Typo fixes and general formatting changes

cobrawap/pipeline/stage01_data_entry/README.rst

@@ -67,7 +67,7 @@ Having the data and metadata already in a standard format (e.g., Neo_) as used w
     * Set the additional metadata for the config parameter as required (*can be revisited when writing the loading script*)
 
 3. Create the corresponding loading script.
-    * Copy the ``enter_data_template.py`` in ``cobrawap/pipeline/stage01_data_entry/scripts/`` and and name it as specified in the just created config file.
+    * Copy the ``enter_data_template.py`` in ``cobrawap/pipeline/stage01_data_entry/scripts/`` and name it as specified in the just created config file.
     * Put the script either in the same folder or in ``<configs_dir>/stage01_data_entry/scripts/`` if defined.
 
 4. Edit the loading script.

cobrawap/pipeline/stage01_data_entry/configs/config_template.yaml

@@ -1,16 +1,16 @@
 # Config file for Stage 1 - Data Entry
 
 # Name of stage, must be identical with folder name
-STAGE_NAME: 'stage01_data_entry'
+STAGE_NAME: "stage01_data_entry"
 
 # The profile name is the key for this parameter configuration. Results are stored in output_path/<PROFILE>/ (output_path is defined in settings.py)
-PROFILE: 'dataset_key'
+PROFILE: "dataset_key"
 
 # Name of stage output file.
-STAGE_OUTPUT: 'data'
+STAGE_OUTPUT: "data"
 
 # File format in which all intermediate neo objects are stored
-NEO_FORMAT: 'nix'
+NEO_FORMAT: "nix"
 
 # If True (default), the output file of a stage is created as symbolic link
 # to the last block output. If False, a duplicate is created (e.g. for cloud
@@ -21,16 +21,16 @@ USE_LINK_AS_STAGE_OUTPUT: True
 # To set as key-value pairs
 # Name_of_dataset: /path/to/data/files/
 DATA_SETS: # Note that only the first entry will be used for the pipeline!
-    data_name: '/path/to/data/'
+    data_name: "/path/to/data/"
 
 # Name of script in scripts/ folder, handling the loading and curation
-CURATION_SCRIPT: 'curate_dataset_key.py'
+CURATION_SCRIPT: "curate_dataset_key.py"
 
 # Distance between electrodes (or pixel size)
 SPATIAL_SCALE: 0.05  # mm
 
 # Rate of acquisition of data points
-# Can be 'None' if information is provided in the data file
+# Can be "None" if information is provided in the data file
 SAMPLING_RATE: 25  # Hz
 
 # Time slice in s
@@ -40,36 +40,36 @@ T_STOP: None
 
 # Orientation of the recorded cortical region
 # recommend top: ventral
-ORIENTATION_TOP: 'ventral'
-# recommended right: 'lateral' for right hemisphere, 'medial' for left hemisphere
-ORIENTATION_RIGHT: 'lateral'
+ORIENTATION_TOP: "ventral"
+# recommended right: "lateral" for right hemisphere, "medial" for left hemisphere
+ORIENTATION_RIGHT: "lateral"
 
 TRIAL: None
 
 # Metadata - general information (non-specific to individual channels)
-# This must include an entry 'grid_size' with an int tuple
+# This must include an entry "grid_size" with an int tuple
 # (x-dim, y-dim). The size of this rectangual grid would thus be
 # (x-dim*SPATIAL_SCALE, y-dim*SPATIAL_SCALE).
 ANNOTATIONS:
-    experimental_lab: 'Santa Corp, Antarctica'
+    experimental_lab: "Santa Corp, Antarctica"
     grid_size: [2, 2]
-    anesthetic: 'Isoflurane'
+    anesthetic: "Isoflurane"
 #
-# # Metadata - channel-wise information (e.g. coordinates, SNR, ...), can be 'None'
+# # Metadata - channel-wise information (e.g. coordinates, SNR, ...), can be "None"
 # # Here, the entries must be lists of the same length as the number of channels
 ARRAY_ANNOTATIONS:
     x_coords: [0, 0, 1, 1]
     y_coords: [0, 1, 0, 1]
 
 # Script parameters
 # Here, additional parameters can be passed to the script,
-# to be used to determine additional annotations or array_annotations. Can be 'None' or empty.
-KWARGS: 'None'
+# to be used to determine additional annotations or array_annotations. Can be "None" or empty.
+KWARGS: "None"
 
 # Plotting parameters
 # These parameters are used to create an example plot to get a first view
 # on the signals and ensure that the data was loaded and annotated correctly
-PLOT_TSTART: 0  # float (in s) or 'None' -> starting time of the input signal is used
-PLOT_TSTOP: 10  # float (in s) or 'None' -> stopping time of the input signal is used
-PLOT_CHANNELS: 'None'  # int, or list of int, or 'None' -> randomly chosen channel
-PLOT_FORMAT: 'png'  # file extension for storing the plot
+PLOT_TSTART: 0  # float (in s) or "None" -> starting time of the input signal is used
+PLOT_TSTOP: 10  # float (in s) or "None" -> stopping time of the input signal is used
+PLOT_CHANNELS: "None"  # int, or list of int, or "None" -> randomly chosen channel
+PLOT_FORMAT: "png"  # file extension for storing the plot

cobrawap/pipeline/stage02_processing/configs/config_template.yaml

@@ -1,27 +1,27 @@
 # Config file for Stage 2 - Processing
 
 # Name of stage, must be identical with folder name
-STAGE_NAME: 'stage02_processing'
+STAGE_NAME: "stage02_processing"
 
 # The profile name is the key for this parameter configuration. Results are stored in output_path/<PROFILE>/ (output_path is defined in settings.py)
-PROFILE: 'dataset_key'
+PROFILE: "dataset_key"
 
 # Name of the output file
 STAGE_OUTPUT: "processed_data"
 
 # File format in which all intermediate neo objects are stored
-NEO_FORMAT: 'nix'
+NEO_FORMAT: "nix"
 
 # If True (default), the output file of a stage is created as symbolic link
 # to the last block output. If False, a duplicate is created (e.g. for cloud
 # application, where sym-links are not supported).
 USE_LINK_AS_STAGE_OUTPUT: True
 
 # Plotting parameters
-PLOT_TSTART: 0  # float (in s) or 'None' -> starting time of the input signal is used
-PLOT_TSTOP: 10  # float (in s) or 'None' -> stopping time of the input signal is used
-PLOT_CHANNELS: 'None'  # int or None. default 'None' -> randomly selected
-PLOT_FORMAT: 'png'
+PLOT_TSTART: 0  # float (in s) or "None" -> starting time of the input signal is used
+PLOT_TSTOP: 10  # float (in s) or "None" -> stopping time of the input signal is used
+PLOT_CHANNELS: "None"  # int or None. default "None" -> randomly selected
+PLOT_FORMAT: "png"
 
 # The block order determines which processing steps (blocks) will be applied
 # and in which order. Execution order is from first to last entry.
@@ -35,9 +35,9 @@ PLOT_FORMAT: 'png'
 BLOCK_ORDER: []
 
 # To make sure that the processing blocks are always executed in the correct
-# order that results from previous runs don't confound the workflow, all blocks
+# order that results from previous runs do not confound the workflow, all blocks
 # are rerun upon each execution. To turn this off, e.g., because the block order
-# didn't change, set to False (do with care!).
+# did not change, set to False (do with care!).
 RERUN_MODE: True
 
 # BLOCK - background_subtraction
@@ -51,8 +51,8 @@ MACRO_PIXEL_DIM: 2
 # BLOCK - normalization
 #######################
 # Normalize the data (divide channels-wise) by either:
-# 'mean', 'median', 'max'
-NORMALIZE_BY: 'max'
+# "mean", "median", "max"
+NORMALIZE_BY: "max"
 
 # BLOCK - frequency_filter
 ##########################
@@ -62,8 +62,8 @@ HIGHPASS_FREQUENCY: 0  # in Hz
 LOWPASS_FREQUENCY: 150  # in Hz
 FILTER_ORDER: 2
 # filter function used in scipy backend.
-# options: ‘filtfilt’, 'lfilter’, ‘sosfiltfilt’
-FILTER_FUNCTION: 'sosfiltfilt'
+# options: "filtfilt", "lfilter", "sosfiltfilt"
+FILTER_FUNCTION: "sosfiltfilt"
 # Plotting parameters for the power spectrum
 PSD_FREQUENCY_RESOLUTION: 5  # in Hz
 PSD_OVERLAP: 0.5
@@ -90,10 +90,10 @@ CROP_TO_SELECTION: True
 MUA_HIGHPASS_FREQUENCY: 50  # in Hz
 MUA_LOWPASS_FREQUENCY: 1200  # in Hz
 # Rate of the logMUA signal. Must be <= the original sampling rate
-# in Hz (default: 'None', takes highpass_frequency)
+# in Hz (default: "None", takes highpass_frequency)
 logMUA_RATE: 70
 # Length of time slice (in s) to estimate the local power spectrum
-# default 'None', takes minimum number of samples determined
+# default "None", takes minimum number of samples determined
 # by the lower bound of the frequency band
 FFT_SLICE: 0.1
 # PSD_OVERLAP: determined by setting in block frequency_filter

cobrawap/pipeline/stage03_trigger_detection/configs/config_template.yaml

@@ -1,48 +1,48 @@
 # Config file for Stage 3 - Trigger Detection
 
 # Name of stage, must be identical with folder name
-STAGE_NAME: 'stage03_trigger_detection'
+STAGE_NAME: "stage03_trigger_detection"
 
 # The profile name is the key for this parameter configuration. Results are stored in output_path/<PROFILE>/ (output_path is defined in settings.py)
-PROFILE: 'dataset_key'
+PROFILE: "dataset_key"
 
 # Name of the stage output file
-STAGE_OUTPUT: 'trigger_times'
+STAGE_OUTPUT: "trigger_times"
 
 # File format in which all intermediate neo objects are stored
-NEO_FORMAT: 'nix'
+NEO_FORMAT: "nix"
 
 # If True (default), the output file of a stage is created as symbolic link
 # to the last block output. If False, a duplicate is created (e.g. for cloud
 # application, where sym-links are not supported).
 USE_LINK_AS_STAGE_OUTPUT: True
 
 # Plotting parameters
-PLOT_TSTART: 0  # float (in s) or 'None' -> starting time of the input signal is used
-PLOT_TSTOP: 10  # float (in s) or 'None' -> stopping time of the input signal is used
-PLOT_CHANNELS: 'None'  # int or None. default 'None' -> randomly selected
-PLOT_FORMAT: 'png'
+PLOT_TSTART: 0  # float (in s) or "None" -> starting time of the input signal is used
+PLOT_TSTOP: 10  # float (in s) or "None" -> stopping time of the input signal is used
+PLOT_CHANNELS: "None"  # int or None. default "None" -> randomly selected
+PLOT_FORMAT: "png"
 
 # DETECTION BLOCK
 #################
-# Available Blocks: 'threshold', 'hilbert_phase', 'minima'
-DETECTION_BLOCK: 'threshold'
+# Available Blocks: "threshold", "hilbert_phase", "minima"
+DETECTION_BLOCK: "threshold"
 
 # TRIGGER FILTER
 #################
-# Available Blocks: 'remove_short_states'
-TRIGGER_FILTER: ['remove_short_states']
+# Available Blocks: "remove_short_states"
+TRIGGER_FILTER: ["remove_short_states"]
 
 # BLOCK - Threshold
 ###################
-# Threshold method: 'fixed', 'fitted'
-THRESHOLD_METHOD: 'fixed'
+# Threshold method: "fixed", "fitted"
+THRESHOLD_METHOD: "fixed"
 
 # Fitting Parameters
-# available fit functions: 'HalfGaussian', 'DoubleGaussian'
+# available fit functions: "HalfGaussian", "DoubleGaussian"
 # FirstGaussian: detects main peak (DOWN), fits Gaussian to left half, set threshold to mean + sigma*SIGMA_FACTOR
 # DoubleGaussian: Fits two Gaussians, threshold = central minima, or if no second peak mean1 + sigma1*SIGMA_FACTOR
-FIT_FUNCTION: 'DoubleGaussian'
+FIT_FUNCTION: "DoubleGaussian"
 BIN_NUM: 100
 # Factor to multiply with the standard deviation
 # to determine threshold
@@ -66,8 +66,8 @@ MIN_PEAK_DISTANCE: 0.28
 # amplitude fraction to set the threshold detecting local maxima
 MAXIMA_THRESHOLD_FRACTION: 0.5
 # time window to use to set the threshold detecting local maxima (s)
-# default value 'None' is meant to set the time window equal to the entire signal length
-MAXIMA_THRESHOLD_WINDOW: 'None'
+# default value "None" is meant to set the time window equal to the entire signal length
+MAXIMA_THRESHOLD_WINDOW: "None"
 # minimum time the signal must be increasing after a minima candidate (s)
 MINIMA_PERSISTENCE: 0.16
 

cobrawap/pipeline/stage04_wave_detection/configs/config_template.yaml

@@ -1,44 +1,44 @@
 # Config file for Stage 4 - Wave Detection
 
 # Name of stage, must be identical with folder name
-STAGE_NAME: 'stage04_wave_detection'
+STAGE_NAME: "stage04_wave_detection"
 
 # The profile name is the key for this parameter configuration. Results are stored in output_path/<PROFILE>/ (output_path is defined in settings.py)
-PROFILE: 'dataset_key'
+PROFILE: "dataset_key"
 
 # Name of the output file
 STAGE_OUTPUT: "waves"
 
 # File format in which all intermediate neo objects are stored
-NEO_FORMAT: 'nix'
+NEO_FORMAT: "nix"
 
 # If True (default), the output file of a stage is created as symbolic link
 # to the last block output. If False, a duplicate is created (e.g. for cloud
 # application, where sym-links are not supported).
 USE_LINK_AS_STAGE_OUTPUT: True
 
 # Plotting parameters
-PLOT_TSTART: 0  # float (in s) or 'None' -> starting time of the input signal is used
-PLOT_TSTOP: 10  # float (in s) or 'None' -> stopping time of the input signal is used
-PLOT_CHANNELS: 'None'  # int or None. default 'None' -> randomly selected
-PLOT_FORMAT: 'png'
+PLOT_TSTART: 0  # float (in s) or "None" -> starting time of the input signal is used
+PLOT_TSTOP: 10  # float (in s) or "None" -> stopping time of the input signal is used
+PLOT_CHANNELS: "None"  # int or None. default "None" -> randomly selected
+PLOT_FORMAT: "png"
 
 
 # DETECTION BLOCK
 ##################
-# Available Blocks: 'trigger_clustering'
-DETECTION_BLOCK: 'trigger_clustering'
+# Available Blocks: "trigger_clustering"
+DETECTION_BLOCK: "trigger_clustering"
 
 # ADDITIONAL PROPERTIES
 #######################
-# Available Blocks: 'optical_flow', 'critical_points', 'wave_mode_clustering'
+# Available Blocks: "optical_flow", "critical_points", "wave_mode_clustering"
 # use empty list [] for selecting none
-ADDITIONAL_PROPERTIES: ['wave_mode_clustering', 'optical_flow']
+ADDITIONAL_PROPERTIES: ["wave_mode_clustering", "optical_flow"]
 
 # Trigger Clustering
 ######################
 # Using sklearn.cluster.DBSCAN
-METRIC: 'euclidean'
+METRIC: "euclidean"
 # eps, maximum distance between points to be neighbours
 NEIGHBOUR_DISTANCE: 15
 MIN_SAMPLES_PER_WAVE: 30
@@ -61,7 +61,7 @@ CONVERGENCE_LIMIT: 0.0001
 GAUSSIAN_SIGMA: [0,3,3]
 # Kernel filter to use to calculate the spatial derivatives.
 # simple_3x3, prewitt_3x3, scharr_3x3, sobel_3x3, sobel_5x5, sobel_7x7
-DERIVATIVE_FILTER: 'scharr_3x3'
+DERIVATIVE_FILTER: "scharr_3x3"
 
 # Critical Point Clustering
 ###########################
@@ -94,7 +94,7 @@ BITRATE: 20M
 # displayed sampling rate, the data will be stretched or compressed to.
 # If None, the inherent sampling rate is used.
 FRAME_RATE: None
-# 'gray', 'viridis' (sequential), 'coolwarm' (diverging), 'twilight' (cyclic)
-COLORMAP: 'twilight'
-PLOT_EVENT: 'wavefronts' # name of neo event to plot, default is None
-MARKER_COLOR: 'r'
+# "gray", "viridis" (sequential), "coolwarm" (diverging), "twilight" (cyclic)
+COLORMAP: "twilight"
+PLOT_EVENT: "wavefronts" # name of neo event to plot, default is None
+MARKER_COLOR: "r"

cobrawap/pipeline/stage05_channel_wave_characterization/configs/config_template.yaml

@@ -1,37 +1,37 @@
 # Config file for Stage 5 - Channel-wise Wave Characterization
 
 # Name of stage, must be identical with folder name
-STAGE_NAME: 'stage05_channel_wave_characterization'
+STAGE_NAME: "stage05_channel_wave_characterization"
 
 # The profile name is the key for this parameter configuration. Results are stored in output_path/<PROFILE>/ (output_path is defined in settings.py)
-PROFILE: 'dataset_key'
+PROFILE: "dataset_key"
 
 # Name of the output file
-STAGE_OUTPUT: 'channel-wise_measures.csv'
+STAGE_OUTPUT: "channel-wise_measures.csv"
 
 # If True (default), the output file of a stage is created as symbolic link
 # to the last block output. If False, a duplicate is created (e.g. for cloud
 # application, where sym-links are not supported).
 USE_LINK_AS_STAGE_OUTPUT: True
 
 # Plotting parameters
-PLOT_TSTART: 0  # float (in s) or 'None' -> starting time of the input signal is used
-PLOT_TSTOP: 10  # float (in s) or 'None' -> stopping time of the input signal is used
-PLOT_CHANNELS: 'None'  # int or None. default 'None' -> randomly selected
-PLOT_FORMAT: 'png'
+PLOT_TSTART: 0  # float (in s) or "None" -> starting time of the input signal is used
+PLOT_TSTOP: 10  # float (in s) or "None" -> stopping time of the input signal is used
+PLOT_CHANNELS: "None"  # int or None. default "None" -> randomly selected
+PLOT_FORMAT: "png"
 
 # Wave event name
-# 'wavefronts', 'wavemodes'
-EVENT_NAME: 'wavefronts'
+# "wavefronts", "wavemodes"
+EVENT_NAME: "wavefronts"
 
 # Measures to compute
-# 'velocity_local', 'direction_local', 'inter_wave_interval_local', 'annotations'
-MEASURES: ['annotations', 'velocity_local', 'direction_local', 'inter_wave_interval_local']
+# "velocity_local", "direction_local", "inter_wave_interval_local", "annotations"
+MEASURES: ["annotations", "velocity_local", "direction_local", "inter_wave_interval_local"]
 
 # Velocity/Direction Local
 ################
 # simple_3x3, prewitt_3x3, scharr_3x3, sobel_3x3, sobel_5x5, sobel_7x7
-KERNEL: 'scharr_3x3'
+KERNEL: "scharr_3x3"
 INTERPOLATE: True
 # smoothing factor for the interpolation (0 = no smoothing)
 SMOOTHING: 0