Configuration files¶
The configuration files copied to the project directory from smartmove/_templates are used for configuration of ___
Project configuration¶
# Datalogger calibration directories
cal:
# Contains acceleration calibration YAMLs for associated tag ID
# Sensors should be calibrated per month--closest calibration used for analysis
w190pd3gt:
34839:
2015:
03: 20150306_W190PD3GT_34839_Notag_Control
34840:
2016:
04: 20160418_W190PD3GT_34840_Skinny_2Neutral
# Parameters for field experiment
experiment:
# 69° 41′ 57.9″ North, 18° 39′ 4.5″ East
coords:
lon: 18.65125
lat: 69.69942
net_depth: 18 #meters
fname_ctd: 'kaldfjorden2016_inner.mat'
Glide analysis¶
# Number of samples per frequency segment in PSD calculation
nperseg: 256
# Threshold above which to find peaks in PSD
peak_thresh: 0.10
# High/low pass cutoff frequency, determined from PSD plot
cutoff_frq: None
# Frequency of stroking, determinded from PSD plot
stroke_frq: 0.4 # Hz
# fraction of `stroke_frq` to calculate cutoff frequency (Wn)
stroke_ratio: 0.4
# Maximum length of stroke signal
# 1/stroke_freq
t_max: 2.5 # seconds
# Minimumn frequency for identifying strokes
# 2 / (180*numpy.pi) (Hz)
J: 0.0349
# For magnetic pry routine
alpha: 25
# Minimum depth at which to recognize a dive
min_depth: 0.4
Sub-glide filtering¶
# Pitch angle (degrees) to consider sgls
pitch_thresh: 30
# Minimum depth at which to recognize a dive (2. Define dives)
min_depth: 0.4
# Maximum cummulative change in depth over a glide
max_depth_delta: 8.0
# Minimum mean speed of sublide
min_speed: 0.3
# Maximum mean speed of sublide
max_speed: 10
# Maximum cummulative change in speed over a glide
max_speed_delta: 1.0
Artificial Neural network¶
# Parameters for compiling data
data:
sgl_cols:
- 'exp_id'
glides:
cutoff_frq: 0.3
J: 0.05
sgls:
dur: 2
filter:
pitch_thresh: 30
max_depth_delta: 8.0
min_speed: 0.3
max_speed: 10
max_speed_delta: 1.0
# Data and network config common to all structures
net_all:
features:
- 'abs_depth_change'
- 'dive_phase_int'
- 'mean_a'
- 'mean_depth'
- 'mean_pitch'
- 'mean_speed'
- 'mean_swdensity'
- 'total_depth_change'
- 'total_speed_change'
target: 'rho_mod'
valid_frac: 0.6
n_targets: 10
# Network tuning parameters, all permutations of these will be trained/validated
net_tuning:
# Number of nodes in each hidden layer
hidden_nodes:
- 10
- 20
- 40
- 60
- 100
- 500
# Number of hidden layers
hidden_layers:
- 1
- 2
- 3
# Trainers (optimizers)
# https://theanets.readthedocs.io/en/stable/api/trainers.html
# http://sebastianruder.com/optimizing-gradient-descent/
algorithm:
- adadelta
- rmsprop
hidden_l1:
- 0.1
- 0.001
- 0.0001
weight_l2:
- 0.1
- 0.001
- 0.0001
momentum:
- 0.9
patience:
- 10
min_improvement:
- 0.999
validate_every:
- 10
learning_rate:
- 0.0001