/main_pystan_model2_corr_cells_NGAWest3CA.py | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Wed Jul 14 14:17:52 2021
@author: glavrent
"""
# Working directory and Packages
# ---------------------------
#load libraries
import os
import sys
import numpy as np
import pandas as pd
import time
#user functions
sys.path.insert(0,'../../../Python_lib/regression/pystan/')
from regression_pystan_model2_corr_cells_unbounded_hyp import RunStan
# Define variables
# ---------------------------
#filename suffix
# synds_suffix = '_small_corr_len'
# synds_suffix = '_large_corr_len'
#synthetic datasets directory
ds_dir = '../../../../Data/Verification/synthetic_datasets/ds2'
ds_dir = r'%s%s/'%(ds_dir, synds_suffix)
# dataset info
#ds_fname_main = 'CatalogNGAWest3CA_synthetic_data'
ds_fname_main = 'CatalogNGAWest3CALite_synthetic_data'
ds_id = np.arange(1,6)
#cell specific anelastic attenuation
ds_fname_cellinfo = 'CatalogNGAWest3CALite_cellinfo'
ds_fname_celldist = 'CatalogNGAWest3CALite_distancematrix'
#stan model
# sm_fname = '../../../Stan_lib/regression_stan_model2_corr_cells_unbounded_hyp.stan'
# sm_fname = '../../../Stan_lib/regression_stan_model2_corr_cells_unbounded_hyp_chol.stan'
# sm_fname = '../../../Stan_lib/regression_stan_model2_corr_cells_unbounded_hyp_chol_efficient.stan'
# sm_fname = '../../../Stan_lib/regression_stan_model2_corr_cells_unbounded_hyp_chol_efficient2.stan'
#output info
#main output filename
out_fname_main = 'NGAWest3CA_syndata'
#main output directory
out_dir_main = '../../../../Data/Verification/regression/ds2/'
#output sub-directory
# out_dir_sub = 'PYSTAN_NGAWest3CA_corr_cells'
# out_dir_sub = 'PYSTAN_NGAWest3CA_corr_cells_chol'
# out_dir_sub = 'PYSTAN_NGAWest3CA_corr_cells_chol_eff'
# out_dir_sub = 'PYSTAN_NGAWest3CA_corr_cells_chol_eff2'
#stan parameters
runstan_flag = True
# pystan_ver = 2
pystan_ver = 3
res_name = 'tot'
n_iter = 1000
n_chains = 4
adapt_delta = 0.8
max_treedepth = 10
#ergodic coefficients
c_a_erg=0.0
#parallel options
# flag_parallel = True
flag_parallel = False
#output sub-dir with corr with suffix info
out_dir_sub = f'%s%s'%(out_dir_sub, synds_suffix)
#load cell dataframes
cellinfo_fname = '%s%s.csv'%(ds_dir, ds_fname_cellinfo)
celldist_fname = '%s%s.csv'%(ds_dir, ds_fname_celldist)
df_cellinfo = pd.read_csv(cellinfo_fname)
df_celldist = pd.read_csv(celldist_fname)
# Run stan regression
# ---------------------------
#create datafame with computation time
df_run_info = list()
#iterate over all synthetic datasets
for d_id in ds_id:
print('Synthetic dataset %i fo %i'%(d_id, len(ds_id)))
#run time start
run_t_strt = time.time()
#input flatfile
ds_fname = '%s%s%s_Y%i.csv'%(ds_dir, ds_fname_main, synds_suffix, d_id)
#load flatfile
df_flatfile = pd.read_csv(ds_fname)
#output file name and directory
out_fname = '%s%s_Y%i'%(out_fname_main, synds_suffix, d_id)
out_dir = '%s/%s/Y%i/'%(out_dir_main, out_dir_sub, d_id)
#run stan model
RunStan(df_flatfile, df_cellinfo, df_celldist, sm_fname,
out_fname, out_dir, res_name, c_a_erg=c_a_erg,
runstan_flag=runstan_flag, n_iter=n_iter, n_chains=n_chains,
adapt_delta=adapt_delta, max_treedepth=max_treedepth,
pystan_ver=pystan_ver, pystan_parallel=flag_parallel)
#run time end
run_t_end = time.time()
#compute run time
run_tm = (run_t_end - run_t_strt)/60
#log run time
df_run_info.append(pd.DataFrame({'computer_name':os.uname()[1],'out_name':out_dir_sub,
'ds_id':d_id,'run_time':run_tm}, index=[d_id]))
#write out run info
out_fname = '%s%s/run_info.csv'%(out_dir_main, out_dir_sub)
pd.concat(df_run_info).reset_index(drop=True).to_csv(out_fname, index=False)
| 3,811 | 30.766667 | 101 | py |
ngmm_tools | ngmm_tools-master/Analyses/Code_Verification/regression/ds3/main_pystan_model3_uncorr_cells_NGAWest3CA.py | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Wed Jul 14 14:17:52 2021
@author: glavrent
"""
# Working directory and Packages
# ---------------------------
#load libraries
import os
import sys
import numpy as np
import pandas as pd
import time
#user functions
sys.path.insert(0,'../../../Python_lib/regression/pystan/')
from regression_pystan_model3_uncorr_cells_unbounded_hyp import RunStan
# Define variables
# ---------------------------
#filename suffix
# synds_suffix = '_small_corr_len'
# synds_suffix = '_large_corr_len'
#synthetic datasets directory
ds_dir = '../../../../Data/Validation/synthetic_datasets/ds3'
ds_dir = r'%s%s/'%(ds_dir, synds_suffix)
# dataset info
#ds_fname_main = 'CatalogNGAWest3CA_synthetic_data'
ds_fname_main = 'CatalogNGAWest3CALite_synthetic_data'
ds_id = np.arange(1,6)
#cell specific anelastic attenuation
ds_fname_cellinfo = 'CatalogNGAWest3CALite_cellinfo'
ds_fname_celldist = 'CatalogNGAWest3CALite_distancematrix'
#stan model
sm_fname = '../../../Stan_lib/regression_stan_model3_uncorr_cells_unbounded_hyp_chol_efficient.stan'
#output info
#main output filename
out_fname_main = 'NGAWest3CA_syndata'
#main output directory
out_dir_main = '../../../../Data/Validation/regression/ds3/'
#output sub-directory
out_dir_sub = 'PYSTAN_NGAWest3CA_uncorr_cells_chol_eff'
#stan parameters
runstan_flag = True
# pystan_ver = 2
pystan_ver = 3
res_name = 'tot'
n_iter = 1000
n_chains = 4
adapt_delta = 0.8
max_treedepth = 10
#ergodic coefficients
c_2_erg=-2.0
c_3_erg=-0.6
c_a_erg=0.0
#parallel options
# flag_parallel = True
flag_parallel = False
#output sub-dir with corr with suffix info
out_dir_sub = f'%s%s'%(out_dir_sub, synds_suffix)
#load cell dataframes
cellinfo_fname = '%s%s.csv'%(ds_dir, ds_fname_cellinfo)
celldist_fname = '%s%s.csv'%(ds_dir, ds_fname_celldist)
df_cellinfo = pd.read_csv(cellinfo_fname)
df_celldist = pd.read_csv(celldist_fname)
# Run stan regression
# ---------------------------
#create datafame with computation time
df_run_info = list()
#iterate over all synthetic datasets
for d_id in ds_id:
print('Synthetic dataset %i fo %i'%(d_id, len(ds_id)))
#run time start
run_t_strt = time.time()
#input flatfile
ds_fname = '%s%s%s_Y%i.csv'%(ds_dir, ds_fname_main, synds_suffix, d_id)
#load flatfile
df_flatfile = pd.read_csv(ds_fname)
#output file name and directory
out_fname = '%s%s_Y%i'%(out_fname_main, synds_suffix, d_id)
out_dir = '%s/%s/Y%i/'%(out_dir_main, out_dir_sub, d_id)
#run stan model
RunStan(df_flatfile, df_cellinfo, df_celldist, sm_fname,
out_fname, out_dir, res_name,
c_2_erg=c_2_erg, c_3_erg=c_3_erg, c_a_erg=c_a_erg,
runstan_flag=runstan_flag, n_iter=n_iter, n_chains=n_chains,
adapt_delta=adapt_delta, max_treedepth=max_treedepth,
pystan_ver=pystan_ver, pystan_parallel=flag_parallel)
#run time end
run_t_end = time.time()
#compute run time
run_tm = (run_t_end - run_t_strt)/60
#log run time
df_run_info.append(pd.DataFrame({'computer_name':os.uname()[1],'out_name':out_dir_sub,
'ds_id':d_id,'run_time':run_tm}, index=[d_id]))
#write out run info
out_fname = '%s%s/run_info.csv'%(out_dir_main, out_dir_sub)
pd.concat(df_run_info).reset_index(drop=True).to_csv(out_fname, index=False)
| 3,445 | 28.20339 | 100 | py |
ngmm_tools | ngmm_tools-master/Analyses/Code_Verification/regression/ds3/main_cmdstan_model3_corr_cells_NGAWest2CA.py | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Wed Dec 29 15:16:15 2021
@author: glavrent
"""
# Working directory and Packages
# ---------------------------
#load libraries
import os
import sys
import numpy as np
import pandas as pd
import time
#user functions
sys.path.insert(0,'../../../Python_lib/regression/cmdstan/')
# from regression_cmdstan_model3_corr_cells_unbounded_hyp import RunStan
# from regression_cmdstan_model3_corr_cells_sparse_unbounded_hyp import RunStan
# Define variables
# ---------------------------
#filename suffix
# synds_suffix = '_small_corr_len'
# synds_suffix = '_large_corr_len'
#synthetic datasets directory
ds_dir = '../../../../Data/Verification/synthetic_datasets/ds3'
ds_dir = r'%s%s/'%(ds_dir, synds_suffix)
# dataset info
#ds_fname_main = 'CatalogNGAWest3CA_synthetic_data'
ds_fname_main = 'CatalogNGAWest3CALite_synthetic_data'
ds_id = np.arange(1,6)
#cell specific anelastic attenuation
ds_fname_cellinfo = 'CatalogNGAWest3CALite_cellinfo'
ds_fname_celldist = 'CatalogNGAWest3CALite_distancematrix'
#stan model
# sm_fname = '../../../Stan_lib/regression_stan_model3_corr_cells_unbounded_hyp_chol_efficient.stan'
# sm_fname = '../../../Stan_lib/regression_stan_model3_corr_cells_sparse_unbounded_hyp_chol_efficient.stan'
#output info
#main output filename
out_fname_main = 'NGAWest2CA_syndata'
#main output directory
out_dir_main = '../../../../Data/Verification/regression/ds3/'
#output sub-directory
# out_dir_sub = 'CMDSTAN_NGAWest2CA_corr_cells_chol_eff'
# out_dir_sub = 'CMDSTAN_NGAWest2CA_corr_cells_chol_eff_sp'
#stan parameters
res_name = 'tot'
n_iter_warmup = 500
n_iter_sampling = 500
n_chains = 4
adapt_delta = 0.8
max_treedepth = 10
#ergodic coefficients
c_2_erg=-2.0
c_3_erg=-0.6
c_a_erg= 0.0
#parallel options
# flag_parallel = True
flag_parallel = False
#output sub-dir with corr with suffix info
out_dir_sub = f'%s%s'%(out_dir_sub, synds_suffix)
#load cell dataframes
cellinfo_fname = '%s%s.csv'%(ds_dir, ds_fname_cellinfo)
celldist_fname = '%s%s.csv'%(ds_dir, ds_fname_celldist)
df_cellinfo = pd.read_csv(cellinfo_fname)
df_celldist = pd.read_csv(celldist_fname)
# Run stan regression
# ---------------------------
#create datafame with computation time
df_run_info = list()
#iterate over all synthetic datasets
for d_id in ds_id:
print('Synthetic dataset %i fo %i'%(d_id, len(ds_id)))
#run time start
run_t_strt = time.time()
#input flatfile
ds_fname = '%s%s%s_Y%i.csv'%(ds_dir, ds_fname_main, synds_suffix, d_id)
#load flatfile
df_flatfile = pd.read_csv(ds_fname)
#keep only NGAWest2 records
df_flatfile = df_flatfile.loc[df_flatfile.dsid==0,:]
#output file name and directory
out_fname = '%s%s_Y%i'%(out_fname_main, synds_suffix, d_id)
out_dir = '%s/%s/Y%i/'%(out_dir_main, out_dir_sub, d_id)
#run stan model
RunStan(df_flatfile, df_cellinfo, df_celldist, sm_fname,
out_fname, out_dir, res_name,
c_2_erg=c_2_erg, c_3_erg=c_3_erg, c_a_erg=c_a_erg,
n_iter_warmup=n_iter_warmup, n_iter_sampling=n_iter_sampling, n_chains=n_chains,
adapt_delta=adapt_delta, max_treedepth=max_treedepth,
stan_parallel=flag_parallel)
#run time end
run_t_end = time.time()
#compute run time
run_tm = (run_t_end - run_t_strt)/60
#log run time
df_run_info.append(pd.DataFrame({'computer_name':os.uname()[1],'out_name':out_dir_sub,
'ds_id':d_id,'run_time':run_tm}, index=[d_id]))
#write out run info
out_fname = '%s%s/run_info.csv'%(out_dir_main, out_dir_sub)
pd.concat(df_run_info).reset_index(drop=True).to_csv(out_fname, index=False)
| 3,778 | 30.491667 | 107 | py |
ngmm_tools | ngmm_tools-master/Analyses/Code_Verification/regression/ds3/main_cmdstan_model3_corr_cells_NGAWest2CANorth.py | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Wed Dec 29 15:16:15 2021
@author: glavrent
"""
# Working directory and Packages
# ---------------------------
#load libraries
import os
import sys
import numpy as np
import pandas as pd
import time
#user functions
sys.path.insert(0,'../../../Python_lib/regression/cmdstan/')
# from regression_cmdstan_model3_corr_cells_unbounded_hyp import RunStan
# from regression_cmdstan_model3_corr_cells_sparse_unbounded_hyp import RunStan
# Define variables
# ---------------------------
#filename suffix
# synds_suffix = '_small_corr_len'
# synds_suffix = '_large_corr_len'
#synthetic datasets directory
ds_dir = '../../../../Data/Verification/synthetic_datasets/ds3'
ds_dir = r'%s%s/'%(ds_dir, synds_suffix)
# dataset info
#ds_fname_main = 'CatalogNGAWest3CA_synthetic_data'
ds_fname_main = 'CatalogNGAWest3CALite_synthetic_data'
ds_id = np.arange(1,6)
#cell specific anelastic attenuation
ds_fname_cellinfo = 'CatalogNGAWest3CALite_cellinfo'
ds_fname_celldist = 'CatalogNGAWest3CALite_distancematrix'
#stan model
# sm_fname = '../../../Stan_lib/regression_stan_model3_corr_cells_unbounded_hyp_chol_efficient.stan'
# sm_fname = '../../../Stan_lib/regression_stan_model3_corr_cells_sparse_unbounded_hyp_chol_efficient.stan'
#output info
#main output filename
out_fname_main = 'NGAWest2CANorth_syndata'
#main output directory
out_dir_main = '../../../../Data/Verification/regression/ds3/'
#output sub-directory
# out_dir_sub = 'CMDSTAN_NGAWest2CANorth_corr_cells_chol_eff'
# out_dir_sub = 'CMDSTAN_NGAWest2CANorth_corr_cells_chol_eff_sp'
#stan parameters
res_name = 'tot'
n_iter_warmup = 500
n_iter_sampling = 500
n_chains = 4
adapt_delta = 0.8
max_treedepth = 10
#ergodic coefficients
c_2_erg=-2.0
c_3_erg=-0.6
c_a_erg= 0.0
#parallel options
# flag_parallel = True
flag_parallel = False
#output sub-dir with corr with suffix info
out_dir_sub = f'%s%s'%(out_dir_sub, synds_suffix)
#load cell dataframes
cellinfo_fname = '%s%s.csv'%(ds_dir, ds_fname_cellinfo)
celldist_fname = '%s%s.csv'%(ds_dir, ds_fname_celldist)
df_cellinfo = pd.read_csv(cellinfo_fname)
df_celldist = pd.read_csv(celldist_fname)
# Run stan regression
# ---------------------------
#create datafame with computation time
df_run_info = list()
#iterate over all synthetic datasets
for d_id in ds_id:
print('Synthetic dataset %i fo %i'%(d_id, len(ds_id)))
#run time start
run_t_strt = time.time()
#input flatfile
ds_fname = '%s%s%s_Y%i.csv'%(ds_dir, ds_fname_main, synds_suffix, d_id)
#load flatfile
df_flatfile = pd.read_csv(ds_fname)
#keep only North records of NGAWest2
df_flatfile = df_flatfile.loc[np.logical_and(df_flatfile.dsid==0,
df_flatfile.sreg==1),:]
#output file name and directory
out_fname = '%s%s_Y%i'%(out_fname_main, synds_suffix, d_id)
out_dir = '%s/%s/Y%i/'%(out_dir_main, out_dir_sub, d_id)
#run stan model
RunStan(df_flatfile, df_cellinfo, df_celldist, sm_fname,
out_fname, out_dir, res_name,
c_2_erg=c_2_erg, c_3_erg=c_3_erg, c_a_erg=c_a_erg,
n_iter_warmup=n_iter_warmup, n_iter_sampling=n_iter_sampling, n_chains=n_chains,
adapt_delta=adapt_delta, max_treedepth=max_treedepth,
stan_parallel=flag_parallel)
#run time end
run_t_end = time.time()
#compute run time
run_tm = (run_t_end - run_t_strt)/60
#log run time
df_run_info.append(pd.DataFrame({'computer_name':os.uname()[1],'out_name':out_dir_sub,
'ds_id':d_id,'run_time':run_tm}, index=[d_id]))
#write out run info
out_fname = '%s%s/run_info.csv'%(out_dir_main, out_dir_sub)
pd.concat(df_run_info).reset_index(drop=True).to_csv(out_fname, index=False)
| 3,888 | 31.140496 | 107 | py |
ngmm_tools | ngmm_tools-master/Analyses/Code_Verification/regression/ds3/main_pystan_model3_corr_cells_NGAWest3CA.py | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Wed Jul 14 14:17:52 2021
@author: glavrent
"""
# Working directory and Packages
# ---------------------------
#load libraries
import os
import sys
import numpy as np
import pandas as pd
import time
#user functions
sys.path.insert(0,'../../../Python_lib/regression/pystan/')
from regression_pystan_model3_corr_cells_unbounded_hyp import RunStan
# Define variables
# ---------------------------
#filename suffix
# synds_suffix = '_small_corr_len'
# synds_suffix = '_large_corr_len'
#synthetic datasets directory
ds_dir = '../../../../Data/Validation/synthetic_datasets/ds3'
ds_dir = r'%s%s/'%(ds_dir, synds_suffix)
# dataset info
#ds_fname_main = 'CatalogNGAWest3CA_synthetic_data'
ds_fname_main = 'CatalogNGAWest3CALite_synthetic_data'
ds_id = np.arange(1,6)
#cell specific anelastic attenuation
ds_fname_cellinfo = 'CatalogNGAWest3CALite_cellinfo'
ds_fname_celldist = 'CatalogNGAWest3CALite_distancematrix'
#stan model
sm_fname = '../../../Stan_lib/regression_stan_model3_corr_cells_unbounded_hyp_chol_efficient.stan'
#output info
#main output filename
out_fname_main = 'NGAWest3CA_syndata'
#main output directory
out_dir_main = '../../../../Data/Validation/regression/ds3/'
#output sub-directory
out_dir_sub = 'PYSTAN_NGAWest3CA_corr_cells_chol_eff'
#stan parameters
runstan_flag = True
# pystan_ver = 2
pystan_ver = 3
res_name = 'tot'
n_iter = 1000
n_chains = 4
adapt_delta = 0.8
max_treedepth = 10
#ergodic coefficients
c_2_erg=-2.0
c_3_erg=-0.6
c_a_erg=0.0
#parallel options
# flag_parallel = True
flag_parallel = False
#output sub-dir with corr with suffix info
out_dir_sub = f'%s%s'%(out_dir_sub, synds_suffix)
#load cell dataframes
cellinfo_fname = '%s%s.csv'%(ds_dir, ds_fname_cellinfo)
celldist_fname = '%s%s.csv'%(ds_dir, ds_fname_celldist)
df_cellinfo = pd.read_csv(cellinfo_fname)
df_celldist = pd.read_csv(celldist_fname)
# Run stan regression
# ---------------------------
#create datafame with computation time
df_run_info = list()
#iterate over all synthetic datasets
for d_id in ds_id:
print('Synthetic dataset %i fo %i'%(d_id, len(ds_id)))
#run time start
run_t_strt = time.time()
#input flatfile
ds_fname = '%s%s%s_Y%i.csv'%(ds_dir, ds_fname_main, synds_suffix, d_id)
#load flatfile
df_flatfile = pd.read_csv(ds_fname)
#output file name and directory
out_fname = '%s%s_Y%i'%(out_fname_main, synds_suffix, d_id)
out_dir = '%s/%s/Y%i/'%(out_dir_main, out_dir_sub, d_id)
#run stan model
RunStan(df_flatfile, df_cellinfo, df_celldist, sm_fname,
out_fname, out_dir, res_name,
c_2_erg=c_2_erg, c_3_erg=c_3_erg, c_a_erg=c_a_erg,
runstan_flag=runstan_flag, n_iter=n_iter, n_chains=n_chains,
adapt_delta=adapt_delta, max_treedepth=max_treedepth,
pystan_ver=pystan_ver, pystan_parallel=flag_parallel)
#run time end
run_t_end = time.time()
#compute run time
run_tm = (run_t_end - run_t_strt)/60
#log run time
df_run_info.append(pd.DataFrame({'computer_name':os.uname()[1],'out_name':out_dir_sub,
'ds_id':d_id,'run_time':run_tm}, index=[d_id]))
#write out run info
out_fname = '%s%s/run_info.csv'%(out_dir_main, out_dir_sub)
pd.concat(df_run_info).reset_index(drop=True).to_csv(out_fname, index=False)
| 3,441 | 28.169492 | 98 | py |
ngmm_tools | ngmm_tools-master/Analyses/Code_Verification/regression/ds3/main_cmdstan_model3_uncorr_cells_NGAWest2CANorth.py | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Wed Dec 29 15:16:15 2021
@author: glavrent
"""
# Working directory and Packages
# ---------------------------
#load libraries
import os
import sys
import numpy as np
import pandas as pd
import time
#user functions
sys.path.insert(0,'../../../Python_lib/regression/cmdstan/')
# from regression_cmdstan_model3_uncorr_cells_unbounded_hyp import RunStan
# from regression_cmdstan_model3_uncorr_cells_sparse_unbounded_hyp import RunStan
# Define variables
# ---------------------------
#filename suffix
# synds_suffix = '_small_corr_len'
# synds_suffix = '_large_corr_len'
#synthetic datasets directory
ds_dir = '../../../../Data/Verification/synthetic_datasets/ds3'
ds_dir = r'%s%s/'%(ds_dir, synds_suffix)
# dataset info
#ds_fname_main = 'CatalogNGAWest3CA_synthetic_data'
ds_fname_main = 'CatalogNGAWest3CALite_synthetic_data'
ds_id = np.arange(1,6)
#cell specific anelastic attenuation
ds_fname_cellinfo = 'CatalogNGAWest3CALite_cellinfo'
ds_fname_celldist = 'CatalogNGAWest3CALite_distancematrix'
#stan model
# sm_fname = '../../../Stan_lib/regression_stan_model3_uncorr_cells_unbounded_hyp_chol_efficient.stan'
# sm_fname = '../../../Stan_lib/regression_stan_model3_uncorr_cells_sparse_unbounded_hyp_chol_efficient.stan'
#output info
#main output filename
out_fname_main = 'NGAWest2CANorth_syndata'
#main output directory
out_dir_main = '../../../../Data/Verification/regression/ds3/'
#output sub-directory
# out_dir_sub = 'CMDSTAN_NGAWest2CANorth_uncorr_cells_chol_eff'
# out_dir_sub = 'CMDSTAN_NGAWest2CANorth_uncorr_cells_chol_eff_sp'
#stan parameters
res_name = 'tot'
n_iter_warmup = 500
n_iter_sampling = 500
n_chains = 4
adapt_delta = 0.8
max_treedepth = 10
#ergodic coefficients
c_2_erg=-2.0
c_3_erg=-0.6
c_a_erg= 0.0
#parallel options
# flag_parallel = True
flag_parallel = False
#output sub-dir with corr with suffix info
out_dir_sub = f'%s%s'%(out_dir_sub, synds_suffix)
#load cell dataframes
cellinfo_fname = '%s%s.csv'%(ds_dir, ds_fname_cellinfo)
celldist_fname = '%s%s.csv'%(ds_dir, ds_fname_celldist)
df_cellinfo = pd.read_csv(cellinfo_fname)
df_celldist = pd.read_csv(celldist_fname)
# Run stan regression
# ---------------------------
#create datafame with computation time
df_run_info = list()
#iterate over all synthetic datasets
for d_id in ds_id:
print('Synthetic dataset %i fo %i'%(d_id, len(ds_id)))
#run time start
run_t_strt = time.time()
#input flatfile
ds_fname = '%s%s%s_Y%i.csv'%(ds_dir, ds_fname_main, synds_suffix, d_id)
#load flatfile
df_flatfile = pd.read_csv(ds_fname)
#keep only North records of NGAWest2
df_flatfile = df_flatfile.loc[np.logical_and(df_flatfile.dsid==0,
df_flatfile.sreg==1),:]
#output file name and directory
out_fname = '%s%s_Y%i'%(out_fname_main, synds_suffix, d_id)
out_dir = '%s/%s/Y%i/'%(out_dir_main, out_dir_sub, d_id)
#run stan model
RunStan(df_flatfile, df_cellinfo, df_celldist, sm_fname,
out_fname, out_dir, res_name,
c_2_erg=c_2_erg, c_3_erg=c_3_erg, c_a_erg=c_a_erg,
n_iter_warmup=n_iter_warmup, n_iter_sampling=n_iter_sampling, n_chains=n_chains,
adapt_delta=adapt_delta, max_treedepth=max_treedepth,
stan_parallel=flag_parallel)
#run time end
run_t_end = time.time()
#compute run time
run_tm = (run_t_end - run_t_strt)/60
#log run time
df_run_info.append(pd.DataFrame({'computer_name':os.uname()[1],'out_name':out_dir_sub,
'ds_id':d_id,'run_time':run_tm}, index=[d_id]))
#write out run info
out_fname = '%s%s/run_info.csv'%(out_dir_main, out_dir_sub)
pd.concat(df_run_info).reset_index(drop=True).to_csv(out_fname, index=False)
| 3,899 | 31.231405 | 109 | py |
ngmm_tools | ngmm_tools-master/Analyses/Code_Verification/regression/ds3/comparison_inla_model3_uncorr_cells.py | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Thu Aug 12 10:26:06 2021
@author: glavrent
"""
# Working directory and Packages
# ---------------------------
#load packages
import os
import sys
import pathlib
import glob
import re #regular expression package
import pickle
#arithmetic libraries
import numpy as np
#statistics libraries
import pandas as pd
#plot libraries
import matplotlib as mpl
import matplotlib.pyplot as plt
from matplotlib.ticker import AutoLocator as plt_autotick
#user functions
sys.path.insert(0,'../../../Python_lib/regression/')
from pylib_stats import CalcRMS
from pylib_stats import CalcLKDivergece
# Define variables
# ---------------------------
# USER SETS DIRECTORIES AND FILE INFO OF SYNTHETIC DS AND REGRESSION RESULTS
# ++++++++++++++++++++++++++++++++++++++++
#processed dataset
# name_dataset = 'NGAWest2CANorth'
# name_dataset = 'NGAWest2CA'
# name_dataset = 'NGAWest3CA'
#correlation info
# 1: Small Correlation Lengths
# 2: Large Correlation Lenghts
corr_id = 1
#kernel function
# 1: Mattern kernel (alpha=2)
# 2: Negative Exp (alpha=3/2)
ker_id = 1
#mesh type
# 1: Fine Mesh
# 2: Medium Mesh
# 3: Coarse Mesh
mesh_id = 3
#directories (synthetic dataset)
if corr_id == 1:
dir_syndata = '../../../../Data/Verification/synthetic_datasets/ds3_small_corr_len'
elif corr_id == 2:
dir_syndata = '../../../../Data/Verification/synthetic_datasets/ds3_large_corr_len'
#directories (regression results)
if mesh_id == 1:
dir_results = f'../../../../Data/Verification/regression/ds3/INLA_%s_uncorr_cells_fine'%name_dataset
elif mesh_id == 2:
dir_results = f'../../../../Data/Verification/regression/ds3/INLA_%s_uncorr_cells_medium'%name_dataset
elif mesh_id == 3:
dir_results = f'../../../../Data/Verification/regression/ds3/INLA_%s_uncorr_cells_coarse'%name_dataset
#cell info
fname_cellinfo = dir_syndata + '/' + 'CatalogNGAWest3CALite_cellinfo.csv'
fname_distmat = dir_syndata + '/' + 'CatalogNGAWest3CALite_distancematrix.csv'
#prefix for synthetic data and results
prfx_syndata = 'CatalogNGAWest3CALite_synthetic'
#regression results filename prefix
prfx_results = f'%s_syndata'%name_dataset
# dataset info
ds_id = np.arange(1,6)
# ++++++++++++++++++++++++++++++++++++++++
# USER NEEDS TO SPECIFY HYPERPARAMETERS OF SYNTHETIC DATASET
# ++++++++++++++++++++++++++++++++++++++++
# hyper-parameters
if corr_id == 1:
# small correlation lengths
hyp = {'omega_0': 0.1, 'omega_1e':0.1, 'omega_1as': 0.35, 'omega_1bs': 0.25,
'ell_1e':60, 'ell_1as':30,
'c_2_erg': -2.0,
'omega_2': 0.2,
'omega_2p': 0.15, 'ell_2p': 80,
'c_3_erg':-0.6,
'omega_3': 0.15,
'omega_3s': 0.15, 'ell_3s': 130,
'c_cap_erg': -0.011,
'omega_cap_mu': 0.005, 'omega_ca1p':0.004, 'omega_ca2p':0.002,
'ell_ca1p': 75,
'phi_0':0.3, 'tau_0':0.25 }
elif corr_id == 2:
# large correlation lengths
hyp = {'omega_0': 0.1, 'omega_1e':0.2, 'omega_1as': 0.4, 'omega_1bs': 0.3,
'ell_1e':100, 'ell_1as':70,
'c_2_erg': -2.0,
'omega_2': 0.2,
'omega_2p': 0.15, 'ell_2e': 140,
'c_3_erg':-0.6,
'omega_3': 0.15,
'omega_3s': 0.15, 'ell_3s': 180,
'c_cap_erg': -0.02,
'omega_cap_mu': 0.008, 'omega_ca1p':0.005, 'omega_ca2p':0.003,
'ell_ca1p': 120,
'phi_0':0.3, 'tau_0':0.25}
# ++++++++++++++++++++++++++++++++++++++++
# FILE INFO FOR REGRESSION RESULTS
# ++++++++++++++++++++++++++++++++++++++++
#output filename sufix
if corr_id == 1:
synds_suffix = '_small_corr_len'
elif corr_id == 2:
synds_suffix = '_large_corr_len'
#kenel info
if ker_id == 1:
ker_suffix = ''
elif ker_id == 2:
ker_suffix = '_nexp'
# ++++++++++++++++++++++++++++++++++++++++
#ploting options
flag_report = True
# Compare results
# ---------------------------
#load cell data
df_cellinfo = pd.read_csv(fname_cellinfo).set_index('cellid')
df_distmat = pd.read_csv(fname_distmat).set_index('rsn')
#initialize misfit metrics dataframe
df_misfit = pd.DataFrame(index=['Y%i'%d_id for d_id in ds_id])
#iterate over different datasets
for d_id in ds_id:
# Load Data
#--- --- --- --- ---
#file names
#synthetic data
fname_sdata_gmotion = '%s/%s_%s%s_Y%i'%(dir_syndata, prfx_syndata, 'data', synds_suffix, d_id) + '.csv'
fname_sdata_atten = '%s/%s_%s%s_Y%i'%(dir_syndata, prfx_syndata, 'atten', synds_suffix, d_id) + '.csv'
#regression results
fname_reg_gmotion = '%s%s/Y%i/%s%s_Y%i_inla_%s'%(dir_results, ker_suffix+synds_suffix, d_id, prfx_results, synds_suffix, d_id, 'residuals') + '.csv'
fname_reg_coeff = '%s%s/Y%i/%s%s_Y%i_inla_%s'%(dir_results, ker_suffix+synds_suffix, d_id, prfx_results, synds_suffix, d_id, 'coefficients') + '.csv'
fname_reg_atten = '%s%s/Y%i/%s%s_Y%i_inla_%s'%(dir_results, ker_suffix+synds_suffix, d_id, prfx_results, synds_suffix, d_id, 'catten') + '.csv'
#load synthetic results
df_sdata_gmotion = pd.read_csv(fname_sdata_gmotion).set_index('rsn')
df_sdata_atten = pd.read_csv(fname_sdata_atten).set_index('cellid')
#load regression results
df_reg_gmotion = pd.read_csv(fname_reg_gmotion).set_index('rsn')
df_reg_coeff = pd.read_csv(fname_reg_coeff).set_index('rsn')
df_reg_atten = pd.read_csv(fname_reg_atten).set_index('cellid')
# Processing
#--- --- --- --- ---
#keep only relevant columns from synthetic dataset
df_sdata_gmotion = df_sdata_gmotion.reindex(df_reg_gmotion.index)
df_sdata_atten = df_sdata_atten.reindex(df_reg_atten.index)
#distance matrix for records of interest
df_dmat = df_distmat.reindex(df_sdata_gmotion.index)
#find unique earthqakes and stations
eq_id, eq_idx, eq_nrec = np.unique(df_sdata_gmotion.eqid, return_index=True, return_counts=True)
sta_id, sta_idx, sta_nrec = np.unique(df_sdata_gmotion.ssn, return_index=True, return_counts=True)
#number of paths per cell
cell_npath = np.sum(df_dmat.loc[:,df_reg_atten.cellname] > 0, axis=0)
# Compute Root Mean Square Error
#--- --- --- --- ---
df_misfit.loc['Y%i'%d_id,'nerg_tot_rms'] = CalcRMS(df_sdata_gmotion.nerg_gm.values, df_reg_gmotion.nerg_mu.values)
df_misfit.loc['Y%i'%d_id,'dc_1e_rms'] = CalcRMS(df_sdata_gmotion['dc_1e'].values[eq_idx], df_reg_coeff['dc_1e_mean'].values[eq_idx])
df_misfit.loc['Y%i'%d_id,'dc_1as_rms'] = CalcRMS(df_sdata_gmotion['dc_1as'].values[sta_idx], df_reg_coeff['dc_1as_mean'].values[sta_idx])
df_misfit.loc['Y%i'%d_id,'dc_1bs_rms'] = CalcRMS(df_sdata_gmotion['dc_1bs'].values[sta_idx], df_reg_coeff['dc_1bs_mean'].values[sta_idx])
df_misfit.loc['Y%i'%d_id,'c_2p_rms'] = CalcRMS(df_sdata_gmotion['c_2p'].values[eq_idx], df_reg_coeff['c_2p_mean'].values[eq_idx])
df_misfit.loc['Y%i'%d_id,'c_3s_rms'] = CalcRMS(df_sdata_gmotion['c_3s'].values[sta_idx], df_reg_coeff['c_3s_mean'].values[sta_idx])
df_misfit.loc['Y%i'%d_id,'c_cap_rms'] = CalcRMS(df_sdata_atten['c_cap'].values, df_reg_atten['c_cap_mean'].values)
# Compute Divergence
#--- --- --- --- ---
df_misfit.loc['Y%i'%d_id,'nerg_tot_KL'] = CalcLKDivergece(df_sdata_gmotion.nerg_gm.values, df_reg_gmotion.nerg_mu.values)
df_misfit.loc['Y%i'%d_id,'dc_1e_KL'] = CalcLKDivergece(df_sdata_gmotion['dc_1e'].values[eq_idx], df_reg_coeff['dc_1e_mean'].values[eq_idx])
df_misfit.loc['Y%i'%d_id,'dc_1as_KL'] = CalcLKDivergece(df_sdata_gmotion['dc_1as'].values[sta_idx], df_reg_coeff['dc_1as_mean'].values[sta_idx])
df_misfit.loc['Y%i'%d_id,'dc_1bs_KL'] = CalcLKDivergece(df_sdata_gmotion['dc_1bs'].values[sta_idx], df_reg_coeff['dc_1bs_mean'].values[sta_idx])
df_misfit.loc['Y%i'%d_id,'c_2p_KL'] = CalcLKDivergece(df_sdata_gmotion['c_2p'].values[eq_idx], df_reg_coeff['c_2p_mean'].values[eq_idx])
df_misfit.loc['Y%i'%d_id,'c_3s_KL'] = CalcLKDivergece(df_sdata_gmotion['c_3s'].values[sta_idx], df_reg_coeff['c_3s_mean'].values[sta_idx])
df_misfit.loc['Y%i'%d_id,'c_cap_KL'] = CalcLKDivergece(df_sdata_atten['c_cap'].values, df_reg_atten['c_cap_mean'].values)
# Output
#--- --- --- --- ---
#figure directory
dir_fig = '%s%s/Y%i/figures_cmp/'%(dir_results, ker_suffix+synds_suffix, d_id)
pathlib.Path(dir_fig).mkdir(parents=True, exist_ok=True)
#compare ground motion predictions
#... ... ... ... ... ...
#figure title
fname_fig = 'Y%i_scatter_tot_res'%d_id
#create figure
fig, ax = plt.subplots(figsize = (10,10))
#median
ax.scatter(df_sdata_gmotion.nerg_gm.values, df_reg_gmotion.nerg_mu.values)
ax.axline((0,0), slope=1, color="black", linestyle="--")
#edit figure
if not flag_report: ax.set_title('Comparison total residuals, Y: %i'%d_id, fontsize=30)
ax.set_xlabel('Synthetic dataset', fontsize=35)
ax.set_ylabel('Estimated', fontsize=35)
ax.grid(which='both')
ax.tick_params(axis='x', labelsize=32)
ax.tick_params(axis='y', labelsize=32)
#plot limits
# plt_lim = np.array([ax.get_xlim(), ax.get_ylim()])
# plt_lim = (plt_lim[:,0].min(), plt_lim[:,1].max())
# ax.set_xlim(plt_lim)
# ax.set_ylim(plt_lim)
ax.set_xlim([-10,2])
ax.set_ylim([-10,2])
fig.tight_layout()
fig.savefig( dir_fig + fname_fig + '.png' )
#compare dc_1e
#... ... ... ... ... ...
#figure title
fname_fig = 'Y%i_dc_1e_scatter'%d_id
#create figure
fig, ax = plt.subplots(figsize = (10,10))
#coefficient scatter
ax.scatter(df_sdata_gmotion['dc_1e'].values[eq_idx], df_reg_coeff['dc_1e_mean'].values[eq_idx])
ax.axline((0,0), slope=1, color="black", linestyle="--")
#edit figure
if not flag_report: ax.set_title(r'Comparison $\delta c_{1,E}$, Y: %i'%d_id, fontsize=30)
ax.set_xlabel('Synthetic dataset', fontsize=25)
ax.set_ylabel('Estimated', fontsize=25)
ax.grid(which='both')
ax.tick_params(axis='x', labelsize=22)
ax.tick_params(axis='y', labelsize=22)
#plot limits
# plt_lim = np.array([ax.get_xlim(), ax.get_ylim()])
# plt_lim = (plt_lim[:,0].min(), plt_lim[:,1].max())
# ax.set_xlim(plt_lim)
# ax.set_ylim(plt_lim)
ax.set_xlim([-2,2])
ax.set_ylim([-2,2])
#save figure
fig.tight_layout()
fig.savefig( dir_fig + fname_fig + '.png' )
#figure title
fname_fig = 'Y%i_dc_1e_accuracy'%d_id
#create figure
fig, ax = plt.subplots(figsize = (10,10))
#coefficient scatter
ax.scatter(df_reg_coeff['dc_1e_sig'].values[eq_idx],
df_sdata_gmotion['dc_1e'].values[eq_idx] - df_reg_coeff['dc_1e_mean'].values[eq_idx])
ax.axline((0,0), slope=0, color="black", linestyle="--")
#edit figure
if not flag_report: ax.set_title(r'Comparison $\delta c_{1,E}$, Y: %i'%d_id, fontsize=30)
ax.set_xlabel('Standard Deviation', fontsize=25)
ax.set_ylabel('Actual - Estimated', fontsize=25)
ax.grid(which='both')
ax.tick_params(axis='x', labelsize=22)
ax.tick_params(axis='y', labelsize=22)
#plot limits
# ax.set_ylim(np.abs(ax.get_ylim()).max()*np.array([-1,1]))
ax.set_xlim([0,.5])
ax.set_ylim([-2,2])
#save figure
fig.tight_layout()
fig.savefig( dir_fig + fname_fig + '.png' )
#figure title
fname_fig = 'Y%i_dc_1e_nrec'%d_id
#create figure
fig, ax = plt.subplots(figsize = (10,10))
#coefficient scatter
ax.scatter(eq_nrec,
df_sdata_gmotion['dc_1e'].values[eq_idx] - df_reg_coeff['dc_1e_mean'].values[eq_idx])
ax.axline((0,0), slope=0, color="black", linestyle="--")
#edit figure
if not flag_report: ax.set_title(r'Comparison $\delta c_{1,E}$, Y: %i'%d_id, fontsize=30)
ax.set_xlabel('Number of records', fontsize=25)
ax.set_ylabel('Actual - Estimated', fontsize=25)
ax.grid(which='both')
ax.set_xscale('log')
ax.tick_params(axis='x', labelsize=22)
ax.tick_params(axis='y', labelsize=22)
#plot limits
# ax.set_ylim(np.abs(ax.get_ylim()).max()*np.array([-1,1]))
ax.set_xlim([0.9,1e3])
ax.set_ylim([-2,2])
#save figure
fig.tight_layout()
fig.savefig( dir_fig + fname_fig + '.png' )
#compare dc_1as
#... ... ... ... ... ...
#figure title
fname_fig = 'Y%i_dc_1as_scatter'%d_id
#create figure
fig, ax = plt.subplots(figsize = (10,10))
#coefficient scatter
ax.scatter(df_sdata_gmotion['dc_1as'].values[sta_idx], df_reg_coeff['dc_1as_mean'].values[sta_idx])
ax.axline((0,0), slope=1, color="black", linestyle="--")
#edit figure
if not flag_report: ax.set_title(r'Comparison $\delta c_{1a,S}$, Y: %i'%d_id, fontsize=30)
ax.set_xlabel('Synthetic dataset', fontsize=25)
ax.set_ylabel('Estimated', fontsize=25)
ax.grid(which='both')
ax.tick_params(axis='x', labelsize=22)
ax.tick_params(axis='y', labelsize=22)
#plot limits
# plt_lim = np.array([ax.get_xlim(), ax.get_ylim()])
# plt_lim = (plt_lim[:,0].min(), plt_lim[:,1].max())
# ax.set_xlim(plt_lim)
# ax.set_ylim(plt_lim)
ax.set_xlim([-2,2])
ax.set_ylim([-2,2])
#save figure
fig.tight_layout()
fig.savefig( dir_fig + fname_fig + '.png' )
#figure title
fname_fig = 'Y%i_dc_1as_accuracy'%d_id
#create figure
fig, ax = plt.subplots(figsize = (10,10))
#accuray
ax.scatter(df_reg_coeff['dc_1as_sig'].values[sta_idx],
df_sdata_gmotion['dc_1as'].values[sta_idx] - df_reg_coeff['dc_1as_mean'].values[sta_idx])
ax.axline((0,0), slope=0, color="black", linestyle="--")
#edit figure
if not flag_report: ax.set_title(r'Comparison $\delta c_{1a,S}$, Y: %i'%d_id, fontsize=30)
ax.set_xlabel('Standard Deviation', fontsize=25)
ax.set_ylabel('Actual - Estimated', fontsize=25)
ax.grid(which='both')
ax.tick_params(axis='x', labelsize=22)
ax.tick_params(axis='y', labelsize=22)
#plot limits
# ax.set_ylim(np.abs(ax.get_ylim()).max()*np.array([-1,1]))
ax.set_xlim([0,.5])
ax.set_ylim([-2,2])
#save figure
fig.tight_layout()
fig.savefig( dir_fig + fname_fig + '.png' )
#figure title
fname_fig = 'Y%i_dc_1as_nrec'%d_id
#create figure
fig, ax = plt.subplots(figsize = (10,10))
#accuray
ax.scatter(sta_nrec,
df_sdata_gmotion['dc_1as'].values[sta_idx] - df_reg_coeff['dc_1as_mean'].values[sta_idx])
ax.axline((0,0), slope=0, color="black", linestyle="--")
#edit figure
if not flag_report: ax.set_title(r'Comparison $\delta c_{1a,S}$, Y: %i'%d_id, fontsize=30)
ax.set_xlabel('Number of records', fontsize=25)
ax.set_ylabel('Actual - Estimated', fontsize=25)
ax.grid(which='both')
ax.set_xscale('log')
ax.tick_params(axis='x', labelsize=22)
ax.tick_params(axis='y', labelsize=22)
#plot limits
# ax.set_ylim(np.abs(ax.get_ylim()).max()*np.array([-1,1]))
ax.set_xlim([.9,1000])
ax.set_ylim([-2,2])
#save figure
fig.tight_layout()
fig.savefig( dir_fig + fname_fig + '.png' )
#compare dc_1bs
#... ... ... ... ... ...
#figure title
fname_fig = 'Y%i_dc_1bs_scatter'%d_id
#create figure
fig, ax = plt.subplots(figsize = (10,10))
#coefficient scatter
ax.scatter(df_sdata_gmotion['dc_1bs'].values[sta_idx], df_reg_coeff['dc_1bs_mean'].values[sta_idx])
ax.axline((0,0), slope=1, color="black", linestyle="--")
#edit figure
if not flag_report: ax.set_title(r'Comparison $\delta c_{1b,S}$, Y: %i'%d_id, fontsize=30)
ax.set_xlabel('Synthetic dataset', fontsize=25)
ax.set_ylabel('Estimated', fontsize=25)
ax.grid(which='both')
ax.tick_params(axis='x', labelsize=22)
ax.tick_params(axis='y', labelsize=22)
#plot limits
# plt_lim = np.array([ax.get_xlim(), ax.get_ylim()])
# plt_lim = (plt_lim[:,0].min(), plt_lim[:,1].max())
# ax.set_xlim(plt_lim)
# ax.set_ylim(plt_lim)
ax.set_xlim([-2,2])
ax.set_ylim([-2,2])
#save figure
fig.tight_layout()
fig.savefig( dir_fig + fname_fig + '.png' )
#figure title
fname_fig = 'Y%i_dc_1bs_accuracy'%d_id
#create figure
fig, ax = plt.subplots(figsize = (10,10))
#accuray
ax.scatter(df_reg_coeff['dc_1bs_sig'].values[sta_idx],
df_sdata_gmotion['dc_1bs'].values[sta_idx] - df_reg_coeff['dc_1bs_mean'].values[sta_idx])
ax.axline((0,0), slope=0, color="black", linestyle="--")
#edit figure
if not flag_report: ax.set_title(r'Comparison $\delta c_{1b,S}$, Y: %i'%d_id, fontsize=30)
ax.set_xlabel('Standard Deviation', fontsize=25)
ax.set_ylabel('Actual - Estimated', fontsize=25)
ax.grid(which='both')
ax.tick_params(axis='x', labelsize=22)
ax.tick_params(axis='y', labelsize=22)
#plot limits
# ax.set_ylim(np.abs(ax.get_ylim()).max()*np.array([-1,1]))
ax.set_xlim([0,.4])
ax.set_ylim([-2,2])
#save figure
fig.tight_layout()
fig.savefig( dir_fig + fname_fig + '.png' )
#figure title
fname_fig = 'Y%i_dc_1bs_nrec'%d_id
#create figure
fig, ax = plt.subplots(figsize = (10,10))
#accuray
ax.scatter(sta_nrec,
df_sdata_gmotion['dc_1bs'].values[sta_idx] - df_reg_coeff['dc_1bs_mean'].values[sta_idx])
ax.axline((0,0), slope=0, color="black", linestyle="--")
#edit figure
if not flag_report: ax.set_title(r'Comparison $\delta c_{1b,S}$, Y: %i'%d_id, fontsize=30)
ax.set_xlabel('Number of records', fontsize=25)
ax.set_ylabel('Actual - Estimated', fontsize=25)
ax.grid(which='both')
ax.set_xscale('log')
ax.tick_params(axis='x', labelsize=22)
ax.tick_params(axis='y', labelsize=22)
#plot limits
# ax.set_ylim(np.abs(ax.get_ylim()).max()*np.array([-1,1]))
ax.set_xlim([.9,1000])
ax.set_ylim([-2,2])
#save figure
fig.tight_layout()
fig.savefig( dir_fig + fname_fig + '.png' )
#compare c_2p
#... ... ... ... ... ...
#figure title
fname_fig = 'Y%i_c_2p_scatter'%d_id
#create figure
fig, ax = plt.subplots(figsize = (10,10))
#coefficient scatter
ax.scatter(df_sdata_gmotion['c_2p'].values[eq_idx], df_reg_coeff['c_2p_mean'].values[eq_idx])
ax.axline((0,0), slope=1, color="black", linestyle="--")
#edit figure
if not flag_report: ax.set_title(r'Comparison $c_{2,P}$, Y: %i'%d_id, fontsize=30)
ax.set_xlabel('Synthetic dataset', fontsize=25)
ax.set_ylabel('Estimated', fontsize=25)
ax.grid(which='both')
ax.tick_params(axis='x', labelsize=22)
ax.tick_params(axis='y', labelsize=22)
#plot limits
# plt_lim = np.array([ax.get_xlim(), ax.get_ylim()])
# plt_lim = (plt_lim[:,0].min(), plt_lim[:,1].max())
# ax.set_xlim(plt_lim)
# ax.set_ylim(plt_lim)
ax.set_xlim([-2.3,-1.6])
ax.set_ylim([-2.3,-1.6])
#save figure
fig.tight_layout()
fig.savefig( dir_fig + fname_fig + '.png' )
#figure title
fname_fig = 'Y%i_c_2p_accuracy'%d_id
#create figure
fig, ax = plt.subplots(figsize = (10,10))
#coefficient scatter
ax.scatter(df_reg_coeff['c_2p_sig'].values[eq_idx],
df_sdata_gmotion['c_2p'].values[eq_idx] - df_reg_coeff['c_2p_mean'].values[eq_idx])
ax.axline((0,0), slope=0, color="black", linestyle="--")
#edit figure
if not flag_report: ax.set_title(r'Comparison $c_{2,P}$, Y: %i'%d_id, fontsize=30)
ax.set_xlabel('Standard Deviation', fontsize=25)
ax.set_ylabel('Actual - Estimated', fontsize=25)
ax.grid(which='both')
ax.tick_params(axis='x', labelsize=22)
ax.tick_params(axis='y', labelsize=22)
#plot limits
# ax.set_ylim(np.abs(ax.get_ylim()).max()*np.array([-1,1]))
ax.set_xlim([0,.15])
ax.set_ylim([-.4,.4])
#save figure
fig.tight_layout()
fig.savefig( dir_fig + fname_fig + '.png' )
#figure title
fname_fig = 'Y%i_c_2p_nrec'%d_id
#create figure
fig, ax = plt.subplots(figsize = (10,10))
#coefficient scatter
ax.scatter(eq_nrec,
df_sdata_gmotion['c_2p'].values[eq_idx] - df_reg_coeff['c_2p_mean'].values[eq_idx])
ax.axline((0,0), slope=0, color="black", linestyle="--")
#edit figure
if not flag_report: ax.set_title(r'Comparison $c_{2,P}$, Y: %i'%d_id, fontsize=30)
ax.set_xlabel('Number of records', fontsize=25)
ax.set_ylabel('Actual - Estimated', fontsize=25)
ax.grid(which='both')
ax.set_xscale('log')
ax.tick_params(axis='x', labelsize=22)
ax.tick_params(axis='y', labelsize=22)
#plot limits
# ax.set_ylim(np.abs(ax.get_ylim()).max()*np.array([-1,1]))
ax.set_xlim([0.9,1e3])
ax.set_ylim([-.4,.4])
#save figure
fig.tight_layout()
fig.savefig( dir_fig + fname_fig + '.png' )
#compare c_3s
#... ... ... ... ... ...
#figure title
fname_fig = 'Y%i_c_3s_scatter'%d_id
#create figure
fig, ax = plt.subplots(figsize = (10,10))
#coefficient scatter
ax.scatter(df_sdata_gmotion['c_3s'].values[sta_idx], df_reg_coeff['c_3s_mean'].values[sta_idx])
ax.axline((0,0), slope=1, color="black", linestyle="--")
#edit figure
if not flag_report: ax.set_title(r'Comparison $c_{3,S}$, Y: %i'%d_id, fontsize=30)
ax.set_xlabel('Synthetic dataset', fontsize=25)
ax.set_ylabel('Estimated', fontsize=25)
ax.grid(which='both')
ax.tick_params(axis='x', labelsize=22)
ax.tick_params(axis='y', labelsize=22)
#plot limits
# plt_lim = np.array([ax.get_xlim(), ax.get_ylim()])
# plt_lim = (plt_lim[:,0].min(), plt_lim[:,1].max())
# ax.set_xlim(plt_lim)
# ax.set_ylim(plt_lim)
ax.set_xlim([-1.2,-.2])
ax.set_ylim([-1.2,-.2])
#save figure
fig.tight_layout()
fig.savefig( dir_fig + fname_fig + '.png' )
#figure title
fname_fig = 'Y%i_c_3s_accuracy'%d_id
#create figure
fig, ax = plt.subplots(figsize = (10,10))
#coefficient scatter
ax.scatter(df_reg_coeff['c_3s_sig'].values[sta_idx],
df_sdata_gmotion['c_3s'].values[sta_idx] - df_reg_coeff['c_3s_mean'].values[sta_idx])
ax.axline((0,0), slope=0, color="black", linestyle="--")
#edit figure
if not flag_report: ax.set_title(r'Comparison $c_{3,S}$, Y: %i'%d_id, fontsize=30)
ax.set_xlabel('Standard Deviation', fontsize=25)
ax.set_ylabel('Actual - Estimated', fontsize=25)
ax.grid(which='both')
ax.tick_params(axis='x', labelsize=22)
ax.tick_params(axis='y', labelsize=22)
#plot limits
# ax.set_ylim(np.abs(ax.get_ylim()).max()*np.array([-1,1]))
ax.set_xlim([0,.3])
ax.set_ylim([-.4,.4])
#save figure
fig.tight_layout()
fig.savefig( dir_fig + fname_fig + '.png' )
#figure title
fname_fig = 'Y%i_c_3s_nrec'%d_id
#create figure
fig, ax = plt.subplots(figsize = (10,10))
#coefficient scatter
ax.scatter(sta_nrec,
df_sdata_gmotion['c_3s'].values[sta_idx] - df_reg_coeff['c_3s_mean'].values[sta_idx])
ax.axline((0,0), slope=0, color="black", linestyle="--")
#edit figure
if not flag_report: ax.set_title(r'Comparison $c_{3,S}$, Y: %i'%d_id, fontsize=30)
ax.set_xlabel('Number of records', fontsize=25)
ax.set_ylabel('Actual - Estimated', fontsize=25)
ax.grid(which='both')
ax.set_xscale('log')
ax.tick_params(axis='x', labelsize=22)
ax.tick_params(axis='y', labelsize=22)
#plot limits
# ax.set_ylim(np.abs(ax.get_ylim()).max()*np.array([-1,1]))
ax.set_xlim([0.9,1e3])
ax.set_ylim([-.4,.4])
#save figure
fig.tight_layout()
fig.savefig( dir_fig + fname_fig + '.png' )
#compare c_cap
#... ... ... ... ... ...
#figure title
fname_fig = 'Y%i_c_cap_scatter'%d_id
#create figure
fig, ax = plt.subplots(figsize = (10,10))
#coefficient scatter
ax.scatter(df_sdata_atten['c_cap'].values, df_reg_atten['c_cap_mean'].values)
ax.axline((0,0), slope=1, color="black", linestyle="--")
#edit figure
if not flag_report: ax.set_title(r'Comparison $c_{ca,P}$, Y: %i'%d_id, fontsize=30)
ax.set_xlabel('Synthetic dataset', fontsize=25)
ax.set_ylabel('Estimated', fontsize=25)
ax.grid(which='both')
ax.tick_params(axis='x', labelsize=22)
ax.tick_params(axis='y', labelsize=22)
#plot limits
# plt_lim = np.array([ax.get_xlim(), ax.get_ylim()])
# plt_lim = (plt_lim[:,0].min(), plt_lim[:,1].max())
# ax.set_xlim(plt_lim)
# ax.set_ylim(plt_lim)
ax.set_xlim([-0.05,0.02])
ax.set_ylim([-0.05,0.02])
#save figure
fig.tight_layout()
fig.savefig( dir_fig + fname_fig + '.png' )
#figure title
fname_fig = 'Y%i_c_cap_accuracy'%d_id
#create figure
fig, ax = plt.subplots(figsize = (10,10))
#coefficient scatter
ax.scatter(df_reg_atten['c_cap_sig'],
df_sdata_atten['c_cap'].values - df_reg_atten['c_cap_mean'].values)
ax.axline((0,0), slope=0, color="black", linestyle="--")
#edit figure
if not flag_report: ax.set_title(r'Comparison $c_{ca,P}$, Y: %i'%d_id, fontsize=30)
ax.set_xlabel('Standard Deviation', fontsize=25)
ax.set_ylabel('Actual - Estimated', fontsize=25)
ax.grid(which='both')
ax.tick_params(axis='x', labelsize=22)
ax.tick_params(axis='y', labelsize=22)
#plot limits
# ax.set_ylim(np.abs(ax.get_ylim()).max()*np.array([-1,1]))
ax.set_xlim([0.00,0.03])
ax.set_ylim([-0.04,0.04])
#save figure
fig.tight_layout()
fig.savefig( dir_fig + fname_fig + '.png' )
#figure title
fname_fig = 'Y%i_c_cap_npath'%d_id
#create figure
fig, ax = plt.subplots(figsize = (10,10))
#coefficient scatter
ax.scatter(cell_npath,
df_sdata_atten['c_cap'].values - df_reg_atten['c_cap_mean'].values)
ax.axline((0,0), slope=0, color="black", linestyle="--")
#edit figure
if not flag_report: ax.set_title(r'Comparison $c_{ca,P}$, Y: %i'%d_id, fontsize=30)
ax.set_xlabel('Number of paths', fontsize=25)
ax.set_ylabel('Actual - Estimated', fontsize=25)
ax.grid(which='both')
ax.set_xscale('log')
ax.tick_params(axis='x', labelsize=22)
ax.tick_params(axis='y', labelsize=22)
#plot limits
# ax.set_ylim(np.abs(ax.get_ylim()).max()*np.array([-1,1]))
ax.set_xlim([.9,5e4])
ax.set_ylim([-0.04,0.04])
#save figure
fig.tight_layout()
fig.savefig( dir_fig + fname_fig + '.png' )
# Compare Misfit Metrics
# ---------------------------
#summary directory
dir_sum = '%s%s/summary/'%(dir_results,ker_suffix+synds_suffix)
pathlib.Path(dir_fig).mkdir(parents=True, exist_ok=True)
#figure directory
dir_fig = '%s/figures/'%(dir_sum)
pathlib.Path(dir_fig).mkdir(parents=True, exist_ok=True)
#save
df_misfit.to_csv(dir_sum + 'misfit_summary.csv')
#RMS misfit
fname_fig = 'misfit_score'
#plot KL divergence
fig, ax = plt.subplots(figsize = (10,10))
ax.plot(ds_id, df_misfit.nerg_tot_rms, linestyle='-', marker='o', linewidth=2, markersize=10, label= 'tot nerg')
ax.plot(ds_id, df_misfit.dc_1e_rms, linestyle='-', marker='o', linewidth=2, markersize=10, label=r'$\delta c_{1,E}$')
ax.plot(ds_id, df_misfit.dc_1as_rms, linestyle='-', marker='o', linewidth=2, markersize=10, label=r'$\delta c_{1a,S}$')
ax.plot(ds_id, df_misfit.dc_1bs_rms, linestyle='-', marker='o', linewidth=2, markersize=10, label=r'$\delta c_{1b,S}$')
ax.plot(ds_id, df_misfit.c_2p_rms, linestyle='-', marker='o', linewidth=2, markersize=10, label=r'$c_{2,E}$')
ax.plot(ds_id, df_misfit.c_3s_rms, linestyle='-', marker='o', linewidth=2, markersize=10, label=r'$c_{3,S}$')
ax.plot(ds_id, df_misfit.c_cap_rms, linestyle='-', marker='o', linewidth=2, markersize=10, label=r'$c_{ca,P}$')
#figure properties
ax.set_ylim([0,0.50])
ax.set_xlabel('synthetic dataset', fontsize=25)
ax.set_ylabel('RSME', fontsize=25)
ax.grid(which='both')
ax.set_xticks(ds_id)
ax.set_xticklabels(labels=df_misfit.index)
ax.tick_params(axis='x', labelsize=22)
ax.tick_params(axis='y', labelsize=22)
#legend
ax.legend(loc='upper left', fontsize=25)
#save figure
fig.tight_layout()
fig.savefig( dir_fig + fname_fig + '.png' )
#KL divergence
fname_fig = 'KLdiv_score'
#plot KL divergence
fig, ax = plt.subplots(figsize = (10,10))
ax.plot(ds_id, df_misfit.nerg_tot_KL, linestyle='-', marker='o', linewidth=2, markersize=10, label= 'tot nerg')
ax.plot(ds_id, df_misfit.dc_1e_KL, linestyle='-', marker='o', linewidth=2, markersize=10, label=r'$\delta c_{1,E}$')
ax.plot(ds_id, df_misfit.dc_1as_KL, linestyle='-', marker='o', linewidth=2, markersize=10, label=r'$\delta c_{1a,S}$')
ax.plot(ds_id, df_misfit.dc_1bs_KL, linestyle='-', marker='o', linewidth=2, markersize=10, label=r'$\delta c_{1b,S}$')
ax.plot(ds_id, df_misfit.c_2p_KL, linestyle='-', marker='o', linewidth=2, markersize=10, label=r'$c_{2,P}$')
ax.plot(ds_id, df_misfit.c_3s_KL, linestyle='-', marker='o', linewidth=2, markersize=10, label=r'$c_{3,S}$')
ax.plot(ds_id, df_misfit.c_cap_KL, linestyle='-', marker='o', linewidth=2, markersize=10, label=r'$c_{ca,P}$')
#figure properties
ax.set_ylim([0,0.50])
ax.set_xlabel('synthetic dataset', fontsize=25)
ax.set_ylabel('KL divergence', fontsize=25)
ax.grid(which='both')
ax.set_xticks(ds_id)
ax.set_xticklabels(labels=df_misfit.index)
ax.tick_params(axis='x', labelsize=22)
ax.tick_params(axis='y', labelsize=22)
#legend
ax.legend(loc='upper left', fontsize=25)
#save figure
fig.tight_layout()
fig.savefig( dir_fig + fname_fig + '.png' )
# Compare hyper-paramters
# ---------------------------
#iterate over different datasets
df_reg_hyp = list()
df_reg_hyp_post = list()
for d_id in ds_id:
# Load Data
#--- --- --- --- ---
#regression hyperparamters results
fname_reg_hyp = '%s%s/Y%i/%s%s_Y%i_inla_%s'%(dir_results, ker_suffix+synds_suffix, d_id,prfx_results, synds_suffix, d_id, 'hyperparameters') + '.csv'
fname_reg_hyp_post = '%s%s/Y%i/%s%s_Y%i_inla_%s'%(dir_results, ker_suffix+synds_suffix, d_id,prfx_results, synds_suffix, d_id, 'hyperposterior') + '.csv'
#load regression results
df_reg_hyp.append( pd.read_csv(fname_reg_hyp, index_col=0) )
df_reg_hyp_post.append( pd.read_csv(fname_reg_hyp_post, index_col=0) )
# Omega_1e
#--- --- --- --- ---
#hyper-paramter name
name_hyp = 'omega_1e'
#figure title
fname_fig = 'post_dist_' + name_hyp
#create figure
fig, ax = plt.subplots(figsize = (10,10))
for d_id, df_r_h, df_r_h_p in zip(ds_id, df_reg_hyp, df_reg_hyp_post):
#estimate vertical line height for mean and mode
ymax_mode = df_r_h_p.loc[:,name_hyp+'_pdf'].max()
# ymax_mean = 1.5*np.ceil(ymax_mode/10)*10
ymax_mean = 40
#plot posterior dist
pl_pdf = ax.plot(df_r_h_p.loc[:,name_hyp], df_r_h_p.loc[:,name_hyp+'_pdf'])
ax.vlines(df_r_h.loc[name_hyp,'mean'], ymin=0, ymax=ymax_mean, linestyle='-', color=pl_pdf[0].get_color(), label='Mean')
ax.vlines(df_r_h.loc[name_hyp,'mode'], ymin=0, ymax=ymax_mode, linestyle='--', color=pl_pdf[0].get_color(), label='Mode')
#plot true value
ymax_hyp = ymax_mean
ax.vlines(hyp[name_hyp], ymin=0, ymax=ymax_hyp, linestyle='-', linewidth=4, color='black', label='True value')
#edit figure
if not flag_report: ax.set_title(r'Comparison $\omega_{1,E}$', fontsize=30)
ax.set_xlabel('$\omega_{1,e}$', fontsize=25)
ax.set_ylabel('probability density function ', fontsize=25)
ax.grid(which='both')
ax.tick_params(axis='x', labelsize=22)
ax.tick_params(axis='y', labelsize=22)
#plot limits
ax.set_xlim([0,0.25])
ax.set_ylim([0,ymax_hyp])
#save figure
fig.tight_layout()
fig.savefig( dir_fig + fname_fig + '.png' )
# Omega_1as
#--- --- --- --- ---
#hyper-paramter name
name_hyp = 'omega_1as'
#figure title
fname_fig = 'post_dist_' + name_hyp
#create figure
fig, ax = plt.subplots(figsize = (10,10))
for d_id, df_r_h, df_r_h_p in zip(ds_id, df_reg_hyp, df_reg_hyp_post):
#estimate vertical line height for mean and mode
ymax_mode = df_r_h_p.loc[:,name_hyp+'_pdf'].max()
# ymax_mean = 1.5*np.ceil(ymax_mode/10)*10
ymax_mean = 30
#plot posterior dist
pl_pdf = ax.plot(df_r_h_p.loc[:,name_hyp], df_r_h_p.loc[:,name_hyp+'_pdf'])
ax.vlines(df_r_h.loc[name_hyp,'mean'], ymin=0, ymax=ymax_mean, linestyle='-', color=pl_pdf[0].get_color(), label='Mean')
ax.vlines(df_r_h.loc[name_hyp,'mode'], ymin=0, ymax=ymax_mode, linestyle='--', color=pl_pdf[0].get_color(), label='Mode')
#plot true value
ymax_hyp = ymax_mean
ax.vlines(hyp[name_hyp], ymin=0, ymax=ymax_hyp, linestyle='-', linewidth=4, color='black', label='True value')
#edit figure
if not flag_report: ax.set_title(r'Comparison $\omega_{1a,S}$', fontsize=30)
ax.set_xlabel('$\omega_{1a,s}$', fontsize=25)
ax.set_ylabel('probability density function ', fontsize=25)
ax.grid(which='both')
ax.tick_params(axis='x', labelsize=22)
ax.tick_params(axis='y', labelsize=22)
#plot limits
ax.set_xlim([0,0.5])
ax.set_ylim([0,ymax_hyp])
#save figure
fig.tight_layout()
fig.savefig( dir_fig + fname_fig + '.png' )
# Omega_1bs
#--- --- --- --- ---
#hyper-paramter name
name_hyp = 'omega_1bs'
#figure title
fname_fig = 'post_dist_' + name_hyp
#create figure
fig, ax = plt.subplots(figsize = (10,10))
for d_id, df_r_h, df_r_h_p in zip(ds_id, df_reg_hyp, df_reg_hyp_post):
#estimate vertical line height for mean and mode
ymax_mode = df_r_h_p.loc[:,name_hyp+'_pdf'].max()
# ymax_mean = 1.5*np.ceil(ymax_mode/10)*10
ymax_mean = 60
#plot posterior dist
pl_pdf = ax.plot(df_r_h_p.loc[:,name_hyp], df_r_h_p.loc[:,name_hyp+'_pdf'])
ax.vlines(df_r_h.loc[name_hyp,'mean'], ymin=0, ymax=ymax_mean, linestyle='-', color=pl_pdf[0].get_color(), label='Mean')
ax.vlines(df_r_h.loc[name_hyp,'mode'], ymin=0, ymax=ymax_mode, linestyle='--', color=pl_pdf[0].get_color(), label='Mode')
#plot true value
ymax_hyp = ymax_mean
ax.vlines(hyp[name_hyp], ymin=0, ymax=ymax_hyp, linestyle='-', linewidth=4, color='black', label='True value')
#edit figure
if not flag_report: ax.set_title(r'Comparison $\omega_{1b,S}$', fontsize=30)
ax.set_xlabel('$\omega_{1b,s}$', fontsize=25)
ax.set_ylabel('probability density function ', fontsize=25)
ax.grid(which='both')
ax.tick_params(axis='x', labelsize=22)
ax.tick_params(axis='y', labelsize=22)
#plot limits
ax.set_xlim([0,0.5])
ax.set_ylim([0,ymax_hyp])
#save figure
fig.tight_layout()
fig.savefig( dir_fig + fname_fig + '.png' )
# Omega_2p
#--- --- --- --- ---
#hyper-paramter name
name_hyp = 'omega_2p'
#figure title
fname_fig = 'post_dist_' + name_hyp
#create figure
fig, ax = plt.subplots(figsize = (10,10))
for d_id, df_r_h, df_r_h_p in zip(ds_id, df_reg_hyp, df_reg_hyp_post):
#estimate vertical line height for mean and mode
ymax_mode = 60
ymax_mean = 60
#plot posterior dist
pl_hyp = ax.vlines(df_r_h.loc['mean',name_hyp], ymin=0, ymax=ymax_mean, linestyle='-', label='Mean')
ax.vlines(df_r_h.loc['prc_0.50',name_hyp], ymin=0, ymax=ymax_mode, linestyle='--', color=pl_hyp.get_color(), label='Mode')
#plot true value
ymax_hyp = ymax_mean
ax.vlines(hyp[name_hyp], ymin=0, ymax=ymax_hyp, linestyle='-', linewidth=4, color='black', label='True value')
#edit figure
if not flag_report: ax.set_title(r'Comparison $\omega_{2,P}$', fontsize=30)
ax.set_xlabel('$\omega_{2,p}$', fontsize=25)
ax.set_ylabel('probability density function ', fontsize=25)
ax.grid(which='both')
ax.tick_params(axis='x', labelsize=22)
ax.tick_params(axis='y', labelsize=22)
#plot limits
ax.set_xlim([0,0.5])
ax.set_ylim([0,ymax_hyp])
#save figure
fig.tight_layout()
fig.savefig( dir_fig + fname_fig + '.png' )
# Omega_3s
#--- --- --- --- ---
#hyper-paramter name
name_hyp = 'omega_3s'
#figure title
fname_fig = 'post_dist_' + name_hyp
#create figure
fig, ax = plt.subplots(figsize = (10,10))
for d_id, df_r_h, df_r_h_p in zip(ds_id, df_reg_hyp, df_reg_hyp_post):
#estimate vertical line height for mean and mode
ymax_mode = 60
ymax_mean = 60
#plot posterior dist
pl_hyp = ax.vlines(df_r_h.loc['mean',name_hyp], ymin=0, ymax=ymax_mean, linestyle='-', label='Mean')
ax.vlines(df_r_h.loc['prc_0.50',name_hyp], ymin=0, ymax=ymax_mode, linestyle='--', color=pl_hyp.get_color(), label='Mode')
#plot true value
ymax_hyp = ymax_mean
ax.vlines(hyp[name_hyp], ymin=0, ymax=ymax_hyp, linestyle='-', linewidth=4, color='black', label='True value')
#edit figure
if not flag_report: ax.set_title(r'Comparison $\omega_{3,S}$', fontsize=30)
ax.set_xlabel('$\omega_{3,s}$', fontsize=25)
ax.set_ylabel('probability density function ', fontsize=25)
ax.grid(which='both')
ax.tick_params(axis='x', labelsize=22)
ax.tick_params(axis='y', labelsize=22)
#plot limits
ax.set_xlim([0,0.5])
ax.set_ylim([0,ymax_hyp])
#save figure
fig.tight_layout()
fig.savefig( dir_fig + fname_fig + '.png' )
# Ell_1e
#--- --- --- --- ---
#hyper-paramter name
name_hyp = 'ell_1e'
#figure title
fname_fig = 'post_dist_' + name_hyp
#create figure
fig, ax = plt.subplots(figsize = (10,10))
for d_id, df_r_h, df_r_h_p in zip(ds_id, df_reg_hyp, df_reg_hyp_post):
#estimate vertical line height for mean and mode
ymax_mode = df_r_h_p.loc[:,name_hyp+'_pdf'].max()
# ymax_mean = 1.5*np.ceil(ymax_mode/10)*10
ymax_mean = 0.02
#plot posterior dist
pl_pdf = ax.plot(df_r_h_p.loc[:,name_hyp], df_r_h_p.loc[:,name_hyp+'_pdf'])
ax.vlines(df_r_h.loc[name_hyp,'mean'], ymin=0, ymax=ymax_mean, linestyle='-', color=pl_pdf[0].get_color(), label='Mean')
ax.vlines(df_r_h.loc[name_hyp,'mode'], ymin=0, ymax=ymax_mode, linestyle='--', color=pl_pdf[0].get_color(), label='Mode')
#plot true value
ymax_hyp = ymax_mean
ax.vlines(hyp[name_hyp], ymin=0, ymax=ymax_hyp, linestyle='-', linewidth=4, color='black', label='True value')
#edit figure
if not flag_report: ax.set_title(r'Comparison $\ell_{1,E}$', fontsize=30)
ax.set_xlabel('$\ell_{1,e}$', fontsize=25)
ax.set_ylabel('probability density function ', fontsize=25)
ax.grid(which='both')
ax.tick_params(axis='x', labelsize=22)
ax.tick_params(axis='y', labelsize=22)
#plot limits
ax.set_xlim([0,500])
ax.set_ylim([0,ymax_hyp])
#save figure
fig.tight_layout()
fig.savefig( dir_fig + fname_fig + '.png' )
# Ell_1as
#--- --- --- --- ---
#hyper-paramter name
name_hyp = 'ell_1as'
#figure title
fname_fig = 'post_dist_' + name_hyp
#create figure
fig, ax = plt.subplots(figsize = (10,10))
for d_id, df_r_h, df_r_h_p in zip(ds_id, df_reg_hyp, df_reg_hyp_post):
#estimate vertical line height for mean and mode
ymax_mode = df_r_h_p.loc[:,name_hyp+'_pdf'].max()
# ymax_mean = 1.5*np.ceil(ymax_mode/10)*10
ymax_mean = 0.1
#plot posterior dist
pl_pdf = ax.plot(df_r_h_p.loc[:,name_hyp], df_r_h_p.loc[:,name_hyp+'_pdf'])
ax.vlines(df_r_h.loc[name_hyp,'mean'], ymin=0, ymax=ymax_mean, linestyle='-', color=pl_pdf[0].get_color(), label='Mean')
ax.vlines(df_r_h.loc[name_hyp,'mode'], ymin=0, ymax=ymax_mode, linestyle='--', color=pl_pdf[0].get_color(), label='Mode')
#plot true value
ymax_hyp = ymax_mean
ax.vlines(hyp[name_hyp], ymin=0, ymax=ymax_hyp, linestyle='-', linewidth=4, color='black', label='True value')
#edit figure
if not flag_report: ax.set_title(r'Comparison $\ell_{1a,S}$', fontsize=30)
ax.set_xlabel('$\ell_{1a,s}$', fontsize=25)
ax.set_ylabel('probability density function ', fontsize=25)
ax.grid(which='both')
ax.tick_params(axis='x', labelsize=22)
ax.tick_params(axis='y', labelsize=22)
#plot limits
ax.set_xlim([0,150])
ax.set_ylim([0,ymax_hyp])
#save figure
fig.tight_layout()
fig.savefig( dir_fig + fname_fig + '.png' )
# Ell_2p
#--- --- --- --- ---
#hyper-paramter name
name_hyp = 'ell_2p'
#figure title
fname_fig = 'post_dist_' + name_hyp
#create figure
fig, ax = plt.subplots(figsize = (10,10))
for d_id, df_r_h, df_r_h_p in zip(ds_id, df_reg_hyp, df_reg_hyp_post):
#estimate vertical line height for mean and mode
ymax_mode = 0.1
ymax_mean = 0.1
#plot posterior dist
pl_hyp = ax.vlines(df_r_h.loc['mean',name_hyp], ymin=0, ymax=ymax_mean, linestyle='-', label='Mean')
ax.vlines(df_r_h.loc['prc_0.50',name_hyp], ymin=0, ymax=ymax_mode, linestyle='--', color=pl_hyp.get_color(), label='Mode')
#plot true value
ymax_hyp = ymax_mean
ax.vlines(hyp[name_hyp], ymin=0, ymax=ymax_hyp, linestyle='-', linewidth=4, color='black', label='True value')
#edit figure
if not flag_report: ax.set_title(r'Comparison $\ell_{2,P}$', fontsize=30)
ax.set_xlabel('$\ell_{2,p}$', fontsize=25)
ax.set_ylabel('probability density function ', fontsize=25)
ax.grid(which='both')
ax.tick_params(axis='x', labelsize=22)
ax.tick_params(axis='y', labelsize=22)
#plot limits
ax.set_xlim([0,150])
ax.set_ylim([0,ymax_hyp])
#save figure
fig.tight_layout()
fig.savefig( dir_fig + fname_fig + '.png' )
# Ell_3s
#--- --- --- --- ---
#hyper-paramter name
name_hyp = 'ell_3s'
#figure title
fname_fig = 'post_dist_' + name_hyp
#create figure
fig, ax = plt.subplots(figsize = (10,10))
for d_id, df_r_h, df_r_h_p in zip(ds_id, df_reg_hyp, df_reg_hyp_post):
#estimate vertical line height for mean and mode
ymax_mode = 0.1
ymax_mean = 0.1
#plot posterior dist
pl_hyp = ax.vlines(df_r_h.loc['mean',name_hyp], ymin=0, ymax=ymax_mean, linestyle='-', label='Mean')
ax.vlines(df_r_h.loc['prc_0.50',name_hyp], ymin=0, ymax=ymax_mode, linestyle='--', color=pl_hyp.get_color(), label='Mode')
#plot true value
ymax_hyp = ymax_mean
ax.vlines(hyp[name_hyp], ymin=0, ymax=ymax_hyp, linestyle='-', linewidth=4, color='black', label='True value')
#edit figure
if not flag_report: ax.set_title(r'Comparison $\ell_{3,S}$', fontsize=30)
ax.set_xlabel('$\ell_{3,s}$', fontsize=25)
ax.set_ylabel('probability density function ', fontsize=25)
ax.grid(which='both')
ax.tick_params(axis='x', labelsize=22)
ax.tick_params(axis='y', labelsize=22)
#plot limits
ax.set_xlim([0,150])
ax.set_ylim([0,ymax_hyp])
#save figure
fig.tight_layout()
fig.savefig( dir_fig + fname_fig + '.png' )
# Tau_0
#--- --- --- --- ---
#hyper-paramter name
name_hyp = 'tau_0'
#figure title
fname_fig = 'post_dist_' + name_hyp
#create figure
fig, ax = plt.subplots(figsize = (10,10))
for d_id, df_r_h, df_r_h_p in zip(ds_id, df_reg_hyp, df_reg_hyp_post):
#estimate vertical line height for mean and mode
ymax_mode = df_r_h_p.loc[:,name_hyp+'_pdf'].max()
# ymax_mean = 1.5*np.ceil(ymax_mode/10)*10
ymax_mean = 60
#plot posterior dist
pl_pdf = ax.plot(df_r_h_p.loc[:,name_hyp], df_r_h_p.loc[:,name_hyp+'_pdf'])
ax.vlines(df_r_h.loc[name_hyp,'mean'], ymin=0, ymax=ymax_mean, linestyle='-', color=pl_pdf[0].get_color(), label='Mean')
ax.vlines(df_r_h.loc[name_hyp,'mode'], ymin=0, ymax=ymax_mode, linestyle='--', color=pl_pdf[0].get_color(), label='Mode')
#plot true value
ymax_hyp = ymax_mean
ax.vlines(hyp[name_hyp], ymin=0, ymax=ymax_hyp, linestyle='-', linewidth=4, color='black', label='True value')
#edit figure
if not flag_report: ax.set_title(r'Comparison $\tau_{0}$', fontsize=30)
ax.set_xlabel(r'$\tau_{0}$', fontsize=25)
ax.set_ylabel(r'probability density function ', fontsize=25)
ax.grid(which='both')
ax.tick_params(axis='x', labelsize=22)
ax.tick_params(axis='y', labelsize=22)
#plot limits
ax.set_xlim([0,0.5])
ax.set_ylim([0,ymax_hyp])
#save figure
fig.tight_layout()
fig.savefig( dir_fig + fname_fig + '.png' )
# Phi_0
#--- --- --- --- ---
#hyper-paramter name
name_hyp = 'phi_0'
#figure title
fname_fig = 'post_dist_' + name_hyp
#create figure
fig, ax = plt.subplots(figsize = (10,10))
for d_id, df_r_h, df_r_h_p in zip(ds_id, df_reg_hyp, df_reg_hyp_post):
#estimate vertical line height for mean and mode
ymax_mode = df_r_h_p.loc[:,name_hyp+'_pdf'].max()
# ymax_mean = 1.5*np.ceil(ymax_mode/10)*10
ymax_mean = 100
#plot posterior dist
pl_pdf = ax.plot(df_r_h_p.loc[:,name_hyp], df_r_h_p.loc[:,name_hyp+'_pdf'])
ax.vlines(df_r_h.loc[name_hyp,'mean'], ymin=0, ymax=ymax_mean, linestyle='-', color=pl_pdf[0].get_color(), label='Mean')
ax.vlines(df_r_h.loc[name_hyp,'mode'], ymin=0, ymax=ymax_mode, linestyle='--', color=pl_pdf[0].get_color(), label='Mode')
#plot true value
ymax_hyp = ymax_mean
ax.vlines(hyp[name_hyp], ymin=0, ymax=ymax_hyp, linestyle='-', linewidth=4, color='black', label='True value')
#edit figure
if not flag_report: ax.set_title(r'Comparison $\phi_{0}$', fontsize=30)
ax.set_xlabel('$\phi_{0}$', fontsize=25)
ax.set_ylabel(r'probability density function ', fontsize=25)
ax.grid(which='both')
ax.tick_params(axis='x', labelsize=22)
ax.tick_params(axis='y', labelsize=22)
#plot limits
ax.set_xlim([0,0.6])
ax.set_ylim([0,ymax_hyp])
#save figure
fig.tight_layout()
fig.savefig( dir_fig + fname_fig + '.png' )
# Omega_ca
#--- --- --- --- ---
#hyper-paramter name
name_hyp = 'omega_cap'
#figure title
fname_fig = 'post_dist_' + name_hyp
#create figure
fig, ax = plt.subplots(figsize = (10,10))
for d_id, df_r_h, df_r_h_p in zip(ds_id, df_reg_hyp, df_reg_hyp_post):
#estimate vertical line height for mean and mode
ymax_mode = df_r_h_p.loc[:,name_hyp+'_pdf'].max()
# ymax_mean = 1.5*np.ceil(ymax_mode/10)*10
ymax_mean = 1500
#plot posterior dist
pl_pdf = ax.plot(df_r_h_p.loc[:,name_hyp], df_r_h_p.loc[:,name_hyp+'_pdf'])
ax.vlines(df_r_h.loc[name_hyp,'mean'], ymin=0, ymax=ymax_mean, linestyle='-', color=pl_pdf[0].get_color(), label='Mean')
ax.vlines(df_r_h.loc[name_hyp,'mode'], ymin=0, ymax=ymax_mode, linestyle='--', color=pl_pdf[0].get_color(), label='Mode')
#plot true value
ymax_hyp = ymax_mean
ax.vlines(np.sqrt(hyp['omega_ca1p']**2+hyp['omega_ca2p']**2), ymin=0, ymax=ymax_hyp, linestyle='-', linewidth=4, color='black', label='True value')
#edit figure
if not flag_report: ax.set_title(r'Comparison $\omega_{ca,P}$', fontsize=30)
ax.set_xlabel('$\omega_{ca,p}$', fontsize=25)
ax.set_ylabel('probability density function ', fontsize=25)
ax.grid(which='both')
ax.tick_params(axis='x', labelsize=22)
ax.tick_params(axis='y', labelsize=22)
#plot limits
ax.set_xlim([0,0.05])
ax.set_ylim([0,ymax_hyp])
#save figure
fig.tight_layout()
fig.savefig( dir_fig + fname_fig + '.png' )
# # Delta c_0
# #--- --- --- --- ---
# #hyper-paramter name
# name_hyp = 'dc_0'
# #figure title
# fname_fig = 'post_dist_' + name_hyp
# #create figure
# fig, ax = plt.subplots(figsize = (10,10))
# for d_id, df_r_h, df_r_h_p in zip(ds_id, df_reg_hyp, df_reg_hyp_post):
# #estimate vertical line height for mean and mode
# ymax_mode = df_r_h_p.loc[:,name_hyp+'_pdf'].max()
# ymax_mean = 1.5*np.ceil(ymax_mode/10)*10
# ymax_mean = 15
# #plot posterior dist
# pl_pdf = ax.plot(df_r_h_p.loc[:,name_hyp], df_r_h_p.loc[:,name_hyp+'_pdf'])
# ax.vlines(df_r_h.loc[name_hyp,'mean'], ymin=0, ymax=ymax_mean, linestyle='-', color=pl_pdf[0].get_color(), label='Mean')
# ax.vlines(df_r_h.loc[name_hyp,'mode'], ymin=0, ymax=ymax_mode, linestyle='--', color=pl_pdf[0].get_color(), label='Mode')
# #plot true value
# ymax_hyp = ymax_mean
# # ax.vlines(hyp[name_hyp], ymin=0, ymax=ymax_hyp, linestyle='-', linewidth=4, color='black', label='True value')
# #edit figure
# ax.set_title(r'Comparison $\delta c_{0}$', fontsize=30)
# ax.set_xlabel('$\delta c_{0}$', fontsize=25)
# ax.set_ylabel('probability density function ', fontsize=25)
# ax.grid(which='both')
# ax.tick_params(axis='x', labelsize=22)
# ax.tick_params(axis='y', labelsize=22)
# #plot limits
# ax.set_xlim([-1,1])
# ax.set_ylim([0,ymax_hyp])
# #save figure
# fig.tight_layout()
# # fig.savefig( dir_fig + fname_fig + '.png' )
| 47,785 | 39.088926 | 160 | py |
ngmm_tools | ngmm_tools-master/Analyses/Code_Verification/regression/ds3/main_pystan_model3_uncorr_cells_NGAWest2CA.py | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Wed Jul 14 14:17:52 2021
@author: glavrent
"""
# Working directory and Packages
# ---------------------------
#load libraries
import os
import sys
import numpy as np
import pandas as pd
import time
#user functions
sys.path.insert(0,'../../../Python_lib/regression/pystan/')
from regression_pystan_model3_uncorr_cells_unbounded_hyp import RunStan
# Define variables
# ---------------------------
#filename suffix
# synds_suffix = '_small_corr_len'
# synds_suffix = '_large_corr_len'
#synthetic datasets directory
ds_dir = '../../../../Data/Validation/synthetic_datasets/ds3'
ds_dir = r'%s%s/'%(ds_dir, synds_suffix)
# dataset info
#ds_fname_main = 'CatalogNGAWest3CA_synthetic_data'
ds_fname_main = 'CatalogNGAWest3CALite_synthetic_data'
ds_id = np.arange(1,6)
#cell specific anelastic attenuation
ds_fname_cellinfo = 'CatalogNGAWest3CALite_cellinfo'
ds_fname_celldist = 'CatalogNGAWest3CALite_distancematrix'
#stan model
sm_fname = '../../../Stan_lib/regression_stan_model3_uncorr_cells_unbounded_hyp_chol_efficient.stan'
#output info
#main output filename
out_fname_main = 'NGAWest2CA_syndata'
#main output directory
out_dir_main = '../../../../Data/Validation/regression/ds3/'
#output sub-directory
out_dir_sub = 'PYSTAN_NGAWest2CA_uncorr_cells_chol_eff'
#stan parameters
runstan_flag = True
# pystan_ver = 2
pystan_ver = 3
res_name = 'tot'
n_iter = 1000
n_chains = 4
adapt_delta = 0.8
max_treedepth = 10
#ergodic coefficients
c_2_erg=-2.0
c_3_erg=-0.6
c_a_erg=0.0
#parallel options
# flag_parallel = True
flag_parallel = False
#output sub-dir with corr with suffix info
out_dir_sub = f'%s%s'%(out_dir_sub, synds_suffix)
#load cell dataframes
cellinfo_fname = '%s%s.csv'%(ds_dir, ds_fname_cellinfo)
celldist_fname = '%s%s.csv'%(ds_dir, ds_fname_celldist)
df_cellinfo = pd.read_csv(cellinfo_fname)
df_celldist = pd.read_csv(celldist_fname)
# Run stan regression
# ---------------------------
#create datafame with computation time
df_run_info = list()
#iterate over all synthetic datasets
for d_id in ds_id:
print('Synthetic dataset %i fo %i'%(d_id, len(ds_id)))
#run time start
run_t_strt = time.time()
#input flatfile
ds_fname = '%s%s%s_Y%i.csv'%(ds_dir, ds_fname_main, synds_suffix, d_id)
#load flatfile
df_flatfile = pd.read_csv(ds_fname)
#keep only NGAWest2 records
df_flatfile = df_flatfile.loc[df_flatfile.dsid==0,:]
#output file name and directory
out_fname = '%s%s_Y%i'%(out_fname_main, synds_suffix, d_id)
out_dir = '%s/%s/Y%i/'%(out_dir_main, out_dir_sub, d_id)
#run stan model
RunStan(df_flatfile, df_cellinfo, df_celldist, sm_fname,
out_fname, out_dir, res_name,
c_2_erg=c_2_erg, c_3_erg=c_3_erg, c_a_erg=c_a_erg,
runstan_flag=runstan_flag, n_iter=n_iter, n_chains=n_chains,
adapt_delta=adapt_delta, max_treedepth=max_treedepth,
pystan_ver=pystan_ver, pystan_parallel=flag_parallel)
#run time end
run_t_end = time.time()
#compute run time
run_tm = (run_t_end - run_t_strt)/60
#log run time
df_run_info.append(pd.DataFrame({'computer_name':os.uname()[1],'out_name':out_dir_sub,
'ds_id':d_id,'run_time':run_tm}, index=[d_id]))
#write out run info
out_fname = '%s%s/run_info.csv'%(out_dir_main, out_dir_sub)
pd.concat(df_run_info).reset_index(drop=True).to_csv(out_fname, index=False)
| 3,534 | 28.458333 | 100 | py |
ngmm_tools | ngmm_tools-master/Analyses/Code_Verification/regression/ds3/main_pystan_model3_corr_cells_NGAWest2CA.py | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Wed Jul 14 14:17:52 2021
@author: glavrent
"""
# Working directory and Packages
# ---------------------------
#load libraries
import os
import sys
import numpy as np
import pandas as pd
import time
#user functions
sys.path.insert(0,'../../../Python_lib/regression/pystan/')
from regression_pystan_model3_corr_cells_unbounded_hyp import RunStan
# Define variables
# ---------------------------
#filename suffix
# synds_suffix = '_small_corr_len'
# synds_suffix = '_large_corr_len'
#synthetic datasets directory
ds_dir = '../../../../Data/Validation/synthetic_datasets/ds3'
ds_dir = r'%s%s/'%(ds_dir, synds_suffix)
# dataset info
#ds_fname_main = 'CatalogNGAWest3CA_synthetic_data'
ds_fname_main = 'CatalogNGAWest3CALite_synthetic_data'
ds_id = np.arange(1,6)
#cell specific anelastic attenuation
ds_fname_cellinfo = 'CatalogNGAWest3CALite_cellinfo'
ds_fname_celldist = 'CatalogNGAWest3CALite_distancematrix'
#stan model
sm_fname = '../../../Stan_lib/regression_stan_model3_corr_cells_unbounded_hyp_chol_efficient.stan'
#output info
#main output filename
out_fname_main = 'NGAWest2CA_syndata'
#main output directory
out_dir_main = '../../../../Data/Validation/regression/ds3/'
#output sub-directory
out_dir_sub = 'PYSTAN_NGAWest2CA_corr_cells_chol_eff'
#stan parameters
runstan_flag = True
# pystan_ver = 2
pystan_ver = 3
res_name = 'tot'
n_iter = 1000
n_chains = 4
adapt_delta = 0.8
max_treedepth = 10
#ergodic coefficients
c_2_erg=-2.0
c_3_erg=-0.6
c_a_erg=0.0
#parallel options
# flag_parallel = True
flag_parallel = False
#output sub-dir with corr with suffix info
out_dir_sub = f'%s%s'%(out_dir_sub, synds_suffix)
#load cell dataframes
cellinfo_fname = '%s%s.csv'%(ds_dir, ds_fname_cellinfo)
celldist_fname = '%s%s.csv'%(ds_dir, ds_fname_celldist)
df_cellinfo = pd.read_csv(cellinfo_fname)
df_celldist = pd.read_csv(celldist_fname)
# Run stan regression
# ---------------------------
#create datafame with computation time
df_run_info = list()
#iterate over all synthetic datasets
for d_id in ds_id:
print('Synthetic dataset %i fo %i'%(d_id, len(ds_id)))
#run time start
run_t_strt = time.time()
#input flatfile
ds_fname = '%s%s%s_Y%i.csv'%(ds_dir, ds_fname_main, synds_suffix, d_id)
#load flatfile
df_flatfile = pd.read_csv(ds_fname)
#keep only NGAWest2 records
df_flatfile = df_flatfile.loc[df_flatfile.dsid==0,:]
#output file name and directory
out_fname = '%s%s_Y%i'%(out_fname_main, synds_suffix, d_id)
out_dir = '%s/%s/Y%i/'%(out_dir_main, out_dir_sub, d_id)
#run stan model
RunStan(df_flatfile, df_cellinfo, df_celldist, sm_fname,
out_fname, out_dir, res_name,
c_2_erg=c_2_erg, c_3_erg=c_3_erg, c_a_erg=c_a_erg,
runstan_flag=runstan_flag, n_iter=n_iter, n_chains=n_chains,
adapt_delta=adapt_delta, max_treedepth=max_treedepth,
pystan_ver=pystan_ver, pystan_parallel=flag_parallel)
#run time end
run_t_end = time.time()
#compute run time
run_tm = (run_t_end - run_t_strt)/60
#log run time
df_run_info.append(pd.DataFrame({'computer_name':os.uname()[1],'out_name':out_dir_sub,
'ds_id':d_id,'run_time':run_tm}, index=[d_id]))
#write out run info
out_fname = '%s%s/run_info.csv'%(out_dir_main, out_dir_sub)
pd.concat(df_run_info).reset_index(drop=True).to_csv(out_fname, index=False)
| 3,530 | 28.425 | 98 | py |
ngmm_tools | ngmm_tools-master/Analyses/Code_Verification/regression/ds3/comparison_stan_model3_corr_cells.py | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Thu Aug 12 10:26:06 2021
@author: glavrent
"""
# Working directory and Packages
# ---------------------------
#load packages
import os
import sys
import pathlib
import glob
import re #regular expression package
import pickle
#arithmetic libraries
import numpy as np
#statistics libraries
import pandas as pd
#plot libraries
import matplotlib as mpl
import matplotlib.pyplot as plt
from matplotlib.ticker import AutoLocator as plt_autotick
#user functions
sys.path.insert(0,'../../../Python_lib/regression/')
from pylib_stats import CalcRMS
from pylib_stats import CalcLKDivergece
# Define variables
# ---------------------------
# USER SETS DIRECTORIES AND FILE INFO OF SYNTHETIC DS AND REGRESSION RESULTS
# ++++++++++++++++++++++++++++++++++++++++
#processed dataset
# name_dataset = 'NGAWest2CANorth'
# name_dataset = 'NGAWest2CA'
# name_dataset = 'NGAWest3CA'
#correlation info
# 1: Small Correlation Lengths
# 2: Large Correlation Lenghts
corr_id = 1
#package
# 1: Pystan v2
# 2: Pystan v3
# 3: stancmd
pkg_id = 3
#approximation type
# 1: multivariate normal
# 2: cholesky
# 3: cholesky efficient
# 4: cholesky efficient v2
# 5: cholesky efficient, sparse cells
aprox_id = 3
#directories (synthetic dataset)
if corr_id == 1:
dir_syndata = '../../../../Data/Verification/synthetic_datasets/ds3_small_corr_len'
elif corr_id == 2:
dir_syndata = '../../../../Data/Verification/synthetic_datasets/ds3_large_corr_len'
#cell info
fname_cellinfo = dir_syndata + '/' + 'CatalogNGAWest3CALite_cellinfo.csv'
fname_distmat = dir_syndata + '/' + 'CatalogNGAWest3CALite_distancematrix.csv'
#directories (regression results)
if pkg_id == 1:
dir_results = f'../../../../Data/Verification/regression/ds3/PYSTAN_%s'%name_dataset
elif pkg_id == 2:
dir_results = f'../../../../Data/Verification/regression/ds3/PYSTAN3_%s'%name_dataset
elif pkg_id == 3:
dir_results = f'../../../../Data/Verification/regression/ds3/CMDSTAN_%s'%name_dataset
#directories (regression results - old results)
if pkg_id == 1:
dir_results = f'../../../../Data/Verification/regression_old/ds3/PYSTAN_%s'%name_dataset
elif pkg_id == 2:
dir_results = f'../../../../Data/Verification/regression_old/ds3/PYSTAN3_%s'%name_dataset
elif pkg_id == 3:
dir_results = f'../../../../Data/Verification/regression_old/ds3/CMDSTAN_%s'%name_dataset
#prefix for synthetic data and results
prfx_syndata = 'CatalogNGAWest3CALite_synthetic'
#regression results filename prefix
prfx_results = f'%s_syndata'%name_dataset
# FILE INFO FOR REGRESSION RESULTS
# ++++++++++++++++++++++++++++++++++++++++
#output filename sufix (synthetic dataset)
if corr_id == 1: synds_suffix = '_small_corr_len'
elif corr_id == 2: synds_suffix = '_large_corr_len'
#output filename sufix (regression results)
if aprox_id == 1: synds_suffix_stan = '_corr_cells' + synds_suffix
elif aprox_id == 2: synds_suffix_stan = '_corr_cells' + '_chol' + synds_suffix
elif aprox_id == 3: synds_suffix_stan = '_corr_cells' + '_chol_eff' + synds_suffix
elif aprox_id == 4: synds_suffix_stan = '_corr_cells' + '_chol_eff2' + synds_suffix
elif aprox_id == 5: synds_suffix_stan = '_corr_cells' + '_chol_eff_sp' + synds_suffix
# dataset info
# ds_id = np.arange(1,6)
ds_id = np.arange(1,2)
# ++++++++++++++++++++++++++++++++++++++++
# USER NEEDS TO SPECIFY HYPERPARAMETERS OF SYNTHETIC DATASET
# ++++++++++++++++++++++++++++++++++++++++
# hyper-parameters
if corr_id == 1:
# small correlation lengths
hyp = {'omega_0': 0.1, 'omega_1e':0.1, 'omega_1as': 0.35, 'omega_1bs': 0.25,
'ell_1e':60, 'ell_1as':30,
'c_2_erg': -2.0,
'omega_2': 0.2,
'omega_2p': 0.15, 'ell_2p': 80,
'c_3_erg':-0.6,
'omega_3': 0.15,
'omega_3s': 0.15, 'ell_3s': 130,
'c_cap_erg': -0.011,
'omega_cap_mu': 0.005, 'omega_ca1p':0.004, 'omega_ca2p':0.002,
'ell_ca1p': 75,
'phi_0':0.3, 'tau_0':0.25 }
elif corr_id == 2:
# large correlation lengths
hyp = {'omega_0': 0.1, 'omega_1e':0.2, 'omega_1as': 0.4, 'omega_1bs': 0.3,
'ell_1e':100, 'ell_1as':70,
'c_2_erg': -2.0,
'omega_2': 0.2,
'omega_2p': 0.15, 'ell_2e': 140,
'c_3_erg':-0.6,
'omega_3': 0.15,
'omega_3s': 0.15, 'ell_3s': 180,
'c_cap_erg': -0.02,
'omega_cap_mu': 0.008, 'omega_ca1p':0.005, 'omega_ca2p':0.003,
'ell_ca1p': 120,
'phi_0':0.3, 'tau_0':0.25}
# ++++++++++++++++++++++++++++++++++++++++
#ploting options
flag_report = True
# Compare results
# ---------------------------
#load cell data
df_cellinfo = pd.read_csv(fname_cellinfo).set_index('cellid')
df_distmat = pd.read_csv(fname_distmat).set_index('rsn')
#initialize misfit metrics dataframe
df_misfit = pd.DataFrame(index=['Y%i'%d_id for d_id in ds_id])
#iterate over different datasets
for d_id in ds_id:
# Load Data
#--- --- --- --- ---
#file names
#synthetic data
fname_sdata_gmotion = '%s/%s_%s%s_Y%i'%(dir_syndata, prfx_syndata, 'data', synds_suffix, d_id) + '.csv'
fname_sdata_atten = '%s/%s_%s%s_Y%i'%(dir_syndata, prfx_syndata, 'atten', synds_suffix, d_id) + '.csv'
#regression results
fname_reg_gmotion = '%s%s/Y%i/%s%s_Y%i_stan_%s'%(dir_results, synds_suffix_stan, d_id, prfx_results, synds_suffix, d_id, 'residuals') + '.csv'
fname_reg_coeff = '%s%s/Y%i/%s%s_Y%i_stan_%s'%(dir_results, synds_suffix_stan, d_id, prfx_results, synds_suffix, d_id, 'coefficients') + '.csv'
fname_reg_atten = '%s%s/Y%i/%s%s_Y%i_stan_%s'%(dir_results, synds_suffix_stan, d_id, prfx_results, synds_suffix, d_id, 'catten') + '.csv'
#load synthetic results
df_sdata_gmotion = pd.read_csv(fname_sdata_gmotion).set_index('rsn')
df_sdata_atten = pd.read_csv(fname_sdata_atten).set_index('cellid')
#load regression results
df_reg_gmotion = pd.read_csv(fname_reg_gmotion, index_col=0)
df_reg_coeff = pd.read_csv(fname_reg_coeff, index_col=0)
df_reg_atten = pd.read_csv(fname_reg_atten, index_col=0)
# Processing
#--- --- --- --- ---
#keep only relevant columns from synthetic dataset
df_sdata_gmotion = df_sdata_gmotion.reindex(df_reg_gmotion.index)
df_sdata_atten = df_sdata_atten.reindex(df_reg_atten.index)
#distance matrix for records of interest
df_dmat = df_distmat.reindex(df_sdata_gmotion.index)
#find unique earthqakes and stations
eq_id, eq_idx, eq_nrec = np.unique(df_sdata_gmotion.eqid, return_index=True, return_counts=True)
sta_id, sta_idx, sta_nrec = np.unique(df_sdata_gmotion.ssn, return_index=True, return_counts=True)
#number of paths per cell
cell_npath = np.sum(df_dmat.loc[:,df_reg_atten.cellname] > 0, axis=0)
# Compute Root Mean Square Error
#--- --- --- --- ---
df_misfit.loc['Y%i'%d_id,'nerg_tot_rms'] = CalcRMS(df_sdata_gmotion.nerg_gm.values, df_reg_gmotion.nerg_mu.values)
df_misfit.loc['Y%i'%d_id,'dc_1e_rms'] = CalcRMS(df_sdata_gmotion['dc_1e'].values[eq_idx], df_reg_coeff['dc_1e_mean'].values[eq_idx])
df_misfit.loc['Y%i'%d_id,'dc_1as_rms'] = CalcRMS(df_sdata_gmotion['dc_1as'].values[sta_idx], df_reg_coeff['dc_1as_mean'].values[sta_idx])
df_misfit.loc['Y%i'%d_id,'dc_1bs_rms'] = CalcRMS(df_sdata_gmotion['dc_1bs'].values[sta_idx], df_reg_coeff['dc_1bs_mean'].values[sta_idx])
df_misfit.loc['Y%i'%d_id,'c_2p_rms'] = CalcRMS(df_sdata_gmotion['c_2p'].values[eq_idx], df_reg_coeff['c_2p_mean'].values[eq_idx])
df_misfit.loc['Y%i'%d_id,'c_3s_rms'] = CalcRMS(df_sdata_gmotion['c_3s'].values[sta_idx], df_reg_coeff['c_3s_mean'].values[sta_idx])
df_misfit.loc['Y%i'%d_id,'c_cap_rms'] = CalcRMS(df_sdata_atten['c_cap'].values, df_reg_atten['c_cap_mean'].values)
# Compute Divergence
#--- --- --- --- ---
df_misfit.loc['Y%i'%d_id,'nerg_tot_KL'] = CalcLKDivergece(df_sdata_gmotion.nerg_gm.values, df_reg_gmotion.nerg_mu.values)
df_misfit.loc['Y%i'%d_id,'dc_1e_KL'] = CalcLKDivergece(df_sdata_gmotion['dc_1e'].values[eq_idx], df_reg_coeff['dc_1e_mean'].values[eq_idx])
df_misfit.loc['Y%i'%d_id,'dc_1as_KL'] = CalcLKDivergece(df_sdata_gmotion['dc_1as'].values[sta_idx], df_reg_coeff['dc_1as_mean'].values[sta_idx])
df_misfit.loc['Y%i'%d_id,'dc_1bs_KL'] = CalcLKDivergece(df_sdata_gmotion['dc_1bs'].values[sta_idx], df_reg_coeff['dc_1bs_mean'].values[sta_idx])
df_misfit.loc['Y%i'%d_id,'c_2p_KL'] = CalcLKDivergece(df_sdata_gmotion['c_2p'].values[eq_idx], df_reg_coeff['c_2p_mean'].values[eq_idx])
df_misfit.loc['Y%i'%d_id,'c_3s_KL'] = CalcLKDivergece(df_sdata_gmotion['c_3s'].values[sta_idx], df_reg_coeff['c_3s_mean'].values[sta_idx])
df_misfit.loc['Y%i'%d_id,'c_cap_KL'] = CalcLKDivergece(df_sdata_atten['c_cap'].values, df_reg_atten['c_cap_mean'].values)
# Output
#--- --- --- --- ---
#figure directory
dir_fig = '%s%s/Y%i/figures_cmp/'%(dir_results,synds_suffix_stan,d_id)
pathlib.Path(dir_fig).mkdir(parents=True, exist_ok=True)
#compare ground motion predictions
#... ... ... ... ... ...
#figure title
fname_fig = 'Y%i_scatter_tot_res'%d_id
#create figure
fig, ax = plt.subplots(figsize = (10,10))
#median
ax.scatter(df_sdata_gmotion.nerg_gm.values, df_reg_gmotion.nerg_mu.values)
ax.axline((0,0), slope=1, color="black", linestyle="--")
#edit figure
if not flag_report: ax.set_title('Comparison total residuals, Y: %i'%d_id, fontsize=30)
ax.set_xlabel('Synthetic dataset', fontsize=25)
ax.set_ylabel('Estimated', fontsize=25)
ax.grid(which='both')
ax.tick_params(axis='x', labelsize=22)
ax.tick_params(axis='y', labelsize=22)
#plot limits
# plt_lim = np.array([ax.get_xlim(), ax.get_ylim()])
# plt_lim = (plt_lim[:,0].min(), plt_lim[:,1].max())
# ax.set_xlim(plt_lim)
# ax.set_ylim(plt_lim)
ax.set_xlim([-10,2])
ax.set_ylim([-10,2])
fig.tight_layout()
fig.savefig( dir_fig + fname_fig + '.png' )
#compare dc_1e
#... ... ... ... ... ...
#figure title
fname_fig = 'Y%i_dc_1e_scatter'%d_id
#create figure
fig, ax = plt.subplots(figsize = (10,10))
#coefficient scatter
ax.scatter(df_sdata_gmotion['dc_1e'].values[eq_idx], df_reg_coeff['dc_1e_mean'].values[eq_idx])
ax.axline((0,0), slope=1, color="black", linestyle="--")
#edit figure
if not flag_report: ax.set_title(r'Comparison $\delta c_{1,E}$, Y: %i'%d_id, fontsize=30)
ax.set_xlabel('Synthetic dataset', fontsize=25)
ax.set_ylabel('Estimated', fontsize=25)
ax.grid(which='both')
ax.tick_params(axis='x', labelsize=22)
ax.tick_params(axis='y', labelsize=22)
#plot limits
# plt_lim = np.array([ax.get_xlim(), ax.get_ylim()])
# plt_lim = (plt_lim[:,0].min(), plt_lim[:,1].max())
# ax.set_xlim(plt_lim)
# ax.set_ylim(plt_lim)
ax.set_xlim([-.4,.4])
ax.set_ylim([-.4,.4])
#save figure
fig.tight_layout()
fig.savefig( dir_fig + fname_fig + '.png' )
#figure title
fname_fig = 'Y%i_dc_1e_accuracy'%d_id
#create figure
fig, ax = plt.subplots(figsize = (10,10))
#coefficient scatter
ax.scatter(df_reg_coeff['dc_1e_sig'].values[eq_idx],
df_sdata_gmotion['dc_1e'].values[eq_idx] - df_reg_coeff['dc_1e_mean'].values[eq_idx])
ax.axline((0,0), slope=0, color="black", linestyle="--")
#edit figure
if not flag_report: ax.set_title(r'Comparison $\delta c_{1,E}$, Y: %i'%d_id, fontsize=30)
ax.set_xlabel('Standard Deviation', fontsize=25)
ax.set_ylabel('Actual - Estimated', fontsize=25)
ax.grid(which='both')
ax.tick_params(axis='x', labelsize=22)
ax.tick_params(axis='y', labelsize=22)
#plot limits
# ax.set_ylim(np.abs(ax.get_ylim()).max()*np.array([-1,1]))
ax.set_xlim([0,.15])
ax.set_ylim([-.4,.4])
#save figure
fig.tight_layout()
fig.savefig( dir_fig + fname_fig + '.png' )
#figure title
fname_fig = 'Y%i_dc_1e_nrec'%d_id
#create figure
fig, ax = plt.subplots(figsize = (10,10))
#coefficient scatter
ax.scatter(eq_nrec,
df_sdata_gmotion['dc_1e'].values[eq_idx] - df_reg_coeff['dc_1e_mean'].values[eq_idx])
ax.axline((0,0), slope=0, color="black", linestyle="--")
#edit figure
if not flag_report: ax.set_title(r'Comparison $\delta c_{1,E}$, Y: %i'%d_id, fontsize=30)
ax.set_xlabel('Number of records', fontsize=25)
ax.set_ylabel('Actual - Estimated', fontsize=25)
ax.grid(which='both')
ax.set_xscale('log')
ax.tick_params(axis='x', labelsize=22)
ax.tick_params(axis='y', labelsize=22)
#plot limits
# ax.set_ylim(np.abs(ax.get_ylim()).max()*np.array([-1,1]))
ax.set_xlim([0.9,1e3])
ax.set_ylim([-.4,.4])
#save figure
fig.tight_layout()
fig.savefig( dir_fig + fname_fig + '.png' )
#compare dc_1as
#... ... ... ... ... ...
#figure title
fname_fig = 'Y%i_dc_1as_scatter'%d_id
#create figure
fig, ax = plt.subplots(figsize = (10,10))
#coefficient scatter
ax.scatter(df_sdata_gmotion['dc_1as'].values[sta_idx], df_reg_coeff['dc_1as_mean'].values[sta_idx])
ax.axline((0,0), slope=1, color="black", linestyle="--")
#edit figure
if not flag_report: ax.set_title(r'Comparison $\delta c_{1a,S}$, Y: %i'%d_id, fontsize=30)
ax.set_xlabel('Synthetic dataset', fontsize=25)
ax.set_ylabel('Estimated', fontsize=25)
ax.grid(which='both')
ax.tick_params(axis='x', labelsize=22)
ax.tick_params(axis='y', labelsize=22)
#plot limits
# plt_lim = np.array([ax.get_xlim(), ax.get_ylim()])
# plt_lim = (plt_lim[:,0].min(), plt_lim[:,1].max())
# ax.set_xlim(plt_lim)
# ax.set_ylim(plt_lim)
ax.set_xlim([-1.5,1.5])
ax.set_ylim([-1.5,1.5])
#save figure
fig.tight_layout()
fig.savefig( dir_fig + fname_fig + '.png' )
#figure title
fname_fig = 'Y%i_dc_1as_accuracy'%d_id
#create figure
fig, ax = plt.subplots(figsize = (10,10))
#accuray
ax.scatter(df_reg_coeff['dc_1as_sig'].values[sta_idx],
df_sdata_gmotion['dc_1as'].values[sta_idx] - df_reg_coeff['dc_1as_mean'].values[sta_idx])
ax.axline((0,0), slope=0, color="black", linestyle="--")
#edit figure
if not flag_report: ax.set_title(r'Comparison $\delta c_{1a,S}$, Y: %i'%d_id, fontsize=30)
ax.set_xlabel('Standard Deviation', fontsize=25)
ax.set_ylabel('Actual - Estimated', fontsize=25)
ax.grid(which='both')
ax.tick_params(axis='x', labelsize=22)
ax.tick_params(axis='y', labelsize=22)
#plot limits
# ax.set_ylim(np.abs(ax.get_ylim()).max()*np.array([-1,1]))
ax.set_xlim([0,.4])
ax.set_ylim([-1.5,1.5])
#save figure
fig.tight_layout()
fig.savefig( dir_fig + fname_fig + '.png' )
#figure title
fname_fig = 'Y%i_dc_1as_nrec'%d_id
#create figure
fig, ax = plt.subplots(figsize = (10,10))
#accuray
ax.scatter(sta_nrec,
df_sdata_gmotion['dc_1as'].values[sta_idx] - df_reg_coeff['dc_1as_mean'].values[sta_idx])
ax.axline((0,0), slope=0, color="black", linestyle="--")
#edit figure
if not flag_report: ax.set_title(r'Comparison $\delta c_{1a,S}$, Y: %i'%d_id, fontsize=30)
ax.set_xlabel('Number of records', fontsize=25)
ax.set_ylabel('Actual - Estimated', fontsize=25)
ax.grid(which='both')
ax.set_xscale('log')
ax.tick_params(axis='x', labelsize=22)
ax.tick_params(axis='y', labelsize=22)
#plot limits
# ax.set_ylim(np.abs(ax.get_ylim()).max()*np.array([-1,1]))
ax.set_xlim([.9,1000])
ax.set_ylim([-1.5,1.5])
#save figure
fig.tight_layout()
fig.savefig( dir_fig + fname_fig + '.png' )
#compare dc_1bs
#... ... ... ... ... ...
#figure title
fname_fig = 'Y%i_dc_1bs_scatter'%d_id
#create figure
fig, ax = plt.subplots(figsize = (10,10))
#coefficient scatter
ax.scatter(df_sdata_gmotion['dc_1bs'].values[sta_idx], df_reg_coeff['dc_1bs_mean'].values[sta_idx])
ax.axline((0,0), slope=1, color="black", linestyle="--")
#edit figure
if not flag_report: ax.set_title(r'Comparison $\delta c_{1b,S}$, Y: %i'%d_id, fontsize=30)
ax.set_xlabel('Synthetic dataset', fontsize=25)
ax.set_ylabel('Estimated', fontsize=25)
ax.grid(which='both')
ax.tick_params(axis='x', labelsize=22)
ax.tick_params(axis='y', labelsize=22)
#plot limits
# plt_lim = np.array([ax.get_xlim(), ax.get_ylim()])
# plt_lim = (plt_lim[:,0].min(), plt_lim[:,1].max())
# ax.set_xlim(plt_lim)
# ax.set_ylim(plt_lim)
ax.set_xlim([-1.5,1.5])
ax.set_ylim([-1.5,1.5])
#save figure
fig.tight_layout()
fig.savefig( dir_fig + fname_fig + '.png' )
#figure title
fname_fig = 'Y%i_dc_1bs_accuracy'%d_id
#create figure
fig, ax = plt.subplots(figsize = (10,10))
#accuray
ax.scatter(df_reg_coeff['dc_1bs_sig'].values[sta_idx],
df_sdata_gmotion['dc_1bs'].values[sta_idx] - df_reg_coeff['dc_1bs_mean'].values[sta_idx])
ax.axline((0,0), slope=0, color="black", linestyle="--")
#edit figure
if not flag_report: ax.set_title(r'Comparison $\delta c_{1b,S}$, Y: %i'%d_id, fontsize=30)
ax.set_xlabel('Standard Deviation', fontsize=25)
ax.set_ylabel('Actual - Estimated', fontsize=25)
ax.grid(which='both')
ax.tick_params(axis='x', labelsize=22)
ax.tick_params(axis='y', labelsize=22)
#plot limits
# ax.set_ylim(np.abs(ax.get_ylim()).max()*np.array([-1,1]))
ax.set_xlim([0,.4])
ax.set_ylim([-1.5,1.5])
#save figure
fig.tight_layout()
fig.savefig( dir_fig + fname_fig + '.png' )
#figure title
fname_fig = 'Y%i_dc_1bs_nrec'%d_id
#create figure
fig, ax = plt.subplots(figsize = (10,10))
#accuray
ax.scatter(sta_nrec,
df_sdata_gmotion['dc_1bs'].values[sta_idx] - df_reg_coeff['dc_1bs_mean'].values[sta_idx])
ax.axline((0,0), slope=0, color="black", linestyle="--")
#edit figure
if not flag_report: ax.set_title(r'Comparison $\delta c_{1b,S}$, Y: %i'%d_id, fontsize=30)
ax.set_xlabel('Number of records', fontsize=25)
ax.set_ylabel('Actual - Estimated', fontsize=25)
ax.grid(which='both')
ax.set_xscale('log')
ax.tick_params(axis='x', labelsize=22)
ax.tick_params(axis='y', labelsize=22)
#plot limits
# ax.set_ylim(np.abs(ax.get_ylim()).max()*np.array([-1,1]))
ax.set_xlim([.9,1000])
ax.set_ylim([-1.5,1.5])
#save figure
fig.tight_layout()
fig.savefig( dir_fig + fname_fig + '.png' )
#compare c_2p
#... ... ... ... ... ...
#figure title
fname_fig = 'Y%i_c_2p_scatter'%d_id
#create figure
fig, ax = plt.subplots(figsize = (10,10))
#coefficient scatter
ax.scatter(df_sdata_gmotion['c_2p'].values[eq_idx], df_reg_coeff['c_2p_mean'].values[eq_idx])
ax.axline((0,0), slope=1, color="black", linestyle="--")
#edit figure
if not flag_report: ax.set_title(r'Comparison $c_{2,P}$, Y: %i'%d_id, fontsize=30)
ax.set_xlabel('Synthetic dataset', fontsize=25)
ax.set_ylabel('Estimated', fontsize=25)
ax.grid(which='both')
ax.tick_params(axis='x', labelsize=22)
ax.tick_params(axis='y', labelsize=22)
#plot limits
# plt_lim = np.array([ax.get_xlim(), ax.get_ylim()])
# plt_lim = (plt_lim[:,0].min(), plt_lim[:,1].max())
# ax.set_xlim(plt_lim)
# ax.set_ylim(plt_lim)
ax.set_xlim([-2.3,-1.6])
ax.set_ylim([-2.3,-1.6])
#save figure
fig.tight_layout()
fig.savefig( dir_fig + fname_fig + '.png' )
#figure title
fname_fig = 'Y%i_c_2p_accuracy'%d_id
#create figure
fig, ax = plt.subplots(figsize = (10,10))
#coefficient scatter
ax.scatter(df_reg_coeff['c_2p_sig'].values[eq_idx],
df_sdata_gmotion['c_2p'].values[eq_idx] - df_reg_coeff['c_2p_mean'].values[eq_idx])
ax.axline((0,0), slope=0, color="black", linestyle="--")
#edit figure
if not flag_report: ax.set_title(r'Comparison $c_{2,P}$, Y: %i'%d_id, fontsize=30)
ax.set_xlabel('Standard Deviation', fontsize=25)
ax.set_ylabel('Actual - Estimated', fontsize=25)
ax.grid(which='both')
ax.tick_params(axis='x', labelsize=22)
ax.tick_params(axis='y', labelsize=22)
#plot limits
# ax.set_ylim(np.abs(ax.get_ylim()).max()*np.array([-1,1]))
ax.set_xlim([0,.15])
ax.set_ylim([-.4,.4])
#save figure
fig.tight_layout()
fig.savefig( dir_fig + fname_fig + '.png' )
#figure title
fname_fig = 'Y%i_c_2p_nrec'%d_id
#create figure
fig, ax = plt.subplots(figsize = (10,10))
#coefficient scatter
ax.scatter(eq_nrec,
df_sdata_gmotion['c_2p'].values[eq_idx] - df_reg_coeff['c_2p_mean'].values[eq_idx])
ax.axline((0,0), slope=0, color="black", linestyle="--")
#edit figure
if not flag_report: ax.set_title(r'Comparison $c_{2,P}$, Y: %i'%d_id, fontsize=30)
ax.set_xlabel('Number of records', fontsize=25)
ax.set_ylabel('Actual - Estimated', fontsize=25)
ax.grid(which='both')
ax.set_xscale('log')
ax.tick_params(axis='x', labelsize=22)
ax.tick_params(axis='y', labelsize=22)
#plot limits
# ax.set_ylim(np.abs(ax.get_ylim()).max()*np.array([-1,1]))
ax.set_xlim([0.9,1e3])
ax.set_ylim([-.4,.4])
#save figure
fig.tight_layout()
fig.savefig( dir_fig + fname_fig + '.png' )
#compare c_3s
#... ... ... ... ... ...
#figure title
fname_fig = 'Y%i_c_3s_scatter'%d_id
#create figure
fig, ax = plt.subplots(figsize = (10,10))
#coefficient scatter
ax.scatter(df_sdata_gmotion['c_3s'].values[sta_idx], df_reg_coeff['c_3s_mean'].values[sta_idx])
ax.axline((0,0), slope=1, color="black", linestyle="--")
#edit figure
if not flag_report: ax.set_title(r'Comparison $c_{3,S}$, Y: %i'%d_id, fontsize=30)
ax.set_xlabel('Synthetic dataset', fontsize=25)
ax.set_ylabel('Estimated', fontsize=25)
ax.grid(which='both')
ax.tick_params(axis='x', labelsize=22)
ax.tick_params(axis='y', labelsize=22)
#plot limits
# plt_lim = np.array([ax.get_xlim(), ax.get_ylim()])
# plt_lim = (plt_lim[:,0].min(), plt_lim[:,1].max())
# ax.set_xlim(plt_lim)
# ax.set_ylim(plt_lim)
ax.set_xlim([-1.2,-.2])
ax.set_ylim([-1.2,-.2])
#save figure
fig.tight_layout()
fig.savefig( dir_fig + fname_fig + '.png' )
#figure title
fname_fig = 'Y%i_c_3s_accuracy'%d_id
#create figure
fig, ax = plt.subplots(figsize = (10,10))
#coefficient scatter
ax.scatter(df_reg_coeff['c_3s_sig'].values[sta_idx],
df_sdata_gmotion['c_3s'].values[sta_idx] - df_reg_coeff['c_3s_mean'].values[sta_idx])
ax.axline((0,0), slope=0, color="black", linestyle="--")
#edit figure
if not flag_report: ax.set_title(r'Comparison $c_{3,S}$, Y: %i'%d_id, fontsize=30)
ax.set_xlabel('Standard Deviation', fontsize=25)
ax.set_ylabel('Actual - Estimated', fontsize=25)
ax.grid(which='both')
ax.tick_params(axis='x', labelsize=22)
ax.tick_params(axis='y', labelsize=22)
#plot limits
# ax.set_ylim(np.abs(ax.get_ylim()).max()*np.array([-1,1]))
ax.set_xlim([0,.3])
ax.set_ylim([-.4,.4])
#save figure
fig.tight_layout()
fig.savefig( dir_fig + fname_fig + '.png' )
#figure title
fname_fig = 'Y%i_c_3s_nrec'%d_id
#create figure
fig, ax = plt.subplots(figsize = (10,10))
#coefficient scatter
ax.scatter(sta_nrec,
df_sdata_gmotion['c_3s'].values[sta_idx] - df_reg_coeff['c_3s_mean'].values[sta_idx])
ax.axline((0,0), slope=0, color="black", linestyle="--")
#edit figure
if not flag_report: ax.set_title(r'Comparison $c_{3,S}$, Y: %i'%d_id, fontsize=30)
ax.set_xlabel('Number of records', fontsize=25)
ax.set_ylabel('Actual - Estimated', fontsize=25)
ax.grid(which='both')
ax.set_xscale('log')
ax.tick_params(axis='x', labelsize=22)
ax.tick_params(axis='y', labelsize=22)
#plot limits
# ax.set_ylim(np.abs(ax.get_ylim()).max()*np.array([-1,1]))
ax.set_xlim([0.9,1e3])
ax.set_ylim([-.4,.4])
#save figure
fig.tight_layout()
fig.savefig( dir_fig + fname_fig + '.png' )
#compare c_cap
#... ... ... ... ... ...
#figure title
fname_fig = 'Y%i_c_cap_scatter'%d_id
#create figure
fig, ax = plt.subplots(figsize = (10,10))
#coefficient scatter
ax.scatter(df_sdata_atten['c_cap'].values, df_reg_atten['c_cap_mean'].values)
ax.axline((0,0), slope=1, color="black", linestyle="--")
#edit figure
if not flag_report: ax.set_title(r'Comparison $c_{ca,P}$, Y: %i'%d_id, fontsize=30)
ax.set_xlabel('Synthetic dataset', fontsize=25)
ax.set_ylabel('Estimated', fontsize=25)
ax.grid(which='both')
ax.tick_params(axis='x', labelsize=22)
ax.tick_params(axis='y', labelsize=22)
#plot limits
# plt_lim = np.array([ax.get_xlim(), ax.get_ylim()])
# plt_lim = (plt_lim[:,0].min(), plt_lim[:,1].max())
# ax.set_xlim(plt_lim)
# ax.set_ylim(plt_lim)
ax.set_xlim([-0.05,0.02])
ax.set_ylim([-0.05,0.02])
#save figure
fig.tight_layout()
fig.savefig( dir_fig + fname_fig + '.png' )
#figure title
fname_fig = 'Y%i_c_cap_accuracy'%d_id
#create figure
fig, ax = plt.subplots(figsize = (10,10))
#coefficient scatter
ax.scatter(df_reg_atten['c_cap_sig'],
df_sdata_atten['c_cap'].values - df_reg_atten['c_cap_mean'].values)
ax.axline((0,0), slope=0, color="black", linestyle="--")
#edit figure
if not flag_report: ax.set_title(r'Comparison $c_{ca,P}$, Y: %i'%d_id, fontsize=30)
ax.set_xlabel('Standard Deviation', fontsize=25)
ax.set_ylabel('Actual - Estimated', fontsize=25)
ax.grid(which='both')
ax.tick_params(axis='x', labelsize=22)
ax.tick_params(axis='y', labelsize=22)
#plot limits
# ax.set_ylim(np.abs(ax.get_ylim()).max()*np.array([-1,1]))
ax.set_xlim([0.00,0.03])
ax.set_ylim([-0.04,0.04])
#save figure
fig.tight_layout()
fig.savefig( dir_fig + fname_fig + '.png' )
#figure title
fname_fig = 'Y%i_c_cap_npath'%d_id
#create figure
fig, ax = plt.subplots(figsize = (10,10))
#coefficient scatter
ax.scatter(cell_npath,
df_sdata_atten['c_cap'].values - df_reg_atten['c_cap_mean'].values)
ax.axline((0,0), slope=0, color="black", linestyle="--")
#edit figure
if not flag_report: ax.set_title(r'Comparison $c_{ca,P}$, Y: %i'%d_id, fontsize=30)
ax.set_xlabel('Number of paths', fontsize=25)
ax.set_ylabel('Actual - Estimated', fontsize=25)
ax.grid(which='both')
ax.set_xscale('log')
ax.tick_params(axis='x', labelsize=22)
ax.tick_params(axis='y', labelsize=22)
#plot limits
# ax.set_ylim(np.abs(ax.get_ylim()).max()*np.array([-1,1]))
ax.set_xlim([.9,5e4])
ax.set_ylim([-0.04,0.04])
#save figure
fig.tight_layout()
fig.savefig( dir_fig + fname_fig + '.png' )
# Compare Misfit Metrics
# ---------------------------
#summary directory
dir_sum = '%s%s/summary/'%(dir_results,synds_suffix_stan)
pathlib.Path(dir_fig).mkdir(parents=True, exist_ok=True)
#figure directory
dir_fig = '%s/figures/'%(dir_sum)
pathlib.Path(dir_fig).mkdir(parents=True, exist_ok=True)
#save
df_misfit.to_csv(dir_sum + 'misfit_summary.csv')
#RMS misfit
fname_fig = 'misfit_score'
#plot KL divergence
fig, ax = plt.subplots(figsize = (10,10))
ax.plot(ds_id, df_misfit.nerg_tot_rms, linestyle='-', marker='o', linewidth=2, markersize=10, label= 'tot nerg')
ax.plot(ds_id, df_misfit.dc_1e_rms, linestyle='-', marker='o', linewidth=2, markersize=10, label=r'$\delta c_{1,E}$')
ax.plot(ds_id, df_misfit.dc_1as_rms, linestyle='-', marker='o', linewidth=2, markersize=10, label=r'$\delta c_{1a,S}$')
ax.plot(ds_id, df_misfit.dc_1bs_rms, linestyle='-', marker='o', linewidth=2, markersize=10, label=r'$\delta c_{1b,S}$')
ax.plot(ds_id, df_misfit.c_2p_rms, linestyle='-', marker='o', linewidth=2, markersize=10, label=r'$c_{2,E}$')
ax.plot(ds_id, df_misfit.c_3s_rms, linestyle='-', marker='o', linewidth=2, markersize=10, label=r'$c_{3,S}$')
ax.plot(ds_id, df_misfit.c_cap_rms, linestyle='-', marker='o', linewidth=2, markersize=10, label=r'$c_{ca,P}$')
#figure properties
ax.set_ylim([0,0.50])
ax.set_xlabel('synthetic dataset', fontsize=25)
ax.set_ylabel('RSME', fontsize=25)
ax.grid(which='both')
ax.set_xticks(ds_id)
ax.set_xticklabels(labels=df_misfit.index)
ax.tick_params(axis='x', labelsize=22)
ax.tick_params(axis='y', labelsize=22)
#legend
ax.legend(loc='upper left', fontsize=25)
#save figure
fig.tight_layout()
fig.savefig( dir_fig + fname_fig + '.png' )
#KL divergence
fname_fig = 'KLdiv_score'
#plot KL divergence
fig, ax = plt.subplots(figsize = (10,10))
ax.plot(ds_id, df_misfit.nerg_tot_KL, linestyle='-', marker='o', linewidth=2, markersize=10, label= 'tot nerg')
ax.plot(ds_id, df_misfit.dc_1e_KL, linestyle='-', marker='o', linewidth=2, markersize=10, label=r'$\delta c_{1,E}$')
ax.plot(ds_id, df_misfit.dc_1as_KL, linestyle='-', marker='o', linewidth=2, markersize=10, label=r'$\delta c_{1a,S}$')
ax.plot(ds_id, df_misfit.dc_1bs_KL, linestyle='-', marker='o', linewidth=2, markersize=10, label=r'$\delta c_{1b,S}$')
ax.plot(ds_id, df_misfit.c_2p_KL, linestyle='-', marker='o', linewidth=2, markersize=10, label=r'$c_{2,P}$')
ax.plot(ds_id, df_misfit.c_3s_KL, linestyle='-', marker='o', linewidth=2, markersize=10, label=r'$c_{3,S}$')
ax.plot(ds_id, df_misfit.c_cap_KL, linestyle='-', marker='o', linewidth=2, markersize=10, label=r'$c_{ca,P}$')
#figure properties
ax.set_ylim([0,0.50])
ax.set_xlabel('synthetic dataset', fontsize=25)
ax.set_ylabel('KL divergence', fontsize=25)
ax.grid(which='both')
ax.set_xticks(ds_id)
ax.set_xticklabels(labels=df_misfit.index)
ax.tick_params(axis='x', labelsize=22)
ax.tick_params(axis='y', labelsize=22)
#legend
ax.legend(loc='upper left', fontsize=25)
#save figure
fig.tight_layout()
fig.savefig( dir_fig + fname_fig + '.png' )
# Compare hyper-paramters
# ---------------------------
#iterate over different datasets
df_reg_hyp = list()
df_reg_hyp_post = list()
for d_id in ds_id:
# Load Data
#--- --- --- --- ---
#regression hyperparamters results
fname_reg_hyp = '%s%s/Y%i/%s%s_Y%i_stan_%s'%(dir_results,synds_suffix_stan, d_id,prfx_results, synds_suffix, d_id, 'hyperparameters') + '.csv'
fname_reg_hyp_post = '%s%s/Y%i/%s%s_Y%i_stan_%s'%(dir_results,synds_suffix_stan, d_id,prfx_results, synds_suffix, d_id, 'hyperposterior') + '.csv'
#load regression results
df_reg_hyp.append( pd.read_csv(fname_reg_hyp, index_col=0) )
df_reg_hyp_post.append( pd.read_csv(fname_reg_hyp_post, index_col=0) )
#figure directory
dir_fig = '%s%s/figures_cmp_hyp/'%(dir_results,synds_suffix_stan)
pathlib.Path(dir_fig).mkdir(parents=True, exist_ok=True)
# Omega_1e
#--- --- --- --- ---
#hyper-paramter name
name_hyp = 'omega_1e'
#figure title
fname_fig = 'post_dist_' + name_hyp
#create figure
fig, ax = plt.subplots(figsize = (10,10))
for d_id, df_r_h, df_r_h_p in zip(ds_id, df_reg_hyp, df_reg_hyp_post):
#estimate vertical line height for mean and mode
ymax_mode = 40
ymax_mean = 40
#plot posterior dist
pl_hyp = ax.vlines(df_r_h.loc['mean',name_hyp], ymin=0, ymax=ymax_mean, linestyle='-', label='Mean')
ax.vlines(df_r_h.loc['prc_0.50',name_hyp], ymin=0, ymax=ymax_mode, linestyle='--', color=pl_hyp.get_color(), label='Mode')
#plot true value
ymax_hyp = ymax_mean
ax.vlines(hyp[name_hyp], ymin=0, ymax=ymax_hyp, linestyle='-', linewidth=4, color='black', label='True value')
#edit figure
if not flag_report: ax.set_title(r'Comparison $\omega_{1,E}$', fontsize=30)
ax.set_xlabel('$\omega_{1,E}$', fontsize=25)
ax.set_ylabel('probability density function ', fontsize=25)
ax.grid(which='both')
ax.tick_params(axis='x', labelsize=22)
ax.tick_params(axis='y', labelsize=22)
#plot limits
ax.set_xlim([0,0.25])
ax.set_ylim([0,ymax_hyp])
#save figure
fig.tight_layout()
fig.savefig( dir_fig + fname_fig + '.png' )
# Omega_1as
#--- --- --- --- ---
#hyper-paramter name
name_hyp = 'omega_1as'
#figure title
fname_fig = 'post_dist_' + name_hyp
#create figure
fig, ax = plt.subplots(figsize = (10,10))
for d_id, df_r_h, df_r_h_p in zip(ds_id, df_reg_hyp, df_reg_hyp_post):
#estimate vertical line height for mean and mode
ymax_mode = 30
ymax_mean = 30
#plot posterior dist
pl_hyp = ax.vlines(df_r_h.loc['mean',name_hyp], ymin=0, ymax=ymax_mean, linestyle='-', label='Mean')
ax.vlines(df_r_h.loc['prc_0.50',name_hyp], ymin=0, ymax=ymax_mode, linestyle='--', color=pl_hyp.get_color(), label='Mode')
#plot true value
ymax_hyp = ymax_mean
ax.vlines(hyp[name_hyp], ymin=0, ymax=ymax_hyp, linestyle='-', linewidth=4, color='black', label='True value')
#edit figure
if not flag_report: ax.set_title(r'Comparison $\omega_{1a,S}$', fontsize=30)
ax.set_xlabel('$\omega_{1a,S}$', fontsize=25)
ax.set_ylabel('probability density function ', fontsize=25)
ax.grid(which='both')
ax.tick_params(axis='x', labelsize=22)
ax.tick_params(axis='y', labelsize=22)
#plot limits
ax.set_xlim([0,0.5])
ax.set_ylim([0,ymax_hyp])
#save figure
fig.tight_layout()
fig.savefig( dir_fig + fname_fig + '.png' )
# Omega_1bs
#--- --- --- --- ---
#hyper-paramter name
name_hyp = 'omega_1bs'
#figure title
fname_fig = 'post_dist_' + name_hyp
#create figure
fig, ax = plt.subplots(figsize = (10,10))
for d_id, df_r_h, df_r_h_p in zip(ds_id, df_reg_hyp, df_reg_hyp_post):
#estimate vertical line height for mean and mode
ymax_mode = 60
ymax_mean = 60
#plot posterior dist
pl_hyp = ax.vlines(df_r_h.loc['mean',name_hyp], ymin=0, ymax=ymax_mean, linestyle='-', label='Mean')
ax.vlines(df_r_h.loc['prc_0.50',name_hyp], ymin=0, ymax=ymax_mode, linestyle='--', color=pl_hyp.get_color(), label='Mode')
#plot true value
ymax_hyp = ymax_mean
ax.vlines(hyp[name_hyp], ymin=0, ymax=ymax_hyp, linestyle='-', linewidth=4, color='black', label='True value')
#edit figure
if not flag_report: ax.set_title(r'Comparison $\omega_{1b,S}$', fontsize=30)
ax.set_xlabel('$\omega_{1b,S}$', fontsize=25)
ax.set_ylabel('probability density function ', fontsize=25)
ax.grid(which='both')
ax.tick_params(axis='x', labelsize=22)
ax.tick_params(axis='y', labelsize=22)
#plot limits
ax.set_xlim([0,0.5])
ax.set_ylim([0,ymax_hyp])
#save figure
fig.tight_layout()
fig.savefig( dir_fig + fname_fig + '.png' )
# Omega_2p
#--- --- --- --- ---
#hyper-paramter name
name_hyp = 'omega_2p'
#figure title
fname_fig = 'post_dist_' + name_hyp
#create figure
fig, ax = plt.subplots(figsize = (10,10))
for d_id, df_r_h, df_r_h_p in zip(ds_id, df_reg_hyp, df_reg_hyp_post):
#estimate vertical line height for mean and mode
ymax_mode = 60
ymax_mean = 60
#plot posterior dist
pl_hyp = ax.vlines(df_r_h.loc['mean',name_hyp], ymin=0, ymax=ymax_mean, linestyle='-', label='Mean')
ax.vlines(df_r_h.loc['prc_0.50',name_hyp], ymin=0, ymax=ymax_mode, linestyle='--', color=pl_hyp.get_color(), label='Mode')
#plot true value
ymax_hyp = ymax_mean
ax.vlines(hyp[name_hyp], ymin=0, ymax=ymax_hyp, linestyle='-', linewidth=4, color='black', label='True value')
#edit figure
if not flag_report: ax.set_title(r'Comparison $\omega_{2,P}$', fontsize=30)
ax.set_xlabel('$\omega_{2,P}$', fontsize=25)
ax.set_ylabel('probability density function ', fontsize=25)
ax.grid(which='both')
ax.tick_params(axis='x', labelsize=22)
ax.tick_params(axis='y', labelsize=22)
#plot limits
ax.set_xlim([0,0.5])
ax.set_ylim([0,ymax_hyp])
#save figure
fig.tight_layout()
fig.savefig( dir_fig + fname_fig + '.png' )
# Omega_3s
#--- --- --- --- ---
#hyper-paramter name
name_hyp = 'omega_3s'
#figure title
fname_fig = 'post_dist_' + name_hyp
#create figure
fig, ax = plt.subplots(figsize = (10,10))
for d_id, df_r_h, df_r_h_p in zip(ds_id, df_reg_hyp, df_reg_hyp_post):
#estimate vertical line height for mean and mode
ymax_mode = 60
ymax_mean = 60
#plot posterior dist
pl_hyp = ax.vlines(df_r_h.loc['mean',name_hyp], ymin=0, ymax=ymax_mean, linestyle='-', label='Mean')
ax.vlines(df_r_h.loc['prc_0.50',name_hyp], ymin=0, ymax=ymax_mode, linestyle='--', color=pl_hyp.get_color(), label='Mode')
#plot true value
ymax_hyp = ymax_mean
ax.vlines(hyp[name_hyp], ymin=0, ymax=ymax_hyp, linestyle='-', linewidth=4, color='black', label='True value')
#edit figure
if not flag_report: ax.set_title(r'Comparison $\omega_{3,S}$', fontsize=30)
ax.set_xlabel('$\omega_{3,S}$', fontsize=25)
ax.set_ylabel('probability density function ', fontsize=25)
ax.grid(which='both')
ax.tick_params(axis='x', labelsize=22)
ax.tick_params(axis='y', labelsize=22)
#plot limits
ax.set_xlim([0,0.5])
ax.set_ylim([0,ymax_hyp])
#save figure
fig.tight_layout()
fig.savefig( dir_fig + fname_fig + '.png' )
# Ell_1e
#--- --- --- --- ---
#hyper-paramter name
name_hyp = 'ell_1e'
#figure title
fname_fig = 'post_dist_' + name_hyp
#create figure
fig, ax = plt.subplots(figsize = (10,10))
for d_id, df_r_h, df_r_h_p in zip(ds_id, df_reg_hyp, df_reg_hyp_post):
#estimate vertical line height for mean and mode
ymax_mode = 0.02
ymax_mean = 0.02
#plot posterior dist
pl_hyp = ax.vlines(df_r_h.loc['mean',name_hyp], ymin=0, ymax=ymax_mean, linestyle='-', label='Mean')
ax.vlines(df_r_h.loc['prc_0.50',name_hyp], ymin=0, ymax=ymax_mode, linestyle='--', color=pl_hyp.get_color(), label='Mode')
#plot true value
ymax_hyp = ymax_mean
ax.vlines(hyp[name_hyp], ymin=0, ymax=ymax_hyp, linestyle='-', linewidth=4, color='black', label='True value')
#edit figure
if not flag_report: ax.set_title(r'Comparison $\ell_{1,E}$', fontsize=30)
ax.set_xlabel('$\ell_{1,E}$', fontsize=25)
ax.set_ylabel('probability density function ', fontsize=25)
ax.grid(which='both')
ax.tick_params(axis='x', labelsize=22)
ax.tick_params(axis='y', labelsize=22)
#plot limits
ax.set_xlim([0,500])
ax.set_ylim([0,ymax_hyp])
#save figure
fig.tight_layout()
fig.savefig( dir_fig + fname_fig + '.png' )
# Ell_1as
#--- --- --- --- ---
#hyper-paramter name
name_hyp = 'ell_1as'
#figure title
fname_fig = 'post_dist_' + name_hyp
#create figure
fig, ax = plt.subplots(figsize = (10,10))
for d_id, df_r_h, df_r_h_p in zip(ds_id, df_reg_hyp, df_reg_hyp_post):
#estimate vertical line height for mean and mode
ymax_mode = 0.1
ymax_mean = 0.1
#plot posterior dist
pl_hyp = ax.vlines(df_r_h.loc['mean',name_hyp], ymin=0, ymax=ymax_mean, linestyle='-', label='Mean')
ax.vlines(df_r_h.loc['prc_0.50',name_hyp], ymin=0, ymax=ymax_mode, linestyle='--', color=pl_hyp.get_color(), label='Mode')
#plot true value
ymax_hyp = ymax_mean
ax.vlines(hyp[name_hyp], ymin=0, ymax=ymax_hyp, linestyle='-', linewidth=4, color='black', label='True value')
#edit figure
if not flag_report: ax.set_title(r'Comparison $\ell_{1a,S}$', fontsize=30)
ax.set_xlabel('$\ell_{1a,S}$', fontsize=25)
ax.set_ylabel('probability density function ', fontsize=25)
ax.grid(which='both')
ax.tick_params(axis='x', labelsize=22)
ax.tick_params(axis='y', labelsize=22)
#plot limits
ax.set_xlim([0,150])
ax.set_ylim([0,ymax_hyp])
#save figure
fig.tight_layout()
fig.savefig( dir_fig + fname_fig + '.png' )
# Ell_2p
#--- --- --- --- ---
#hyper-paramter name
name_hyp = 'ell_2p'
#figure title
fname_fig = 'post_dist_' + name_hyp
#create figure
fig, ax = plt.subplots(figsize = (10,10))
for d_id, df_r_h, df_r_h_p in zip(ds_id, df_reg_hyp, df_reg_hyp_post):
#estimate vertical line height for mean and mode
ymax_mode = 0.1
ymax_mean = 0.1
#plot posterior dist
pl_hyp = ax.vlines(df_r_h.loc['mean',name_hyp], ymin=0, ymax=ymax_mean, linestyle='-', label='Mean')
ax.vlines(df_r_h.loc['prc_0.50',name_hyp], ymin=0, ymax=ymax_mode, linestyle='--', color=pl_hyp.get_color(), label='Mode')
#plot true value
ymax_hyp = ymax_mean
ax.vlines(hyp[name_hyp], ymin=0, ymax=ymax_hyp, linestyle='-', linewidth=4, color='black', label='True value')
#edit figure
if not flag_report: ax.set_title(r'Comparison $\ell_{2,P}$', fontsize=30)
ax.set_xlabel('$\ell_{2,P}$', fontsize=25)
ax.set_ylabel('probability density function ', fontsize=25)
ax.grid(which='both')
ax.tick_params(axis='x', labelsize=22)
ax.tick_params(axis='y', labelsize=22)
#plot limits
ax.set_xlim([0,150])
ax.set_ylim([0,ymax_hyp])
#save figure
fig.tight_layout()
fig.savefig( dir_fig + fname_fig + '.png' )
# Ell_3s
#--- --- --- --- ---
#hyper-paramter name
name_hyp = 'ell_3s'
#figure title
fname_fig = 'post_dist_' + name_hyp
#create figure
fig, ax = plt.subplots(figsize = (10,10))
for d_id, df_r_h, df_r_h_p in zip(ds_id, df_reg_hyp, df_reg_hyp_post):
#estimate vertical line height for mean and mode
ymax_mode = 0.1
ymax_mean = 0.1
#plot posterior dist
pl_hyp = ax.vlines(df_r_h.loc['mean',name_hyp], ymin=0, ymax=ymax_mean, linestyle='-', label='Mean')
ax.vlines(df_r_h.loc['prc_0.50',name_hyp], ymin=0, ymax=ymax_mode, linestyle='--', color=pl_hyp.get_color(), label='Mode')
#plot true value
ymax_hyp = ymax_mean
ax.vlines(hyp[name_hyp], ymin=0, ymax=ymax_hyp, linestyle='-', linewidth=4, color='black', label='True value')
#edit figure
if not flag_report: ax.set_title(r'Comparison $\ell_{3,S}$', fontsize=30)
ax.set_xlabel('$\ell_{3,S}$', fontsize=25)
ax.set_ylabel('probability density function ', fontsize=25)
ax.grid(which='both')
ax.tick_params(axis='x', labelsize=22)
ax.tick_params(axis='y', labelsize=22)
#plot limits
ax.set_xlim([0,150])
ax.set_ylim([0,ymax_hyp])
#save figure
fig.tight_layout()
fig.savefig( dir_fig + fname_fig + '.png' )
# Tau_0
#--- --- --- --- ---
#hyper-paramter name
name_hyp = 'tau_0'
#figure title
fname_fig = 'post_dist_' + name_hyp
#create figure
fig, ax = plt.subplots(figsize = (10,10))
for d_id, df_r_h, df_r_h_p in zip(ds_id, df_reg_hyp, df_reg_hyp_post):
#estimate vertical line height for mean and mode
ymax_mode = 150
ymax_mean = 150
#plot posterior dist
pl_hyp = ax.vlines(df_r_h.loc['mean',name_hyp], ymin=0, ymax=ymax_mean, linestyle='-', label='Mean')
ax.vlines(df_r_h.loc['prc_0.50',name_hyp], ymin=0, ymax=ymax_mode, linestyle='--', color=pl_hyp.get_color(), label='Mode')
#plot true value
ymax_hyp = ymax_mean
ax.vlines(hyp[name_hyp], ymin=0, ymax=ymax_hyp, linestyle='-', linewidth=4, color='black', label='True value')
#edit figure
if not flag_report: ax.set_title(r'Comparison $\tau_{0}$', fontsize=30)
ax.set_xlabel(r'$\tau_{0}$', fontsize=25)
ax.set_ylabel(r'probability density function ', fontsize=25)
ax.grid(which='both')
ax.tick_params(axis='x', labelsize=22)
ax.tick_params(axis='y', labelsize=22)
#plot limits
ax.set_xlim([0,0.5])
ax.set_ylim([0,ymax_hyp])
#save figure
fig.tight_layout()
fig.savefig( dir_fig + fname_fig + '.png' )
# Phi_0
#--- --- --- --- ---
#hyper-paramter name
name_hyp = 'phi_0'
#figure title
fname_fig = 'post_dist_' + name_hyp
#create figure
fig, ax = plt.subplots(figsize = (10,10))
for d_id, df_r_h, df_r_h_p in zip(ds_id, df_reg_hyp, df_reg_hyp_post):
#estimate vertical line height for mean and mode
ymax_mode = 1000
ymax_mean = 1000
#plot posterior dist
pl_hyp = ax.vlines(df_r_h.loc['mean',name_hyp], ymin=0, ymax=ymax_mean, linestyle='-', label='Mean')
ax.vlines(df_r_h.loc['prc_0.50',name_hyp], ymin=0, ymax=ymax_mode, linestyle='--', color=pl_hyp.get_color(), label='Mode')
#plot true value
ymax_hyp = ymax_mean
ax.vlines(hyp[name_hyp], ymin=0, ymax=ymax_hyp, linestyle='-', linewidth=4, color='black', label='True value')
#edit figure
if not flag_report: ax.set_title(r'Comparison $\phi_{0}$', fontsize=30)
ax.set_xlabel('$\phi_{0}$', fontsize=25)
ax.set_ylabel(r'probability density function ', fontsize=25)
ax.grid(which='both')
ax.tick_params(axis='x', labelsize=22)
ax.tick_params(axis='y', labelsize=22)
#plot limits
ax.set_xlim([0,0.6])
ax.set_ylim([0,ymax_hyp])
#save figure
fig.tight_layout()
fig.savefig( dir_fig + fname_fig + '.png' )
# Ell_ca1p
#--- --- --- --- ---
#hyper-paramter name
name_hyp = 'ell_ca1p'
#figure title
fname_fig = 'post_dist_' + name_hyp
#create figure
fig, ax = plt.subplots(figsize = (10,10))
for d_id, df_r_h, df_r_h_p in zip(ds_id, df_reg_hyp, df_reg_hyp_post):
#estimate vertical line height for mean and mode
ymax_mode = 0.02
ymax_mean = 0.02
#plot posterior dist
pl_hyp = ax.vlines(df_r_h.loc['mean',name_hyp], ymin=0, ymax=ymax_mean, linestyle='-', label='Mean')
ax.vlines(df_r_h.loc['prc_0.50',name_hyp], ymin=0, ymax=ymax_mode, linestyle='--', color=pl_hyp.get_color(), label='Mode')
#plot true value
ymax_hyp = ymax_mean
ax.vlines(hyp[name_hyp], ymin=0, ymax=ymax_hyp, linestyle='-', linewidth=4, color='black', label='True value')
#edit figure
if not flag_report: ax.set_title(r'Comparison $\ell_{ca1,P}$', fontsize=30)
ax.set_xlabel('$\ell_{ca1,P}$', fontsize=25)
ax.set_ylabel('probability density function ', fontsize=25)
ax.grid(which='both')
ax.tick_params(axis='x', labelsize=22)
ax.tick_params(axis='y', labelsize=22)
#plot limits
ax.set_xlim([0,500])
ax.set_ylim([0,ymax_hyp])
#save figure
fig.tight_layout()
fig.savefig( dir_fig + fname_fig + '.png' )
# Omega_ca1
#--- --- --- --- ---
#hyper-paramter name
name_hyp = 'omega_ca1p'
#figure title
fname_fig = 'post_dist_' + name_hyp
#create figure
fig, ax = plt.subplots(figsize = (10,10))
for d_id, df_r_h, df_r_h_p in zip(ds_id, df_reg_hyp, df_reg_hyp_post):
#estimate vertical line height for mean and mode
ymax_mode = 1500
ymax_mean = 1500
#plot posterior dist
pl_hyp = ax.vlines(df_r_h.loc['mean',name_hyp], ymin=0, ymax=ymax_mean, linestyle='-', label='Mean')
ax.vlines(df_r_h.loc['prc_0.50',name_hyp], ymin=0, ymax=ymax_mode, linestyle='--', color=pl_hyp.get_color(), label='Mode')
#plot true value
ymax_hyp = ymax_mean
ax.vlines(hyp['omega_ca2p'], ymin=0, ymax=ymax_hyp, linestyle='-', linewidth=4, color='black', label='True value')
#edit figure
if not flag_report: ax.set_title(r'Comparison $\omega_{ca1,P}$', fontsize=30)
ax.set_xlabel('$\omega_{ca1,P}$', fontsize=25)
ax.set_ylabel('probability density function ', fontsize=25)
ax.grid(which='both')
ax.tick_params(axis='x', labelsize=22)
ax.tick_params(axis='y', labelsize=22)
#plot limits
ax.set_xlim([0,0.05])
ax.set_ylim([0,ymax_hyp])
#save figure
fig.tight_layout()
fig.savefig( dir_fig + fname_fig + '.png' )
# Omega_ca2
#--- --- --- --- ---
#hyper-paramter name
name_hyp = 'omega_ca2p'
#figure title
fname_fig = 'post_dist_' + name_hyp
#create figure
fig, ax = plt.subplots(figsize = (10,10))
for d_id, df_r_h, df_r_h_p in zip(ds_id, df_reg_hyp, df_reg_hyp_post):
#estimate vertical line height for mean and mode
ymax_mode = 1500
ymax_mean = 1500
#plot posterior dist
pl_hyp = ax.vlines(df_r_h.loc['mean',name_hyp], ymin=0, ymax=ymax_mean, linestyle='-', label='Mean')
ax.vlines(df_r_h.loc['prc_0.50',name_hyp], ymin=0, ymax=ymax_mode, linestyle='--', color=pl_hyp.get_color(), label='Mode')
#plot true value
ymax_hyp = ymax_mean
ax.vlines(hyp['omega_ca2p'], ymin=0, ymax=ymax_hyp, linestyle='-', linewidth=4, color='black', label='True value')
#edit figure
if not flag_report: ax.set_title(r'Comparison $\omega_{ca2,p}$', fontsize=30)
ax.set_xlabel('$\omega_{ca2,P}$', fontsize=25)
ax.set_ylabel('probability density function ', fontsize=25)
ax.grid(which='both')
ax.tick_params(axis='x', labelsize=22)
ax.tick_params(axis='y', labelsize=22)
#plot limits
ax.set_xlim([0,0.05])
ax.set_ylim([0,ymax_hyp])
#save figure
fig.tight_layout()
fig.savefig( dir_fig + fname_fig + '.png' )
# # Delta c_0
# #--- --- --- --- ---
# #hyper-paramter name
# name_hyp = 'dc_0'
# #figure title
# fname_fig = 'post_dist_' + name_hyp
# #create figure
# fig, ax = plt.subplots(figsize = (10,10))
# for d_id, df_r_h, df_r_h_p in zip(ds_id, df_reg_hyp, df_reg_hyp_post):
# #estimate vertical line height for mean and mode
# ymax_mode = df_r_h_p.loc[:,name_hyp+'_pdf'].max()
# ymax_mean = 1.5*np.ceil(ymax_mode/10)*10
# ymax_mean = 15
# #plot posterior dist
# pl_pdf = ax.plot(df_r_h_p.loc[:,name_hyp], df_r_h_p.loc[:,name_hyp+'_pdf'])
# ax.vlines(df_r_h.loc[name_hyp,'mean'], ymin=0, ymax=ymax_mean, linestyle='-', color=pl_pdf[0].get_color(), label='Mean')
# ax.vlines(df_r_h.loc[name_hyp,'mode'], ymin=0, ymax=ymax_mode, linestyle='--', color=pl_pdf[0].get_color(), label='Mode')
# #plot true value
# ymax_hyp = ymax_mean
# # ax.vlines(hyp[name_hyp], ymin=0, ymax=ymax_hyp, linestyle='-', linewidth=4, color='black', label='True value')
# #edit figure
# ax.set_title(r'Comparison $\delta c_{0}$', fontsize=30)
# ax.set_xlabel('$\delta c_{0}$', fontsize=25)
# ax.set_ylabel('probability density function ', fontsize=25)
# ax.grid(which='both')
# ax.tick_params(axis='x', labelsize=22)
# ax.tick_params(axis='y', labelsize=22)
# #plot limits
# ax.set_xlim([-1,1])
# ax.set_ylim([0,ymax_hyp])
# #save figure
# fig.tight_layout()
# # fig.savefig( dir_fig + fname_fig + '.png' )
| 49,612 | 38.658673 | 153 | py |
ngmm_tools | ngmm_tools-master/Analyses/Code_Verification/regression/ds3/main_cmdstan_model3_uncorr_cells_NGAWest3CA.py | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Wed Dec 29 15:16:15 2021
@author: glavrent
"""
# Working directory and Packages
# ---------------------------
#load libraries
import os
import sys
import numpy as np
import pandas as pd
import time
#user functions
sys.path.insert(0,'../../../Python_lib/regression/cmdstan/')
# from regression_cmdstan_model3_uncorr_cells_unbounded_hyp import RunStan
# from regression_cmdstan_model3_uncorr_cells_sparse_unbounded_hyp import RunStan
# Define variables
# ---------------------------
#filename suffix
# synds_suffix = '_small_corr_len'
# synds_suffix = '_large_corr_len'
#synthetic datasets directory
ds_dir = '../../../../Data/Verification/synthetic_datasets/ds3'
ds_dir = r'%s%s/'%(ds_dir, synds_suffix)
# dataset info
#ds_fname_main = 'CatalogNGAWest3CA_synthetic_data'
ds_fname_main = 'CatalogNGAWest3CALite_synthetic_data'
ds_id = np.arange(1,6)
#cell specific anelastic attenuation
ds_fname_cellinfo = 'CatalogNGAWest3CALite_cellinfo'
ds_fname_celldist = 'CatalogNGAWest3CALite_distancematrix'
#stan model
# sm_fname = '../../../Stan_lib/regression_stan_model3_uncorr_cells_unbounded_hyp_chol_efficient.stan'
sm_fname = '../../../Stan_lib/regression_stan_model3_uncorr_cells_sparse_unbounded_hyp_chol_efficient.stan'
#output info
#main output filename
out_fname_main = 'NGAWest3CA_syndata'
#main output directory
out_dir_main = '../../../../Data/Verification/regression/ds3/'
#output sub-directory
# out_dir_sub = 'CMDSTAN_NGAWest3CA_uncorr_cells_chol_eff'
# out_dir_sub = 'CMDSTAN_NGAWest3CA_uncorr_cells_chol_eff_sp'
#stan parameters
res_name = 'tot'
n_iter_warmup = 500
n_iter_sampling = 500
n_chains = 4
adapt_delta = 0.8
max_treedepth = 10
#ergodic coefficients
c_2_erg=-2.0
c_3_erg=-0.6
c_a_erg= 0.0
#parallel options
# flag_parallel = True
flag_parallel = False
#output sub-dir with corr with suffix info
out_dir_sub = f'%s%s'%(out_dir_sub, synds_suffix)
#load cell dataframes
cellinfo_fname = '%s%s.csv'%(ds_dir, ds_fname_cellinfo)
celldist_fname = '%s%s.csv'%(ds_dir, ds_fname_celldist)
df_cellinfo = pd.read_csv(cellinfo_fname)
df_celldist = pd.read_csv(celldist_fname)
# Run stan regression
# ---------------------------
#create datafame with computation time
df_run_info = list()
#iterate over all synthetic datasets
for d_id in ds_id:
print('Synthetic dataset %i fo %i'%(d_id, len(ds_id)))
#run time start
run_t_strt = time.time()
#input flatfile
ds_fname = '%s%s%s_Y%i.csv'%(ds_dir, ds_fname_main, synds_suffix, d_id)
#load flatfile
df_flatfile = pd.read_csv(ds_fname)
#output file name and directory
out_fname = '%s%s_Y%i'%(out_fname_main, synds_suffix, d_id)
out_dir = '%s/%s/Y%i/'%(out_dir_main, out_dir_sub, d_id)
#run stan model
RunStan(df_flatfile, df_cellinfo, df_celldist, sm_fname,
out_fname, out_dir, res_name,
c_2_erg=c_2_erg, c_3_erg=c_3_erg, c_a_erg=c_a_erg,
n_iter_warmup=n_iter_warmup, n_iter_sampling=n_iter_sampling, n_chains=n_chains,
adapt_delta=adapt_delta, max_treedepth=max_treedepth,
stan_parallel=flag_parallel)
#run time end
run_t_end = time.time()
#compute run time
run_tm = (run_t_end - run_t_strt)/60
#log run time
df_run_info.append(pd.DataFrame({'computer_name':os.uname()[1],'out_name':out_dir_sub,
'ds_id':d_id,'run_time':run_tm}, index=[d_id]))
#write out run info
out_fname = '%s%s/run_info.csv'%(out_dir_main, out_dir_sub)
pd.concat(df_run_info).reset_index(drop=True).to_csv(out_fname, index=False)
| 3,698 | 30.347458 | 107 | py |
ngmm_tools | ngmm_tools-master/Analyses/Code_Verification/regression/ds3/main_cmdstan_model3_uncorr_cells_NGAWest2CA.py | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Wed Dec 29 15:16:15 2021
@author: glavrent
"""
# Working directory and Packages
# ---------------------------
#load libraries
import os
import sys
import numpy as np
import pandas as pd
import time
#user functions
sys.path.insert(0,'../../../Python_lib/regression/cmdstan/')
# from regression_cmdstan_model3_uncorr_cells_unbounded_hyp import RunStan
# from regression_cmdstan_model3_uncorr_cells_sparse_unbounded_hyp import RunStan
# Define variables
# ---------------------------
#filename suffix
# synds_suffix = '_small_corr_len'
# synds_suffix = '_large_corr_len'
#synthetic datasets directory
ds_dir = '../../../../Data/Verification/synthetic_datasets/ds3'
ds_dir = r'%s%s/'%(ds_dir, synds_suffix)
# dataset info
#ds_fname_main = 'CatalogNGAWest3CA_synthetic_data'
ds_fname_main = 'CatalogNGAWest3CALite_synthetic_data'
ds_id = np.arange(1,6)
#cell specific anelastic attenuation
ds_fname_cellinfo = 'CatalogNGAWest3CALite_cellinfo'
ds_fname_celldist = 'CatalogNGAWest3CALite_distancematrix'
#stan model
# sm_fname = '../../../Stan_lib/regression_stan_model3_uncorr_cells_unbounded_hyp_chol_efficient.stan'
# sm_fname = '../../../Stan_lib/regression_stan_model3_uncorr_cells_sparse_unbounded_hyp_chol_efficient.stan'
#output info
#main output filename
out_fname_main = 'NGAWest2CA_syndata'
#main output directory
out_dir_main = '../../../../Data/Verification/regression/ds3/'
#output sub-directory
# out_dir_sub = 'CMDSTAN_NGAWest2CA_uncorr_cells_chol_eff'
# out_dir_sub = 'CMDSTAN_NGAWest2CA_uncorr_cells_chol_eff_sp'
#stan parameters
res_name = 'tot'
n_iter_warmup = 500
n_iter_sampling = 500
n_chains = 4
adapt_delta = 0.8
max_treedepth = 10
#ergodic coefficients
c_2_erg=-2.0
c_3_erg=-0.6
c_a_erg= 0.0
#parallel options
# flag_parallel = True
flag_parallel = False
#output sub-dir with corr with suffix info
out_dir_sub = f'%s%s'%(out_dir_sub, synds_suffix)
#load cell dataframes
cellinfo_fname = '%s%s.csv'%(ds_dir, ds_fname_cellinfo)
celldist_fname = '%s%s.csv'%(ds_dir, ds_fname_celldist)
df_cellinfo = pd.read_csv(cellinfo_fname)
df_celldist = pd.read_csv(celldist_fname)
# Run stan regression
# ---------------------------
#create datafame with computation time
df_run_info = list()
#iterate over all synthetic datasets
for d_id in ds_id:
print('Synthetic dataset %i fo %i'%(d_id, len(ds_id)))
#run time start
run_t_strt = time.time()
#input flatfile
ds_fname = '%s%s%s_Y%i.csv'%(ds_dir, ds_fname_main, synds_suffix, d_id)
#load flatfile
df_flatfile = pd.read_csv(ds_fname)
#keep only NGAWest2 records
df_flatfile = df_flatfile.loc[df_flatfile.dsid==0,:]
#output file name and directory
out_fname = '%s%s_Y%i'%(out_fname_main, synds_suffix, d_id)
out_dir = '%s/%s/Y%i/'%(out_dir_main, out_dir_sub, d_id)
#run stan model
RunStan(df_flatfile, df_cellinfo, df_celldist, sm_fname,
out_fname, out_dir, res_name,
c_2_erg=c_2_erg, c_3_erg=c_3_erg, c_a_erg=c_a_erg,
n_iter_warmup=n_iter_warmup, n_iter_sampling=n_iter_sampling, n_chains=n_chains,
adapt_delta=adapt_delta, max_treedepth=max_treedepth,
stan_parallel=flag_parallel)
#run time end
run_t_end = time.time()
#compute run time
run_tm = (run_t_end - run_t_strt)/60
#log run time
df_run_info.append(pd.DataFrame({'computer_name':os.uname()[1],'out_name':out_dir_sub,
'ds_id':d_id,'run_time':run_tm}, index=[d_id]))
#write out run info
out_fname = '%s%s/run_info.csv'%(out_dir_main, out_dir_sub)
pd.concat(df_run_info).reset_index(drop=True).to_csv(out_fname, index=False)
| 3,789 | 30.583333 | 109 | py |
ngmm_tools | ngmm_tools-master/Analyses/Code_Verification/regression/ds3/main_cmdstan_model3_corr_cells_NGAWest3CA.py | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Wed Dec 29 15:16:15 2021
@author: glavrent
"""
# Working directory and Packages
# ---------------------------
#load libraries
import os
import sys
import numpy as np
import pandas as pd
import time
#user functions
sys.path.insert(0,'../../../Python_lib/regression/cmdstan/')
# from regression_cmdstan_model3_corr_cells_unbounded_hyp import RunStan
# from regression_cmdstan_model3_corr_cells_sparse_unbounded_hyp import RunStan
# Define variables
# ---------------------------
#filename suffix
# synds_suffix = '_small_corr_len'
# synds_suffix = '_large_corr_len'
#synthetic datasets directory
ds_dir = '../../../../Data/Verification/synthetic_datasets/ds3'
ds_dir = r'%s%s/'%(ds_dir, synds_suffix)
# dataset info
#ds_fname_main = 'CatalogNGAWest3CA_synthetic_data'
ds_fname_main = 'CatalogNGAWest3CALite_synthetic_data'
ds_id = np.arange(1,6)
#cell specific anelastic attenuation
ds_fname_cellinfo = 'CatalogNGAWest3CALite_cellinfo'
ds_fname_celldist = 'CatalogNGAWest3CALite_distancematrix'
#stan model
# sm_fname = '../../../Stan_lib/regression_stan_model3_corr_cells_unbounded_hyp_chol_efficient.stan'
# sm_fname = '../../../Stan_lib/regression_stan_model3_corr_cells_sparse_unbounded_hyp_chol_efficient.stan'
#output info
#main output filename
out_fname_main = 'NGAWest3CA_syndata'
#main output directory
out_dir_main = '../../../../Data/Verification/regression/ds3/'
#output sub-directory
# out_dir_sub = 'CMDSTAN_NGAWest3CA_corr_cells_chol_eff'
# out_dir_sub = 'CMDSTAN_NGAWest3CA_corr_cells_chol_eff_sp'
#stan parameters
res_name = 'tot'
n_iter_warmup = 500
n_iter_sampling = 500
n_chains = 4
adapt_delta = 0.8
max_treedepth = 10
#ergodic coefficients
c_2_erg=-2.0
c_3_erg=-0.6
c_a_erg= 0.0
#parallel options
# flag_parallel = True
flag_parallel = False
#output sub-dir with corr with suffix info
out_dir_sub = f'%s%s'%(out_dir_sub, synds_suffix)
#load cell dataframes
cellinfo_fname = '%s%s.csv'%(ds_dir, ds_fname_cellinfo)
celldist_fname = '%s%s.csv'%(ds_dir, ds_fname_celldist)
df_cellinfo = pd.read_csv(cellinfo_fname)
df_celldist = pd.read_csv(celldist_fname)
# Run stan regression
# ---------------------------
#create datafame with computation time
df_run_info = list()
#iterate over all synthetic datasets
for d_id in ds_id:
print('Synthetic dataset %i fo %i'%(d_id, len(ds_id)))
#run time start
run_t_strt = time.time()
#input flatfile
ds_fname = '%s%s%s_Y%i.csv'%(ds_dir, ds_fname_main, synds_suffix, d_id)
#load flatfile
df_flatfile = pd.read_csv(ds_fname)
#keep only NGAWest2 records
df_flatfile = df_flatfile.loc[df_flatfile.dsid==0,:]
#output file name and directory
out_fname = '%s%s_Y%i'%(out_fname_main, synds_suffix, d_id)
out_dir = '%s/%s/Y%i/'%(out_dir_main, out_dir_sub, d_id)
#run stan model
RunStan(df_flatfile, df_cellinfo, df_celldist, sm_fname,
out_fname, out_dir, res_name,
c_2_erg=c_2_erg, c_3_erg=c_3_erg, c_a_erg=c_a_erg,
n_iter_warmup=n_iter_warmup, n_iter_sampling=n_iter_sampling, n_chains=n_chains,
adapt_delta=adapt_delta, max_treedepth=max_treedepth,
stan_parallel=flag_parallel)
#run time end
run_t_end = time.time()
#compute run time
run_tm = (run_t_end - run_t_strt)/60
#log run time
df_run_info.append(pd.DataFrame({'computer_name':os.uname()[1],'out_name':out_dir_sub,
'ds_id':d_id,'run_time':run_tm}, index=[d_id]))
#write out run info
out_fname = '%s%s/run_info.csv'%(out_dir_main, out_dir_sub)
pd.concat(df_run_info).reset_index(drop=True).to_csv(out_fname, index=False)
| 3,777 | 30.747899 | 107 | py |
ngmm_tools | ngmm_tools-master/Analyses/Code_Verification/regression/ds3/comparison_stan_model3_uncorr_cells.py | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Thu Aug 12 10:26:06 2021
@author: glavrent
"""
# Working directory and Packages
# ---------------------------
#load packages
import os
import sys
import pathlib
import glob
import re #regular expression package
import pickle
#arithmetic libraries
import numpy as np
#statistics libraries
import pandas as pd
#plot libraries
import matplotlib as mpl
import matplotlib.pyplot as plt
from matplotlib.ticker import AutoLocator as plt_autotick
#user functions
sys.path.insert(0,'../../../Python_lib/regression/')
from pylib_stats import CalcRMS
from pylib_stats import CalcLKDivergece
# Define variables
# ---------------------------
# USER SETS DIRECTORIES AND FILE INFO OF SYNTHETIC DS AND REGRESSION RESULTS
# ++++++++++++++++++++++++++++++++++++++++
#processed dataset
# name_dataset = 'NGAWest2CANorth'
# name_dataset = 'NGAWest2CA'
# name_dataset = 'NGAWest3CA'
#correlation info
# 1: Small Correlation Lengths
# 2: Large Correlation Lenghts
corr_id = 1
#package
# 1: Pystan v2
# 2: Pystan v3
# 3: stancmd
pkg_id = 3
#approximation type
# 1: multivariate normal
# 2: cholesky
# 3: cholesky efficient
# 4: cholesky efficient v2
# 5: cholesky efficient, sparse cells
aprox_id = 3
#directories (synthetic dataset)
if corr_id == 1:
dir_syndata = '../../../../Data/Verification/synthetic_datasets/ds3_small_corr_len'
elif corr_id == 2:
dir_syndata = '../../../../Data/Verification/synthetic_datasets/ds3_large_corr_len'
#cell info
fname_cellinfo = dir_syndata + '/' + 'CatalogNGAWest3CALite_cellinfo.csv'
fname_distmat = dir_syndata + '/' + 'CatalogNGAWest3CALite_distancematrix.csv'
#directories (regression results)
if pkg_id == 1:
dir_results = f'../../../../Data/Verification/regression/ds3/PYSTAN_%s'%name_dataset
elif pkg_id == 2:
dir_results = f'../../../../Data/Verification/regression/ds3/PYSTAN3_%s'%name_dataset
elif pkg_id == 3:
dir_results = f'../../../../Data/Verification/regression/ds3/CMDSTAN_%s'%name_dataset
#directories (regression results - old results)
if pkg_id == 1:
dir_results = f'../../../../Data/Verification/regression_old/ds3/PYSTAN_%s'%name_dataset
elif pkg_id == 2:
dir_results = f'../../../../Data/Verification/regression_old/ds3/PYSTAN3_%s'%name_dataset
elif pkg_id == 3:
dir_results = f'../../../../Data/Verification/regression_old/ds3/CMDSTAN_%s'%name_dataset
#prefix for synthetic data and results
prfx_syndata = 'CatalogNGAWest3CALite_synthetic'
#regression results filename prefix
prfx_results = f'%s_syndata'%name_dataset
# FILE INFO FOR REGRESSION RESULTS
# ++++++++++++++++++++++++++++++++++++++++
#output filename sufix (synthetic dataset)
if corr_id == 1: synds_suffix = '_small_corr_len'
elif corr_id == 2: synds_suffix = '_large_corr_len'
#output filename sufix (regression results)
if aprox_id == 1: synds_suffix_stan = '_uncorr_cells' + synds_suffix
elif aprox_id == 2: synds_suffix_stan = '_uncorr_cells' + '_chol' + synds_suffix
elif aprox_id == 3: synds_suffix_stan = '_uncorr_cells' + '_chol_eff' + synds_suffix
elif aprox_id == 4: synds_suffix_stan = '_uncorr_cells' + '_chol_eff2' + synds_suffix
elif aprox_id == 5: synds_suffix_stan = '_uncorr_cells' + '_chol_eff_sp' + synds_suffix
# dataset info
ds_id = np.arange(1,2)
# ++++++++++++++++++++++++++++++++++++++++
# USER NEEDS TO SPECIFY HYPERPARAMETERS OF SYNTHETIC DATASET
# ++++++++++++++++++++++++++++++++++++++++
# hyper-parameters
if corr_id == 1:
# small correlation lengths
hyp = {'omega_0': 0.1, 'omega_1e':0.1, 'omega_1as': 0.35, 'omega_1bs': 0.25,
'ell_1e':60, 'ell_1as':30,
'c_2_erg': -2.0,
'omega_2': 0.2,
'omega_2p': 0.15, 'ell_2p': 80,
'c_3_erg':-0.6,
'omega_3': 0.15,
'omega_3s': 0.15, 'ell_3s': 130,
'c_cap_erg': -0.011,
'omega_cap_mu': 0.005, 'omega_ca1p':0.004, 'omega_ca2p':0.002,
'ell_ca1p': 75,
'phi_0':0.3, 'tau_0':0.25 }
elif corr_id == 2:
# large correlation lengths
hyp = {'omega_0': 0.1, 'omega_1e':0.2, 'omega_1as': 0.4, 'omega_1bs': 0.3,
'ell_1e':100, 'ell_1as':70,
'c_2_erg': -2.0,
'omega_2': 0.2,
'omega_2p': 0.15, 'ell_2e': 140,
'c_3_erg':-0.6,
'omega_3': 0.15,
'omega_3s': 0.15, 'ell_3s': 180,
'c_cap_erg': -0.02,
'omega_cap_mu': 0.008, 'omega_ca1p':0.005, 'omega_ca2p':0.003,
'ell_ca1p': 120,
'phi_0':0.3, 'tau_0':0.25}
# ++++++++++++++++++++++++++++++++++++++++
#ploting options
flag_report = True
# Compare results
# ---------------------------
#load cell data
df_cellinfo = pd.read_csv(fname_cellinfo).set_index('cellid')
df_distmat = pd.read_csv(fname_distmat).set_index('rsn')
#initialize misfit metrics dataframe
df_misfit = pd.DataFrame(index=['Y%i'%d_id for d_id in ds_id])
#iterate over different datasets
for d_id in ds_id:
# Load Data
#--- --- --- --- ---
#file names
#synthetic data
fname_sdata_gmotion = '%s/%s_%s%s_Y%i'%(dir_syndata, prfx_syndata, 'data', synds_suffix, d_id) + '.csv'
fname_sdata_atten = '%s/%s_%s%s_Y%i'%(dir_syndata, prfx_syndata, 'atten', synds_suffix, d_id) + '.csv'
#regression results
fname_reg_gmotion = '%s%s/Y%i/%s%s_Y%i_stan_%s'%(dir_results, synds_suffix_stan, d_id, prfx_results, synds_suffix, d_id, 'residuals') + '.csv'
fname_reg_coeff = '%s%s/Y%i/%s%s_Y%i_stan_%s'%(dir_results, synds_suffix_stan, d_id, prfx_results, synds_suffix, d_id, 'coefficients') + '.csv'
fname_reg_atten = '%s%s/Y%i/%s%s_Y%i_stan_%s'%(dir_results, synds_suffix_stan, d_id, prfx_results, synds_suffix, d_id, 'catten') + '.csv'
#load synthetic results
df_sdata_gmotion = pd.read_csv(fname_sdata_gmotion).set_index('rsn')
df_sdata_atten = pd.read_csv(fname_sdata_atten).set_index('cellid')
#load regression results
df_reg_gmotion = pd.read_csv(fname_reg_gmotion, index_col=0)
df_reg_coeff = pd.read_csv(fname_reg_coeff, index_col=0)
df_reg_atten = pd.read_csv(fname_reg_atten, index_col=0)
# Processing
#--- --- --- --- ---
#keep only relevant columns from synthetic dataset
df_sdata_gmotion = df_sdata_gmotion.reindex(df_reg_gmotion.index)
df_sdata_atten = df_sdata_atten.reindex(df_reg_atten.index)
#distance matrix for records of interest
df_dmat = df_distmat.reindex(df_sdata_gmotion.index)
#find unique earthqakes and stations
eq_id, eq_idx, eq_nrec = np.unique(df_sdata_gmotion.eqid, return_index=True, return_counts=True)
sta_id, sta_idx, sta_nrec = np.unique(df_sdata_gmotion.ssn, return_index=True, return_counts=True)
#number of paths per cell
cell_npath = np.sum(df_dmat.loc[:,df_reg_atten.cellname] > 0, axis=0)
# Compute Root Mean Square Error
#--- --- --- --- ---
df_misfit.loc['Y%i'%d_id,'nerg_tot_rms'] = CalcRMS(df_sdata_gmotion.nerg_gm.values, df_reg_gmotion.nerg_mu.values)
df_misfit.loc['Y%i'%d_id,'dc_1e_rms'] = CalcRMS(df_sdata_gmotion['dc_1e'].values[eq_idx], df_reg_coeff['dc_1e_mean'].values[eq_idx])
df_misfit.loc['Y%i'%d_id,'dc_1as_rms'] = CalcRMS(df_sdata_gmotion['dc_1as'].values[sta_idx], df_reg_coeff['dc_1as_mean'].values[sta_idx])
df_misfit.loc['Y%i'%d_id,'dc_1bs_rms'] = CalcRMS(df_sdata_gmotion['dc_1bs'].values[sta_idx], df_reg_coeff['dc_1bs_mean'].values[sta_idx])
df_misfit.loc['Y%i'%d_id,'c_2p_rms'] = CalcRMS(df_sdata_gmotion['c_2p'].values[eq_idx], df_reg_coeff['c_2p_mean'].values[eq_idx])
df_misfit.loc['Y%i'%d_id,'c_3s_rms'] = CalcRMS(df_sdata_gmotion['c_3s'].values[sta_idx], df_reg_coeff['c_3s_mean'].values[sta_idx])
df_misfit.loc['Y%i'%d_id,'c_cap_rms'] = CalcRMS(df_sdata_atten['c_cap'].values, df_reg_atten['c_cap_mean'].values)
# Compute Divergence
#--- --- --- --- ---
df_misfit.loc['Y%i'%d_id,'nerg_tot_KL'] = CalcLKDivergece(df_sdata_gmotion.nerg_gm.values, df_reg_gmotion.nerg_mu.values)
df_misfit.loc['Y%i'%d_id,'dc_1e_KL'] = CalcLKDivergece(df_sdata_gmotion['dc_1e'].values[eq_idx], df_reg_coeff['dc_1e_mean'].values[eq_idx])
df_misfit.loc['Y%i'%d_id,'dc_1as_KL'] = CalcLKDivergece(df_sdata_gmotion['dc_1as'].values[sta_idx], df_reg_coeff['dc_1as_mean'].values[sta_idx])
df_misfit.loc['Y%i'%d_id,'dc_1bs_KL'] = CalcLKDivergece(df_sdata_gmotion['dc_1bs'].values[sta_idx], df_reg_coeff['dc_1bs_mean'].values[sta_idx])
df_misfit.loc['Y%i'%d_id,'c_2p_KL'] = CalcLKDivergece(df_sdata_gmotion['c_2p'].values[eq_idx], df_reg_coeff['c_2p_mean'].values[eq_idx])
df_misfit.loc['Y%i'%d_id,'c_3s_KL'] = CalcLKDivergece(df_sdata_gmotion['c_3s'].values[sta_idx], df_reg_coeff['c_3s_mean'].values[sta_idx])
df_misfit.loc['Y%i'%d_id,'c_cap_KL'] = CalcLKDivergece(df_sdata_atten['c_cap'].values, df_reg_atten['c_cap_mean'].values)
# Output
#--- --- --- --- ---
#figure directory
dir_fig = '%s%s/Y%i/figures_cmp/'%(dir_results,synds_suffix_stan,d_id)
pathlib.Path(dir_fig).mkdir(parents=True, exist_ok=True)
#compare ground motion predictions
#... ... ... ... ... ...
#figure title
fname_fig = 'Y%i_scatter_tot_res'%d_id
#create figure
fig, ax = plt.subplots(figsize = (10,10))
#median
ax.scatter(df_sdata_gmotion.nerg_gm.values, df_reg_gmotion.nerg_mu.values)
ax.axline((0,0), slope=1, color="black", linestyle="--")
#edit figure
if not flag_report: ax.set_title('Comparison total residuals, Y: %i'%d_id, fontsize=30)
ax.set_xlabel('Synthetic dataset', fontsize=25)
ax.set_ylabel('Estimated', fontsize=25)
ax.grid(which='both')
ax.tick_params(axis='x', labelsize=22)
ax.tick_params(axis='y', labelsize=22)
#plot limits
# plt_lim = np.array([ax.get_xlim(), ax.get_ylim()])
# plt_lim = (plt_lim[:,0].min(), plt_lim[:,1].max())
# ax.set_xlim(plt_lim)
# ax.set_ylim(plt_lim)
ax.set_xlim([-10,2])
ax.set_ylim([-10,2])
fig.tight_layout()
fig.savefig( dir_fig + fname_fig + '.png' )
#compare dc_1e
#... ... ... ... ... ...
#figure title
fname_fig = 'Y%i_dc_1e_scatter'%d_id
#create figure
fig, ax = plt.subplots(figsize = (10,10))
#coefficient scatter
ax.scatter(df_sdata_gmotion['dc_1e'].values[eq_idx], df_reg_coeff['dc_1e_mean'].values[eq_idx])
ax.axline((0,0), slope=1, color="black", linestyle="--")
#edit figure
if not flag_report: ax.set_title(r'Comparison $\delta c_{1,E}$, Y: %i'%d_id, fontsize=30)
ax.set_xlabel('Synthetic dataset', fontsize=25)
ax.set_ylabel('Estimated', fontsize=25)
ax.grid(which='both')
ax.tick_params(axis='x', labelsize=22)
ax.tick_params(axis='y', labelsize=22)
#plot limits
# plt_lim = np.array([ax.get_xlim(), ax.get_ylim()])
# plt_lim = (plt_lim[:,0].min(), plt_lim[:,1].max())
# ax.set_xlim(plt_lim)
# ax.set_ylim(plt_lim)
ax.set_xlim([-.4,.4])
ax.set_ylim([-.4,.4])
#save figure
fig.tight_layout()
fig.savefig( dir_fig + fname_fig + '.png' )
#figure title
fname_fig = 'Y%i_dc_1e_accuracy'%d_id
#create figure
fig, ax = plt.subplots(figsize = (10,10))
#coefficient scatter
ax.scatter(df_reg_coeff['dc_1e_sig'].values[eq_idx],
df_sdata_gmotion['dc_1e'].values[eq_idx] - df_reg_coeff['dc_1e_mean'].values[eq_idx])
ax.axline((0,0), slope=0, color="black", linestyle="--")
#edit figure
if not flag_report: ax.set_title(r'Comparison $\delta c_{1,E}$, Y: %i'%d_id, fontsize=30)
ax.set_xlabel('Standard Deviation', fontsize=25)
ax.set_ylabel('Actual - Estimated', fontsize=25)
ax.grid(which='both')
ax.tick_params(axis='x', labelsize=22)
ax.tick_params(axis='y', labelsize=22)
#plot limits
# ax.set_ylim(np.abs(ax.get_ylim()).max()*np.array([-1,1]))
ax.set_xlim([0,.15])
ax.set_ylim([-.4,.4])
#save figure
fig.tight_layout()
fig.savefig( dir_fig + fname_fig + '.png' )
#figure title
fname_fig = 'Y%i_dc_1e_nrec'%d_id
#create figure
fig, ax = plt.subplots(figsize = (10,10))
#coefficient scatter
ax.scatter(eq_nrec,
df_sdata_gmotion['dc_1e'].values[eq_idx] - df_reg_coeff['dc_1e_mean'].values[eq_idx])
ax.axline((0,0), slope=0, color="black", linestyle="--")
#edit figure
if not flag_report: ax.set_title(r'Comparison $\delta c_{1,E}$, Y: %i'%d_id, fontsize=30)
ax.set_xlabel('Number of records', fontsize=25)
ax.set_ylabel('Actual - Estimated', fontsize=25)
ax.grid(which='both')
ax.set_xscale('log')
ax.tick_params(axis='x', labelsize=22)
ax.tick_params(axis='y', labelsize=22)
#plot limits
# ax.set_ylim(np.abs(ax.get_ylim()).max()*np.array([-1,1]))
ax.set_xlim([0.9,1e3])
ax.set_ylim([-.4,.4])
#save figure
fig.tight_layout()
fig.savefig( dir_fig + fname_fig + '.png' )
#compare dc_1as
#... ... ... ... ... ...
#figure title
fname_fig = 'Y%i_dc_1as_scatter'%d_id
#create figure
fig, ax = plt.subplots(figsize = (10,10))
#coefficient scatter
ax.scatter(df_sdata_gmotion['dc_1as'].values[sta_idx], df_reg_coeff['dc_1as_mean'].values[sta_idx])
ax.axline((0,0), slope=1, color="black", linestyle="--")
#edit figure
if not flag_report: ax.set_title(r'Comparison $\delta c_{1a,S}$, Y: %i'%d_id, fontsize=30)
ax.set_xlabel('Synthetic dataset', fontsize=25)
ax.set_ylabel('Estimated', fontsize=25)
ax.grid(which='both')
ax.tick_params(axis='x', labelsize=22)
ax.tick_params(axis='y', labelsize=22)
#plot limits
# plt_lim = np.array([ax.get_xlim(), ax.get_ylim()])
# plt_lim = (plt_lim[:,0].min(), plt_lim[:,1].max())
# ax.set_xlim(plt_lim)
# ax.set_ylim(plt_lim)
ax.set_xlim([-1.5,1.5])
ax.set_ylim([-1.5,1.5])
#save figure
fig.tight_layout()
fig.savefig( dir_fig + fname_fig + '.png' )
#figure title
fname_fig = 'Y%i_dc_1as_accuracy'%d_id
#create figure
fig, ax = plt.subplots(figsize = (10,10))
#accuray
ax.scatter(df_reg_coeff['dc_1as_sig'].values[sta_idx],
df_sdata_gmotion['dc_1as'].values[sta_idx] - df_reg_coeff['dc_1as_mean'].values[sta_idx])
ax.axline((0,0), slope=0, color="black", linestyle="--")
#edit figure
if not flag_report: ax.set_title(r'Comparison $\delta c_{1a,S}$, Y: %i'%d_id, fontsize=30)
ax.set_xlabel('Standard Deviation', fontsize=25)
ax.set_ylabel('Actual - Estimated', fontsize=25)
ax.grid(which='both')
ax.tick_params(axis='x', labelsize=22)
ax.tick_params(axis='y', labelsize=22)
#plot limits
# ax.set_ylim(np.abs(ax.get_ylim()).max()*np.array([-1,1]))
ax.set_xlim([0,.4])
ax.set_ylim([-1.5,1.5])
#save figure
fig.tight_layout()
fig.savefig( dir_fig + fname_fig + '.png' )
#figure title
fname_fig = 'Y%i_dc_1as_nrec'%d_id
#create figure
fig, ax = plt.subplots(figsize = (10,10))
#accuray
ax.scatter(sta_nrec,
df_sdata_gmotion['dc_1as'].values[sta_idx] - df_reg_coeff['dc_1as_mean'].values[sta_idx])
ax.axline((0,0), slope=0, color="black", linestyle="--")
#edit figure
if not flag_report: ax.set_title(r'Comparison $\delta c_{1a,S}$, Y: %i'%d_id, fontsize=30)
ax.set_xlabel('Number of records', fontsize=25)
ax.set_ylabel('Actual - Estimated', fontsize=25)
ax.grid(which='both')
ax.set_xscale('log')
ax.tick_params(axis='x', labelsize=22)
ax.tick_params(axis='y', labelsize=22)
#plot limits
# ax.set_ylim(np.abs(ax.get_ylim()).max()*np.array([-1,1]))
ax.set_xlim([.9,1000])
ax.set_ylim([-1.5,1.5])
#save figure
fig.tight_layout()
fig.savefig( dir_fig + fname_fig + '.png' )
#compare dc_1bs
#... ... ... ... ... ...
#figure title
fname_fig = 'Y%i_dc_1bs_scatter'%d_id
#create figure
fig, ax = plt.subplots(figsize = (10,10))
#coefficient scatter
ax.scatter(df_sdata_gmotion['dc_1bs'].values[sta_idx], df_reg_coeff['dc_1bs_mean'].values[sta_idx])
ax.axline((0,0), slope=1, color="black", linestyle="--")
#edit figure
if not flag_report: ax.set_title(r'Comparison $\delta c_{1b,S}$, Y: %i'%d_id, fontsize=30)
ax.set_xlabel('Synthetic dataset', fontsize=25)
ax.set_ylabel('Estimated', fontsize=25)
ax.grid(which='both')
ax.tick_params(axis='x', labelsize=22)
ax.tick_params(axis='y', labelsize=22)
#plot limits
# plt_lim = np.array([ax.get_xlim(), ax.get_ylim()])
# plt_lim = (plt_lim[:,0].min(), plt_lim[:,1].max())
# ax.set_xlim(plt_lim)
# ax.set_ylim(plt_lim)
ax.set_xlim([-1.5,1.5])
ax.set_ylim([-1.5,1.5])
#save figure
fig.tight_layout()
fig.savefig( dir_fig + fname_fig + '.png' )
#figure title
fname_fig = 'Y%i_dc_1bs_accuracy'%d_id
#create figure
fig, ax = plt.subplots(figsize = (10,10))
#accuray
ax.scatter(df_reg_coeff['dc_1bs_sig'].values[sta_idx],
df_sdata_gmotion['dc_1bs'].values[sta_idx] - df_reg_coeff['dc_1bs_mean'].values[sta_idx])
ax.axline((0,0), slope=0, color="black", linestyle="--")
#edit figure
if not flag_report: ax.set_title(r'Comparison $\delta c_{1b,S}$, Y: %i'%d_id, fontsize=30)
ax.set_xlabel('Standard Deviation', fontsize=25)
ax.set_ylabel('Actual - Estimated', fontsize=25)
ax.grid(which='both')
ax.tick_params(axis='x', labelsize=22)
ax.tick_params(axis='y', labelsize=22)
#plot limits
# ax.set_ylim(np.abs(ax.get_ylim()).max()*np.array([-1,1]))
ax.set_xlim([0,.4])
ax.set_ylim([-1.5,1.5])
#save figure
fig.tight_layout()
fig.savefig( dir_fig + fname_fig + '.png' )
#figure title
fname_fig = 'Y%i_dc_1bs_nrec'%d_id
#create figure
fig, ax = plt.subplots(figsize = (10,10))
#accuray
ax.scatter(sta_nrec,
df_sdata_gmotion['dc_1bs'].values[sta_idx] - df_reg_coeff['dc_1bs_mean'].values[sta_idx])
ax.axline((0,0), slope=0, color="black", linestyle="--")
#edit figure
if not flag_report: ax.set_title(r'Comparison $\delta c_{1b,S}$, Y: %i'%d_id, fontsize=30)
ax.set_xlabel('Number of records', fontsize=25)
ax.set_ylabel('Actual - Estimated', fontsize=25)
ax.grid(which='both')
ax.set_xscale('log')
ax.tick_params(axis='x', labelsize=22)
ax.tick_params(axis='y', labelsize=22)
#plot limits
# ax.set_ylim(np.abs(ax.get_ylim()).max()*np.array([-1,1]))
ax.set_xlim([.9,1000])
ax.set_ylim([-1.5,1.5])
#save figure
fig.tight_layout()
fig.savefig( dir_fig + fname_fig + '.png' )
#compare c_2p
#... ... ... ... ... ...
#figure title
fname_fig = 'Y%i_c_2p_scatter'%d_id
#create figure
fig, ax = plt.subplots(figsize = (10,10))
#coefficient scatter
ax.scatter(df_sdata_gmotion['c_2p'].values[eq_idx], df_reg_coeff['c_2p_mean'].values[eq_idx])
ax.axline((0,0), slope=1, color="black", linestyle="--")
#edit figure
if not flag_report: ax.set_title(r'Comparison $c_{2,P}$, Y: %i'%d_id, fontsize=30)
ax.set_xlabel('Synthetic dataset', fontsize=25)
ax.set_ylabel('Estimated', fontsize=25)
ax.grid(which='both')
ax.tick_params(axis='x', labelsize=22)
ax.tick_params(axis='y', labelsize=22)
#plot limits
# plt_lim = np.array([ax.get_xlim(), ax.get_ylim()])
# plt_lim = (plt_lim[:,0].min(), plt_lim[:,1].max())
# ax.set_xlim(plt_lim)
# ax.set_ylim(plt_lim)
ax.set_xlim([-2.3,-1.6])
ax.set_ylim([-2.3,-1.6])
#save figure
fig.tight_layout()
fig.savefig( dir_fig + fname_fig + '.png' )
#figure title
fname_fig = 'Y%i_c_2p_accuracy'%d_id
#create figure
fig, ax = plt.subplots(figsize = (10,10))
#coefficient scatter
ax.scatter(df_reg_coeff['c_2p_sig'].values[eq_idx],
df_sdata_gmotion['c_2p'].values[eq_idx] - df_reg_coeff['c_2p_mean'].values[eq_idx])
ax.axline((0,0), slope=0, color="black", linestyle="--")
#edit figure
if not flag_report: ax.set_title(r'Comparison $c_{2,P}$, Y: %i'%d_id, fontsize=30)
ax.set_xlabel('Standard Deviation', fontsize=25)
ax.set_ylabel('Actual - Estimated', fontsize=25)
ax.grid(which='both')
ax.tick_params(axis='x', labelsize=22)
ax.tick_params(axis='y', labelsize=22)
#plot limits
# ax.set_ylim(np.abs(ax.get_ylim()).max()*np.array([-1,1]))
ax.set_xlim([0,.15])
ax.set_ylim([-.4,.4])
#save figure
fig.tight_layout()
fig.savefig( dir_fig + fname_fig + '.png' )
#figure title
fname_fig = 'Y%i_c_2p_nrec'%d_id
#create figure
fig, ax = plt.subplots(figsize = (10,10))
#coefficient scatter
ax.scatter(eq_nrec,
df_sdata_gmotion['c_2p'].values[eq_idx] - df_reg_coeff['c_2p_mean'].values[eq_idx])
ax.axline((0,0), slope=0, color="black", linestyle="--")
#edit figure
if not flag_report: ax.set_title(r'Comparison $c_{2,P}$, Y: %i'%d_id, fontsize=30)
ax.set_xlabel('Number of records', fontsize=25)
ax.set_ylabel('Actual - Estimated', fontsize=25)
ax.grid(which='both')
ax.set_xscale('log')
ax.tick_params(axis='x', labelsize=22)
ax.tick_params(axis='y', labelsize=22)
#plot limits
# ax.set_ylim(np.abs(ax.get_ylim()).max()*np.array([-1,1]))
ax.set_xlim([0.9,1e3])
ax.set_ylim([-.4,.4])
#save figure
fig.tight_layout()
fig.savefig( dir_fig + fname_fig + '.png' )
#compare c_3s
#... ... ... ... ... ...
#figure title
fname_fig = 'Y%i_c_3s_scatter'%d_id
#create figure
fig, ax = plt.subplots(figsize = (10,10))
#coefficient scatter
ax.scatter(df_sdata_gmotion['c_3s'].values[sta_idx], df_reg_coeff['c_3s_mean'].values[sta_idx])
ax.axline((0,0), slope=1, color="black", linestyle="--")
#edit figure
if not flag_report: ax.set_title(r'Comparison $c_{3,S}$, Y: %i'%d_id, fontsize=30)
ax.set_xlabel('Synthetic dataset', fontsize=25)
ax.set_ylabel('Estimated', fontsize=25)
ax.grid(which='both')
ax.tick_params(axis='x', labelsize=22)
ax.tick_params(axis='y', labelsize=22)
#plot limits
# plt_lim = np.array([ax.get_xlim(), ax.get_ylim()])
# plt_lim = (plt_lim[:,0].min(), plt_lim[:,1].max())
# ax.set_xlim(plt_lim)
# ax.set_ylim(plt_lim)
ax.set_xlim([-1.2,-.2])
ax.set_ylim([-1.2,-.2])
#save figure
fig.tight_layout()
fig.savefig( dir_fig + fname_fig + '.png' )
#figure title
fname_fig = 'Y%i_c_3s_accuracy'%d_id
#create figure
fig, ax = plt.subplots(figsize = (10,10))
#coefficient scatter
ax.scatter(df_reg_coeff['c_3s_sig'].values[sta_idx],
df_sdata_gmotion['c_3s'].values[sta_idx] - df_reg_coeff['c_3s_mean'].values[sta_idx])
ax.axline((0,0), slope=0, color="black", linestyle="--")
#edit figure
if not flag_report: ax.set_title(r'Comparison $c_{3,S}$, Y: %i'%d_id, fontsize=30)
ax.set_xlabel('Standard Deviation', fontsize=25)
ax.set_ylabel('Actual - Estimated', fontsize=25)
ax.grid(which='both')
ax.tick_params(axis='x', labelsize=22)
ax.tick_params(axis='y', labelsize=22)
#plot limits
# ax.set_ylim(np.abs(ax.get_ylim()).max()*np.array([-1,1]))
ax.set_xlim([0,.3])
ax.set_ylim([-.4,.4])
#save figure
fig.tight_layout()
fig.savefig( dir_fig + fname_fig + '.png' )
#figure title
fname_fig = 'Y%i_c_3s_nrec'%d_id
#create figure
fig, ax = plt.subplots(figsize = (10,10))
#coefficient scatter
ax.scatter(sta_nrec,
df_sdata_gmotion['c_3s'].values[sta_idx] - df_reg_coeff['c_3s_mean'].values[sta_idx])
ax.axline((0,0), slope=0, color="black", linestyle="--")
#edit figure
if not flag_report: ax.set_title(r'Comparison $c_{3,S}$, Y: %i'%d_id, fontsize=30)
ax.set_xlabel('Number of records', fontsize=25)
ax.set_ylabel('Actual - Estimated', fontsize=25)
ax.grid(which='both')
ax.set_xscale('log')
ax.tick_params(axis='x', labelsize=22)
ax.tick_params(axis='y', labelsize=22)
#plot limits
# ax.set_ylim(np.abs(ax.get_ylim()).max()*np.array([-1,1]))
ax.set_xlim([0.9,1e3])
ax.set_ylim([-.4,.4])
#save figure
fig.tight_layout()
fig.savefig( dir_fig + fname_fig + '.png' )
#compare c_cap
#... ... ... ... ... ...
#figure title
fname_fig = 'Y%i_c_cap_scatter'%d_id
#create figure
fig, ax = plt.subplots(figsize = (10,10))
#coefficient scatter
ax.scatter(df_sdata_atten['c_cap'].values, df_reg_atten['c_cap_mean'].values)
ax.axline((0,0), slope=1, color="black", linestyle="--")
#edit figure
if not flag_report: ax.set_title(r'Comparison $c_{ca,P}$, Y: %i'%d_id, fontsize=30)
ax.set_xlabel('Synthetic dataset', fontsize=25)
ax.set_ylabel('Estimated', fontsize=25)
ax.grid(which='both')
ax.tick_params(axis='x', labelsize=22)
ax.tick_params(axis='y', labelsize=22)
#plot limits
# plt_lim = np.array([ax.get_xlim(), ax.get_ylim()])
# plt_lim = (plt_lim[:,0].min(), plt_lim[:,1].max())
# ax.set_xlim(plt_lim)
# ax.set_ylim(plt_lim)
ax.set_xlim([-0.05,0.02])
ax.set_ylim([-0.05,0.02])
#save figure
fig.tight_layout()
fig.savefig( dir_fig + fname_fig + '.png' )
#figure title
fname_fig = 'Y%i_c_cap_accuracy'%d_id
#create figure
fig, ax = plt.subplots(figsize = (10,10))
#coefficient scatter
ax.scatter(df_reg_atten['c_cap_sig'],
df_sdata_atten['c_cap'].values - df_reg_atten['c_cap_mean'].values)
ax.axline((0,0), slope=0, color="black", linestyle="--")
#edit figure
if not flag_report: ax.set_title(r'Comparison $c_{ca,P}$, Y: %i'%d_id, fontsize=30)
ax.set_xlabel('Standard Deviation', fontsize=25)
ax.set_ylabel('Actual - Estimated', fontsize=25)
ax.grid(which='both')
ax.tick_params(axis='x', labelsize=22)
ax.tick_params(axis='y', labelsize=22)
#plot limits
# ax.set_ylim(np.abs(ax.get_ylim()).max()*np.array([-1,1]))
ax.set_xlim([0.00,0.03])
ax.set_ylim([-0.04,0.04])
#save figure
fig.tight_layout()
fig.savefig( dir_fig + fname_fig + '.png' )
#figure title
fname_fig = 'Y%i_c_cap_npath'%d_id
#create figure
fig, ax = plt.subplots(figsize = (10,10))
#coefficient scatter
ax.scatter(cell_npath,
df_sdata_atten['c_cap'].values - df_reg_atten['c_cap_mean'].values)
ax.axline((0,0), slope=0, color="black", linestyle="--")
#edit figure
if not flag_report: ax.set_title(r'Comparison $c_{ca,P}$, Y: %i'%d_id, fontsize=30)
ax.set_xlabel('Number of paths', fontsize=25)
ax.set_ylabel('Actual - Estimated', fontsize=25)
ax.grid(which='both')
ax.set_xscale('log')
ax.tick_params(axis='x', labelsize=22)
ax.tick_params(axis='y', labelsize=22)
#plot limits
# ax.set_ylim(np.abs(ax.get_ylim()).max()*np.array([-1,1]))
ax.set_xlim([.9,5e4])
ax.set_ylim([-0.04,0.04])
#save figure
fig.tight_layout()
fig.savefig( dir_fig + fname_fig + '.png' )
# Compare Misfit Metrics
# ---------------------------
#summary directory
dir_sum = '%s%s/summary/'%(dir_results,synds_suffix_stan)
pathlib.Path(dir_fig).mkdir(parents=True, exist_ok=True)
#figure directory
dir_fig = '%s/figures/'%(dir_sum)
pathlib.Path(dir_fig).mkdir(parents=True, exist_ok=True)
#save
df_misfit.to_csv(dir_sum + 'misfit_summary.csv')
#RMS misfit
fname_fig = 'misfit_score'
#plot KL divergence
fig, ax = plt.subplots(figsize = (10,10))
ax.plot(ds_id, df_misfit.nerg_tot_rms, linestyle='-', marker='o', linewidth=2, markersize=10, label= 'tot nerg')
ax.plot(ds_id, df_misfit.dc_1e_rms, linestyle='-', marker='o', linewidth=2, markersize=10, label=r'$\delta c_{1,E}$')
ax.plot(ds_id, df_misfit.dc_1as_rms, linestyle='-', marker='o', linewidth=2, markersize=10, label=r'$\delta c_{1a,S}$')
ax.plot(ds_id, df_misfit.dc_1bs_rms, linestyle='-', marker='o', linewidth=2, markersize=10, label=r'$\delta c_{1b,S}$')
ax.plot(ds_id, df_misfit.c_2p_rms, linestyle='-', marker='o', linewidth=2, markersize=10, label=r'$c_{2,E}$')
ax.plot(ds_id, df_misfit.c_3s_rms, linestyle='-', marker='o', linewidth=2, markersize=10, label=r'$c_{3,S}$')
ax.plot(ds_id, df_misfit.c_cap_rms, linestyle='-', marker='o', linewidth=2, markersize=10, label=r'$c_{ca,P}$')
#figure properties
ax.set_ylim([0,0.50])
ax.set_xlabel('synthetic dataset', fontsize=25)
ax.set_ylabel('RSME', fontsize=25)
ax.grid(which='both')
ax.set_xticks(ds_id)
ax.set_xticklabels(labels=df_misfit.index)
ax.tick_params(axis='x', labelsize=22)
ax.tick_params(axis='y', labelsize=22)
#legend
ax.legend(loc='upper left', fontsize=25)
#save figure
fig.tight_layout()
fig.savefig( dir_fig + fname_fig + '.png' )
#KL divergence
fname_fig = 'KLdiv_score'
#plot KL divergence
fig, ax = plt.subplots(figsize = (10,10))
ax.plot(ds_id, df_misfit.nerg_tot_KL, linestyle='-', marker='o', linewidth=2, markersize=10, label= 'tot nerg')
ax.plot(ds_id, df_misfit.dc_1e_KL, linestyle='-', marker='o', linewidth=2, markersize=10, label=r'$\delta c_{1,E}$')
ax.plot(ds_id, df_misfit.dc_1as_KL, linestyle='-', marker='o', linewidth=2, markersize=10, label=r'$\delta c_{1a,S}$')
ax.plot(ds_id, df_misfit.dc_1bs_KL, linestyle='-', marker='o', linewidth=2, markersize=10, label=r'$\delta c_{1b,S}$')
ax.plot(ds_id, df_misfit.c_2p_KL, linestyle='-', marker='o', linewidth=2, markersize=10, label=r'$c_{2,P}$')
ax.plot(ds_id, df_misfit.c_3s_KL, linestyle='-', marker='o', linewidth=2, markersize=10, label=r'$c_{3,S}$')
ax.plot(ds_id, df_misfit.c_cap_KL, linestyle='-', marker='o', linewidth=2, markersize=10, label=r'$c_{ca,P}$')
#figure properties
ax.set_ylim([0,0.50])
ax.set_xlabel('synthetic dataset', fontsize=25)
ax.set_ylabel('KL divergence', fontsize=25)
ax.grid(which='both')
ax.set_xticks(ds_id)
ax.set_xticklabels(labels=df_misfit.index)
ax.tick_params(axis='x', labelsize=22)
ax.tick_params(axis='y', labelsize=22)
#legend
ax.legend(loc='upper left', fontsize=25)
#save figure
fig.tight_layout()
fig.savefig( dir_fig + fname_fig + '.png' )
# Compare hyper-paramters
# ---------------------------
#iterate over different datasets
df_reg_hyp = list()
df_reg_hyp_post = list()
for d_id in ds_id:
# Load Data
#--- --- --- --- ---
#regression hyperparamters results
fname_reg_hyp = '%s%s/Y%i/%s%s_Y%i_stan_%s'%(dir_results,synds_suffix_stan, d_id,prfx_results, synds_suffix, d_id, 'hyperparameters') + '.csv'
fname_reg_hyp_post = '%s%s/Y%i/%s%s_Y%i_stan_%s'%(dir_results,synds_suffix_stan, d_id,prfx_results, synds_suffix, d_id, 'hyperposterior') + '.csv'
#load regression results
df_reg_hyp.append( pd.read_csv(fname_reg_hyp, index_col=0) )
df_reg_hyp_post.append( pd.read_csv(fname_reg_hyp_post, index_col=0) )
#figure directory
dir_fig = '%s%s/figures_cmp_hyp/'%(dir_results,synds_suffix_stan)
pathlib.Path(dir_fig).mkdir(parents=True, exist_ok=True)
# Omega_1e
#--- --- --- --- ---
#hyper-paramter name
name_hyp = 'omega_1e'
#figure title
fname_fig = 'post_dist_' + name_hyp
#create figure
fig, ax = plt.subplots(figsize = (10,10))
for d_id, df_r_h, df_r_h_p in zip(ds_id, df_reg_hyp, df_reg_hyp_post):
#estimate vertical line height for mean and mode
ymax_mode = 40
ymax_mean = 40
#plot posterior dist
pl_hyp = ax.vlines(df_r_h.loc['mean',name_hyp], ymin=0, ymax=ymax_mean, linestyle='-', label='Mean')
ax.vlines(df_r_h.loc['prc_0.50',name_hyp], ymin=0, ymax=ymax_mode, linestyle='--', color=pl_hyp.get_color(), label='Mode')
#plot true value
ymax_hyp = ymax_mean
ax.vlines(hyp[name_hyp], ymin=0, ymax=ymax_hyp, linestyle='-', linewidth=4, color='black', label='True value')
#edit figure
if not flag_report: ax.set_title(r'Comparison $\omega_{1,E}$', fontsize=30)
ax.set_xlabel('$\omega_{1,e}$', fontsize=25)
ax.set_ylabel('probability density function ', fontsize=25)
ax.grid(which='both')
ax.tick_params(axis='x', labelsize=22)
ax.tick_params(axis='y', labelsize=22)
#plot limits
ax.set_xlim([0,0.25])
ax.set_ylim([0,ymax_hyp])
#save figure
fig.tight_layout()
fig.savefig( dir_fig + fname_fig + '.png' )
# Omega_1as
#--- --- --- --- ---
#hyper-paramter name
name_hyp = 'omega_1as'
#figure title
fname_fig = 'post_dist_' + name_hyp
#create figure
fig, ax = plt.subplots(figsize = (10,10))
for d_id, df_r_h, df_r_h_p in zip(ds_id, df_reg_hyp, df_reg_hyp_post):
#estimate vertical line height for mean and mode
ymax_mode = 30
ymax_mean = 30
#plot posterior dist
pl_hyp = ax.vlines(df_r_h.loc['mean',name_hyp], ymin=0, ymax=ymax_mean, linestyle='-', label='Mean')
ax.vlines(df_r_h.loc['prc_0.50',name_hyp], ymin=0, ymax=ymax_mode, linestyle='--', color=pl_hyp.get_color(), label='Mode')
#plot true value
ymax_hyp = ymax_mean
ax.vlines(hyp[name_hyp], ymin=0, ymax=ymax_hyp, linestyle='-', linewidth=4, color='black', label='True value')
#edit figure
if not flag_report: ax.set_title(r'Comparison $\omega_{1a,S}$', fontsize=30)
ax.set_xlabel('$\omega_{1a,s}$', fontsize=25)
ax.set_ylabel('probability density function ', fontsize=25)
ax.grid(which='both')
ax.tick_params(axis='x', labelsize=22)
ax.tick_params(axis='y', labelsize=22)
#plot limits
ax.set_xlim([0,0.5])
ax.set_ylim([0,ymax_hyp])
#save figure
fig.tight_layout()
fig.savefig( dir_fig + fname_fig + '.png' )
# Omega_1bs
#--- --- --- --- ---
#hyper-paramter name
name_hyp = 'omega_1bs'
#figure title
fname_fig = 'post_dist_' + name_hyp
#create figure
fig, ax = plt.subplots(figsize = (10,10))
for d_id, df_r_h, df_r_h_p in zip(ds_id, df_reg_hyp, df_reg_hyp_post):
#estimate vertical line height for mean and mode
ymax_mode = 60
ymax_mean = 60
#plot posterior dist
pl_hyp = ax.vlines(df_r_h.loc['mean',name_hyp], ymin=0, ymax=ymax_mean, linestyle='-', label='Mean')
ax.vlines(df_r_h.loc['prc_0.50',name_hyp], ymin=0, ymax=ymax_mode, linestyle='--', color=pl_hyp.get_color(), label='Mode')
#plot true value
ymax_hyp = ymax_mean
ax.vlines(hyp[name_hyp], ymin=0, ymax=ymax_hyp, linestyle='-', linewidth=4, color='black', label='True value')
#edit figure
if not flag_report: ax.set_title(r'Comparison $\omega_{1b,S}$', fontsize=30)
ax.set_xlabel('$\omega_{1b,s}$', fontsize=25)
ax.set_ylabel('probability density function ', fontsize=25)
ax.grid(which='both')
ax.tick_params(axis='x', labelsize=22)
ax.tick_params(axis='y', labelsize=22)
#plot limits
ax.set_xlim([0,0.5])
ax.set_ylim([0,ymax_hyp])
#save figure
fig.tight_layout()
fig.savefig( dir_fig + fname_fig + '.png' )
# Omega_2p
#--- --- --- --- ---
#hyper-paramter name
name_hyp = 'omega_2p'
#figure title
fname_fig = 'post_dist_' + name_hyp
#create figure
fig, ax = plt.subplots(figsize = (10,10))
for d_id, df_r_h, df_r_h_p in zip(ds_id, df_reg_hyp, df_reg_hyp_post):
#estimate vertical line height for mean and mode
ymax_mode = 60
ymax_mean = 60
#plot posterior dist
pl_hyp = ax.vlines(df_r_h.loc['mean',name_hyp], ymin=0, ymax=ymax_mean, linestyle='-', label='Mean')
ax.vlines(df_r_h.loc['prc_0.50',name_hyp], ymin=0, ymax=ymax_mode, linestyle='--', color=pl_hyp.get_color(), label='Mode')
#plot true value
ymax_hyp = ymax_mean
ax.vlines(hyp[name_hyp], ymin=0, ymax=ymax_hyp, linestyle='-', linewidth=4, color='black', label='True value')
#edit figure
if not flag_report: ax.set_title(r'Comparison $\omega_{2,P}$', fontsize=30)
ax.set_xlabel('$\omega_{2,p}$', fontsize=25)
ax.set_ylabel('probability density function ', fontsize=25)
ax.grid(which='both')
ax.tick_params(axis='x', labelsize=22)
ax.tick_params(axis='y', labelsize=22)
#plot limits
ax.set_xlim([0,0.5])
ax.set_ylim([0,ymax_hyp])
#save figure
fig.tight_layout()
fig.savefig( dir_fig + fname_fig + '.png' )
# Omega_3s
#--- --- --- --- ---
#hyper-paramter name
name_hyp = 'omega_3s'
#figure title
fname_fig = 'post_dist_' + name_hyp
#create figure
fig, ax = plt.subplots(figsize = (10,10))
for d_id, df_r_h, df_r_h_p in zip(ds_id, df_reg_hyp, df_reg_hyp_post):
#estimate vertical line height for mean and mode
ymax_mode = 60
ymax_mean = 60
#plot posterior dist
pl_hyp = ax.vlines(df_r_h.loc['mean',name_hyp], ymin=0, ymax=ymax_mean, linestyle='-', label='Mean')
ax.vlines(df_r_h.loc['prc_0.50',name_hyp], ymin=0, ymax=ymax_mode, linestyle='--', color=pl_hyp.get_color(), label='Mode')
#plot true value
ymax_hyp = ymax_mean
ax.vlines(hyp[name_hyp], ymin=0, ymax=ymax_hyp, linestyle='-', linewidth=4, color='black', label='True value')
#edit figure
if not flag_report: ax.set_title(r'Comparison $\omega_{3,S}$', fontsize=30)
ax.set_xlabel('$\omega_{3,s}$', fontsize=25)
ax.set_ylabel('probability density function ', fontsize=25)
ax.grid(which='both')
ax.tick_params(axis='x', labelsize=22)
ax.tick_params(axis='y', labelsize=22)
#plot limits
ax.set_xlim([0,0.5])
ax.set_ylim([0,ymax_hyp])
#save figure
fig.tight_layout()
fig.savefig( dir_fig + fname_fig + '.png' )
# Ell_1e
#--- --- --- --- ---
#hyper-paramter name
name_hyp = 'ell_1e'
#figure title
fname_fig = 'post_dist_' + name_hyp
#create figure
fig, ax = plt.subplots(figsize = (10,10))
for d_id, df_r_h, df_r_h_p in zip(ds_id, df_reg_hyp, df_reg_hyp_post):
#estimate vertical line height for mean and mode
ymax_mode = 0.02
ymax_mean = 0.02
#plot posterior dist
pl_hyp = ax.vlines(df_r_h.loc['mean',name_hyp], ymin=0, ymax=ymax_mean, linestyle='-', label='Mean')
ax.vlines(df_r_h.loc['prc_0.50',name_hyp], ymin=0, ymax=ymax_mode, linestyle='--', color=pl_hyp.get_color(), label='Mode')
#plot true value
ymax_hyp = ymax_mean
ax.vlines(hyp[name_hyp], ymin=0, ymax=ymax_hyp, linestyle='-', linewidth=4, color='black', label='True value')
#edit figure
if not flag_report: ax.set_title(r'Comparison $\ell_{1,E}$', fontsize=30)
ax.set_xlabel('$\ell_{1,e}$', fontsize=25)
ax.set_ylabel('probability density function ', fontsize=25)
ax.grid(which='both')
ax.tick_params(axis='x', labelsize=22)
ax.tick_params(axis='y', labelsize=22)
#plot limits
ax.set_xlim([0,500])
ax.set_ylim([0,ymax_hyp])
#save figure
fig.tight_layout()
fig.savefig( dir_fig + fname_fig + '.png' )
# Ell_1as
#--- --- --- --- ---
#hyper-paramter name
name_hyp = 'ell_1as'
#figure title
fname_fig = 'post_dist_' + name_hyp
#create figure
fig, ax = plt.subplots(figsize = (10,10))
for d_id, df_r_h, df_r_h_p in zip(ds_id, df_reg_hyp, df_reg_hyp_post):
#estimate vertical line height for mean and mode
ymax_mode = 0.1
ymax_mean = 0.1
#plot posterior dist
pl_hyp = ax.vlines(df_r_h.loc['mean',name_hyp], ymin=0, ymax=ymax_mean, linestyle='-', label='Mean')
ax.vlines(df_r_h.loc['prc_0.50',name_hyp], ymin=0, ymax=ymax_mode, linestyle='--', color=pl_hyp.get_color(), label='Mode')
#plot true value
ymax_hyp = ymax_mean
ax.vlines(hyp[name_hyp], ymin=0, ymax=ymax_hyp, linestyle='-', linewidth=4, color='black', label='True value')
#edit figure
if not flag_report: ax.set_title(r'Comparison $\ell_{1a,S}$', fontsize=30)
ax.set_xlabel('$\ell_{1a,s}$', fontsize=25)
ax.set_ylabel('probability density function ', fontsize=25)
ax.grid(which='both')
ax.tick_params(axis='x', labelsize=22)
ax.tick_params(axis='y', labelsize=22)
#plot limits
ax.set_xlim([0,150])
ax.set_ylim([0,ymax_hyp])
#save figure
fig.tight_layout()
fig.savefig( dir_fig + fname_fig + '.png' )
# Ell_2p
#--- --- --- --- ---
#hyper-paramter name
name_hyp = 'ell_2p'
#figure title
fname_fig = 'post_dist_' + name_hyp
#create figure
fig, ax = plt.subplots(figsize = (10,10))
for d_id, df_r_h, df_r_h_p in zip(ds_id, df_reg_hyp, df_reg_hyp_post):
#estimate vertical line height for mean and mode
ymax_mode = 0.1
ymax_mean = 0.1
#plot posterior dist
pl_hyp = ax.vlines(df_r_h.loc['mean',name_hyp], ymin=0, ymax=ymax_mean, linestyle='-', label='Mean')
ax.vlines(df_r_h.loc['prc_0.50',name_hyp], ymin=0, ymax=ymax_mode, linestyle='--', color=pl_hyp.get_color(), label='Mode')
#plot true value
ymax_hyp = ymax_mean
ax.vlines(hyp[name_hyp], ymin=0, ymax=ymax_hyp, linestyle='-', linewidth=4, color='black', label='True value')
#edit figure
if not flag_report: ax.set_title(r'Comparison $\ell_{2,P}$', fontsize=30)
ax.set_xlabel('$\ell_{2,p}$', fontsize=25)
ax.set_ylabel('probability density function ', fontsize=25)
ax.grid(which='both')
ax.tick_params(axis='x', labelsize=22)
ax.tick_params(axis='y', labelsize=22)
#plot limits
ax.set_xlim([0,150])
ax.set_ylim([0,ymax_hyp])
#save figure
fig.tight_layout()
fig.savefig( dir_fig + fname_fig + '.png' )
# Ell_3s
#--- --- --- --- ---
#hyper-paramter name
name_hyp = 'ell_3s'
#figure title
fname_fig = 'post_dist_' + name_hyp
#create figure
fig, ax = plt.subplots(figsize = (10,10))
for d_id, df_r_h, df_r_h_p in zip(ds_id, df_reg_hyp, df_reg_hyp_post):
#estimate vertical line height for mean and mode
ymax_mode = 0.1
ymax_mean = 0.1
#plot posterior dist
pl_hyp = ax.vlines(df_r_h.loc['mean',name_hyp], ymin=0, ymax=ymax_mean, linestyle='-', label='Mean')
ax.vlines(df_r_h.loc['prc_0.50',name_hyp], ymin=0, ymax=ymax_mode, linestyle='--', color=pl_hyp.get_color(), label='Mode')
#plot true value
ymax_hyp = ymax_mean
ax.vlines(hyp[name_hyp], ymin=0, ymax=ymax_hyp, linestyle='-', linewidth=4, color='black', label='True value')
#edit figure
if not flag_report: ax.set_title(r'Comparison $\ell_{3,S}$', fontsize=30)
ax.set_xlabel('$\ell_{3,s}$', fontsize=25)
ax.set_ylabel('probability density function ', fontsize=25)
ax.grid(which='both')
ax.tick_params(axis='x', labelsize=22)
ax.tick_params(axis='y', labelsize=22)
#plot limits
ax.set_xlim([0,150])
ax.set_ylim([0,ymax_hyp])
#save figure
fig.tight_layout()
fig.savefig( dir_fig + fname_fig + '.png' )
# Tau_0
#--- --- --- --- ---
#hyper-paramter name
name_hyp = 'tau_0'
#figure title
fname_fig = 'post_dist_' + name_hyp
#create figure
fig, ax = plt.subplots(figsize = (10,10))
for d_id, df_r_h, df_r_h_p in zip(ds_id, df_reg_hyp, df_reg_hyp_post):
#estimate vertical line height for mean and mode
ymax_mode = 150
ymax_mean = 150
#plot posterior dist
pl_hyp = ax.vlines(df_r_h.loc['mean',name_hyp], ymin=0, ymax=ymax_mean, linestyle='-', label='Mean')
ax.vlines(df_r_h.loc['prc_0.50',name_hyp], ymin=0, ymax=ymax_mode, linestyle='--', color=pl_hyp.get_color(), label='Mode')
#plot true value
ymax_hyp = ymax_mean
ax.vlines(hyp[name_hyp], ymin=0, ymax=ymax_hyp, linestyle='-', linewidth=4, color='black', label='True value')
#edit figure
if not flag_report: ax.set_title(r'Comparison $\tau_{0}$', fontsize=30)
ax.set_xlabel(r'$\tau_{0}$', fontsize=25)
ax.set_ylabel(r'probability density function ', fontsize=25)
ax.grid(which='both')
ax.tick_params(axis='x', labelsize=22)
ax.tick_params(axis='y', labelsize=22)
#plot limits
ax.set_xlim([0,0.5])
ax.set_ylim([0,ymax_hyp])
#save figure
fig.tight_layout()
fig.savefig( dir_fig + fname_fig + '.png' )
# Phi_0
#--- --- --- --- ---
#hyper-paramter name
name_hyp = 'phi_0'
#figure title
fname_fig = 'post_dist_' + name_hyp
#create figure
fig, ax = plt.subplots(figsize = (10,10))
for d_id, df_r_h, df_r_h_p in zip(ds_id, df_reg_hyp, df_reg_hyp_post):
#estimate vertical line height for mean and mode
ymax_mode = 1000
ymax_mean = 1000
#plot posterior dist
pl_hyp = ax.vlines(df_r_h.loc['mean',name_hyp], ymin=0, ymax=ymax_mean, linestyle='-', label='Mean')
ax.vlines(df_r_h.loc['prc_0.50',name_hyp], ymin=0, ymax=ymax_mode, linestyle='--', color=pl_hyp.get_color(), label='Mode')
#plot true value
ymax_hyp = ymax_mean
ax.vlines(hyp[name_hyp], ymin=0, ymax=ymax_hyp, linestyle='-', linewidth=4, color='black', label='True value')
#edit figure
if not flag_report: ax.set_title(r'Comparison $\phi_{0}$', fontsize=30)
ax.set_xlabel('$\phi_{0}$', fontsize=25)
ax.set_ylabel(r'probability density function ', fontsize=25)
ax.grid(which='both')
ax.tick_params(axis='x', labelsize=22)
ax.tick_params(axis='y', labelsize=22)
#plot limits
ax.set_xlim([0,0.6])
ax.set_ylim([0,ymax_hyp])
#save figure
fig.tight_layout()
fig.savefig( dir_fig + fname_fig + '.png' )
# Omega_ca
#--- --- --- --- ---
#hyper-paramter name
name_hyp = 'omega_cap'
#figure title
fname_fig = 'post_dist_' + name_hyp
#create figure
fig, ax = plt.subplots(figsize = (10,10))
for d_id, df_r_h, df_r_h_p in zip(ds_id, df_reg_hyp, df_reg_hyp_post):
#estimate vertical line height for mean and mode
ymax_mode = 1500
ymax_mean = 1500
#plot posterior dist
pl_hyp = ax.vlines(df_r_h.loc['mean',name_hyp], ymin=0, ymax=ymax_mean, linestyle='-', label='Mean')
ax.vlines(df_r_h.loc['prc_0.50',name_hyp], ymin=0, ymax=ymax_mode, linestyle='--', color=pl_hyp.get_color(), label='Mode')
#plot true value
ymax_hyp = ymax_mean
ax.vlines(np.sqrt(hyp['omega_ca1p']**2+hyp['omega_ca2p']**2), ymin=0, ymax=ymax_hyp, linestyle='-', linewidth=4, color='black', label='True value')
#edit figure
if not flag_report: ax.set_title(r'Comparison $\omega_{ca,P}$', fontsize=30)
ax.set_xlabel('$\omega_{ca,p}$', fontsize=25)
ax.set_ylabel('probability density function ', fontsize=25)
ax.grid(which='both')
ax.tick_params(axis='x', labelsize=22)
ax.tick_params(axis='y', labelsize=22)
#plot limits
ax.set_xlim([0,0.05])
ax.set_ylim([0,ymax_hyp])
#save figure
fig.tight_layout()
fig.savefig( dir_fig + fname_fig + '.png' )
# # Delta c_0
# #--- --- --- --- ---
# #hyper-paramter name
# name_hyp = 'dc_0'
# #figure title
# fname_fig = 'post_dist_' + name_hyp
# #create figure
# fig, ax = plt.subplots(figsize = (10,10))
# for d_id, df_r_h, df_r_h_p in zip(ds_id, df_reg_hyp, df_reg_hyp_post):
# #estimate vertical line height for mean and mode
# ymax_mode = df_r_h_p.loc[:,name_hyp+'_pdf'].max()
# ymax_mean = 1.5*np.ceil(ymax_mode/10)*10
# ymax_mean = 15
# #plot posterior dist
# pl_pdf = ax.plot(df_r_h_p.loc[:,name_hyp], df_r_h_p.loc[:,name_hyp+'_pdf'])
# ax.vlines(df_r_h.loc[name_hyp,'mean'], ymin=0, ymax=ymax_mean, linestyle='-', color=pl_pdf[0].get_color(), label='Mean')
# ax.vlines(df_r_h.loc[name_hyp,'mode'], ymin=0, ymax=ymax_mode, linestyle='--', color=pl_pdf[0].get_color(), label='Mode')
# #plot true value
# ymax_hyp = ymax_mean
# # ax.vlines(hyp[name_hyp], ymin=0, ymax=ymax_hyp, linestyle='-', linewidth=4, color='black', label='True value')
# #edit figure
# ax.set_title(r'Comparison $\delta c_{0}$', fontsize=30)
# ax.set_xlabel('$\delta c_{0}$', fontsize=25)
# ax.set_ylabel('probability density function ', fontsize=25)
# ax.grid(which='both')
# ax.tick_params(axis='x', labelsize=22)
# ax.tick_params(axis='y', labelsize=22)
# #plot limits
# ax.set_xlim([-1,1])
# ax.set_ylim([0,ymax_hyp])
# #save figure
# fig.tight_layout()
# # fig.savefig( dir_fig + fname_fig + '.png' )
| 47,186 | 38.686291 | 153 | py |
ngmm_tools | ngmm_tools-master/Analyses/Code_Verification/regression/ds3/main_pystan_model3_corr_cells_NGAWest2CANorth.py | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Wed Jul 14 14:17:52 2021
@author: glavrent
"""
# Working directory and Packages
# ---------------------------
#load libraries
import os
import sys
import numpy as np
import pandas as pd
import time
#user functions
sys.path.insert(0,'../../../Python_lib/regression/pystan/')
from regression_pystan_model3_corr_cells_unbounded_hyp import RunStan
# Define variables
# ---------------------------
#filename suffix
# synds_suffix = '_small_corr_len'
# synds_suffix = '_large_corr_len'
#synthetic datasets directory
ds_dir = '../../../../Data/Validation/synthetic_datasets/ds3'
ds_dir = r'%s%s/'%(ds_dir, synds_suffix)
# dataset info
#ds_fname_main = 'CatalogNGAWest3CA_synthetic_data'
ds_fname_main = 'CatalogNGAWest3CALite_synthetic_data'
ds_id = np.arange(1,6)
#cell specific anelastic attenuation
ds_fname_cellinfo = 'CatalogNGAWest3CALite_cellinfo'
ds_fname_celldist = 'CatalogNGAWest3CALite_distancematrix'
#stan model
sm_fname = '../../../Stan_lib/regression_stan_model3_corr_cells_unbounded_hyp_chol_efficient.stan'
#output info
#main output filename
out_fname_main = 'NGAWest2CANorth_syndata'
#main output directory
out_dir_main = '../../../../Data/Validation/regression/ds3/'
#output sub-directory
out_dir_sub = 'PYSTAN_NGAWest2CANorth_corr_cells_chol_eff'
#stan parameters
runstan_flag = True
# pystan_ver = 2
pystan_ver = 3
res_name = 'tot'
n_iter = 1000
n_chains = 4
adapt_delta = 0.8
max_treedepth = 10
#ergodic coefficients
c_2_erg=-2.0
c_3_erg=-0.6
c_a_erg=0.0
#parallel options
# flag_parallel = True
flag_parallel = False
#output sub-dir with corr with suffix info
out_dir_sub = f'%s%s'%(out_dir_sub, synds_suffix)
#load cell dataframes
cellinfo_fname = '%s%s.csv'%(ds_dir, ds_fname_cellinfo)
celldist_fname = '%s%s.csv'%(ds_dir, ds_fname_celldist)
df_cellinfo = pd.read_csv(cellinfo_fname)
df_celldist = pd.read_csv(celldist_fname)
# Run stan regression
# ---------------------------
#create datafame with computation time
df_run_info = list()
#iterate over all synthetic datasets
for d_id in ds_id:
print('Synthetic dataset %i fo %i'%(d_id, len(ds_id)))
#run time start
run_t_strt = time.time()
#input flatfile
ds_fname = '%s%s%s_Y%i.csv'%(ds_dir, ds_fname_main, synds_suffix, d_id)
#load flatfile
df_flatfile = pd.read_csv(ds_fname)
#keep only North records of NGAWest2
df_flatfile = df_flatfile.loc[np.logical_and(df_flatfile.dsid==0,
df_flatfile.sreg==1),:]
#output file name and directory
out_fname = '%s%s_Y%i'%(out_fname_main, synds_suffix, d_id)
out_dir = '%s/%s/Y%i/'%(out_dir_main, out_dir_sub, d_id)
#run stan model
RunStan(df_flatfile, df_cellinfo, df_celldist, sm_fname,
out_fname, out_dir, res_name,
c_2_erg=c_2_erg, c_3_erg=c_3_erg, c_a_erg=c_a_erg,
runstan_flag=runstan_flag, n_iter=n_iter, n_chains=n_chains,
adapt_delta=adapt_delta, max_treedepth=max_treedepth,
pystan_ver=pystan_ver, pystan_parallel=flag_parallel)
#run time end
run_t_end = time.time()
#compute run time
run_tm = (run_t_end - run_t_strt)/60
#log run time
df_run_info.append(pd.DataFrame({'computer_name':os.uname()[1],'out_name':out_dir_sub,
'ds_id':d_id,'run_time':run_tm}, index=[d_id]))
#write out run info
out_fname = '%s%s/run_info.csv'%(out_dir_main, out_dir_sub)
pd.concat(df_run_info).reset_index(drop=True).to_csv(out_fname, index=False)
| 3,635 | 29.049587 | 98 | py |
ngmm_tools | ngmm_tools-master/Analyses/Code_Verification/regression/ds3/main_pystan_model3_uncorr_cells_NGAWest2CANorth.py | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Wed Jul 14 14:17:52 2021
@author: glavrent
"""
# Working directory and Packages
# ---------------------------
#load libraries
import os
import sys
import numpy as np
import pandas as pd
import time
#user functions
sys.path.insert(0,'../../../Python_lib/regression/pystan/')
from regression_pystan_model3_uncorr_cells_unbounded_hyp import RunStan
# Define variables
# ---------------------------
#filename suffix
# synds_suffix = '_small_corr_len'
# synds_suffix = '_large_corr_len'
#synthetic datasets directory
ds_dir = '../../../../Data/Validation/synthetic_datasets/ds3'
ds_dir = r'%s%s/'%(ds_dir, synds_suffix)
# dataset info
#ds_fname_main = 'CatalogNGAWest3CA_synthetic_data'
ds_fname_main = 'CatalogNGAWest3CALite_synthetic_data'
ds_id = np.arange(1,6)
#cell specific anelastic attenuation
ds_fname_cellinfo = 'CatalogNGAWest3CALite_cellinfo'
ds_fname_celldist = 'CatalogNGAWest3CALite_distancematrix'
#stan model
sm_fname = '../../../Stan_lib/regression_stan_model3_uncorr_cells_unbounded_hyp_chol_efficient.stan'
#output info
#main output filename
out_fname_main = 'NGAWest2CANorth_syndata'
#main output directory
out_dir_main = '../../../../Data/Validation/regression/ds3/'
#output sub-directory
out_dir_sub = 'PYSTAN_NGAWest2CANorth_uncorr_cells_chol_eff'
#stan parameters
runstan_flag = True
# pystan_ver = 2
pystan_ver = 3
res_name = 'tot'
n_iter = 1000
n_chains = 4
adapt_delta = 0.8
max_treedepth = 10
#ergodic coefficients
c_2_erg=-2.0
c_3_erg=-0.6
c_a_erg=0.0
#parallel options
# flag_parallel = True
flag_parallel = False
#output sub-dir with corr with suffix info
out_dir_sub = f'%s%s'%(out_dir_sub, synds_suffix)
#load cell dataframes
cellinfo_fname = '%s%s.csv'%(ds_dir, ds_fname_cellinfo)
celldist_fname = '%s%s.csv'%(ds_dir, ds_fname_celldist)
df_cellinfo = pd.read_csv(cellinfo_fname)
df_celldist = pd.read_csv(celldist_fname)
# Run stan regression
# ---------------------------
#create datafame with computation time
df_run_info = list()
#iterate over all synthetic datasets
for d_id in ds_id:
print('Synthetic dataset %i fo %i'%(d_id, len(ds_id)))
#run time start
run_t_strt = time.time()
#input flatfile
ds_fname = '%s%s%s_Y%i.csv'%(ds_dir, ds_fname_main, synds_suffix, d_id)
#load flatfile
df_flatfile = pd.read_csv(ds_fname)
#keep only North records of NGAWest2
df_flatfile = df_flatfile.loc[np.logical_and(df_flatfile.dsid==0,
df_flatfile.sreg==1),:]
#output file name and directory
out_fname = '%s%s_Y%i'%(out_fname_main, synds_suffix, d_id)
out_dir = '%s/%s/Y%i/'%(out_dir_main, out_dir_sub, d_id)
#run stan model
RunStan(df_flatfile, df_cellinfo, df_celldist, sm_fname,
out_fname, out_dir, res_name,
c_2_erg=c_2_erg, c_3_erg=c_3_erg, c_a_erg=c_a_erg,
runstan_flag=runstan_flag, n_iter=n_iter, n_chains=n_chains,
adapt_delta=adapt_delta, max_treedepth=max_treedepth,
pystan_ver=pystan_ver, pystan_parallel=flag_parallel)
#run time end
run_t_end = time.time()
#compute run time
run_tm = (run_t_end - run_t_strt)/60
#log run time
df_run_info.append(pd.DataFrame({'computer_name':os.uname()[1],'out_name':out_dir_sub,
'ds_id':d_id,'run_time':run_tm}, index=[d_id]))
#write out run info
out_fname = '%s%s/run_info.csv'%(out_dir_main, out_dir_sub)
pd.concat(df_run_info).reset_index(drop=True).to_csv(out_fname, index=False)
| 3,639 | 29.082645 | 100 | py |
ngmm_tools | ngmm_tools-master/Analyses/Code_Verification/preprocessing/CreateCatalogNGAWest2CA.py | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Sun Jun 27 22:58:16 2021
@author: glavrent
"""
# %% Required Packages
# ======================================
#load libraries
import os
import sys
import pathlib
import glob
import re #regular expression package
#arithmetic libraries
import numpy as np
import pandas as pd
#geographic coordinates
import pyproj
#plotting libraries
from matplotlib import pyplot as plt
import matplotlib.ticker as mticker
#user-derfined functions
sys.path.insert(0,'../../Python_lib/catalog')
sys.path.insert(0,'../../Python_lib/plotting')
import pylib_catalog as pylib_catalog
import pylib_contour_plots as pylib_cplt
# %% Define Input Data
# ======================================
#thresholds
thres_dist = 0.01 #collocated stations
# projection system
utm_zone = '11S'
# region id
region_id = 1
#input file names
fname_flatfile_NGA2ASK14 = '../../../Raw_files/nga_w2_resid/resid_T0.200.out2.txt'
fname_flatfile_NGA2coor = '../../../Raw_files/nga_w2/Updated_NGA_West2_Flatfile_coordinates.csv'
#flatfile file
fname_flatfile = 'CatalogNGAWest2CA_ASK14'
#output directory
dir_out = '../../../Data/Verification/preprocessing/flatfiles/NGAWest2_CA/'
dir_fig = dir_out + 'figures/'
# %% Load Data
# ======================================
#NGAWest2
df_flatfile_NGA2ASK14 = pd.read_csv(fname_flatfile_NGA2ASK14, delim_whitespace=True)
df_flatfile_NGA2coor = pd.read_csv(fname_flatfile_NGA2coor)
df_flatfile_NGA2 = pd.merge(df_flatfile_NGA2ASK14, df_flatfile_NGA2coor, left_on='recID', right_on='Record Sequence Number')
# %% Cleaning files
# ======================================
# NGA2
#keep only CA for NGA2
df_flatfile_NGA2 = df_flatfile_NGA2[ df_flatfile_NGA2.region == region_id ]
#reset indices
df_flatfile_NGA2.reset_index(inplace=True)
# %% Process Data
# ======================================
#coordinates and projection system
# projection system
utmProj = pyproj.Proj("+proj=utm +zone="+utm_zone+", +ellps=WGS84 +datum=WGS84 +units=m +no_defs")
#earthquake and station ids
eq_id_NGA2 = df_flatfile_NGA2['eqid'].values.astype(int)
sta_id_NGA2 = df_flatfile_NGA2['Station Sequence Number'].values.astype(int)
#unique earthquake and station ids
eq_id_NGA2_unq, eq_idx_NGA2 = np.unique(eq_id_NGA2, return_index=True)
sta_id_NGA2_unq, sta_idx_NGA2 = np.unique(sta_id_NGA2, return_index=True)
#number of earthquake and stations
neq_NGA2 = len(eq_id_NGA2_unq)
nsta_NGA2 = len(sta_id_NGA2_unq)
#earthquake and station coordinates
eq_latlon_NGA2_all = df_flatfile_NGA2[['Hypocenter Latitude (deg)','Hypocenter Longitude (deg)']].values
sta_latlon_NGA2_all = df_flatfile_NGA2[['Station Latitude','Station Longitude']].values
#utm coordinates
eq_X_NGA2_all = np.array([utmProj(e_lon, e_lat) for e_lat, e_lon in zip(eq_latlon_NGA2_all[:,0], eq_latlon_NGA2_all[:,1])]) / 1000
eq_z_NGA2_all = -1*df_flatfile_NGA2['Hypocenter Depth (km)'].values
sta_X_NGA2_all = np.array([utmProj(s_lon, s_lat) for s_lat, s_lon in zip(sta_latlon_NGA2_all[:,0], sta_latlon_NGA2_all[:,1])]) / 1000
mpt_X_NGA2_all = (eq_X_NGA2_all + sta_X_NGA2_all) / 2
#mid point coordinates
mpt_latlon_NGA2_all = np.flip( np.array([utmProj(pt_x, pt_y, inverse=True) for pt_x, pt_y in
zip(mpt_X_NGA2_all[:,0], mpt_X_NGA2_all[:,1]) ]), axis=1)
#ground motion parameteres
mag_NGA2 = df_flatfile_NGA2['mag'].values
rup_NGA2 = np.sqrt(np.linalg.norm(eq_X_NGA2_all-sta_X_NGA2_all, axis=1)**2+eq_z_NGA2_all**2)
vs30_NGA2 = df_flatfile_NGA2['VS30'].values
# %% Process Data to save
# ======================================
i_data2keep = np.full(len(df_flatfile_NGA2), True)
#records' info
rsn_array = df_flatfile_NGA2['recID'].values[i_data2keep].astype(int)
eqid_array = eq_id_NGA2[i_data2keep]
ssn_array = sta_id_NGA2[i_data2keep]
year_array = df_flatfile_NGA2['YEAR'].values[i_data2keep]
#records' parameters
mag_array = mag_NGA2[i_data2keep]
rrup_array = rup_NGA2[i_data2keep]
vs30_array = vs30_NGA2[i_data2keep]
#earthquake, station, mid-point latlon coordinates
eq_latlon = eq_latlon_NGA2_all[i_data2keep,:]
sta_latlon = sta_latlon_NGA2_all[i_data2keep,:]
mpt_latlon = mpt_latlon_NGA2_all[i_data2keep,:]
#earthquake, station, mid-point UTM coordinates
eq_utm = eq_X_NGA2_all[i_data2keep,:]
sta_utm = sta_X_NGA2_all[i_data2keep,:]
mpt_utm = mpt_X_NGA2_all[i_data2keep,:]
#earthquake source depth
eq_z = eq_z_NGA2_all[i_data2keep]
#indices for unique earthquakes and stations
_, eq_idx, eq_inv = np.unique(eqid_array, return_index=True, return_inverse=True)
_, sta_idx, sta_inv = np.unique(ssn_array, return_index=True, return_inverse=True)
n_eq_orig = len(eq_idx)
n_sta_orig = len(sta_idx)
# NGAWest2 dataframe
# ---- ---- ---- ---- ----
data_full = {'rsn':rsn_array, 'eqid':eqid_array, 'ssn':ssn_array,
'mag':mag_array, 'Rrup':rrup_array, 'Vs30': vs30_array, 'year': year_array,
'eqLat':eq_latlon[:,0], 'eqLon':eq_latlon[:,1], 'staLat':sta_latlon[:,0], 'staLon':sta_latlon[:,1], 'mptLat':mpt_latlon[:,0], 'mptLon':mpt_latlon[:,1],
'UTMzone':utm_zone,
'eqX':eq_utm[:,0], 'eqY':eq_utm[:,1], 'eqZ':eq_z, 'staX':sta_utm[:,0], 'staY':sta_utm[:,1], 'mptX':mpt_utm[:,0], 'mptY':mpt_utm[:,1]}
df_flatfile_full = pd.DataFrame(data_full)
# colocate stations
# ---- ---- ---- ---- ----
#update ssn for colocated stations
df_flatfile_full = pylib_catalog.ColocatePt(df_flatfile_full, 'ssn', ['staX','staY'], thres_dist=thres_dist)
#keep single record from each event after collocation
# ---- ---- ---- ---- ----
i_unq_eq_sta = np.unique(df_flatfile_full[['eqid','ssn']].values, return_index=True, axis=0)[1]
df_flatfile_full = df_flatfile_full.iloc[i_unq_eq_sta, :].sort_index()
_, eq_idx, eq_inv = np.unique(df_flatfile_full.loc[:,'eqid'], axis=0, return_index=True, return_inverse=True)
_, sta_idx, sta_inv = np.unique(df_flatfile_full.loc[:,'ssn'], axis=0, return_index=True, return_inverse=True)
n_eq = len(eq_idx)
n_sta = len(sta_idx)
# average gm parameters
# ---- ---- ---- ---- ----
df_flatfile_full = pylib_catalog.IndexAvgColumns(df_flatfile_full, 'eqid', ['mag','eqLat','eqLon','eqX','eqY','eqZ'])
df_flatfile_full = pylib_catalog.IndexAvgColumns(df_flatfile_full, 'ssn', ['Vs30','staLat','staLon','staX','staY'])
# create event and station dataframes
# ---- ---- ---- ---- ----
#event dataframe
df_flatfile_event = df_flatfile_full.iloc[eq_idx,:][['eqid','mag','year','eqLat','eqLon','UTMzone','eqX','eqY','eqZ']].reset_index(drop=True)
#station dataframe
df_flatfile_station = df_flatfile_full.iloc[sta_idx,:][['ssn','Vs30','staLat','staLon','UTMzone','staX','staY']].reset_index(drop=True)
# %% Save data
# ======================================
# create output directories
if not os.path.isdir(dir_out): pathlib.Path(dir_out).mkdir(parents=True, exist_ok=True)
if not os.path.isdir(dir_fig): pathlib.Path(dir_fig).mkdir(parents=True, exist_ok=True)
#save processed dataframes
fname_flatfile_full= '%s%s'%(dir_out, fname_flatfile)
df_flatfile_full.to_csv(fname_flatfile_full + '.csv', index=False)
df_flatfile_event.to_csv(fname_flatfile_full + '_event.csv', index=False)
df_flatfile_station.to_csv(fname_flatfile_full + '_station.csv', index=False)
# create figures
# ---- ---- ---- ---- ----
# Mag-Dist distribution
fname_fig = 'M-R_dist'
#create figure
fig, ax = plt.subplots(figsize = (10,9))
pl1 = ax.scatter(df_flatfile_full.Rrup, df_flatfile_full.mag, label='NGAWest2 CA')
#edit figure properties
ax.set_xlabel(r'Distance ($km$)', fontsize=30)
ax.set_ylabel(r'Magnitude', fontsize=30)
ax.grid(which='both')
ax.set_xscale('log')
# ax.set_xlim([0.1, 2000])
ax.set_ylim([2, 8])
ax.tick_params(axis='x', labelsize=25)
ax.tick_params(axis='y', labelsize=25)
# ax.legend(fontsize=25, loc='upper left')
ax.xaxis.set_tick_params(which='major', size=10, width=2, direction='in', top='on')
ax.xaxis.set_tick_params(which='minor', size=7, width=2, direction='in', top='on')
ax.yaxis.set_tick_params(which='major', size=10, width=2, direction='in', right='on')
ax.yaxis.set_tick_params(which='minor', size=7, width=2, direction='in', right='on')
fig.tight_layout()
#save figure
fig.savefig( dir_fig + fname_fig + '.png' )
# Mag-Year distribution
fname_fig = 'M-date_dist'
#create figure
fig, ax = plt.subplots(figsize = (10,9))
pl1 = ax.scatter(df_flatfile_event['year'].values, df_flatfile_event['mag'].values, label='NGAWest2 CA')
#edit figure properties
ax.set_xlabel(r'time ($year$)', fontsize=30)
ax.set_ylabel(r'Magnitude', fontsize=30)
ax.grid(which='both')
# ax.set_xscale('log')
ax.set_xlim([1965, 2025])
ax.set_ylim([2, 8])
ax.tick_params(axis='x', labelsize=25)
ax.tick_params(axis='y', labelsize=25)
# ax.legend(fontsize=25, loc='upper left')
ax.xaxis.set_tick_params(which='major', size=10, width=2, direction='in', top='on')
ax.xaxis.set_tick_params(which='minor', size=7, width=2, direction='in', top='on')
ax.yaxis.set_tick_params(which='major', size=10, width=2, direction='in', right='on')
ax.yaxis.set_tick_params(which='minor', size=7, width=2, direction='in', right='on')
fig.tight_layout()
#save figure
fig.savefig( dir_fig + fname_fig + '.png' )
#eq and sta location
fname_fig = 'eq_sta_locations'
fig, ax, data_crs, gl = pylib_cplt.PlotMap(flag_grid=True)
#plot earthquake and station locations
ax.plot(df_flatfile_event['eqLon'].values, df_flatfile_event['eqLat'].values, '*', transform = data_crs, markersize = 10, zorder=13, label='Events')
ax.plot(df_flatfile_station['staLon'].values, df_flatfile_station['staLat'].values, 'o', transform = data_crs, markersize = 6, zorder=12, label='Stations')
#edit figure properties
gl.xlabel_style = {'size': 25}
gl.ylabel_style = {'size': 25}
# gl.xlocator = mticker.FixedLocator([-124, -122, -120, -118, -116, -114])
# gl.ylocator = mticker.FixedLocator([32, 34, 36, 38, 40])
ax.legend(fontsize=25, loc='lower left')
# ax.set_xlim(plt_latlon_win[:,1])
# ax.set_ylim(plt_latlon_win[:,0])
#save figure
fig.tight_layout()
fig.savefig( dir_fig + fname_fig + '.png' )
# Print data info
# ---------------------------
print(f'NGAWest2:')
print(f'\tnumber of rec: %.i'%len(df_flatfile_full))
print(f'\tnumber of rec (R<200km): %.i'%np.sum(df_flatfile_full.Rrup<=200))
print(f'\tnumber of rec (R<300km): %.i'%np.sum(df_flatfile_full.Rrup<=300))
print(f'\tnumber of eq: %.i'%len(df_flatfile_event))
print(f'\tnumber of sta: %.i'%len(df_flatfile_station))
print(f'\tcoverage: %.i to %i'%(df_flatfile_full.year.min(), df_flatfile_full.year.max()))
#write out summary
# ---- ---- ---- ---- ----
f = open(dir_out + 'summary_data' + '.txt', 'w')
f.write(f'NGAWest2:\n')
f.write(f'\tnumber of rec: %.i\n'%len(df_flatfile_full))
f.write(f'\tnumber of rec (R<200km): %.i\n'%np.sum(df_flatfile_full.Rrup<=200))
f.write(f'\tnumber of rec (R<300km): %.i\n'%np.sum(df_flatfile_full.Rrup<=300))
f.write(f'\tnumber of eq: %.i\n'%len(df_flatfile_event))
f.write(f'\tnumber of sta: %.i\n'%len(df_flatfile_station))
f.write(f'\tcoverage: %.i to %i\n'%(df_flatfile_full.year.min(), df_flatfile_full.year.max()))
f.close()
| 11,132 | 39.631387 | 164 | py |
ngmm_tools | ngmm_tools-master/Analyses/Code_Verification/preprocessing/CreateCatalogNewEvents2017.py | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Sun Jun 27 16:12:57 2021
@author: glavrent
"""
# Required Packages
# ======================================
#load libraries
import os
import sys
import pathlib
import glob
import re #regular expression package
#arithmetic libraries
import numpy as np
import pandas as pd
#geographic coordinates
import pyproj
#plotting libraries
from matplotlib import pyplot as plt
import matplotlib.ticker as mticker
#user-derfined functions
sys.path.insert(0,'../../Python_lib/ground_motions')
sys.path.insert(0,'../../Python_lib/plotting')
import pylib_Willis15CA_Vs30 as pylib_W15_Vs30
import pylib_contour_plots as pylib_cplt
# %% Define Input Data
# ======================================
#thresholds
dt_thres = 0.025
rrup_thres = 300
# projection system
utm_zone = '11S'
#input flatfiles
fname_flatfile_newrec_eq = '../../../Raw_files/nga_w3/California2011-2017/eventcatalog.csv'
fname_flatfile_newrec_sta = '../../../Raw_files/nga_w3/California2011-2017/Recorddata.csv'
#flatfile file
fname_flatfile = 'CatalogNewRecords_2011-2017_CA_NV'
#output directory
dir_out = '../../../Data/NGAWest_expansion/CA_NV_2011-2017/'
dir_out = '../../../Data/Verification/preprocessing/flatfiles/CA_NV_2011-2017/'
dir_fig = dir_out + 'figures/'
# %% Load Data
# ======================================
#merge event and station info
df_flatfile_newrec_eq = pd.read_csv(fname_flatfile_newrec_eq)
df_flatfile_newrec_sta = pd.read_csv(fname_flatfile_newrec_sta)
df_flatfile_newrec = pd.merge(df_flatfile_newrec_eq, df_flatfile_newrec_sta, left_on='EventIDs.i.', right_on='EventID')
# %% Cleaning files
# ======================================
#set -999 to nan
df_flatfile_newrec.replace(-999, np.nan, inplace=True)
#remove data based on timestep
df_flatfile_newrec = df_flatfile_newrec[ df_flatfile_newrec.timeStep <= dt_thres ]
#remove data with unknown mag
df_flatfile_newrec = df_flatfile_newrec[ ~np.isnan(df_flatfile_newrec['mag.event']) ]
#remove data with unknown coordinates
df_flatfile_newrec = df_flatfile_newrec[ ~np.isnan(df_flatfile_newrec[['latitude.event', 'longitude.event']]).any(axis=1) ]
df_flatfile_newrec = df_flatfile_newrec[ ~np.isnan(df_flatfile_newrec[['stnlat', 'stnlon']]).any(axis=1) ]
#keep single record from
df_flatfile_newrec.loc[:,'EventID'] = df_flatfile_newrec['EventID'].values.astype(int)
df_flatfile_newrec.loc[:,'stationID'] = np.unique(df_flatfile_newrec['station'], return_inverse=True)[1]
i_unq_eq_sta = np.unique( np.unique(df_flatfile_newrec[['stationID','EventID']].values, return_index=True, axis=0)[1] )
df_flatfile_newrec = df_flatfile_newrec.iloc[i_unq_eq_sta, :]
#reset indices
df_flatfile_newrec.reset_index(inplace=True)
# %% Process Data
# ======================================
#coordinates and projection system
# projection system
utmProj = pyproj.Proj("+proj=utm +zone="+utm_zone+", +ellps=WGS84 +datum=WGS84 +units=m +no_defs")
#earthquake and station ids
eq_id_newrec = df_flatfile_newrec['EventIDs.i.'].values.astype(int)
sta_id_newrec = df_flatfile_newrec['station'].values
sta_net_newrec = df_flatfile_newrec['network'].values
sta_netid_newrec = [f'{s_net}-{s_id}' for s_net, s_id in zip(sta_net_newrec, sta_id_newrec)]
#unique earthquake and station ids
eq_id_newrec_unq, eq_idx_newrec = np.unique(eq_id_newrec, return_index=True)
# sta_id_newrec_unq, sta_inv_newrec = np.unique(sta_id_newrec, return_inverse=True)
sta_id_newrec_unq, sta_inv_newrec = np.unique(sta_netid_newrec, return_inverse=True)
#number of earthquake and stations
neq_newrec = len(eq_id_newrec_unq)
nsta_newrec = len(sta_id_newrec_unq)
#earthquake and station coordinates
eq_latlon_newrec_all = df_flatfile_newrec[['latitude.event', 'longitude.event']].values
sta_latlon_newrec_all = df_flatfile_newrec[['stnlat', 'stnlon']].values
#utm coordinates
eq_X_newrec_all = np.array([utmProj(e_lon, e_lat) for e_lat, e_lon in zip(eq_latlon_newrec_all[:,0], eq_latlon_newrec_all[:,1])]) / 1000
eq_z_newrec_all = np.minimum(-1*df_flatfile_newrec['depth.event.1000'].values, 0)
sta_X_newrec_all = np.array([utmProj(s_lon, s_lat) for s_lat, s_lon in zip(sta_latlon_newrec_all[:,0], sta_latlon_newrec_all[:,1])]) / 1000
mpt_X_newrec_all = (eq_X_newrec_all + sta_X_newrec_all) / 2
#mid point coordinates
mpt_latlon_newrec_all = np.flip( np.array([utmProj(pt_x, pt_y, inverse=True) for pt_x, pt_y in
zip(mpt_X_newrec_all[:,0], mpt_X_newrec_all[:,1]) ]), axis=1 )
#earthquake parameteres
mag_newrec = df_flatfile_newrec['mag.event'].values
rup_newrec = np.sqrt(np.linalg.norm(eq_X_newrec_all-sta_X_newrec_all, axis=1)**2+eq_z_newrec_all**2)
#year of recording
df_flatfile_newrec['year'] = pd.DatetimeIndex(df_flatfile_newrec['rec.stime']).year
#estimate station vs30
Wills15Vs30 = pylib_W15_Vs30.Willis15Vs30CA()
vs30_newrec = Wills15Vs30.lookup(np.fliplr(sta_latlon_newrec_all))[0]
vs30_newrec[vs30_newrec<=50] = np.nan
# %% Process Data to save
# ======================================
#distance threshold for data to keep
i_data2keep = rup_newrec <= rrup_thres
#records' info
rsn_array = df_flatfile_newrec.loc[i_data2keep,'index'].values
eqid_array = eq_id_newrec[i_data2keep]
ssn_array = sta_inv_newrec[i_data2keep]
sid_array = sta_id_newrec[i_data2keep]
snet_array = sta_net_newrec[i_data2keep]
year_array = df_flatfile_newrec['year'].values[i_data2keep]
#records' parameters
mag_array = mag_newrec[i_data2keep]
rrup_array = rup_newrec[i_data2keep]
vs30_array = vs30_newrec[i_data2keep]
#earthquake, station, mid-point latlon coordinates
eq_latlon = eq_latlon_newrec_all[i_data2keep,:]
sta_latlon = sta_latlon_newrec_all[i_data2keep,:]
mpt_latlon = mpt_latlon_newrec_all[i_data2keep,:]
#earthquake, station, mid-point UTM coordinates
eq_utm = eq_X_newrec_all[i_data2keep,:]
sta_utm = sta_X_newrec_all[i_data2keep,:]
mpt_utm = mpt_X_newrec_all[i_data2keep,:]
#earthquake source depth
eq_z = eq_z_newrec_all[i_data2keep]
#indices for unique earthquakes and stations
eq_idx = np.unique(eqid_array, return_index=True)[1]
sta_idx = np.unique(ssn_array, return_index=True)[1]
#data to save
data_full = {'rsn':rsn_array, 'eqid':eqid_array, 'ssn':ssn_array,
'mag':mag_array, 'Rrup':rrup_array, 'Vs30': vs30_array, 'year': year_array,
'eqLat':eq_latlon[:,0], 'eqLon':eq_latlon[:,1], 'staLat':sta_latlon[:,0], 'staLon':sta_latlon[:,1], 'mptLat':mpt_latlon[:,0], 'mptLon':mpt_latlon[:,1],
'UTMzone':utm_zone,
'eqX':eq_utm[:,0], 'eqY':eq_utm[:,1], 'eqZ':eq_z, 'staX':sta_utm[:,0], 'staY':sta_utm[:,1], 'mptX':mpt_utm[:,0], 'mptY':mpt_utm[:,1]}
#processed dataframes
df_flatfile_full = pd.DataFrame(data_full)
#event dataframe
df_flatfile_event = df_flatfile_full.loc[eq_idx, ['eqid','mag','year','eqLat','eqLon','UTMzone','eqX','eqY','eqZ']].reset_index(drop=True)
#station dataframe
df_flatfile_station = df_flatfile_full.loc[sta_idx, ['ssn','Vs30','staLat','staLon','UTMzone','staX','staY']].reset_index(drop=True)
# %% Save data
# ======================================
# create output directories
if not os.path.isdir(dir_out): pathlib.Path(dir_out).mkdir(parents=True, exist_ok=True)
if not os.path.isdir(dir_fig): pathlib.Path(dir_fig).mkdir(parents=True, exist_ok=True)
#save processed dataframes
fname_flatfile_full= '%s%s'%(dir_out, fname_flatfile)
df_flatfile_full.to_csv(fname_flatfile_full + '.csv', index=True)
df_flatfile_event.to_csv(fname_flatfile_full + '_event.csv', index=False)
df_flatfile_station.to_csv(fname_flatfile_full + '_station.csv', index=False)
# create figures
# ---- ---- ---- ---- ----
# Mag-Dist distribution
fname_fig = 'M-R_dist'
#create figure
fig, ax = plt.subplots(figsize = (10,9))
pl1 = ax.scatter(df_flatfile_full.Rrup, df_flatfile_full.mag, label='New Records')
#edit figure properties
ax.set_xlabel(r'Distance ($km$)', fontsize=30)
ax.set_ylabel(r'Magnitude', fontsize=30)
ax.grid(which='both')
ax.set_xscale('log')
# ax.set_xlim([0.1, 2000])
ax.set_ylim([2, 8])
ax.tick_params(axis='x', labelsize=25)
ax.tick_params(axis='y', labelsize=25)
ax.legend(fontsize=25, loc='upper left')
ax.xaxis.set_tick_params(which='major', size=10, width=2, direction='in', top='on')
ax.xaxis.set_tick_params(which='minor', size=7, width=2, direction='in', top='on')
ax.yaxis.set_tick_params(which='major', size=10, width=2, direction='in', right='on')
ax.yaxis.set_tick_params(which='minor', size=7, width=2, direction='in', right='on')
fig.tight_layout()
#save figure
fig.savefig( dir_fig + fname_fig + '.png' )
# Mag-Year distribution
fname_fig = 'M-date_dist'
#create figure
fig, ax = plt.subplots(figsize = (10,9))
pl1 = ax.scatter(df_flatfile_event['year'].values, df_flatfile_event['mag'].values, label='New Records')
#edit figure properties
ax.set_xlabel(r'time ($year$)', fontsize=30)
ax.set_ylabel(r'Magnitude', fontsize=30)
ax.grid(which='both')
# ax.set_xscale('log')
ax.set_xlim([1965, 2025])
ax.set_ylim([2, 8])
ax.tick_params(axis='x', labelsize=25)
ax.tick_params(axis='y', labelsize=25)
ax.legend(fontsize=25, loc='upper left')
ax.xaxis.set_tick_params(which='major', size=10, width=2, direction='in', top='on')
ax.xaxis.set_tick_params(which='minor', size=7, width=2, direction='in', top='on')
ax.yaxis.set_tick_params(which='major', size=10, width=2, direction='in', right='on')
ax.yaxis.set_tick_params(which='minor', size=7, width=2, direction='in', right='on')
fig.tight_layout()
#save figure
fig.savefig( dir_fig + fname_fig + '.png' )
#eq and sta location
fname_fig = 'eq_sta_locations'
fig, ax, data_crs, gl = pylib_cplt.PlotMap(flag_grid=True)
#plot earthquake and station locations
ax.plot(df_flatfile_event['eqLon'].values, df_flatfile_event['eqLat'].values, '*', transform = data_crs, markersize = 10, zorder=13)
ax.plot(df_flatfile_station['staLon'].values, df_flatfile_station['staLat'].values, 'o', transform = data_crs, markersize = 6, zorder=13, label='STA')
ax.plot(df_flatfile_event['eqLon'].values, df_flatfile_event['eqLat'].values, '*', color='black', transform = data_crs, markersize = 14, zorder=13, label='EQ')
#edit figure properties
gl.xlabel_style = {'size': 25}
gl.ylabel_style = {'size': 25}
# gl.xlocator = mticker.FixedLocator([-124, -122, -120, -118, -116, -114])
# gl.ylocator = mticker.FixedLocator([32, 34, 36, 38, 40])
ax.legend(fontsize=25, loc='lower left')
# ax.set_xlim(plt_latlon_win[:,1])
# ax.set_ylim(plt_latlon_win[:,0])
#save figure
fig.tight_layout()
fig.savefig( dir_fig + fname_fig + '.png' )
# Print data info
# ---------------------------
print(r'New Records:')
print(f'\tnumber of rec: %.i'%len(df_flatfile_full))
print(f'\tnumber of rec (R<200km): %.i'%np.sum(df_flatfile_full.Rrup<=200))
print(f'\tnumber of rec (R<%.1f): %.i'%(rrup_thres, np.sum(df_flatfile_full.Rrup<=rrup_thres)))
print(f'\tnumber of eq: %.i'%len(df_flatfile_event))
print(f'\tnumber of sta: %.i'%len(df_flatfile_station))
print(f'\tnumber of sta (R<300km): %.i'%len(np.unique(ssn_array[df_flatfile_full.Rrup<=300])))
print(f'\tcoverage: %.i to %i'%(df_flatfile_full.year.min(), df_flatfile_full.year.max()))
| 11,196 | 41.25283 | 165 | py |
ngmm_tools | ngmm_tools-master/Analyses/Code_Verification/preprocessing/CreateCatalogNewEvents2021Raw.py | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Tue Oct 5 09:37:23 2021
@author: glavrent
"""
# %% Required Packages
# ======================================
#load libraries
import os
import sys
import pathlib
import re
#arithmetic libraries
import numpy as np
import pandas as pd
#geographical libraries
import geopy
from geopy.distance import distance
#plotting libraries
from matplotlib import pyplot as plt
import matplotlib.ticker as mticker
#user-derfined functions
sys.path.insert(0,'../../Python_lib/catalog')
sys.path.insert(0,'../../Python_lib/plotting')
import pylib_catalog as pylib_catalog
import pylib_contour_plots as pylib_cplt
# %% Define variables
# ======================================
#input file names
fname_nrec_eq = '../../../Raw_files/nga_w3/IRIS/fdsnws-events_CA_2011-2021.csv'
fname_nerc_sta = '../../../Raw_files/nga_w3/IRIS/fdsn-station_USA_[HB][HN]?.csv'
fname_mag_rup_lim = '../../../Data/Verification/preprocessing/flatfiles/usable_mag_rrup/usable_Mag_Rrup_coeffs.csv'
#output directoy
dir_out = '../../../Data/Verification/preprocessing/flatfiles/CA_NV_2011-2021_raw/'
dir_fig = dir_out + 'figures/'
fname_new_cat = 'Catalog_California_2011-2021.ver02'
#station
srate_min = 10 #minimum sample rate for accepting instrument
net_seis = np.array(['AZ','BK','CI','NC','NN','NP','PB','PG','SB','WR','US','CJ','UO'])
# %% Load Data
# ======================================
df_nrec_eq = pd.read_csv(fname_nrec_eq, delimiter='|', skiprows=4)
df_nrec_sta = pd.read_csv(fname_nerc_sta, delimiter='|', skiprows=4)
#M/R limitis
df_lim_mag_rrup = pd.read_csv(fname_mag_rup_lim, index_col=0)
# %% Process Data
# ======================================
#rename earthquake and station coordinates
df_nrec_eq.rename(columns={'Latitude':'eqLat', 'Longitude':'eqLon', 'Depth':'eqDepth'}, inplace=True)
df_nrec_sta.rename(columns={'Latitude':'staLat', 'Longitude':'staLon','Depth':'staDepth'}, inplace=True)
#remove rows with nna columns
df_nrec_eq = df_nrec_eq.loc[~df_nrec_eq.isnull().any(axis=1).values,:]
df_nrec_sta = df_nrec_sta.loc[~df_nrec_sta.isnull().any(axis=1).values,:]
df_nrec_eq = df_nrec_eq.loc[~df_nrec_eq.isna().any(axis=1).values,:]
df_nrec_sta = df_nrec_sta.loc[~df_nrec_sta.isna().any(axis=1).values,:]
#remove invalid columns
i_nan_rec = np.array([bool(re.match('^#.*$',n)) for n in df_nrec_sta['Network']])
df_nrec_sta = df_nrec_sta.loc[~i_nan_rec,:]
#keep networks of interest
i_net = df_nrec_sta.Network.isin(net_seis)
df_nrec_sta = df_nrec_sta.loc[i_net,:]
#with only records with sufficient sampling rate
df_nrec_sta.SampleRate = df_nrec_sta.SampleRate.astype(float)
i_srate = df_nrec_sta.SampleRate > srate_min
df_nrec_sta = df_nrec_sta.loc[i_srate,:]
#create network and station IDs
_, df_nrec_sta.loc[:,'NetworkID'] = np.unique(df_nrec_sta['Network'].values.astype(str), return_inverse=True)
_, df_nrec_sta.loc[:,'StationID'] = np.unique(df_nrec_sta['Station'].values.astype(str), return_inverse=True)
#reduce to unique stations
_, i_sta_unq = np.unique(df_nrec_sta[['NetworkID','StationID']], return_index=True, axis=0)
df_nrec_sta = df_nrec_sta.iloc[i_sta_unq,:]
#station coordinates
sta_latlon = df_nrec_sta[['staLat','staLon']].values
#initialize rec catalog
cat_new_rec = []
#number of events and stations
n_eq = len(df_nrec_eq)
n_sta = len(df_nrec_sta)
#iterate evetns
for (k, eq) in df_nrec_eq.iterrows():
print('Processing event %i of %i'%(k, n_eq))
#earthquake info
eq_latlon = eq[['eqLat','eqLon']].values
eq_depth = eq['eqDepth']
eq_mag = eq['Magnitude']
#epicenter and hypocenter dist
dist_epi = np.array([distance(eq_latlon, sta_ll).km for sta_ll in sta_latlon])
dist_hyp = np.sqrt(dist_epi**2 + eq_depth**2)
#stations that satisfy the M/R limit
i_sta_event = pylib_catalog.UsableSta(np.full(n_sta, eq_mag), dist_hyp, df_lim_mag_rrup)
#create catalog for k^th event
df_new_r = df_nrec_sta.loc[i_sta_event,:].assign(**eq)
#add rupture info
df_new_r.loc[:,'HypDist'] = dist_hyp[i_sta_event]
#combine sta with event info
cat_new_rec.append(df_new_r)
#combine catalogs of all events into one
df_cat_new_rec = pd.concat(cat_new_rec).reset_index()
#re-roder columns
df_cat_new_rec = df_cat_new_rec[np.concatenate([df_nrec_eq.columns, df_nrec_sta.columns,['HypDist']])]
#create event and station dataframes
#indices for unique earthquakes and stations
eq_idx = np.unique(df_cat_new_rec.EventID, return_index=True)[1]
sta_idx = np.unique(df_cat_new_rec[['NetworkID','StationID']], return_index=True, axis=0)[1]
#event dataframe
df_cat_new_rec_eq = df_cat_new_rec.loc[eq_idx, df_nrec_eq.columns].reset_index(drop=True)
#station dataframe
df_cat_new_rec_sta = df_cat_new_rec.loc[sta_idx, df_nrec_sta.columns].reset_index(drop=True)
# %% Output
# ======================================
# create output directories
if not os.path.isdir(dir_out): pathlib.Path(dir_out).mkdir(parents=True, exist_ok=True)
if not os.path.isdir(dir_fig): pathlib.Path(dir_fig).mkdir(parents=True, exist_ok=True)
#save processed dataframes
fname_cat = '%s%s'%(dir_out, fname_new_cat )
df_cat_new_rec.to_csv(fname_cat + '.csv', index=False)
df_cat_new_rec_eq.to_csv(fname_cat + '_event.csv', index=False)
df_cat_new_rec_sta.to_csv(fname_cat + '_station.csv', index=False)
# create figures
# ---- ---- ---- ---- ----
# Mag-Dist distribution
fname_fig = 'M-R_dist'
#create figure
fig, ax = plt.subplots(figsize = (10,9))
pl1 = ax.scatter(df_cat_new_rec.HypDist, df_cat_new_rec.Magnitude)
#edit figure properties
ax.set_xlabel(r'Distance ($km$)', fontsize=30)
ax.set_ylabel(r'Magnitude', fontsize=30)
ax.grid(which='both')
# ax.set_xscale('log')
# ax.set_xlim([0.1, 2000])
ax.set_ylim([1, 8])
ax.tick_params(axis='x', labelsize=25)
ax.tick_params(axis='y', labelsize=25)
# ax.legend(fontsize=25, loc='upper left')
ax.xaxis.set_tick_params(which='major', size=10, width=2, direction='in', top='on')
ax.xaxis.set_tick_params(which='minor', size=7, width=2, direction='in', top='on')
ax.yaxis.set_tick_params(which='major', size=10, width=2, direction='in', right='on')
ax.yaxis.set_tick_params(which='minor', size=7, width=2, direction='in', right='on')
fig.tight_layout()
#save figure
fig.savefig( dir_fig + fname_fig + '.png' )
#log scale figure
ax.set_xscale('log')
fig.tight_layout()
#save figure
fig.savefig( dir_fig + fname_fig + '_log' + '.png' )
# Mag-Year distribution
fname_fig = 'M-date_dist'
#create figure
fig, ax = plt.subplots(figsize = (10,9))
pl1 = ax.scatter(pd.DatetimeIndex(df_cat_new_rec['Time']).year, df_cat_new_rec['Magnitude'].values)
#edit figure properties
ax.set_xlabel(r'time ($year$)', fontsize=30)
ax.set_ylabel(r'Magnitude', fontsize=30)
ax.grid(which='both')
# ax.set_xscale('log')
ax.set_xlim([1965, 2025])
ax.set_ylim([1, 8])
ax.tick_params(axis='x', labelsize=25)
ax.tick_params(axis='y', labelsize=25)
# ax.legend(fontsize=25, loc='upper left')
ax.xaxis.set_tick_params(which='major', size=10, width=2, direction='in', top='on')
ax.xaxis.set_tick_params(which='minor', size=7, width=2, direction='in', top='on')
ax.yaxis.set_tick_params(which='major', size=10, width=2, direction='in', right='on')
ax.yaxis.set_tick_params(which='minor', size=7, width=2, direction='in', right='on')
fig.tight_layout()
#save figure
fig.savefig( dir_fig + fname_fig + '.png' )
#eq and sta location
fname_fig = 'eq_sta_locations'
fig, ax, data_crs, gl = pylib_cplt.PlotMap(flag_grid=True)
#plot earthquake and station locations
ax.plot(df_cat_new_rec_eq['eqLon'].values, df_cat_new_rec_eq['eqLat'].values, '*', transform = data_crs, markersize = 10, zorder=13, label='Events')
ax.plot(df_cat_new_rec_sta['staLon'].values, df_cat_new_rec_sta['staLat'].values, 'o', transform = data_crs, markersize = 6, zorder=13, label='Stations')
#edit figure properties
gl.xlabel_style = {'size': 25}
gl.ylabel_style = {'size': 25}
# gl.xlocator = mticker.FixedLocator([-124, -122, -120, -118, -116, -114])
# gl.ylocator = mticker.FixedLocator([32, 34, 36, 38, 40])
ax.legend(fontsize=25, loc='lower left')
# ax.set_xlim(plt_latlon_win[:,1])
# ax.set_ylim(plt_latlon_win[:,0])
#save figure
fig.tight_layout()
fig.savefig( dir_fig + fname_fig + '.png' )
# Print data info
# ---------------------------
print(f'New Records:')
print(f'\tnumber of rec: %.i'%len(df_cat_new_rec))
print(f'\tnumber of rec (R<200km): %.i'%np.sum(df_cat_new_rec.HypDist<=200))
print(f'\tnumber of rec (R<300km): %.i'%np.sum(df_cat_new_rec.HypDist<=300))
print(f'\tnumber of eq: %.i'%len(df_cat_new_rec_eq))
print(f'\tnumber of sta: %.i'%len(df_cat_new_rec_sta))
| 8,640 | 36.569565 | 154 | py |
ngmm_tools | ngmm_tools-master/Analyses/Code_Verification/preprocessing/CreateCatalogNewEvents2021.py | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Sun Jun 27 16:12:57 2021
@author: glavrent
"""
# Required Packages
# ======================================
#load libraries
import os
import sys
import pathlib
import glob
import re #regular expression package
#arithmetic libraries
import numpy as np
import pandas as pd
#geographic coordinates
import pyproj
#plotting libraries
from matplotlib import pyplot as plt
import matplotlib.ticker as mticker
#user-derfined functions
sys.path.insert(0,'../../Python_lib/catalog')
sys.path.insert(0,'../../Python_lib/plotting')
import pylib_catalog as pylib_catalog
import pylib_contour_plots as pylib_cplt
# %% Define Input Data
# ======================================
#thresholds
#min sampling rate
thres_dt = 0.025
#collocated stations
thres_dist = 0.01
#maximum depth
eq_mag_depth = 25
#distance range
# rrup_thres = 300
rrup_thres = 400
#year range
year_min = 2011
year_max = 2021
# year_max = 2013
# projection system
utm_zone = '11S'
#input flatfiles
fname_flatfile_newrec = '../../../Data/Verification/preprocessing/flatfiles/CA_NV_2011-2021_raw/Catalog_California_2011-2021.ver02.csv'
fname_sta_vs30 = '../../../Raw_files/nga_w3/IRIS/Catalog_California_2011-2021.ver02_station_Vs30PW.csv'
#flatfile file
fname_flatfile = 'CatalogNewRecords_%.i-%.i_CA_NV'%(year_min, year_max )
#output directory
dir_out = '../../../Data/Verification/preprocessing/flatfiles/CA_NV_%.i-%.i/'%(year_min, year_max)
dir_fig = dir_out + 'figures/'
#latlon window
win_latlon = np.array([[30, 43],[-125, -110]])
#win_latlon = np.array([[32, 43],[-125, -114]])
# %% Load Data
# ======================================
#read event and station info
df_flatfile_newrec = pd.read_csv(fname_flatfile_newrec)
#read vs30 info
if fname_sta_vs30:
df_sta_vs30 = pd.read_csv(fname_sta_vs30)
else:
df_sta_vs30 = None
# %% Process Data
# ======================================
# projection system
# ---- ---- ---- ---- ----
utmProj = pyproj.Proj("+proj=utm +zone="+utm_zone+", +ellps=WGS84 +datum=WGS84 +units=km +no_defs")
# rename columns
# ---- ---- ---- ---- ----
df_flatfile_newrec.rename(columns={'EventID':'eventid', 'Time':'time', 'Latitude':'eqLat', 'Longitude':'eqLon',
'Author':'author', 'Catalog':'cat', 'Contributor':'contributor', 'ContributorID':'contributor_id',
'Magnitude':'mag', 'MagType':'mag_type', 'MagAuthor':'mag_author',
'EventLocationName':'eq_loc', 'ESN':'esn', 'Double.event':'double_event',
'event.ESN':'esn_y', 'event.EventID':'eventid', 'Network':'network', 'Station':'station',
'Latitude':'staLat', 'Longitude':'staLon', 'Elevation':'staElev'},
inplace=True)
#year of event
df_flatfile_newrec['year'] = pd.DatetimeIndex(df_flatfile_newrec['time']).year
#station vs30
if fname_sta_vs30:
df_sta_vs30.rename(columns={'Network':'network', 'Station':'station', 'Vs30_lnSD': 'Vs30sig'}, inplace=True)
_, df_sta_vs30.loc[:,'sta_id'] = np.unique(df_sta_vs30.station, return_inverse=True)
_, df_sta_vs30.loc[:,'net_id'] = np.unique(df_sta_vs30.network, return_inverse=True)
assert(len(df_sta_vs30) == len(np.unique(df_sta_vs30[['net_id','sta_id']], axis=1))),'Error. Non-unique network stations'
# cleaning files
# ---- ---- ---- ---- ----
#set -999 to nan
df_flatfile_newrec.replace(-999, np.nan, inplace=True)
#remove data with unknown mag
df_flatfile_newrec = df_flatfile_newrec[ ~np.isnan(df_flatfile_newrec['mag']) ]
#remove data with unknown coordinates
df_flatfile_newrec = df_flatfile_newrec[ ~np.isnan(df_flatfile_newrec[['eqLat', 'eqLon']]).any(axis=1) ]
df_flatfile_newrec = df_flatfile_newrec[ ~np.isnan(df_flatfile_newrec[['staLat', 'staLon']]).any(axis=1) ]
# keep only data in spatio-temporal window
# ---- ---- ---- ---- ----
#earthquakes
i_space_win_eq = np.all(np.array([df_flatfile_newrec.eqLat >= win_latlon[0,0],
df_flatfile_newrec.eqLat < win_latlon[0,1],
df_flatfile_newrec.eqLon >= win_latlon[1,0],
df_flatfile_newrec.eqLon < win_latlon[1,1]]),axis=0)
#stations
i_space_win_sta = np.all(np.array([df_flatfile_newrec.staLat >= win_latlon[0,0],
df_flatfile_newrec.staLat < win_latlon[0,1],
df_flatfile_newrec.staLon >= win_latlon[1,0],
df_flatfile_newrec.staLon < win_latlon[1,1]]),axis=0)
#depth limit
i_eq_depth = df_flatfile_newrec.eqDepth <= eq_mag_depth
#time
i_time_win = np.logical_and(df_flatfile_newrec.year >= year_min, df_flatfile_newrec.year <= year_max)
#records to keep
i_win = np.all(np.array([i_space_win_eq, i_space_win_sta, i_eq_depth, i_time_win]),axis=0)
df_flatfile_newrec = df_flatfile_newrec[i_win]
# Vs30
# ---- ---- ---- ---- ----
#Wills 2015 model for CA
# Wills15Vs30 = pylib_W15_Vs30.Willis15Vs30CA()
# df_flatfile_newrec.loc[:,'Vs30'] = Wills15Vs30.lookup(np.fliplr(df_flatfile_newrec[['staLat', 'staLon']]))[0]
# df_flatfile_newrec.loc[df_flatfile_newrec.loc[:,'Vs30'] < 50,'Vs30'] = np.nan
#geology based Vs30
if not df_sta_vs30 is None:
#Vs30 estimates from geology and topography (Pengfei, personal communication)
df_flatfile_newrec = pd.merge(df_flatfile_newrec, df_sta_vs30[['station','network','Vs30','Vs30sig']], on=['station','network'] )
else:
#Unavailable Vs30
df_flatfile_newrec.loc[:,['Vs30','Vs30sig']] = np.nan
# define earthquake, station and network ids
# ---- ---- ---- ---- ----
#original earthquake and station ids
df_flatfile_newrec.loc[:,'eventid'] = df_flatfile_newrec['eventid'].values.astype(int)
df_flatfile_newrec.loc[:,'staid'] = np.unique(df_flatfile_newrec['station'], return_inverse=True)[1] + 1
df_flatfile_newrec.loc[:,'netid'] = np.unique(df_flatfile_newrec['network'], return_inverse=True)[1] + 1
#keep single record from each event
i_unq_eq_sta = np.unique(df_flatfile_newrec[['eventid','staid','netid']].values, return_index=True, axis=0)[1]
df_flatfile_newrec = df_flatfile_newrec.iloc[i_unq_eq_sta, :]
# define rsn eqid and ssn
# ---- ---- ---- ---- ----
#reset indices
df_flatfile_newrec.reset_index(drop=True, inplace=True)
df_flatfile_newrec.rename_axis('rsn', inplace=True)
#updated earthquake and station ids
_, eq_idx, eq_inv = np.unique(df_flatfile_newrec.loc[:,'eventid'], axis=0, return_index=True, return_inverse=True)
_, sta_idx, sta_inv = np.unique(df_flatfile_newrec.loc[:,['netid','staid']], axis=0, return_index=True, return_inverse=True)
df_flatfile_newrec.loc[:,'eqid'] = eq_inv+1
df_flatfile_newrec.loc[:,'ssn'] = sta_inv+1
n_eq_orig = len(eq_idx)
n_sta_orig = len(sta_idx)
# cartesian coordinates
# ---- ---- ---- ---- ----
#utm coordinates
eq_X = np.array([utmProj(e.eqLon, e.eqLat) for _, e in df_flatfile_newrec.iloc[eq_idx,:].iterrows()])
sta_X = np.array([utmProj(s.staLon, s.staLat) for _, s in df_flatfile_newrec.iloc[sta_idx,:].iterrows()])
df_flatfile_newrec.loc[:,['eqX','eqY']] = eq_X[eq_inv,:]
df_flatfile_newrec.loc[:,['staX','staY']] = sta_X[sta_inv,:]
#eq depth
df_flatfile_newrec.loc[:,'eqZ'] = np.minimum(-1*df_flatfile_newrec['eqDepth'].values, 0)
#utm zone
df_flatfile_newrec.loc[:,'UTMzone'] = utm_zone
# rupture distance
# ---- ---- ---- ---- ----
df_flatfile_newrec.loc[:,'Rrup'] = np.sqrt(np.linalg.norm(df_flatfile_newrec[['eqX','eqY']].values-df_flatfile_newrec[['staX','staY']].values, axis=1)**2 +
df_flatfile_newrec['eqZ']**2)
#remove records based on rupture distance
i_rrup = df_flatfile_newrec['Rrup'] < rrup_thres
df_flatfile_newrec = df_flatfile_newrec.loc[i_rrup,:]
# colocate stations
# ---- ---- ---- ---- ----
#update ssn for colocated stations
df_flatfile_newrec = pylib_catalog.ColocatePt(df_flatfile_newrec, 'ssn', ['staX','staY'], thres_dist=thres_dist)
#keep single record from each event
i_unq_eq_sta = np.unique(df_flatfile_newrec[['eqid','ssn']].values, return_index=True, axis=0)[1]
df_flatfile_newrec = df_flatfile_newrec.iloc[i_unq_eq_sta, :].sort_index()
# average gm parameters
# ---- ---- ---- ---- ----
df_flatfile_newrec = pylib_catalog.IndexAvgColumns(df_flatfile_newrec, 'eqid', ['mag','eqX','eqY','eqZ'])
df_flatfile_newrec = pylib_catalog.IndexAvgColumns(df_flatfile_newrec, 'ssn', ['Vs30','staX','staY','staElev'])
#recalculated lat/lon coordinates
_, eq_idx, eq_inv = np.unique(df_flatfile_newrec.loc[:,'eqid'], axis=0, return_index=True, return_inverse=True)
_, sta_idx, sta_inv = np.unique(df_flatfile_newrec.loc[:,'ssn'], axis=0, return_index=True, return_inverse=True)
n_eq = len(eq_idx)
n_sta = len(sta_idx)
eq_latlon = np.flip([utmProj(e.eqX, e.eqY, inverse=True) for _, e in df_flatfile_newrec.iloc[eq_idx,:].iterrows()], axis=1)
sta_latlon = np.flip([utmProj(s.staX, s.staY, inverse=True) for _, s in df_flatfile_newrec.iloc[sta_idx,:].iterrows()], axis=1)
df_flatfile_newrec.loc[:,['eqLat','eqLon']] = eq_latlon[eq_inv,:]
df_flatfile_newrec.loc[:,['staLat','staLon']] = sta_latlon[sta_inv,:]
# midpoint coordinates
# ---- ---- ---- ---- ----
df_flatfile_newrec.loc[:,['mptX','mptY']] = (df_flatfile_newrec.loc[:,['eqX','eqY']].values + df_flatfile_newrec.loc[:,['staX','staY']].values) / 2
df_flatfile_newrec.loc[:,['mptLat','mptLon']] = np.flip( np.array([utmProj(pt.mptX, pt.mptY, inverse=True) for _, pt in df_flatfile_newrec.iterrows()]), axis=1 )
#recalculate rupture distance after averaging
df_flatfile_newrec.loc[:,'Rrup'] = np.sqrt(np.linalg.norm(df_flatfile_newrec[['eqX','eqY']].values-df_flatfile_newrec[['staX','staY']].values, axis=1)**2 +
df_flatfile_newrec['eqZ']**2)
# %% Save Data
# ======================================
# create output directories
if not os.path.isdir(dir_out): pathlib.Path(dir_out).mkdir(parents=True, exist_ok=True)
if not os.path.isdir(dir_fig): pathlib.Path(dir_fig).mkdir(parents=True, exist_ok=True)
#full dataframe
df_flatfile_full = df_flatfile_newrec[['eqid','ssn','eventid','staid','netid','station','network',
'mag','mag_type','mag_author','Rrup','Vs30','time','year',
'eqLat','eqLon','staLat','staLon','mptLat','mptLon',
'UTMzone','eqX','eqY','eqZ','staX','staY','staElev','mptX','mptY',
'author','cat','contributor','contributor_id','eq_loc']]
#event dataframe
df_flatfile_event = df_flatfile_newrec.iloc[eq_idx,:][['eqid','eventid','mag','mag_type','mag_author','year',
'eqLat','eqLon','UTMzone','eqX','eqY','eqZ',
'author','cat','contributor','contributor_id','eq_loc']].reset_index(drop=True)
#station dataframe
df_flatfile_station = df_flatfile_newrec.iloc[sta_idx,:][['ssn','Vs30',
'staLat','staLon','UTMzone','staX','staY','staElev']].reset_index(drop=True)
# save dataframe
# ---- ---- ---- ---- ----
#save processed dataframes
fname_flatfile_full= '%s%s'%(dir_out, fname_flatfile)
df_flatfile_full.to_csv(fname_flatfile_full + '.csv', index=True)
df_flatfile_event.to_csv(fname_flatfile_full + '_event.csv', index=False)
df_flatfile_station.to_csv(fname_flatfile_full + '_station.csv', index=False)
# create figures
# ---- ---- ---- ---- ----
# Mag-Dist distribution
fname_fig = 'M-R_dist'
#create figure
fig, ax = plt.subplots(figsize = (10,9))
pl1 = ax.scatter(df_flatfile_full.Rrup, df_flatfile_full.mag, label='new records')
#edit figure properties
ax.set_xlabel(r'Distance ($km$)', fontsize=30)
ax.set_ylabel(r'Magnitude', fontsize=30)
ax.grid(which='both')
ax.set_xscale('log')
# ax.set_xlim([0.1, 2000])
ax.set_ylim([1, 8])
ax.tick_params(axis='x', labelsize=25)
ax.tick_params(axis='y', labelsize=25)
# ax.legend(fontsize=25, loc='upper left')
ax.xaxis.set_tick_params(which='major', size=10, width=2, direction='in', top='on')
ax.xaxis.set_tick_params(which='minor', size=7, width=2, direction='in', top='on')
ax.yaxis.set_tick_params(which='major', size=10, width=2, direction='in', right='on')
ax.yaxis.set_tick_params(which='minor', size=7, width=2, direction='in', right='on')
fig.tight_layout()
#save figure
fig.savefig( dir_fig + fname_fig + '.png' )
# Mag-Year distribution
fname_fig = 'M-date_dist'
#create figure
fig, ax = plt.subplots(figsize = (10,9))
pl1 = ax.scatter(df_flatfile_event['year'].values, df_flatfile_event['mag'].values, label='new records')
#edit figure properties
ax.set_xlabel(r'time ($year$)', fontsize=30)
ax.set_ylabel(r'Magnitude', fontsize=30)
ax.grid(which='both')
# ax.set_xscale('log')
ax.set_xlim([1965, 2025])
ax.set_ylim([2, 8])
ax.tick_params(axis='x', labelsize=25)
ax.tick_params(axis='y', labelsize=25)
# ax.legend(fontsize=25, loc='upper left')
ax.xaxis.set_tick_params(which='major', size=10, width=2, direction='in', top='on')
ax.xaxis.set_tick_params(which='minor', size=7, width=2, direction='in', top='on')
ax.yaxis.set_tick_params(which='major', size=10, width=2, direction='in', right='on')
ax.yaxis.set_tick_params(which='minor', size=7, width=2, direction='in', right='on')
fig.tight_layout()
#save figure
fig.savefig( dir_fig + fname_fig + '.png' )
#eq and sta location
fname_fig = 'eq_sta_locations'
fig, ax, data_crs, gl = pylib_cplt.PlotMap(flag_grid=True)
#plot earthquake and station locations
ax.plot(df_flatfile_event['eqLon'].values, df_flatfile_event['eqLat'].values, '*', transform = data_crs, markersize = 10, zorder=12, label='Events')
ax.plot(df_flatfile_station['staLon'].values, df_flatfile_station['staLat'].values, 'o', transform = data_crs, markersize = 6, zorder=13, label='Stations')
#edit figure properties
gl.xlabel_style = {'size': 25}
gl.ylabel_style = {'size': 25}
# gl.xlocator = mticker.FixedLocator([-124, -122, -120, -118, -116, -114])
# gl.ylocator = mticker.FixedLocator([32, 34, 36, 38, 40])
ax.legend(fontsize=25, loc='lower left')
# ax.set_xlim(plt_latlon_win[:,1])
# ax.set_ylim(plt_latlon_win[:,0])
#save figure
fig.tight_layout()
fig.savefig( dir_fig + fname_fig + '.png' )
# print data info
# ---- ---- ---- ---- ----
print(r'New Records:')
print(f'\tnumber of rec: %.i'%len(df_flatfile_newrec))
print(f'\tnumber of rec (R<200km): %.i'%np.sum(df_flatfile_newrec.Rrup<=200))
print(f'\tnumber of rec (R<%.1f): %.i'%(rrup_thres, np.sum(df_flatfile_newrec.Rrup<=rrup_thres)))
print(f'\tnumber of eq: %.i'%n_eq)
print(f'\tnumber of sta: %.i'%n_sta)
print(f'\tmin magnitude: %.1f'%df_flatfile_newrec.mag.min())
print(f'\tmax magnitude: %.1f'%df_flatfile_newrec.mag.max())
print(f'\tcoverage: %.i to %i'%(df_flatfile_newrec.year.min(), df_flatfile_newrec.year.max()))
#write out summary
# ---- ---- ---- ---- ----
f = open(dir_out + 'summary_data' + '.txt', 'w')
f.write(f'New Records:\n')
f.write(f'\tnumber of rec: %.i\n'%len(df_flatfile_newrec))
f.write(f'\tnumber of rec (R<200km): %.i\n'%np.sum(df_flatfile_newrec.Rrup<=200))
f.write(f'\tnumber of rec (R<%.1f): %.i\n'%(rrup_thres, np.sum(df_flatfile_newrec.Rrup<=rrup_thres)))
f.write(f'\tnumber of eq: %.i\n'%n_eq)
f.write(f'\tnumber of sta: %.i\n'%n_sta)
f.write(f'\tmin magnitude: %.1f\n'%df_flatfile_newrec.mag.min())
f.write(f'\tmax magnitude: %.1f\n'%df_flatfile_newrec.mag.max())
f.write(f'\tcoverage: %.i to %i\n'%(df_flatfile_newrec.year.min(), df_flatfile_newrec.year.max()))
f.close()
| 15,785 | 44.889535 | 162 | py |
ngmm_tools | ngmm_tools-master/Analyses/Code_Verification/preprocessing/PlotCellPaths.py | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Sun Sep 13 18:00:32 2020
@author: glavrent
"""
# %% Required Packages
# ======================================
#load variables
import os
import sys
import pathlib
import glob
import re #regular expression package
#arithmetic libraries
import numpy as np
import pandas as pd
#geographic coordinates
import pyproj
#plottign libraries
from matplotlib import pyplot as plt
#user-derfined functions
sys.path.insert(0,'../../Python_lib/plotting')
import pylib_contour_plots as pycplt
# Define Input Data
# ---------------------------
fname_flatfile = '../../../Data/Verification/preprocessing/flatfiles/CA_NV_2011-2021Lite/CatalogNewRecordsLite_2011-2021_CA_NV.csv'
fname_cellinfo = '../../../Data/Verification/preprocessing/cell_distances/CatalogNGAWest3CALite_cellinfo.csv'
fname_celldistfile = '../../../Data/Verification/preprocessing/cell_distances/CatalogNGAWest3CALite_distancematrix.csv'
#grid limits and size
coeff_latlon_win = np.array([[32, -125],[42.5, -114]])
#log scale for number of paths
flag_logscl = True
#output directory
dir_out = '../../../Data/Verification/preprocessing/cell_distances/figures/'
# Load Data
# ---------------------------
df_flatfile = pd.read_csv(fname_flatfile)
df_cellinfo = pd.read_csv(fname_cellinfo)
#cell distance file
df_celldata = pd.read_csv(fname_celldistfile, index_col=0).reindex(df_flatfile.rsn)
# Process Data
# ---------------------------
#coordinates and projection system
# projection system
utm_zone = np.unique(df_flatfile.UTMzone)[0] #utm zone
utmProj = pyproj.Proj("+proj=utm +zone="+utm_zone+", +ellps=WGS84 +datum=WGS84 +units=m +no_defs")
#cell edge coordinates
cell_edge_latlon = []
for cell_edge in [['q5X','q5Y'], ['q6X','q6Y'], ['q8X','q8Y'],
['q7X','q7Y'], ['q5X','q5Y']]:
cell_edge_latlon.append( np.fliplr(np.array([utmProj(c_xy[0]*1000, c_xy[1]*1000, inverse=True) for c_xy in
df_cellinfo.loc[:,cell_edge].values])) )
cell_edge_latlon = np.hstack(cell_edge_latlon)
#cell mid-coordinates
cell_latlon = np.fliplr(np.array([utmProj(c_xy[0]*1000, c_xy[1]*1000, inverse=True) for c_xy in
df_cellinfo.loc[:,['mptX','mptY']].values]))
#earthquake and station ids
eq_id_train = df_flatfile['eqid'].values.astype(int)
sta_id_train = df_flatfile['ssn'].values.astype(int)
eq_id, eq_idx_inv = np.unique(eq_id_train, return_index=True)
sta_id, sta_idx_inv = np.unique(sta_id_train, return_index=True)
#earthquake and station coordinates
eq_latlon_train = df_flatfile[['eqLat', 'eqLon']].values
stat_latlon_train = df_flatfile[['staLat', 'staLon']].values
#unique earthquake and station coordinates
eq_latlon = eq_latlon_train[eq_idx_inv,:]
stat_latlon = stat_latlon_train[sta_idx_inv,:]
#cell names
cell_i = [bool(re.match('^c\\..*$',c_n)) for c_n in df_celldata.columns.values] #indices for cell columns
cell_names = df_celldata.columns.values[cell_i]
#cell-distance matrix with all cells
cell_dist = df_celldata[cell_names]
cell_n_paths = (cell_dist > 0).sum()
# Create cell figures
# ---------------------------
if not os.path.isdir(dir_out): pathlib.Path(dir_out).mkdir(parents=True, exist_ok=True)
# if flag_pub:
# # mpl.rcParams['font.family'] = 'Avenir'
# plt.rcParams['axes.linewidth'] = 2
# Plot cell paths
fname_fig = 'cA_paths'
fig, ax, data_crs, gl = pycplt.PlotMap()
#plot earthquake and station locations
ax.plot(eq_latlon[:,1], eq_latlon[:,0], '*', transform = data_crs, markersize = 10, zorder=13, label='Events')
ax.plot(stat_latlon[:,1], stat_latlon[:,0], 'o', transform = data_crs, markersize = 6, zorder=12, label='Stations')
# ax.plot(eq_latlon[:,1], eq_latlon[:,0], '^', transform = data_crs, color = 'black', markersize = 10, zorder=13, label='Earthquake')
# ax.plot(stat_latlon[:,1], stat_latlon[:,0], 'o', transform = data_crs, color = 'black', markersize = 3, zorder=12, label='Station')
#plot earthquake-station paths
for rec in df_flatfile[['eqLat','eqLon','staLat','staLon']].iterrows():
ax.plot(rec[1][['eqLon','staLon']], rec[1][['eqLat','staLat']], transform = data_crs, color = 'gray', linewidth=0.05, zorder=10, alpha=0.2)
#plot cells
for ce_xy in cell_edge_latlon:
ax.plot(ce_xy[[1,3,5,7,9]],ce_xy[[0,2,4,6,8]],color='gray', transform = data_crs)
#figure limits
ax.set_xlim( coeff_latlon_win[:,1] )
ax.set_ylim( coeff_latlon_win[:,0] )
#edit figure properties
#grid lines
gl = ax.gridlines(draw_labels=True)
gl.xlabels_top = False
gl.ylabels_right = False
gl.xlabel_style = {'size': 25}
gl.ylabel_style = {'size': 25}
#add legend
ax.legend(fontsize=25, loc='lower left')
#apply tight layout
fig.show()
fig.tight_layout()
fig.savefig( dir_out + fname_fig + '.png')
# Plot cell paths
fname_fig = 'cA_num_paths'
cbar_label = 'Number of paths'
data2plot = np.vstack([cell_latlon.T, cell_n_paths.values]).T
#color limits
cmin = 0
cmax = 2000
#log scale options
if flag_logscl:
# data2plot[:,2] = np.maximum(data2plot[:,2], 1)
cmin = np.log(1)
cmax = np.log(cmax)
#create figure
fig, ax, cbar, data_crs, gl = pycplt.PlotCellsCAMap(data2plot, cmin=cmin, cmax=cmax, log_cbar = flag_logscl,
frmt_clb = '%.0f', cmap='OrRd')
#plot cells
for ce_xy in cell_edge_latlon:
ax.plot(ce_xy[[1,3,5,7]],ce_xy[[0,2,4,6]],color='gray', transform = data_crs)
#figure limits
ax.set_xlim( coeff_latlon_win[:,1] )
ax.set_ylim( coeff_latlon_win[:,0] )
#edit figure properties
#grid lines
gl = ax.gridlines(draw_labels=True)
gl.xlabels_top = False
gl.ylabels_right = False
gl.xlabel_style = {'size': 25}
gl.ylabel_style = {'size': 25}
#update colorbar
cbar.set_label(cbar_label, size=30)
cbar.ax.tick_params(labelsize=25)
#apply tight layout
fig.show()
fig.tight_layout()
fig.savefig( dir_out + fname_fig + '.png')
| 5,935 | 34.54491 | 143 | py |
ngmm_tools | ngmm_tools-master/Analyses/Code_Verification/preprocessing/PlotUsableMagRrupCatalog.py | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Mon Oct 4 16:32:37 2021
@author: glavrent
"""
# %% Required Packages
# ======================================
#load libraries
import os
import pathlib
#arithmetic libraries
import numpy as np
import pandas as pd
#plotting libraries
from matplotlib import pyplot as plt
import matplotlib.ticker as mticker
# %% Define variables
# ======================================
#input file names
fname_flatfile_NGA2 = '../../../Raw_files/nga_w2/Updated_NGA_West2_Flatfile_RotD50_d050_public_version.xlsx'
fname_mag_rrup_lim = '../../../Data/Verification/preprocessing/flatfiles/usable_mag_rrup/usable_Mag_Rrup_coeffs.csv'
#output directoy
dir_fig = '../../../Data/Verification/preprocessing/flatfiles/usable_mag_rrup/'
# %% Load Data
# ======================================
#NGAWest2
df_flatfile_NGA2 = pd.read_excel(fname_flatfile_NGA2)
#M/R limit
df_m_r_lim = pd.read_csv(fname_mag_rrup_lim,index_col=0)
#remove rec with unavailable data
df_flatfile_NGA2 = df_flatfile_NGA2.loc[df_flatfile_NGA2.EQID>0,:]
df_flatfile_NGA2 = df_flatfile_NGA2.loc[df_flatfile_NGA2['ClstD (km)']>0,:]
#mag and distance arrays
mag_array = df_flatfile_NGA2['Earthquake Magnitude']
rrup_array = df_flatfile_NGA2['ClstD (km)']
#compute limit
rrup_lim1 = np.arange(0,1001)
mag_lim1 = (df_m_r_lim.loc['b0','coefficients'] +
df_m_r_lim.loc['b1','coefficients'] * rrup_lim1 +
df_m_r_lim.loc['b2','coefficients'] * rrup_lim1**2)
rrup_lim2 = df_m_r_lim.loc['max_rrup','coefficients']
# %% Process Data
# ======================================
if not os.path.isdir(dir_fig): pathlib.Path(dir_fig).mkdir(parents=True, exist_ok=True)
# create figures
# ---- ---- ---- ---- ----
# Mag-Dist distribution
fname_fig = 'M-R_limits'
#create figure
fig, ax = plt.subplots(figsize = (10,9))
pl1 = ax.scatter(rrup_array, mag_array, label='NGAWest2 CA')
pl2 = ax.plot(rrup_lim1, mag_lim1, linewidth=2, color='black')
pl3 = ax.vlines(rrup_lim2, ymin=0, ymax=10, linewidth=2, color='black', linestyle='--')
#edit figure properties
ax.set_xlabel(r'Distance ($km$)', fontsize=30)
ax.set_ylabel(r'Magnitude', fontsize=30)
ax.grid(which='both')
# ax.set_xscale('log')
ax.set_xlim([0, 1000])
ax.set_ylim([2, 8])
ax.tick_params(axis='x', labelsize=25)
ax.tick_params(axis='y', labelsize=25)
# ax.legend(fontsize=25, loc='upper left')
ax.xaxis.set_tick_params(which='major', size=10, width=2, direction='in', top='on')
ax.xaxis.set_tick_params(which='minor', size=7, width=2, direction='in', top='on')
ax.yaxis.set_tick_params(which='major', size=10, width=2, direction='in', right='on')
ax.yaxis.set_tick_params(which='minor', size=7, width=2, direction='in', right='on')
fig.tight_layout()
#save figure
fig.savefig( dir_fig + fname_fig + '.png' )
| 2,814 | 33.753086 | 117 | py |
ngmm_tools | ngmm_tools-master/Analyses/Code_Verification/preprocessing/ComputeCellDistance.py | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Thu Apr 9 11:04:25 2020
@author: glavrent
"""
# Required Packages
# ======================================
#load libraries
import os
import sys
import pathlib
import numpy as np
import pandas as pd
from scipy import sparse
#geographic libraries
import pyproj
#user libraries
sys.path.insert(0,'../../Python_lib/ground_motions')
import pylib_cell_dist as pylib_cells
# %% Define Input Data
# ======================================
#input flatfile
# fname_flatfile = 'CatalogNGAWest3CA'
# fname_flatfile = 'CatalogNGAWest3CA_2013'
fname_flatfile = 'CatalogNGAWest3CALite'
# fname_flatfile = 'CatalogNGAWest3NCA'
# fname_flatfile = 'CatalogNGAWest3SCA'
dir_flatfile = '../../../Data/Verification/preprocessing/flatfiles/merged/'
#output files
dir_out = '../../../Data/Verification/preprocessing/cell_distances/'
# %% Read and Porcess Input Data
# ======================================
# read ground-motion data
fullname_flatfile = dir_flatfile + fname_flatfile + '.csv'
df_flatfile = pd.read_csv(fullname_flatfile)
n_rec = len(df_flatfile)
#define projection system
assert(len(np.unique(df_flatfile.UTMzone))==1),'Error. Multiple UTM zones defined.'
utm_zone = df_flatfile.UTMzone[0]
utmProj = pyproj.Proj("+proj=utm +zone="+utm_zone+", +ellps=WGS84 +datum=WGS84 +units=m +no_defs")
#create output directory
if not os.path.isdir(dir_out): pathlib.Path(dir_out).mkdir(parents=True, exist_ok=True)
#create object with source and station locations
#utm coordinates
data4celldist = df_flatfile.loc[:,['eqX','eqY','eqZ','staX','staY']].values
flagUTM = True
#add elevation for stations
data4celldist = np.hstack([data4celldist,np.zeros([n_rec,1])])
# %% Create Cell Grid
# ======================================
#grid range
grid_lims_x = [data4celldist[:,[0,3]].min(), data4celldist[:,[0,3]].max()]
grid_lims_y = [data4celldist[:,[1,4]].min(), data4celldist[:,[1,4]].max()]
grid_lims_z = [data4celldist[:,[2,5]].min(), data4celldist[:,[2,5]].max()]
#manual limits
#utm limits
# #NGAWest3 full
# grid_lims_x = [-200, 1100]
# grid_lims_y = [3300, 4800]
# grid_lims_z = [-100, 0]
#NGAWest3 lite
grid_lims_x = [-200, 800]
grid_lims_y = [3450, 4725]
grid_lims_z = [-50, 0]
#cell size
cell_size = [25, 25, 50]
#lat-lon grid spacing
grid_x = np.arange(grid_lims_x[0], grid_lims_x[1]+0.1, cell_size[0])
grid_y = np.arange(grid_lims_y[0], grid_lims_y[1]+0.1, cell_size[1])
grid_z = np.arange(grid_lims_z[0], grid_lims_z[1]+0.1, cell_size[2])
#cell schematic
# (7)-----(8) (surface, top face)
# / | / |
# (5)-----(6) |
# | | | |
# | (3)---|-(4) (bottom face)
# |/ |/
# (1)-----(2)
#create cells
j1 = 0
j2 = 0
j3 = 0
cells = []
for j1 in range(len(grid_x)-1):
for j2 in range(len(grid_y)-1):
for j3 in range(len(grid_z)-1):
#cell corners (bottom-face)
cell_c1 = [grid_x[j1], grid_y[j2], grid_z[j3]]
cell_c2 = [grid_x[j1+1], grid_y[j2], grid_z[j3]]
cell_c3 = [grid_x[j1], grid_y[j2+1], grid_z[j3]]
cell_c4 = [grid_x[j1+1], grid_y[j2+1], grid_z[j3]]
#cell corners (top-face)
cell_c5 = [grid_x[j1], grid_y[j2], grid_z[j3+1]]
cell_c6 = [grid_x[j1+1], grid_y[j2], grid_z[j3+1]]
cell_c7 = [grid_x[j1], grid_y[j2+1], grid_z[j3+1]]
cell_c8 = [grid_x[j1+1], grid_y[j2+1], grid_z[j3+1]]
#cell center
cell_cent = np.mean(np.stack([cell_c1,cell_c2,cell_c3,cell_c4,
cell_c5,cell_c6,cell_c7,cell_c8]),axis = 0).tolist()
#summarize all cell coordinates in a list
cell_info = cell_c1 + cell_c2 + cell_c3 + cell_c4 + \
cell_c5 + cell_c6 + cell_c7 + cell_c8 + cell_cent
#add cell info
cells.append(cell_info)
del j1, j2, j3, cell_info
del cell_c1, cell_c2, cell_c3, cell_c4, cell_c5, cell_c6, cell_c7, cell_c8
cells = np.array(cells)
n_cells = len(cells)
#cell info
cell_ids = np.arange(n_cells)
cell_names = ['c.%i'%(i) for i in cell_ids]
cell_q_names = ['q1X','q1Y','q1Z','q2X','q2Y','q2Z','q3X','q3Y','q3Z','q4X','q4Y','q4Z',
'q5X','q5Y','q5Z','q6X','q6Y','q6Z','q7X','q7Y','q7Z','q8X','q8Y','q8Z',
'mptX','mptY','mptZ']
# Create cell info dataframe
# ---- ---- ---- ---- ----
#cell names
df_data1 = pd.DataFrame({'cellid': cell_ids, 'cellname': cell_names})
#cell coordinates
df_data2 = pd.DataFrame(cells, columns = cell_q_names)
df_cellinfo = pd.merge(df_data1,df_data2,left_index=True,right_index=True)
# add cell utm zone
df_cellinfo.loc[:,'UTMzone'] = utm_zone
# Compute Lat\Lon of cells
# ---- ---- ---- ---- ----
#cell verticies
for q in range(1,9):
c_X = ['q%iX'%q, 'q%iY'%q]
c_latlon = ['q%iLat'%q, 'q%iLon'%q]
df_cellinfo.loc[:,c_latlon] = np.flip( np.array([utmProj(pt_xy[0]*1e3, pt_xy[1]*1e3, inverse=True)
for _, pt_xy in df_cellinfo[c_X].iterrows() ]), axis=1)
#cell midpoints
c_X = ['mptX', 'mptY']
c_latlon = ['mptLat','mptLon']
df_cellinfo.loc[:,c_latlon] = np.flip( np.array([utmProj(pt_xy[0]*1e3, pt_xy[1]*1e3, inverse=True)
for _, pt_xy in df_cellinfo[c_X].iterrows() ]), axis=1)
# %% Compute Cell distances
# ======================================
cells4dist = cells[:,[0,1,2,21,22,23]]
distancematrix = np.zeros([len(data4celldist), len(cells4dist)])
for i in range(len(data4celldist)):
print('Computing cell distances, record',i)
pt1 = data4celldist[i,(0,1,2)]
pt2 = data4celldist[i,(3,4,5)]
dm = pylib_cells.ComputeDistGridCells(pt1,pt2,cells4dist, flagUTM)
distancematrix[i] = dm
#print Rrup missfits
dist_diff = df_flatfile.Rrup - distancematrix.sum(axis=1)
print('max R_rup misfit', max(dist_diff))
print('min R_rup misfit', min(dist_diff))
#convert cell distances to sparse matrix
distmatrix_sparce = sparse.coo_matrix(distancematrix)
# Create cell distances data-frame
# ---- ---- ---- ---- ----
#record info
df_recinfo = df_flatfile[['rsn','eqid','ssn']]
#cell distances
df_celldist = pd.DataFrame(distancematrix, columns = cell_names)
df_celldist = pd.merge(df_recinfo, df_celldist, left_index=True, right_index=True)
#spase cell distances
df_celldist_sp = pd.DataFrame({'row': distmatrix_sparce.row+1, 'col': distmatrix_sparce.col+1, 'data': distmatrix_sparce.data})
# %% Save data
# ======================================
#save cell info
fname_cellinfo = fname_flatfile + '_cellinfo'
df_cellinfo.to_csv(dir_out + fname_cellinfo + '.csv', index=False)
# #save distance metrics
fname_celldist = fname_flatfile + '_distancematrix'
df_celldist.to_csv(dir_out + fname_celldist + '.csv', index=False)
# #save distance matrix as sparce
fname_celldist = fname_flatfile + '_distancematrix_sparce'
df_celldist_sp.to_csv(dir_out + fname_celldist + '.csv', index=False)
| 7,014 | 33.219512 | 127 | py |
Subsets and Splits