Locomotion Code Doc

Locomotion.MWW_test(result, result_Ctr, exp_parameter='STN', mouse_type='Vglut2D2')

Return two-sided Mann-Whitney test for ON-laser period velocity between Ctr and ChR2.

Parameters

• result (df) – a data frame containing mean velocity for ChR2 mice.

• result_Ctr (df) – a data frame containing mean velocity for Control mice.

• mouse_type (str) – mouse type as a string e.g. FoxP2, D2, Vglut2 ..

Returns

stat – MWW stat results

Return type

obj

Locomotion.align_right_left(right, left)

Correct if for any reason there has been a shift between labelings of right and left side

Parameters

• right (1-D array) – position in time detected from the right camera

• left (1-D array) – position in time detected from the left camera

Locomotion.annotate_conditions(ax, conditions, y=0.9, fontsize=30)

write grouping condition for each double column of “ON”-“OFF”.

Parameters

• ax (obj) – plot axis.

• y (float) – y axes-fraction of the text.

• conditions (list(str)) – conditions corresponding to each double column

Return type

None.

Locomotion.annotate_n_trials(ax, n_dict, key, xy=(0.5, 0.1), txt='N = ', fontsize=20)

Annotate the number of trials for the provided key within the n_dict.

Parameters

• ax (Axesplot) – matplotlib axis.

• n_dict (dict-like) – dictionary of number of trials.

• key (str, or tuple) – key for which the number needs to be retrieved.

• xy (tuple(float), optional) – x and y coordinate of the text annotated in axes fraction. The default is (0.5, 0.1).

• txt (str, optional) – Text informing about the number. The default is ‘N = ‘.

• fontsize (float, optional) – fontsize of text. The default is 20.

Returns

exists – Returns True if the key exists and False otherwise.

Return type

bool

Locomotion.annotate_n_trials_all_conditions(ax, n_trials_dict, y=0.02, fontsize=30)

write number of trials for each double column of “ON”-“OFF”.

Parameters

• ax (obj) – plot axis.

• n_trials_dict (dict) – dictionary with values as number of trials and keys as condition of the column.

• y (float) – y axes-fraction of the text.

Return type

None.

Locomotion.average_position_r_l(df, window, misdetection_dict, cor, body_part, plot_param)

Return three-level correction of the average between right and left side for multiple selected body-parts.

Extract the right and left trace for each selected body part, correct right and left traces with “compare_r_l_correct_misdetect”, then average over right and left and remove jitters by calling “correct_labeling_jitter”. Then return the moving average of all selected body-parts.

Parameters

• df (dataframe) – Dataframe derived from DLC for one session of one animal

• window (int) – Moving average window for smoothing the position time series

• misdetection_dict (dict-like) – dict-like containing constants for misdetection corrections

• cor (str) – Which coordinate to work with. e.g. ‘x’ or ‘y’

• body_part (list(str)) – Which body parts to average over.

• plot_param (str) – Parameter for analysis e.g. position or velocity.

Returns

corrected_averaged – Moving average of all selected body-parts.

Return type

1-D array

Locomotion.categorize_pre_x_and_v(result, back_front_boundary, v_threshold, pre_stim_inter)

Set threshold to velocity and x position averaged over pre_stim_inter.

Parameters

• result (dataframe) – dataframe containing information of all animals

• back_front_boundary (float) – Threshold to set the position of the animal before the laser onset as either ‘back’ or ‘front’

• v_threshold (float) – Threshold to set the velocity of the animal before the laser onset as either ‘pos’ or ‘neg’

• pre_stim_inter (int) – Pre interval for averaging the positions/velocity and accelaration and report as pre_info

Returns

result – the dataframe as the input but with the difference that pre_X_front_back and pre_velocity_sign set to binary(str) values accorfing to values.

Return type

dataframe

Locomotion.check_DLC_corresponds_to_laser(filepath_laser, filepath_DLC)

Return true if the filename except for the last elemet is the same between the DLC and the laser files.

Locomotion.choose_n_from_colormap(cmap, minval=0.0, maxval=1.0, n_colors=100)

Return ‘n’ equally distanced colors from the colormap

Locomotion.compare_analysis_file(path1, path2)

match experiment analysis filenames between two directories and report comparison

Locomotion.compare_r_l_correct_misdetect(right_p, left_p, acc_deviance, t_s, internal_ctr_dev=0.5, percent_thresh_align=0.8, n_iter_jitter=1, jitter_threshold=0.5)

Compare the right and left sides and correct if the difference between

the two is more than an acceptable amount.

Parameters

• right_p (1-D array) – Position in time detected from the right camera

• left_p (1-D array) – Position in time detected from the left camera

• acc_deviance (float) – Passed within misdetect dict-like. Acceptable deviance of the right and left side detections

• t_s (int) – The number of procedeeing and post time bins to average from and correct the misdetection with

• internal_ctr_dev (float) – The threshold (cm) by which it is allowed for left and right detections to differ.

• percent_thresh_align (int) – The percentage above which it’s decided that there is a systematic shift between the right and left side detections.

Returns

• right (1-D array) – Corrected position in time detected from the right camera

• left (1-D array) – Corrected position in time detected from the left camera

Locomotion.convert_all_xlsx_to_csv(path)

conver all xlsx files to csvs under the given path

Locomotion.convert_csv_to_xlsx(path)

Check if a .xlsx version of all the .csv files exists, if not convert to this format and remove the .csv to save space

Locomotion.correct_accep_interval_range_for_beta(accep_interval_range, stim_type)

beta stimulation doesn’t include the last half cycle so it’s shorter. Therefore the acceptable range is coorected to be smaller

Locomotion.correct_labeling_jitter(x, jitter_threshold, n_iter_jitter, t_s, acc_deviance=0, internal_ctr_dev=0, percent_thresh_align=0)

Correct the detections exceeding the max speed of the mouse.

The detected jitters are replaced by the average of before and after time stamps.

Parameters

• x (1-D array) – The time series that needs correction (usually the average of right and left cameras).

• jitter_threshold (float) – The allowed maximum movement in one timebin with the max allowed velocity.

• n_iter_jitter (int) – Number of times to repeat the correction algorithm.

• t_s (int) – The number of timebins to average from when correcting the jitter point.

Returns

x – The corrected time series.

Return type

1-D array

Locomotion.create_colormaps_for_beta_square_pulses(beta=['Oranges_r', 'Blues'], square=['Reds_r', 'Greens'])

create colormaps for square and beta stimulations by creating a spectrum between the two given colors.

Parameters

• beta (list(str), optional) – colormaps to be joined representing beta stimulations. The default is [‘Oranges_r’, ‘Blues’].

• square (list(str), optional) – colormaps to be joined representing beta stimulations. The default is [‘Reds_r’, ‘Greens’].

Returns

• beta_cm (obj) – beta colormap.

• square_cm (obj) – square colormap.

Locomotion.create_df_from_data_summary(summary_files_list, intervals_dict, opto_par='ChR2', report_stats=True)

‘append the summary df of all files given in the list

Locomotion.create_mouse_color_dict(mouse_dict, mouse_color_opto_dict)

Assign colors to each individual mouse in the mouse_dict.

Parameters

• mouse_dict (dict) – dictionary of mouse numbers for each mouse line and optogenetic expression.

• mouse_color_opto_dict (dict) – dictionary of color lists for the different optogenetic expressions.

Returns

mouse_color_dict – dictionary of colors for each mouse number in mouse_dict.

Return type

dict

Locomotion.create_pulse_specific_color_lists(pulse_cmap, stim_type_dict)

create color dict for different stimulation protocols

Locomotion.create_x_position_pairs(ax, pad=0.2)

Create x pairs between pairs of x ticks.

Parameters

• ax (axes.SubplotBase) – The axes of the subplot..

• pad (float, optional) – distancing from the tick positions. The default is 0.2.

Returns

x_series – array of pairs of x positions.

Return type

2D-array(float)

Locomotion.csv_from_excel(filepath)

Convert xlsx file to csv

Locomotion.delta_v_vs_laser_intensity(pre_direct, intervals_dict, opto_par='ChR2', stim_loc='STR', mouse_type='Vglut2D2', pulse='beta', measure='mean')

extract the (means, std) of the changes from baseline to when laser-ON velocity for different laser intensities. Note that this function requires sorted file hierarchy.

Locomotion.derivative(x, delta_t, fps)

Take the derivative over delta_t.

Locomotion.derivative_mov_ave(x, delta_t, window_veloc, fps)

Take the derivative with delta_t and do a moving average.

Locomotion.epochs_stats(epochs, epochs_spont)

Return mean and confidence intervals of epochs

Parameters

• epochs (2D array(float)) – Trials containtning laser stimulation aligned in rows. n_col = pre_interval + interval + post_interval

• epochs_spont (2D array(float)) – same as epochs only for spontaneous sessions with no laser stimulation.

• Returns –

• -------- –

• epochs_mean (2D array(float)) – average of epochs for all trials (averaged over rows)

• epochs_mean_spont (2D array(float)) – average of epochs_spont for all trials (averaged over rows)

• confidence_inter (2D array(float)) – 95% confidence interval of epochs

• confidence_inter_spont (2D array(float)) – 95% confidence interval of epochs_spont

Locomotion.extract_epochs(bins, x, smallest_accep_inter, largest_accep_inter, pre_interval, interval, post_interval, session_length)

Extract the (pre | Laser ON | post) epochs.

Check reported (start,end) of trials from bins and discard trial with unacceptable duration. Return the stacked the corresponding frames of all trials from the measurment array x.

Parameters

• bins (2D-array(int)) – array of start and end times for trials

• smallest_accep_inter (int) – Smallest acceptable duration of an interval in frames

• largest_accep_inter (int) – Largest acceptable duration of an interval in frames

• pre_interval (int) – Number of time bins to be taken into account before laser onset

• interval (int) – Laser duration in timebins.

• post_interval (int) – Number of time bins to be taken into account after laser onset

Returns

• epochs (2D array(float)) – Trials containtning laser stimulation aligned in rows. n_col = pre_interval + interval + post_interval

• n_trials (int) – Number of trials

• take (1D array(int)) – Array of indices corresponding to pre | on | post epoch frmaes.

Locomotion.extract_epochs_over_trials(files_list_DLC, files_list_laser, direct, stim_loc, stim_type, scale_pix_to_cm, accep_interval_range, default_treadmill_velocity, spont_trial_dict, misdetection_dict, study_param_dict, pre_interval, interval, post_interval, pre_stim_inter, fps, n_timebin, window_veloc, window_pos, n_samples=25, spont=True)

Return all the epochs of all similar trials for one mouse.

Parameters

• files_list_DLC (list(str)) – List of paths to all DLC .csv files for sessions

• files_list_laser (list(str)) – List of paths to all .csv files that have (start,end) laser times.

• direct (str) – Path to the parent directory containing all stim_types

• stim_loc (str) – the folder with the corresponding experiment brain location e.g. “STR”.

• stim_type (str) – folder containing sessions for this particular protocol

• scale_pix_to_cm (float) – Scaling coefficient converting pixels to cm

• spont (str) – Either False –> for trials containing laser stimulation or ‘y’ –> for spontaneous trials with no laser

• accep_interval_range (tuple(int)) – Smallest and largest acceptable trial sizes in timebins

• spont_trial_dict (dict-like) – {‘max_distance’ : int (max n_timebins between sampled epochs for spontaneous), ‘min_distance’ : int (min #n_timebines between sampled epochs for spontaneous), ‘n_trials_spont’ : int (the average number of trials extracted from one spontaneous session)}

• misdetection_dict (dict-like) – Dictionary of constants used in the misdetection correction algorithm: {‘acc_deviance’ : float (acceptable deviance between right and left detections), ‘internal_ctr_dev’ : float, ‘percent_thresh_align’ : int, ‘t_s’ : int (number of time steps before and after to look at), ‘n_iter_jitter’ : int (how many times go over data to find jitters and clear them out), ‘jitter_threshold’ : int (max_speed/fps)}

• study_param_dict (dict-like) – Dictionary of values regarding coordinate (x,y), body part (list of body parts) and plot parameter (position, velocity, acceleration) packed from input of user

• pre_interval (int) – Number of time bins to be taken into account before laser onset

• interval (int) – Laser duration in timebins.

• post_interval (int) – Number of time bins to be taken into account after laser onset

• pre_stim_inter (int) – Pre interval for averaging the positions/velocity and accelaration and report as pre_info

• fps (int) – Frame per second of the video

• n_timebin (int) – Number of frames as timebins for the x derivative to yields velocity

• window_veloc (int) – Velocity moving average window in frames

• window_pos (int) – Position moving average window in frames

• n_samples (int, optional) – Number of samples to extract from one spontaneous session. Only necessary for spont = ‘y’ cases.

Returns

• epochs (2D array(float)) – (pre | Laser ON | post) epochs with shape (n_trials, pre_interval+post_interval+interval+1)

• pre_info (2D array(float)) – Three columns with averages of : pre_x , pre_v, pre_acceleration over pre_stim_interval duration. with shape (n_trials, 3)

Locomotion.extract_epochs_over_trials_one_side_one_body_part(files_list_DLC, files_list_Laser, direct, stim_loc, stim_type, scale_pix_to_cm, accep_interval_range, treadmill_velocity, misdetection_dict, pre_interval, interval, post_interval, fps, n_timebin, window_veloc, window_pos, cor, body_part, plot_param, left_or_right='right')

Return all the epochs of all simillar trials for one mouse.

Parameters

• files_list_DLC (list(str)) – List of paths to all DLC .csv files for sessions

• files_list_laser (list(str)) – List of paths to all .csv files that have (start,end) laser times.

• direct (str) – Path to the parent directory containing all stim_types

• stim_loc (str) – the folder with the corresponding experiment brain location e.g. “STR”.

• stim_type (str) – Folder containing sessions for this protocol

• scale_pix_to_cm (float) –

• accep_interval_range (tuple(int)) – Smallest and largest acceptable trial sizes in timebins

• misdetection_dict (dict-like) – dictionary of constants used in the misdetection correction algorithm: {‘acc_deviance’ : float (acceptable deviance between right and left detections), ‘internal_ctr_dev’ : float, ‘percent_thresh_align’ : int, ‘t_s’ : int (number of time steps before and after to look at), ‘n_iter_jitter’ : int (how many times go over data to find jitters and clear them out), ‘jitter_threshold’ : int (max_speed/fps)}

• pre_interval (int) – Number of time bins to be taken into account before laser onset

• interval (int) – Laser duration in timebins.

• post_interval (int) – Number of time bins to be taken into account after laser onset

• pre_stim_inter (int) – Pre interval for averaging the positions/velocity and accelaration and report as pre_info

• fps (int) – Frame per second of the video

• n_timebin (int) – Number of frames as timebins for the x derivative to yields velocity

• window_veloc (int) – Velocity moving average window in frames

• window_pos (int) – Position moving average window in frames

• left_or_right (str, optional) – Either left side or right side. Default value is set to ‘right’.

Returns

epochs – (pre | Laser ON | post) epochs with shape (n_trials, pre_interval+post_interval+interval+1)

Return type

2D-array

Locomotion.extract_info_from_npz_filename(filename)

extract info about experiment from the summary npz file

Locomotion.extract_info_from_saved_data(filename, intervals_dict, opto_par='ChR2', report_stats=True)

for the given filename extract information from filename and create the mean velocity pre/post laser

in a dataframe

Parameters

• filename (str) – DESCRIPTION.

• intervals_dict (dict-like) –

  dict-like of interals:{‘pre_interval’int(.5*fps), # interval before laser onset

  ’interval’ : int(interval_in_sec * fps), # number of timebins of stimulation ‘post_interval’ : int(.5*fps*2), # interval after laser onset ‘pre_stim_inter’ : pre_stim_inter }

• opto_par (str, optional) – e.g. ChR2 or Control. The default is ‘ChR2’.

• report_stats (bool, optional) – whether report the statistical tests. Default is True.

Returns

• df (dataframe) – dataframe summerizing all data.

• info (dict) – information about the file extracted from its name.

Locomotion.extract_pre_laser_x_epochs_over_trials(files_list, files_list_laser, direct, stim_loc, stim_type, scale_pix_to_cm, window_pos, misdetection_dict, smallest_accep_inter, largest_accep_inter, pre_stim_inter)

Return all the x positions in epochs preceding to a ON-laser of all trials for one mouse.

Parameters

• files_list (list(str)) – List of the file names of the sessions

• files_list_laser – List of file names for laser detections

• pre_direct (str) – Path to the root directory of the project

• stim_loc (str) – the folder with the corresponding experiment brain location e.g. “STR”.

• stim_type (str) – Folder containing this specific protocol.

• scale_pix_to_cm (float) – Scaling coeficient for pixels to cm.

• window_pos (int) – Moving average window for position

• misdetection_dict (dict-like) – dict-like containing constants for misdetection corrections

• smallest_accep_inter (int) – Smallest acceptable duration of an interval in frames

• largest_accep_inter (int) – Largest acceptable duration of an interval in frames

• pre_stim_inter (int) – Pre interval for averaging the positions/velocity and accelaration and report as pre_info

Returns

epochs – X positions prior to laser stimulation aligned in rows. n_col = pre_stim_inter

Return type

2D array(float)

Locomotion.filter_based_on_substring(string_list, contains, not_contains=[])

keep only strings that include the all the given substrings in contains

Locomotion.find_filepaths_for_stim_type_and_loc_list(stim_loc_list, pre_direct, stim_type_list, opto_par)

Parameters

• stim_loc_list (list(str)) – stimulation locaiton list.

• pre_direct (str) – path to the project root.

• stim_type_list (list(str)) – stimulation type (pulse + intesity) list.

• opto_par (str) – optogenetic expression.

Returns

summary_files_list – list of file paths satisfying the input arguments.

Return type

list(str)

Locomotion.find_filepaths_of_corresponding_stim_loc_for_mouse_type(pre_direct, opto_par, stim_type_list, mouse_type_to_loc_dict)

Find filepaths of the summary files with corresponding stim type –> stim_loc –> mouse_type charecterestics.

Parameters

• pre_direct (str) – path to project root.

• opto_par ({'ChR2', 'Control'}) – optogenetic expression.

• stim_type_list (list(str)) – stim type protocol.

• mouse_type_to_loc_dict (dict-like) – dictionary coupling mouse type to the desired stim location.

Returns

summary_files_list – filepaths.

Return type

list(str)

Locomotion.find_files_containing_substring(path, substring)

Walk in all subfolders and return all files containing substring.

Parameters

• path (str) – path to lood under for files.

• substring (str) – substring that is required to be contained in path.

Returns

files – list of files under path containing substring.

Return type

list(str)

Locomotion.find_header_line_laser_file_csv(path)

Find which line includes the headers of ‘ON’ and ‘OFF in the csv file

Locomotion.find_header_line_laser_file_excel(path)

Find which line includes the headers of ‘ON’ and ‘OFF in the excel file

Locomotion.find_mouse_no(filepath)

extract the mouse number based on filename.

Parameters

filepath (str) – path of the DLC file.

Returns

mouse_no – mouse number.

Return type

str

Locomotion.find_session_day_tag(filepath)

extract the session tag that is e.g. [a, b, …][01, 02, ..] based on filename.

Parameters

filepath (str) – path of the DLC file.

Returns

day tag – 3 charecter tag assigned to one session of recording.

Return type

str

Locomotion.find_treadmill_velocity(filepath, default=15)

Determine the treadmill velocity based on filename.

Parameters

filepath (str) – path of the DLC file.

Returns

treadmill_velocity – The treadmill velocity in cm/s. If not specified in filename, default = 15 is returned.

Return type

int

Locomotion.flatten(t)

create a flattened list of the list of lists

Locomotion.get_DLC_files(path)

Find DLC files

Locomotion.get_extension(path)

return file extension from path

Locomotion.get_input_cor_body_part(cor_list, body_part_list, plot_param_list)

Ask for the body part and coordinate from user.

Parameters

• cor_list (list(str)) – list of available coordinates e.g. ‘x’, ‘y’

• body_part_list (list(str)) – list of DLC labeled body parts

• plot_param_list (list(str)) – List of available parameters for analysis e.g position or velocity.

Returns

study_param_dict – selected items from the options in the inputs

Return type

dict-like

Locomotion.get_laser_files(path)

Find laser files

Locomotion.get_name(path)

Return filename from path

Locomotion.get_nested_keys(dictionary)

Return the set of the keys of the dictionaries that are nested whithin the original dictionary

Locomotion.get_pulse_list(protocol_dict, mouse_type, opto_par='ChR2')

get the list of all pulse types from the protocol dict for the specified mouse line

Locomotion.get_set_of_sorted(d)

get set of dict values sort them and put back in dict

Locomotion.get_sorted_laser_DLC_files(path)

print sorted files in laser and DLC folders to see if the match

Locomotion.handle_legend_label(ax, label, stim_type, body_part, epochs, annotate_n)

Create the legend label based on the input parameters.

Parameters

• ax (object) – plot axis.

• label (str) – Determining the variable to be reported as legend.

• stim_type (str) – stimulation type and string joined with underscore.

• body_part (list (str)) – list of tracked body parts.

• epochs (nd-array (float)) – time epochs of all trials.

• annotate_n (bool) – if True is True the number of trials is written as text in the plot otherwise it will be reported in the legend.

Returns

• ax (object) – plot axis.

• label (TYPE) – text to be put in the plot object.

Locomotion.input_plot(df, laser_t, mouse_type, mouse_no, trial_no, opto_par, pre_direct, exp_dict, t_window_dict, misdetection_dict, save_as_format='.pdf')

Get the specifics of the plot as input and call the corresponding plot function to plot the session.

Parameters

• df (Dataframe) – Dataframe derived from DLC for one session of one animal

• laser_t (Dataframe) – Dataframe with laser onset and offset times as columns

• mouse_type (str) – Mouse type. For example FoxP2

• mouse_no (int) – Mouse number

• trial_no (int) – Trial number specified in file name

• opto_par (str) – Optogenetic specification. For example ChR2 or Control

• pre_direct (str) – Path to the root directory of the project

• exp_dict (dict-like) – Experiment dictionary for cor_list, body_part_list and plot_param_list

• t_window_dict (dict-like) – Dictionary of time constants

• misdetection_dict (dict-like) – Dictionary for misdetection algorithm constants

• save_as_format (str, optional) – The extension of the saved figure

Return type

None

Locomotion.keep_corresponding_stim_loc_for_mouse_type(summary_files_list, mouse_type_to_loc_dict)

from the provided list of files only keep ones that correspond to the specified mouse_type –> stim_loc coupling in the dictionary

Parameters

• summary_files_list (list(str)) – filepaths.

• mouse_type_to_loc_dict (dict-like) – dictionary coupling mouse type to the desired stim location .

Returns

summary_files_list – filepaths.

Return type

list(str)

Locomotion.lighten_color(color, amount=0.5)

Lightens the given color by multiplying (1-luminosity) by the given amount. Input can be matplotlib color string, hex string, or RGB tuple.

Examples: >> lighten_color(‘g’, 0.3) >> lighten_color(‘#F034A3’, 0.6) >> lighten_color((.3,.55,.1), 0.5)

Locomotion.list_all_files(path, extensions=['.xlsx', '.csv'])

List all the files with this extention in this directory

Locomotion.min_and_mean_on_off(epochs, measure, pre_interval, interval, post_interval, pre_stim_inter)

Report the min or mean (specified by measure) velocity in the off-on periods.

Parameters

• epochs (2D array(float)) – Trial variables in rows. n_col = pre_interval + interval + post_interval

• measure (str) – ‘mean’ or ‘min’

• pre_interval (int) – Number of time bins to be taken into account before laser onset

• interval (int) – Laser duration in timebins.

• post_interval (int) – Number of time bins to be taken into account after laser onset

• pre_stim_inter (int) – Unpacked in dict-like. Not used here.

Returns

average_of_off_on_off – array of 3 columns. first column is the aversge of pre-intevsl, second the ‘measure’ during laser interval third again the average of post

Return type

2D array

Locomotion.min_velocity_diff_inten_box_plot(file_path_list, path_to_save, intervals_dict, pre_x_v_dict, ylim=[- 40, 15], save_as_format='.pdf')

Compare min velocity during laser for trials with positive and negative pre laser velocity.

Box plots to show this comparison for different intensity in same animal types.

Parameters

• file_path_list (list(str)) – list of paths to .npz files

• path_to_save (str) – Path to save figure

• intervals_dict (dict-like) –

  dict-like of interals:{‘pre_interval’int(.5*fps), # interval before laser onset

  ’interval’ : int(interval_in_sec * fps), # number of timebins of stimulation ‘post_interval’ : int(.5*fps*2), # interval after laser onset ‘pre_stim_inter’ : pre_stim_inter }

• pre_x_v_dict (dict-like) –

  {‘back_front_boundary’(treadmill_len-elec_shock)/2, # set the limit below which is considered back of the treadmill

  ’v_threshold’ : 0, ‘pre_stim_inter’ : pre_stim_inter # number of timebins in the pre-stimulus period}

• ylim (list(float), optional) – y axis limits for figure. Default is set to [-40,15]

• save_as_format (str, optional) – format to save the figure. Default is set to ‘.pdf’

Locomotion.min_velocity_mouse_type_box_plot(file_path_list, path_to_save, intervals_dict, pre_x_v_dict, ylim=[- 40, 15], save_as_format='.pdf')

Compare min velocity during laser.

Box plots to show this comparison FOR trials with positive and negative pre laser velocity for same intensity in different animal types.

Parameters

• file_path_list (list(str)) – list of paths to .npz files

• path_to_save (str) – Path to save figure

• intervals_dict (dict-like) –

  dict-like of interals:{‘pre_interval’int(.5*fps), # interval before laser onset

  ’interval’ : int(interval_in_sec * fps), # number of timebins of stimulation ‘post_interval’ : int(.5*fps*2), # interval after laser onset ‘pre_stim_inter’ : pre_stim_inter }

• pre_x_v_dict (dict-like) –

  {‘back_front_boundary’(treadmill_len-elec_shock)/2, # set the limit below which is considered back of the treadmill

  ’v_threshold’ : 0, ‘pre_stim_inter’ : pre_stim_inter # number of timebins in the pre-stimulus period}

• ylim (list(float), optional) – y axis limits for figure. Default is set to [-40,15]

• save_as_format (str, optional) – format to save the figure. Default is set to ‘.pdf’

Locomotion.move_unwanter_files_out_of_folder(path)

move all non csv xlsx files out of the Laser and DLC subdirectory

Locomotion.moving_average_array(X, n)

Return the moving average over X with window n without changing dimesions of X

Locomotion.no_files(files, mouse_no, dtype='DLC')

Report True the there are no ‘dtype’ files for this mouse

Locomotion.plot_ON_OFF_measure(result, pre_direct, mouse_type, opto_par, stim_loc, stim_type, fps, back_front_boundary, v_threshold, pre_stim_inter, ylim=[- 1, 2], velocity_measure='norm_velocity (mean)', save_as_format='.pdf', axes=[], save=True, title=True, legend=False, group_by=['pre_velocity_sign', 'epoch'], conditions=['pos', 'neg'], colors=['teal', 'blueviolet'], alphas=[1, 1], lstyles=['-', '-'])

Plot the mean laser-ON laser-OFF velocity measure with distinction of the groupby elements provided prior to laser stim.

Parameters

• result (dataframe) – dataframe containing information of all animals

• pre_direct (str) – Path to save the figure

• opto_par (str) – optogenetic expression.

• mouse_type (str) – mouse line.

• stim_loc (str) – the folder with the corresponding experiment brain location e.g. “STR”.

• stim_type (str) – Folder containing the experiment sessions

• back_front_boundary (float) – Threshold to set the position of the animal before the laser onset as either ‘back’ or ‘front’

• v_threshold (float) – Threshold to set the velocity of the animal before the laser onset as either ‘pos’ or ‘neg’

• pre_stim_inter (int) – Pre interval for averaging the positions/velocity and accelaration and report as pre_info

• ylim (list(float), optional) – Y axis limits for the figure, default set to [-20,15]

• axes (list(Axes), optional) – list of 2 Axes. The default is an empty list.

• velocity_measure (['norm_velocity (mean)', 'norm_velocity (min)']) – value reported in plot.

• save_as_format (str, optional) – Format to save the figure default is ‘.pdf’

• {[] (axes =) – if axes provided the two conditions are plotted in separate axes. Otherwise will be plotted on the same axis.

• axes} – if axes provided the two conditions are plotted in separate axes. Otherwise will be plotted on the same axis.

• optional – if axes provided the two conditions are plotted in separate axes. Otherwise will be plotted on the same axis.

• save (bool(default: True)) – if save figure.

• title (bool(default=True)) – if put title.

• legend (bool(default=True)) – if put legend.

• groupby ({['pre_velocity_sign', epoch'],) –

  [‘pre_accel_sign’, epoch’],

  [‘pre_accel_sign’, epoch’], ..}, optional

  The columns of result dataframe to group by. Note that when providing three columns the conditions list should be adjusted to include the conditions other that ‘epoch’ in each element. Default is [‘pre_velocity_sign’, epoch’].

• conditions ({['pos', 'neg'],) – [[‘pos’, ‘front’], [‘neg’, ‘front’]], …}, optional The list of conditions to plot the measure for. the default is [‘pos’, ‘neg’].

• colors (list of matplotlib colors, optional) – list of colors for different conditions. The default is colors = [‘teal’, ‘blueviolet’].

• alphas (list(float), optional) – list of saturation alphas for the lines drawn. The elements should correspond to the number of elements in conditions.The default is [1, 1].

• ls (list of matplotlib linestyles, optional.) – linestyles of each plotted line. The defautl is [‘-’, ‘-‘].

Return type

None

Locomotion.plot_ON_OFF_measure_with_x_distionction(result, pre_direct, mouse_type, opto_par, stim_loc, stim_type, fps, back_front_boundary, v_threshold, pre_stim_inter, ylim=[- 1, 2], velocity_measure='norm_velocity (mean)', save_as_format='.pdf', axes=[], save=False, title=False, legend=False, group_by=['pre_velocity_sign', 'pre_x_front_back', 'epoch'], conditions=[['pos', 'front'], ['neg', 'front']], colors=['teal', 'blueviolet'], alphas=[1, 1], lstyles=['-', '-'], position_conditions=['front', 'back'])

Plot the mean laser-ON laser-OFF velocity measure with distinction of the groupby elements provided prior to laser stim which includes position.

Parameters

• result (dataframe) – dataframe containing information of all animals

• pre_direct (str) – Path to save the figure

• opto_par (str) – optogenetic expression.

• mouse_type (str) – mouse line.

• stim_loc (str) – the folder with the corresponding experiment brain location e.g. “STR”.

• stim_type (str) – Folder containing the experiment sessions

• back_front_boundary (float) – Threshold to set the position of the animal before the laser onset as either ‘back’ or ‘front’

• v_threshold (float) – Threshold to set the velocity of the animal before the laser onset as either ‘pos’ or ‘neg’

• pre_stim_inter (int) – Pre interval for averaging the positions/velocity and accelaration and report as pre_info

• ylim (list(float), optional) – Y axis limits for the figure, default set to [-20,15]

• axes (list(Axes), optional) – list of 2 Axes. The default is an empty list.

• velocity_measure (['norm_velocity (mean)', 'norm_velocity (min)']) – value reported in plot.

• save_as_format (str, optional) – Format to save the figure default is ‘.pdf’

• {[] (axes =) – if axes provided the two conditions are plotted in separate axes. Otherwise will be plotted on the same axis.

• axes} – if axes provided the two conditions are plotted in separate axes. Otherwise will be plotted on the same axis.

• optional – if axes provided the two conditions are plotted in separate axes. Otherwise will be plotted on the same axis.

• save (bool(default: True)) – if save figure.

• title (bool(default=True)) – if put title.

• legend (bool(default=True)) – if put legend.

• groupby ({['pre_velocity_sign', epoch'],) –

  [‘pre_accel_sign’, epoch’],

  [‘pre_accel_sign’, epoch’], ..}, optional

  The columns of result dataframe to group by. Note that when providing three columns the conditions list should be adjusted to include the conditions other that ‘epoch’ in each element. Default is [‘pre_velocity_sign’, ‘pre_x_front_back’, ‘epoch’].

• conditions ({['pos', 'neg'],) – [[‘pos’, ‘front’], [‘neg’, ‘front’]], …}, optional The list of conditions to plot the measure for. the default is [[‘pos’, ‘front’], [‘neg’, ‘front’]].

• colors (list of matplotlib colors, optional) – list of colors for different conditions. The default is colors = [‘teal’, ‘blueviolet’].

• alphas (list(float), optional) – list of saturation alphas for the lines drawn. The elements should correspond to the number of elements in conditions.The default is [1, 1].

• ls (list of matplotlib linestyles, optional.) – linestyles of each plotted line. The defautl is [‘-’, ‘-‘].

Locomotion.plot_every_mouse_mean(epochs_mean_each_mouse)

plot mean of each mouse for epochs OFF-ON-OFF

Locomotion.plot_pre_on_post(pre_direct, mouse_type, opto_par, stim_loc, stim_type, epochs, epochs_spont, treadmill_velocity, ylim, fps, n_timebin, window_pos, window_veloc, cor, body_part, plot_param, pre_interval, interval, post_interval, pre_stim_inter, average='all_mice', c_laser='deepskyblue', c_spont='k', save_as_format='.pdf', title=True, plot_spont=True, label='body-part', annotate_n=False, axvspan=True, multi_region=False, save_fig=True, ax=None, x_label_list=[], y_label_list=[], legend_loc='upper left', legend_fontsize=15, bbox_to_anch_leg=(0.1, 1))

Plot (pre Laser | Laser | post Laser) velocity/position/acceleration comparison between laser and spontaneous trials.

Parameters

• ax (ax) –

• pre_direct (str) – Path to project directory

• mouse_type (str) – Type of the animal that makes it different in the experiment e.g. for transgenic mice ‘FoxP2’ or ‘Vglut2’.

• opto_par (str) – Optogentic parameter distinguishing injections. e.g. ‘Control’ or ‘ChR2’.

• stim_loc (str) – the folder with the corresponding experiment brain location e.g. “STR”.

• stim_type (str) – folder containing this specific protocol’s sessions e.g. ‘Square_1_mW’

• epochs (2D array(float)) – Trials containtning laser stimulation aligned in rows. n_col is equal to pre_interval + interval + post_interval

• epochs_spont (2D array(float)) – Spontaneous trials aligned in rows. n_col is equal to pre_interval + interval + post_interval

• treadmill_velocity (float) – Treadmill velocity in cm/s

• ylim (list(float)) – Y axis limits for the figure given as a list : [ymin, ymax]

• fps (int) – frame per second of the video

• n_timebin (int) – number of timebins for taking derivatives

• window_pos (int) – Moving average window for position

• window_veloc (int) – Moving average window for velocity

• cor (str) – Which coordinate to work with. e.g. ‘x’ or ‘y’

• body_part (list(str)) – Which body parts to average over.

• plot_param (str) – Parameter for analysis e.g. position or velocity.

• pre_interval (int) – Number of time bins to be taken into account before laser onset

• interval (int) – Laser duration in timebins.

• post_interval (int) – Number of time bins to be taken into account after laser onset

• pre_stim_inter (int) – Pre interval for averaging the positions/velocity and accelaration and report as pre_info

• average (str, optional) – Parameter specifying the output figure. Here default set to ‘Averg_trials_all_mice’.

• c_laser (str, optional) – color for plotting trials with laser stimulation. Default value is set to ‘deepskyblue’.

• c_spont (str, optional) – color for plotting trials with laser stimulation. Default value is set to ‘k’ which is black.

• save_as_format (str, optional) – Format to save the figure with. Default set to ‘.pdf’

Return type

None

Locomotion.plot_two_protocols_with_mouse_distinction(mouse_type, mouse_list, stim_loc, stim_type1, stim_type2, opto_par)

Extract data over all mice of one group in two stim_types intensity and plot with subplots of individual animals

Locomotion.plot_velocity_phase_space(result, pre_direct, mouse_type, opto_par, stim_loc, stim_type, fps, back_front_boundary, v_threshold, pre_stim_inter, xlim=[- 1, 3], ylim=[- 1, 3], velocity_measure='norm_velocity (mean)', save_as_format='.pdf')

Plot the phase space of laser-ON vs. laser-OFF velocity for all trials of all mice.

Parameters

• result (dataframe) – dataframe containing information of all animals pre_direct : str Path to save the figure

• opto_par (str) – optogenetic expression.

• mouse_type (str) – mouse line.

• stim_loc (str) – the folder with the corresponding experiment brain location e.g. “STR”.

• stim_type (str) – Folder containing the experiment sessions

• back_front_boundary (float) – Threshold to set the position of the animal before the laser onset as either ‘back’ or ‘front’

• v_threshold (float) – Threshold to set the velocity of the animal before the laser onset as either ‘pos’ or ‘neg’

• pre_stim_inter (int) – Pre interval for averaging the positions/velocity and accelaration and report as pre_info

• xlim (list(float), optional) – X axis limits for the figure, default set to [-25,32]

• ylim (list(float), optional) – Y axis limits for the figure, default set to [-20,15]

• velocity_measure (['norm_velocity (mean)', 'norm_velocity (min)']) – value reported in plot.

• save_as_format (str, optional) – Format to save the figure default is ‘.pdf’

Locomotion.plot_what_which_where(df, laser_t, mouse_type, mouse_no, trial_no, opto_par, pre_direct, misdetection_dict, cor, body_part, plot_param, fps, n_timebin, window_pos, window_veloc, save_as_format='.pdf')

Choose to see averaged velocity, position or acceleration for a chosen combination of body_parts for either x or y coordiante.

Locomotion.plot_what_which_where_r_l(df, laser_t, mouse_type, mouse_no, trial_no, opto_par, pre_direct, misdetection_dict, cor, body_part, plot_param, fps, n_timebin, window_pos, window_veloc, save_as_format='.pdf')

Choose which body part/what measure/ x or y to see the left vs. right traces.

Locomotion.position_r_l(df, window, misdetection_dict, cor, body_part, plot_param)

Remove jitters with “correct_labeling_jitter” on right and left on the selected body parts and return right and left separately.

Parameters

• df (dataframe) – Dataframe derived from DLC for one session of one animal

• window (int) – Moving average window for smoothing the position time series

• misdetection_dict (dict-like) – dict-like containing constants for misdetection corrections

• cor (str) – Which coordinate to work with. e.g. ‘x’ or ‘y’

• body_part (list(str)) – Which body parts to average over.

• plot_param (str) – Parameter for analysis e.g. position or velocity.

Returns

• right_corrected (1-D array) – Moving average of right side for all selected body-parts

• left_corrected (1-D array) – Moving average of left side for all selected body-parts

Locomotion.produce_random_bins_for_spont(max_time, n_sample, pre_interval, interval, post_interval, max_distance, min_distance, n_trials_spont)

Produce a grid for start of bins then perturb to have randomly spaced trials with a minimum and maximum distance between them.

Parameters

• max_time (int) – number of timebins of the spontaneous session

• n_sample (int) – number of trial samples that needs to be extracted from this session

• max_distance (int) – maximum number of timebins between extracted trials

• min_distance (int) – minimum number of timebins between extracted trials

Returns

bins – an array with two columns each row containing (start,end) of each trial

Return type

2D-array(int)

Locomotion.read_DLC(filepath, scale_pix_to_cm)

Read DeepLabCut trackings of bodyparts.

Parameters

• filepath (str) – path of the DLC file.

• scale_pix_to_cm (float) – scaling factor to transform DLC pixel values into cm.

Raises

ValueError – Raises error if file type is not csv or xlsx.

Returns

df – DLC output dataframe.

Return type

dataframe

Locomotion.read_laser(laser_file_name, DLC_file_name)

Read laser onset offset times for Square pulse stimulation

Locomotion.read_laser_beta_stim(laser_t, min_t_between_stim=200)

Read laser onset offset times for laser detections of every beta cycle (Gilles’s)

Locomotion.remove_epochs_with_unaccep_pre_post_interval(bins, pre_interval, post_interval, session_length)

Remove trials where extending the width to pre interval and post interval exceeds the session boundaries

Locomotion.remove_xlsx_if_csv_exists(path)

remove matching xlsx if the equivalent csv file exists

Locomotion.rename_laser_files_according_to_DLC(path)

rename matching the laser files to DLC files

Locomotion.rm_shared_axis_labels(axes)

Remove repeated axis labels

Locomotion.rm_val_from_dict_if_exists_in_another(dictionary, ref_dictionary)

remove values that exist in the same key of the ref dictionary from dictionary and return

Locomotion.run_one_intensity_save_data(pre_direct, scale_pix_to_cm, mouse_type, mouse_dict, stim_loc, stim_type, opto_par, treadmill_velocity, ylim, spont_trial_dict, misdetection_dict, intervals_dict, t_window_dict, accep_interval_range, study_param_dict, max_distance, min_distance, n_trials_spont, c_laser='deepskyblue', c_spont='k', fig=None, outer=None, n_inner=0, inner=None, axes=[], remove_empty_ax=True, label=None, annotate_n=False, title_manually=True, plot_spont=True, save_fig=True, suptitle_y=0.95, multi_region=False, axvspan=True, x_label_list=[], y_label_list=[])

Save data of epochs and individal mice to a npz file.

by running over all mice of one group and one intensity.

Parameters

• pre_direct (str) – path to rooot directory

• scale_pix_to_cm (float) – SCaling coefficient for converting pixels to cm.

• mouse_type (str) – mouse type such as FoxP2 etc

• mouse_list (list(int)) – list of mouse identification numbers

• stim_loc (str) – the folder with the corresponding experiment brain location e.g. “STR”.

• stim_type (str) – the folder with the corresponding experiment protocol e.g. “Square_1_mW”.

• opto_par (str) – ChR2 for ChR2 injected animals Control for Control group

• treadmill_velocity (float) – Treadmill velocity in cm/s

• ylim (list(float)) –

• spont_trial_dict (dict-like) –

  dict-like with values{‘max_distance’int(4.5*fps), # max #n_timebines between sampled epochs for spontaneous

  ’min_distance’ : int(3*fps), # min #n_timebines between sampled epochs for spontaneous ‘n_trials_spont’ : 25 # the average number of trials extracted from one spontaneous session}

• misdetection_dict (dict-like) – dictionary of constants used in the misdetection correction algorithm:

• intervals_dict (dict-like) –

  dict-like of interals:{‘pre_interval’int(.5*fps), # interval before laser onset

  ’interval’ : int(interval_in_sec * fps), # number of timebins of stimulation ‘post_interval’ : int(.5*fps*2), # interval after laser onset ‘pre_stim_inter’ : pre_stim_inter }

• t_window_dict (dict-like) – dict-like containing information about time contants of the project: t_window_dict = {fps : int, ‘n_timebin’ : int, ‘window_pos’ : int , ‘window_veloc’ : int}

• accep_interval_range (tuple(int)) – Smallest and largest acceptable trial sizes in timebins

• study_param_dict (dictionray) – dict-like of values regarding coordinate (x,y), body part (list of body parts) and plot parameter (position, velocity, acceleration) packed from input of user

• max_distance (int) – maximum number of frames between sampled epochs for spontaneous

• min_distance (int) – mainimum number of frames between sampled epochs for spontaneous

• n_trials_spont (int) – number of spontaneous trials

Locomotion.save_npz(pre_direct, mouse_type, opto_par, stim_loc, stim_type, fps, window, n_timebin, file_name_ext, epochs_all_mice, epochs_mean_each_mouse, epochs_spont_all_mice, epochs_tag, mouse_no_list, pre_info, cor, body_part, plot_param)

Save the trial epochs in one .npz file.

Parameters

• pre_direct (str) – path to rooot directory

• mouse_type (str) – mouse type such as ‘FoxP2’ or ..

• opto_par (str) – “ChR2” –> for ChR2 injected animals “Control” –> for Control group

• stim_loc (str) – the folder with the corresponding experiment brain location e.g. “STR”.

• stim_type (str) – the folder with the corresponding experiment protocol e.g. “Square_1_mW”.

• pulse_inten (int) – Pulse intensity in mW

• fps (int) – Frame per second

• window (int) – Moving average window for position

• n_timebin (int) – Number of frames to take derivative over

• file_name_ext (str) – File name extension

• epochs_all_mice (2D-array(float)) – (pre | Laser ON | post) epochs with shape (n_trials, pre_interval+post_interval+interval+1)

• epochs_mean_each_mouse (2D-array(float)) – average of (pre | Laser ON | post) epochs with shape (n_animals, 3) averaged for individual animals

• epochs_spont_all_mice (2D-array(float)) – (pre | Laser ON | post) spontaneous epochs with shape (n_trials, pre_interval+post_interval+interval+1)

• epochs_tag (1D-array(str)) – trial tags of each extracted laser epoch.

• pre_info (2D array(float)) – Three columns with averages of : pre_x , pre_v, pre_acceleration over pre_stim_interval duration. with shape (n_trials, 3)

• cor_list (list(str)) – list of available coordinates e.g. ‘x’, ‘y’

• body_part_list (list(str)) – list of DLC labeled body parts

• plot_param_list (list(str)) – List of available parameters for analysis e.g position or velocity.

Locomotion.save_npz_limb_and_tail(pre_direct, scale_pix_to_cm, mouse_type, mouse_list, stim_loc, stim_type, opto_par, misdetection_dict, pre_interval, interval, post_interval, t_window_dict, accep_interval_range, cor_list, body_part_list, plot_param_list)

Save data of epochs and individal mice to a npz file.

Run over all mice of one group and one intensity append epochs.

Parameters

• pre_direct (str) – path to rooot directory

• scale_pix_to_cm (float) – Scaling coeficient for pixels to cm.

• mouse_type (str) – mouse type such as FoxP2 or etc

• mouse_list (list(int)) – list of mouse identification numbers

• stim_loc (str) – the folder with the corresponding experiment brain location e.g. “STR”.

• stim_type (str) – the folder with the corresponding experiment protocol e.g. Square_1_mW

• opto_par (str) – ChR2 for ChR2 injected animals, Control for Control group

• misdetection_dict (dict-like) – dictionary of constants used in the misdetection correction algorithm

• pre_interval (int) – Number of time bins to be taken into account before laser onset

• interval (int) – Laser duration in timebins.

• post_interval (int) – Number of time bins to be taken into account after laser onset

• t_window_dict (dict-like) – dictionary containing information about time contants of the project

• accep_interval_range (tuple(int)) – Smallest and largest acceptable trial sizes in timebins

• cor_list (list(str)) – list of available coordinates e.g. x, y

• body_part_list (list(str)) – list of DLC labeled body parts

• plot_param_list (list(str)) – List of available parameters for analysis e.g position or velocity

Locomotion.save_pdf_png(fig, figname, size=(8, 6))

Save fig as pdf and png format

Locomotion.set_ticks(ax)

set tick length and label fontsize

Locomotion.set_x_ticks_one_ax(ax, label_list)

set x axis tick labels from list

Parameters

• ax (obj) – plot axis.

• label_list (list (int)) – x axis tick labels.

Returns

ax – plot axis.

Return type

obj

Locomotion.set_xy_ticks_one_ax(ax, x_label_list, y_label_list)

set x and y axis tick labels from list

Parameters

• ax (obj) – plot axis.

• x_label_list (list (int)) – x axis tick labels.

• y_label_list (list (int)) – y axis tick labels.

Returns

ax – plot axis.

Return type

obj

Locomotion.set_y_ticks_one_ax(ax, label_list)

set y axis tick labels from list

Parameters

• ax (obj) – plot axis.

• label_list (list (int)) – y axis tick labels.

Returns

ax – plot axis.

Return type

obj

Locomotion.superimpose_intensities(opto_par_list, pulse_cmap_dict, protocol_dict, stim_loc_dict, pre_direct, scale_pix_to_cm, mouse_type, mouse_dict, treadmill_velocity, ylim, spont_trial_dict, misdetection_dict, intervals_dict, t_window_dict, accep_interval_range, study_param_dict, max_distance, min_distance, n_trials_spont, plot_spont=True, ylabel_x=0, xlabel_y=0.07, suptitle_y=0.98, c_spont='k', x_label_list=[], y_label_list=[])

Create a set of subplots with mice as rows and stimulation location as columns, superimposeing all intensities and creating new figures for different types of stimuli

Locomotion.violin_plot_laser_ON_OFF(pre_direct, intervals_dict, opto_par, mouse_color_dict, mouse_type, subplot_parameter='stim_loc', fig_w=2, fig_h=4, velocity_measure='norm_velocity (mean)', stim_type_list=None, stim_loc_list=None, summary_files_list=None, savefig=True, pulse=None)

Plot distributions of normalized velocity in the laser ON and OFF epochs as subplots distinguishing the subplot_parameter.

Parameters

• pre_direct (str) – Path to the root directory of the project

• intervals_dict (dict-like) –

  dict-like of intervals:{‘pre_interval’int(.5*fps), # interval before laser onset

  ’interval’ : int(interval_in_sec * fps), # number of timebins of stimulation ‘post_interval’ : int(.5*fps*2), # interval after laser onset ‘pre_stim_inter’ : pre_stim_inter

• opto_par (str) – optogenetic expression.

• mouse_color_dict (dict-like) – dictionary of colors for each mouse number.

• mouse_type (str) – mouse line.

• subplot_parameter (str, optional) – The parameter changing between the columns e.g. laser internsity or stim location. The default is ‘stim_loc’.

• fig_w (float, optional) – individual double column width in inches. The default is 1.

• fig_h (float, optional) – figure height in inches. The default is 4.

• velocity_measure ({'norm_velocity (mean)', 'norm_velocity (min)'}, optional) – the measurement of the laser-ON and -OFF epochs.

• stim_type_list (list(str), optional) – type of stimulation that includes the pulse type and intensity. The default is None.

• stim_loc_list (list(str), optional) – list of stimulation brain structures.. The default is None.

• summary_files_list (list(str), optional) – list of filepaths of experiments. The default is None where the function searches for the provided stim types and locs for the specified mouse type, otherwise input list is used.

• savefig (bool, True) – if save generated figure.

• pulse (str, optional) – stimulation type, to be added to the figname when subplot parameter is intensity

Returns

ax – plot axis.

Return type

obj

Locomotion.violin_plot_summary(data_init, names, measure)

plot violin plot for different columns in data

Locomotion.violin_plot_with_distiction(result, pre_direct, mouse_type, opto_par, stim_loc, stim_type, fps, back_front_boundary, v_threshold, pre_stim_inter, ylim=[- 1.5, 3], velocity_measure='norm_velocity (mean)', violin_alpha=0.5, x_distinction=True, save_as_format='.pdf', scale='area', colors=['teal', 'blueviolet'], velocity_conditions=['pos', 'neg'], position_conditions=['front', 'back'], alphas=[1, 0.6], ls=['-', '--'], column='pre_velocity_sign')

Plot violin plots with columns for pre laser velocity distinction and hue for back and front distinction on the treadmill.

Parameters

• result (dataframe) – dataframe containing information of all animals

• pre_direct (str) – Path to save the figure

• opto_par (str) – optogenetic expression.

• mouse_type (str) – mouse line.

• stim_loc (str) – the folder with the corresponding experiment brain location e.g. “STR”.

• stim_type (str) – Folder containing the experiment sessions

• back_front_boundary (float) – Threshold to set the position of the animal before the laser onset as either back or front

• v_threshold (float) – Threshold to set the velocity of the animal before the laser onset as either pos or neg

• pre_stim_inter (int) – Pre interval for averaging the positions/velocity and accelaration and report as pre_info

• ylim (list(float), optional) –

  Y axis limits for the figure, default set to [-1, 2] velocity_measure: [‘norm_velocity (mean)’, ‘norm_velocity (min)’]

  value reported in plot.

• alpha (float) – transparency of distribution

• x_distinction (True, optional) – Whether separate x position as hue. Default is True

• save_as_format (str, optional) – Format to save the figure default is ‘.pdf’

• scale ({“area”, “count”, “width”}, optional) – The method used to scale the width of each violin. If area, each violin will have the same area. If count, the width of the violins will be scaled by the number of observations in that bin. If width, each violin will have the same width. The default is ‘area’.

• colors (list of matplotlib colors, optional) – list of colors for different conditions. The default is [‘teal’, ‘blueviolet’].

• velocity_conditions (list(str), optional) – The conditions for the pre_velocity_sign column of the data. The default is [‘pos’, ‘neg’].

• position_conditions (list(str), optional) – The conditions for the pre_x_front_back column of the data. The default is [‘front’, ‘back’].

• alphas (list(float), optional) – list of saturation alphas for the lines drawn. The elements should correspond to the number of elements in conditions.The default is [1, 1].

• ls (list of matplotlib linestyles, optional.) – linestyles of each plotted line. The defautl is [‘-’, ‘-‘].

• column (str, optional) – The measure that separates the columns of the catplot. The default is ‘pre_velocity_sign’.

Return type

None.