diff --git a/.gitignore b/.gitignore index 7613c5b06..38efed855 100644 --- a/.gitignore +++ b/.gitignore @@ -46,8 +46,10 @@ barrel_cortex/* getting_started/example_data/**/metadata.json getting_started/biophysical_constraints/86_CDK_20041214_BAC_run5_soma_Hay2013_C2center_apic_rec.param getting_started/functional_constraints/network.param -getting_started/example_simulation_data/C2_evoked_UpState_INH_PW_1.0_SuW_0.5_C2center/20150815-1530_20240/20240_network_model.param -getting_started/example_simulation_data/C2_evoked_UpState_INH_PW_1.0_SuW_0.5_C2center/20150815-1530_20240/20240_neuron_model.param +getting_started/example_data/biophysical_constraints/86_CDK_20041214_BAC_run5_soma_Hay2013_C2center_apic_rec.param +getting_started/example_data/functional_constraints/network.param +getting_started/example_data/simulation_data/C2_evoked_UpState_INH_PW_1.0_SuW_0.5_C2center/20150815-1530_20240/20240_network_model.param +getting_started/example_data/simulation_data/C2_evoked_UpState_INH_PW_1.0_SuW_0.5_C2center/20150815-1530_20240/20240_neuron_model.param getting_started/radii/data/neuron1/output** # Ignore example param files generated from templates, but don't ignore templates themselves diff --git a/Interface/__init__.py b/Interface/__init__.py index 92db7205e..c0f094bdb 100644 --- a/Interface/__init__.py +++ b/Interface/__init__.py @@ -260,7 +260,7 @@ def print_module_versions(): logger.info("Loaded modules with __version__ attribute are:\n" + ', '.join(module_versions)) -def get_client(ip=None, client_port=38786, timeout=120): +def get_client(ip=None, client_port=38786, timeout=120, cluster=None): """ Gets the distributed.client object if dask has been setup @@ -288,7 +288,10 @@ def get_client(ip=None, client_port=38786, timeout=120): hostname ) # fetches the ip of the current host logger.info("Getting dask client with ip {}".format(ip)) - c = Client(ip + ':' + client_port, timeout=timeout) + if cluster: + c = cluster.get_client() + else: + c = Client(ip + ':' + client_port, timeout=timeout) logger.info("Got dask client {}".format(c)) logger.debug("Making mechanisms visible on client side") local_pythonpath = os.environ.get('PYTHONPATH', '') diff --git a/README.md b/README.md index 6a0cbfd6d..df3f9889c 100644 --- a/README.md +++ b/README.md @@ -31,7 +31,7 @@ Installation instructions can be found [here](https://mpinb.github.io/in_silico_ ISF is available for Linux, Windows and macOS. For installation and environment management, ISF uses [pixi](https://pixi.sh/latest/). -Please follow the installation instructions on the [`pixi` documentation](https://pixi.sh/latest/#installation) +Please follow the installation instructions on the [pixi documentation](https://pixi.sh/latest/#installation) To install ISF with pixi, simply: diff --git a/biophysics_fitting/L5tt_parameter_setup.py b/biophysics_fitting/L5tt_parameter_setup.py index 9b5d7528a..37aec6a0d 100644 --- a/biophysics_fitting/L5tt_parameter_setup.py +++ b/biophysics_fitting/L5tt_parameter_setup.py @@ -187,15 +187,15 @@ def get_L5tt_template(): 'spatial': 'uniform' }, 'Ca_HVA': { - 'begin': 900.0, - 'end': 1100.0, + 'begin': None, + 'end': None, 'gCa_HVAbar': None, 'outsidescale': 0.1, 'spatial': 'uniform_range' }, 'Ca_LVAst': { - 'begin': 900.0, - 'end': 1100.0, + 'begin': None, + 'end': None, 'gCa_LVAstbar': None, 'outsidescale': 0.01, 'spatial': 'uniform_range' @@ -391,6 +391,7 @@ def get_L5tt_template_v2(): return NTParameterSet(p['neuron']) + def set_morphology(cell_param, filename=None): """Add the morphology to a cell parameter object. diff --git a/biophysics_fitting/exploration_from_seedpoint/RW.py b/biophysics_fitting/exploration_from_seedpoint/RW.py index 19af33090..799f7bcea 100644 --- a/biophysics_fitting/exploration_from_seedpoint/RW.py +++ b/biophysics_fitting/exploration_from_seedpoint/RW.py @@ -29,13 +29,14 @@ import numpy as np import cloudpickle import shutil -from .utils import get_vector_norm, convert_all_check_columns_bool_to_float -from .RW_analysis import read_pickle from data_base.utils import silence_stdout import time import sys import math import glob +from typing import Literal +from .utils import get_vector_norm, convert_all_check_columns_bool_to_float +from .RW_analysis import read_pickle import logging logger = logging.getLogger("ISF").getChild(__name__) @@ -65,10 +66,13 @@ class RW: checkpoint_every (int): save the results every n iterations checkpoint_by_time (float): time interval in minutes for checkpointing for using time-based checkpointing. If both checkpoint_every and checkpoint_by_time are set, checkpointing will be done by time. - concat_every_n_save (int): number of checkpoints after which the pickle files are concatenated and cleaned n_iterations (int): number of iterations to run the random walk - mode (str): Random walk mode. Options: (None, 'expand'). default: None + mode (str): Random walk mode. Options: (None, 'expand', 'custom'). default: None 'expand': only propose new points that move further away from seedpoint + 'custom': use custom conditions to propose new points. If this mode is used, mode_condition_fun must be provided to evaluate the new points. + mode_condition_fun (Callable): + Function that takes a parameter vector and returns a boolean indicating whether the point is acceptable or not. + This function is used in 'custom' mode to propose new points. aim_params (dict): this param will make the exploration algorithm propose only new points such that a set of parameters aims certain values during exploration. Default: {} @@ -87,9 +91,9 @@ def __init__( max_step_size = 0.02, checkpoint_every = 100, checkpoint_by_time = None, - concat_every_n_save = 60, n_iterations = 60000, mode = None, + mode_condition_fun = None, aim_params=None, stop_n_inside_with_aim_params = -1): ''' @@ -99,16 +103,19 @@ def __init__( params_to_explore (list): list of parameters that should be explored. If None, all parameters are explored. evaluation_function (Callable): takes one argument (a new parameter vector) and returns: - - inside: boolean that indicates if the parameter vector is within experimental constraits (i.e. results in acceptable physiology) or not. - evaluation: dictionary that will be saved alongside the parameters. For example, this should contain ephys features. - - checkpoint_every (int): save the results every n iterations - check_point_by_time (float): time interval in minutes for checkpointing for using time-based checkpointing. If both + checkpoint_every (int): iteration interval at which the results are saved e.i., n for checkpointing every n iterations. If both checkpoint_every and checkpoint_by_time are set, checkpointing will be done by time. - concat_every_n_save (int): number of checkpoints after which the intermediate ``.pickle` files are concatenated to a single ``.parquet`` dataframe. - mode (str): Random walk mode. Options: (None, 'expand'). default: None + check_point_by_time (float): time interval (in minutes) at which results are saved e.i., n for checkpointing every n minutes. If both + checkpoint_every and checkpoint_by_time are set, checkpointing will be done by time. + mode (str): Random walk mode. Options: (None, 'expand', 'custom'). If none, no additional constraints will be considered when proposing new points. 'expand': only propose new points that move further away from seedpoint + 'custom': use custom conditions to propose new points. If this mode is used, mode_condition_fun must be provided to evaluate the new points. + Default: None + mode_condition_fun (Callable): + A function that takes a parameter vector and returns a boolean indicating if the proposed point + is acceptable. Used if mode is set to custom. aim_params (dict): this param will make the exploration algorithm propose only new points such that a set of parameters aims certain values during exploration. Default: {} @@ -124,35 +131,16 @@ def __init__( self.max_step_size = max_step_size self.checkpoint_every = checkpoint_every self.checkpoint_by_time = checkpoint_by_time - self.concat_every_n_save = concat_every_n_save self.all_param_names = list(self.param_ranges.index) self.n_iterations = n_iterations self.mode = mode - if params_to_explore is None: - self.params_to_explore = self.all_param_names - else: - self.params_to_explore = params_to_explore + self.mode_condition_fun = mode_condition_fun + self.params_to_explore = params_to_explore or self.all_param_names if aim_params is None: self.aim_params = {} else: self.aim_params = aim_params self.normalized_aim_params = self._normalize_aim_params(aim_params) self.stop_n_inside_with_aim_params = stop_n_inside_with_aim_params - def _normalize_aim_params(self,aim_params): - """Normalize aim parameters to be between 0 and 1. - - Args: - aim_params (dict): aim parameters - - Returns: - pd.Series: normalized aim parameters - """ - normalized_params = pd.Series(aim_params) - for key in normalized_params.keys(): - min_ = self.param_ranges['min'][key] - max_ = self.param_ranges['max'][key] - normalized_params[key] = (normalized_params[key]-min_)/(max_-min_) - return normalized_params - def _normalize_params(self,p): """Normalize parameters to be between 0 and 1. @@ -180,10 +168,165 @@ def _unnormalize_params(self, p): min_ = self.param_ranges['min'] max_ = self.param_ranges['max'] return p*(max_-min_)+min_ + + def _normalize_aim_params(self,aim_params): + """Normalize aim parameters to be between 0 and 1. - def _concatenate_and_clean(self,seed_folder, particle_id, iteration = None): - """Concatenate the intermediate ``.pickle`` results and save as one parquet file. + Args: + aim_params (dict): aim parameters + + Returns: + pd.Series: normalized aim parameters + """ + normalized_params = pd.Series(aim_params) + for key in normalized_params.keys(): + min_ = self.param_ranges['min'][key] + max_ = self.param_ranges['max'][key] + normalized_params[key] = (normalized_params[key]-min_)/(max_-min_) + return normalized_params + + def assess_aim_params_reached(self, normalized_params, tolerance=1e-4): + """Check whether the aim parameters have been reached. + + For each parameter in the aim_params dictionary, check whether the parameter has been reached. + A parameter is reached if it lies within a certain tolerance of the aim parameter. + + Args: + normalized_params (np.array): normalized parameter vector + tolerance (float): tolerance for the aim parameters to be reached. Default: 1e-4 + + Returns: + list: boolean values indicating whether each aim parameter has been reached or not. + """ + reached_aim_params = [] + for key in self.aim_params.keys(): + idx = self.params_to_explore.index(key) + reached_aim_params.append(math.isclose(normalized_params[idx],self.normalized_aim_params[key], abs_tol=tolerance)) + return reached_aim_params + + def _ensure_direction_toward_aim(self, movement, reached_aim_params, seed_pt_pd): + """Ensure that the movement is in the right direction for the aim parameters, + or that there is no movement if aim value is reached. + + Args: + movement (np.array): movement vector to adjust + reached_aim_params (list): list of booleans indicating whether each aim parameter has been reached or not. + seed_point (pd.Series): seed point parameters + + Returns: + np.array: adjusted movement vector + """ + for aim_param, reached in zip(self.aim_params.keys(), reached_aim_params): + idx = self.params_to_explore.index(aim_param) + if reached: + movement[idx] = 0 + else: + direction = 1 if (self.aim_params[aim_param]- seed_pt_pd[aim_param])>0 else -1 + movement[idx] = abs(movement[idx]) * direction + return movement + + def _check_movement_towards_aim(self, current_np, proposal): + """ + Check if the proposed parameters are closer to the aim parameters than the current parameters. + + Args: + current_np (np.array): current normalized parameters + proposal (np.array): proposed normalized parameters + + Returns: + bool: True if the proposed parameters are closer to the aim parameters, False otherwise. + """ + for key in self.aim_params: + idx = self.params_to_explore.index(key) + prev_dist = abs(current_np[idx] - self.normalized_aim_params[key]) + new_dist = abs(proposal[idx] - self.normalized_aim_params[key]) + if new_dist >= prev_dist: + return False + return True + + def _flag_aim_params_success(self, seed_dir): + """Create a flag in the seedpoint directory to indicate that aim parameters have been reached + or adjust the flag to indicate how many times the aim parameters have been reached. + + Args: + seed_dir (str): directory where the seedpoint is located + + Returns: + int: count of how many times the aim parameters have been reached + """ + flag = glob.glob(os.path.join(seed_dir, 'aim_params_successful_model_*')) + count = int(flag[0].split('_')[-1]) + 1 if flag else 1 + new_flag = os.path.join(seed_dir, f'aim_params_successful_model_{count}') + if flag: + os.rename(flag[0], new_flag) + else: + open(new_flag, 'a').close() + return count + + def _save_checkpoint(self, iteration, out, op_dir): + """Save the results saved in "out" to a pickle file. + Additionally, save the state of the random number generator. + + Args: + iteration (int): current iteration number + out (list): list of dictionaries containing the evaluation results for each parameter vector + op_dir (str): directory where the results are saved + + Returns: + None. Saves the results to a pickle file in the specified directory. + """ + path = os.path.join(op_dir, f'{iteration}.pickle') + pd.DataFrame(out).to_pickle(path + '.saving') + logger.info('Checkpointing') + with open(path + '.rngn', 'wb') as f: + cloudpickle.dump(np.random.get_state(), f) + shutil.move(path + '.saving', path) + + def _propose_new_position(self, p_normalized_np, seed_normalized_np, dist, reached_aim_params, seed_pt_pd): + """Propose a new position to move to in the parameter space. + If applicable, the new position is proposed according to the mode and aim parameters. + + Args: + p_normalized_np (np.array): normalized parameter vector of the current position + seed_normalized_np (np.array): normalized parameter vector of the seed point + dist (float): distance from the seed point to the current position + reached_aim_params (list): list of booleans indicating whether each aim parameter has been reached or not. + seed_pt_pd (pd.Series): seed point parameters + + Returns: + tuple: proposed position (np.array) and number of suggestions it took to find an acceptable position (int) + """ + if self.mode not in [None, 'expand', 'custom']: + raise ValueError('Mode must be None, "expand" or "custom"') + n_suggestion = 0 + mode_fulfilled = False + while not mode_fulfilled: + n_suggestion += 1 + movement = np.random.randn(len(self.params_to_explore)) + movement = self._ensure_direction_toward_aim(movement, reached_aim_params, seed_pt_pd) + movement /= get_vector_norm(movement) + step_size = np.random.rand() * (self.max_step_size - self.min_step_size) + self.min_step_size + movement *= step_size + p_proposal = p_normalized_np + movement + if p_proposal.max() > 1 or p_proposal.min() < 0: + continue + if self.mode is None: + mode_fulfilled = True + elif (self.mode == 'expand' and + get_vector_norm(p_proposal - seed_normalized_np) - dist > 0): + mode_fulfilled = True + elif self.mode == 'custom': + if self.mode_condition_fun is None: + raise ValueError('mode_condition_fun must be provided in "custom" mode') + mode_fulfilled = self.mode_condition_fun(p_proposal) + if len(self.normalized_aim_params.keys()) > 0 and not self._check_movement_towards_aim(p_normalized_np, p_proposal): + continue + logger.info(f"Position within boundaries found, step size is {step_size}, Tested {n_suggestion} positions to find one.") + return p_proposal, n_suggestion + + def _concatenate_and_clean(self, seed_folder, particle_id): + """Concatenate the intermediate ``.pickle`` results and save as one parquet file. Removes the pickle files. Args: @@ -191,7 +334,6 @@ def _concatenate_and_clean(self,seed_folder, particle_id, iteration = None): particle_id (int): id of the particle iteration (int): iteration number. Default: None """ - # check that we are doing this for the latest iteration outdir = os.path.join(seed_folder, str(particle_id)) iterations = sorted(list(set([int(f.split('.')[0]) for f in os.listdir(outdir) if f.endswith('.pickle') or f.endswith('.parquet')])), reverse=True) @@ -225,7 +367,7 @@ def _clean_the_pickles(self,outdir, files, iteration): if not path.endswith(f'{iteration}.pickle'): # do not remove the last rngn os.remove(path + '.rngn') - def _load_pickle_or_parquet(self,outdir, iteration, mode = 'parquet_load'): + def _load_pickle_or_parquet(self,outdir, iteration, mode: Literal['pickle', 'parquet']): """Load the results of a iteration from a pickle or parquet file. Args: @@ -236,35 +378,38 @@ def _load_pickle_or_parquet(self,outdir, iteration, mode = 'parquet_load'): Returns: tuple: dataframe and path to the file """ - assert mode in ['pickle_load', 'parquet_load'], 'mode must be "pickle_load" or "parquet_load"' - if mode == 'pickle_load': - df_path = os.path.join(outdir, '{}.pickle'.format(iteration)) - df = pd.read_pickle(df_path) - if mode == 'parquet_load': - df_path = os.path.join(outdir, '{}.parquet'.format(iteration)) - df = pd.read_parquet(df_path) - return df, df_path + df_path = os.path.join(outdir, '.'.join([str(iteration), mode])) + return getattr(pd, 'read_' + mode)(df_path), df_path + + def _load_existing_exploration(self, OPERATION_DIR, iteration_files, file_list): + """Load the existing exploration results from the specified directory to continue the random walk. - - def assess_aim_params_reached(self, normalized_params, tolerance=1e-4): - """Check whether the aim parameters have been reached. - - For each parameter in the aim_params dictionary, check whether the parameter has been reached. - A parameter is reached if it lies within a certain tolerance of the aim parameter. - Args: - normalized_params (np.array): normalized parameter vector - tolerance (float): tolerance for the aim parameters to be reached. Default: 1e-4 - + OPERATION_DIR (str): directory where the results are stored + iteration_files (list): list of iteration files + file_list (list): list of files in the directory + Returns: - list: boolean values indicating whether each aim parameter has been reached or not. + tuple: parameters of the last iteration, next iteration number, count of pickle files, and + the state of the random number generator. """ - reached_aim_params = [] - for key in self.aim_params.keys(): - idx = self.params_to_explore.index(key) - reached_aim_params.append(math.isclose(normalized_params[idx],self.normalized_aim_params[key], abs_tol=tolerance)) - return reached_aim_params - + pickle_count = len([f for f in file_list if f.endswith('.pickle')]) + parquet_count = len([f for f in file_list if f.endswith('.parquet')]) + load_list = ['pickle'] * pickle_count + ['parquet'] * parquet_count + for i, iteration in enumerate(iteration_files): + df, df_path = self._load_pickle_or_parquet(OPERATION_DIR, iteration, load_list[i]) + logger.info(f'Loaded file {df_path}') + df = df[df.inside] + try: + p = df.iloc[-1][self.all_param_names] + break + except IndexError: + logger.info("Didn't find a model inside the space, trying previous iteration") + assert max(iteration_files) == iteration_files[0] + with open(os.path.join(OPERATION_DIR, f'{iteration_files[0]}.pickle.rngn'), 'rb') as f: + rngn = cloudpickle.load(f) + return p, iteration_files[0] + 1, pickle_count, rngn + def run_RW(self, selected_seedpoint, particle_id, seed = None): """Run random walk exploration from a seed point. @@ -281,222 +426,98 @@ def run_RW(self, selected_seedpoint, particle_id, seed = None): except AttributeError as e: self.mode = None # get the parameters of the seed point (there might be more info in df_seeds than just the parameters) - seed_point_for_exploration_pd = self.df_seeds[self.params_to_explore].iloc[selected_seedpoint] - logger.info(len(seed_point_for_exploration_pd)) # this is the point in space to start exploring + seed_pt_pd = self.df_seeds[self.params_to_explore].iloc[selected_seedpoint] + logger.info(str(len(seed_pt_pd))) # normalize seed point parameters - seed_point_for_exploration_normalized_pd = self._normalize_params(seed_point_for_exploration_pd) - seed_point_for_exploration_normalized_selected_np = seed_point_for_exploration_normalized_pd[self.params_to_explore].values - + seed_pt_normalized_pd = self._normalize_params(seed_pt_pd) + seed_pt_normalized_np = seed_pt_normalized_pd[self.params_to_explore].values # set seed assert(seed is not None) - np.random.seed(seed) - + np.random.seed(seed) + logger.info(f'My random number generator seed is {seed}') + # set up folder structure - logger.info('My random number generator seed is', seed) - SEED_DIR = os.path.join(self.MAIN_DIRECTORY, str(selected_seedpoint)) - OPERATION_DIR = os.path.join(SEED_DIR, str(particle_id)) - if not os.path.exists(OPERATION_DIR): - os.makedirs(OPERATION_DIR) - logger.info('I am particle', particle_id, 'and I write to', OPERATION_DIR) + SEED_PT_DIR = os.path.join(self.MAIN_DIRECTORY, str(selected_seedpoint)) + OPERATION_DIR = os.path.join(SEED_PT_DIR, str(particle_id)) + os.makedirs(OPERATION_DIR, exist_ok=True) + logger.info(f"I am particle {particle_id}, and I write to {OPERATION_DIR}") + + concat_every = 60 #number of checkpoints after which the intermediate pickle files are concatenated to a single dataframe (".parquet") # check if we start from scratch or if we resume an exploration - file_list = os.listdir(OPERATION_DIR) #newnewnew - iterations = list(set([int(f.split('.')[0]) for f in file_list - if f.endswith('.pickle') or f.endswith('.parquet')])) #set to list to drop duplicates - iterations = sorted(iterations,reverse=True) - if iterations and max(iterations) > self.n_iterations: - logger.info('Max iterations reached. exit gracefully') + file_list = os.listdir(OPERATION_DIR) + iteration_files = list(set([int(f.split('.')[0]) for f in file_list + if f.endswith('.pickle') or f.endswith('.parquet')])) #set to list to drop duplicates + iteration_files = sorted(iteration_files,reverse=True) + if iteration_files and max(iteration_files) > self.n_iterations: + logger.info('Max iterations reached. Exiting gracefully') + self._concatenate_and_clean(SEED_PT_DIR, particle_id) return - #sys.exit(0) - if len(iterations) == 0: - logger.info('So far nothing simulated, start from seedpoint', selected_seedpoint) - p = seed_point_for_exploration_pd # p is pandas and the full vector and unnormalized + if len(iteration_files) == 0: + logger.info(f"Starting fresh from seedpoint {selected_seedpoint}") + p = seed_pt_pd iteration = 0 - inside, initial_evaluation = self.evaluation_function(p) # inside determines if the evaluation has been successful or not - assert(inside) + inside, initial_evaluation = self.evaluation_function(p) + assert inside initial_evaluation['inside'] = inside - out = [initial_evaluation] # out is what will be saved - save_count = 0 #newnewnew + out = [initial_evaluation] + save_count = 0 else: - # we resume the exploration. check how to load the saved files - save_count = len([f for f in file_list if f.endswith('.pickle')]) #count pickle files - parquet_count = len([f for f in file_list if f.endswith('.parquet')]) - load_mode = None - if parquet_count == 0: - load_mode = 'pickle_load' - elif save_count == 0: - load_mode = 'parquet_load' - else: #there are both pickle and parquet files - #check: last iteration is not saved in both a pickle and parquet file - if os.path.isfile(os.path.join(OPERATION_DIR, '{}.parquet'.format(iterations[0]))): - self._concatenate_and_clean() - save_count = 0 - load_mode = 'parquet_load' - # first load the pickles, then the parquet - load_list = ['pickle_load']*save_count + ['parquet_load']*parquet_count - - # search for last model inside the space, starting from the iteration saved the latest - for i, iteration in enumerate(iterations): - if not load_mode: - df, df_path = self._load_pickle_or_parquet(OPERATION_DIR,iteration,load_list[i]) - else: - df, df_path = self._load_pickle_or_parquet(OPERATION_DIR,iteration,load_mode) - - logger.info('Found preexisting RW, continue from there. Iteration', iteration) - logger.info('Loaded file', df_path) - df = df[df.inside] - try: - p = df.iloc[-1][self.all_param_names] # p is pandas and the full vector and unnormalized - break - except IndexError: - logger.info("didn't find a model inside the space, try previous iteration") - - # set the random number generator to the latest state - assert(max(iterations) == iterations[0]) - rngn_path = os.path.join(OPERATION_DIR, '{}.pickle.rngn'.format(iterations[0])) - with open(rngn_path, 'rb') as f: - rngn = cloudpickle.load(f) + p, iteration, save_count, rngn = self._load_existing_exploration(OPERATION_DIR, iteration_files, file_list) + logger.info(f'Found preexisting RW, continue from there. Iteration {iteration}') logger.info('set state of random number generator') np.random.set_state(rngn) - - # set current iteration to follow up on the latest saved iteration - iteration = iterations[0] + 1 - out = [] # out is what is saved - + out = [] + p_normalized = self._normalize_params(p) - p_normalized_selected_np = p_normalized[self.params_to_explore].values + p_normalized_np = p_normalized[self.params_to_explore].values reached_aim_params = self.assess_aim_params_reached(p_normalized) - - # exploration loop - logger.info('exploration loop') + + logger.info("Exploration loop") save_time = time.time() - save = False while True: - logger.info('New loop. Current iteration', iteration) + logger.info(f"New loop. Current iteration {iteration}") if self.checkpoint_by_time: - current_time = time.time() - time_since_last_save = (current_time - save_time)/60 #in minutes - if time_since_last_save>self.checkpoint_by_time and len(out) > 0: - logger.info(f'It\'s been {time_since_last_save} minutes since last checkpoint. Saving!') - save = True - save_time = current_time - - elif iteration % self.checkpoint_every == 0 and iteration > 0: - logger.info('Saving') - save = True - - if save: - df_path = os.path.join(OPERATION_DIR, '{}.pickle'.format(iteration)) - df = pd.DataFrame(out) - df.to_pickle(df_path + '.saving') - with open(df_path + '.rngn', 'wb') as f: - cloudpickle.dump(np.random.get_state(), f) - # deal with the case that exploration was interupted while saving the dataframe - shutil.move(df_path + '.saving', df_path) - out = [] # reset output after saving - save = False - save_count += 1 - - if save_count != 0 and save_count % self.concat_every_n_save == 0: - logger.info(f'{save_count} saved pickle files. Concatenating and saving as parquet.') - self._concatenate_and_clean(SEED_DIR, particle_id, iteration) + since_last_save = (time.time() - save_time)/60 #in minutes + if since_last_save>self.checkpoint_by_time and len(out) > 0: + logger.info(f'It\'s been {since_last_save} minutes since last checkpoint. Checkpointing.') + self._save_checkpoint(iteration, out, OPERATION_DIR) + save_time = time.time() + out = [] + save_count += 1 + elif iteration > 0 and iteration % self.checkpoint_every == 0: + self._save_checkpoint(iteration, out, OPERATION_DIR) + out = [] + save_count += 1 + if save_count != 0 and save_count % concat_every == 0: + self._concatenate_and_clean(SEED_PT_DIR, particle_id) save_count = 0 - - # this inner loop suggests new movements until the suggested step is within bounds - # Here we enforce the algorithm to run in the specified mode (once we make the suggested particle move - # according to the mode mode_fulfilled becomes true), and also the aim params are forced to move in - # the right direction to reach the aim values (unidir_params_fulfilled becomes true). - # If we've enforced both things, the while stops, as we have the desired movement. - logger.info('Get new position') - n_suggestion = 0 - dist = get_vector_norm(p_normalized_selected_np-seed_point_for_exploration_normalized_selected_np) - mode_fulfilled = False - unidir_params_fulfilled = False - while not mode_fulfilled or not unidir_params_fulfilled: - n_suggestion += 1 - movement = np.random.randn(len(self.params_to_explore)) - # Ensure that the movement is in the right direction for the aim parameters, - # or that they do not move if they reached their aim value - for aim_param, reached_aim_param in zip(self.aim_params.keys(),reached_aim_params): - idx = self.params_to_explore.index(aim_param) - if reached_aim_param: - movement[idx] = 0 - else: - positive_movement = (self.aim_params[aim_param]-seed_point_for_exploration_pd[aim_param])>0 - if positive_movement: - movement[idx] = abs(movement[idx]) - else: - movement[idx] = -abs(movement[idx]) - - movement = movement/get_vector_norm(movement) - #sample step size from a normal distribution - step_size = np.random.rand()*(self.max_step_size-self.min_step_size)+self.min_step_size - movement = movement * step_size - - p_proposal = p_normalized_selected_np + movement - if p_proposal.max() <= 1 and p_proposal.min() >= 0: - if self.mode is None: - mode_fulfilled = True - elif self.mode == 'expand': - delta_dist = get_vector_norm(p_proposal-seed_point_for_exploration_normalized_selected_np) - dist - if delta_dist > 0: - logger.info('new position increases distance by {}'.format(delta_dist)) - mode_fulfilled = True - else: - raise ValueError('mode must be None or "expand"') - - if len(self.normalized_aim_params.keys()) == 0: - unidir_params_fulfilled = True - else: - right_direction = [] - for i,key in enumerate(self.normalized_aim_params.keys()): - idx = self.params_to_explore.index(key) - previous_dist = abs(p_normalized_selected_np[idx]-self.normalized_aim_params[key]) - current_dist = abs(p_proposal[idx]-self.normalized_aim_params[key]) - if current_distself.n_iterations: + self._concatenate_and_clean(SEED_PT_DIR, particle_id) + logger.info('Max iterations reached. Exiting gracefully') + return iteration += 1 \ No newline at end of file diff --git a/pixi.lock b/pixi.lock index 2ec773780..b3a754bd6 100644 --- a/pixi.lock +++ b/pixi.lock @@ -9,6 +9,9 @@ environments: linux-64: - conda: https://conda.anaconda.org/conda-forge/linux-64/_libgcc_mutex-0.1-conda_forge.tar.bz2 - conda: https://conda.anaconda.org/conda-forge/linux-64/_openmp_mutex-4.5-2_gnu.tar.bz2 + - conda: https://conda.anaconda.org/conda-forge/noarch/aiohappyeyeballs-2.4.3-pyhd8ed1ab_0.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/aiohttp-3.10.5-py38h2019614_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/aiosignal-1.3.1-pyhd8ed1ab_0.tar.bz2 - conda: https://conda.anaconda.org/conda-forge/linux-64/alsa-lib-1.2.14-hb9d3cd8_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/anyio-3.7.1-pyhd8ed1ab_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/anywidget-0.9.13-pyhd8ed1ab_0.conda @@ -16,6 +19,7 @@ environments: - conda: https://conda.anaconda.org/conda-forge/linux-64/argon2-cffi-bindings-21.2.0-py38h01eb140_4.conda - 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python + - cffi >=1.11 + - zstd >=1.5.7,<1.5.8.0a0 + - vc >=14.3,<15 + - vc14_runtime >=14.44.35208 + - ucrt >=10.0.20348.0 + - vc >=14.3,<15 + - vc14_runtime >=14.44.35208 + - ucrt >=10.0.20348.0 + - python_abi 3.12.* *_cp312 + - zstd >=1.5.7,<1.6.0a0 + license: BSD-3-Clause + license_family: BSD + size: 374897 + timestamp: 1757930143303 - conda: https://conda.anaconda.org/conda-forge/linux-64/zstd-1.5.7-hb8e6e7a_2.conda sha256: a4166e3d8ff4e35932510aaff7aa90772f84b4d07e9f6f83c614cba7ceefe0eb md5: 6432cb5d4ac0046c3ac0a8a0f95842f9 diff --git a/pyproject.toml b/pyproject.toml index 8591e61df..d6b4e3f1f 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -38,9 +38,9 @@ requires = ["hatchling"] cmasher = ">=1.6.3,<2" cython = ">=3.0.11,<4" dash = ">=2.18.1,<3" -dask = "2.30.0.*" +dask = ">=2022.6" fasteners = ">=0.17.3,<0.18" -flask = "==1.1.4" +flask = ">=2.3" ipywidgets = ">=8.1.5,<9" jupyterlab = ">=3.5.3,<4" jupyter-scatter = ">=0.19.1,<0.20" @@ -59,6 +59,8 @@ seaborn = ">=0.12.2,<0.13" scipy = "1.5.2.*" wheel = ">=0.45.1,<0.46" zarr = ">=2.15.0,<3" +dask-gateway = ">=2023.1.1,<2024" +async-timeout = ">=4.0.3,<5" [tool.pixi.pypi-dependencies] bluepyopt = "==1.9.126" @@ -70,15 +72,15 @@ blenderspike-py = { git = "https://github.com/ArtemKirsanov/BlenderSpike.git" } # OS specific dependencies [tool.pixi.target.osx] -pypi-dependencies = { neuron = ">=8.2", "distributed"= ">=2.30.0"} +pypi-dependencies = { neuron = ">=8.2", distributed = ">=2022.6"} dependencies = { numpy = "==1.21", pytables = ">=3.7.0,<4" } [tool.pixi.target.linux] -dependencies = { neuron = "==7.8.2", numpy = "==1.19.2", distributed = "==2.30.0", gcc = "<15" } +dependencies = { neuron = "==7.8.2", numpy = "==1.19.2", distributed = ">=2022.6", gcc = "<15" } pypi-dependencies = { tables = ">=3.8.0,<4" } [tool.pixi.target.win] -dependencies = { numpy = "==1.19.2", distributed = "==2.30.0"} +dependencies = { numpy = "==1.19.2", distributed = ">=2022.6"} pypi-dependencies = { tables = ">=3.8.0,<4" } # Docs does not inherit the default dependencies diff --git a/single_cell_parser/cell_parser.py b/single_cell_parser/cell_parser.py index b4ac8a0c5..86fc9d2a4 100644 --- a/single_cell_parser/cell_parser.py +++ b/single_cell_parser/cell_parser.py @@ -431,7 +431,7 @@ def insert_range_mechanisms(self, label, mechs): h.pop_section() elif mech.spatial == 'linear': - ''' spatially linear distribution with negative slope''' + ''' spatially linear distribution with slope''' maxDist = self.cell.max_distance(label) # set origin to 0 of first branch with this label if label == 'Soma': @@ -459,8 +459,11 @@ def insert_range_mechanisms(self, label, mechs): dist = self.cell.distance_to_soma(sec, seg.x) if relDistance: dist = dist / maxDist - #rangeVarVal = mech[param]*(dist*slope + offset) - rangeVarVal = max(mech[param] * (dist * slope + 1), + if slope > 0: # positive slope + rangeVarVal = min(mech[param] * (dist * slope + 1), + mech[param] * offset) + else: + rangeVarVal = max(mech[param] * (dist * slope + 1), mech[param] * offset) s = param + '=' + str(rangeVarVal) paramStrings.append(s) diff --git a/visualize/cell_morphology_visualizer.py b/visualize/cell_morphology_visualizer.py index 8f9bc2600..b2d082aad 100644 --- a/visualize/cell_morphology_visualizer.py +++ b/visualize/cell_morphology_visualizer.py @@ -1487,6 +1487,7 @@ def interactive_app( port=5050, host=self.dash_ip) + def get_3d_plot_morphology( lookup_table=None, colors="grey", @@ -1502,7 +1503,9 @@ def get_3d_plot_morphology( synapse_legend=True, legend=None, return_figax = True, - proj_type="ortho" + proj_type="ortho", + fig = None, + ax = None ): """Constructs a 3d matplotlib plot of a cell morphology, overlayed with some scalar data. @@ -1528,21 +1531,25 @@ def get_3d_plot_morphology( synapse_legend (bool): Whether the synapse activations legend should appear in the plot legend (bool): Whether the legend for scalar data (e.g. membrane voltage) should appear in the plot return_figax (bool): Whether to return the figure and axis objects. Default: True - proj_type (str): Projection type for the 3D plot. Default: "ortho" + proj_type (str): Projection type for the 3D plot, ignored if fig and ax are provided. Default: "ortho" + fig (matplotlib.figure.Figure): Figure object to plot on. If None, a new figure and axes will be created. + ax (matplotlib.axes._subplots.Axes3DSubplot): Axes object. Ignored if fig is None. Note that it must be created as a 3D axes with the intended projection type. Returns: tuple | None: fig and ax object if :paramref:`return_figax` is True. None otherwise. """ #----------------- Generic axes setup - fig = plt.figure( - figsize=(15, 15), - dpi=dpi, - num=str(time_point) if time_point is not None else None) - ax = plt.axes(projection='3d', proj_type=proj_type) + if not fig: + assert not ax, "If no figure is given, ax argument is ignored!" + fig = plt.figure( + figsize=(15, 15), + dpi=dpi, + num=str(time_point) if time_point is not None else None) + ax = plt.axes(projection='3d', proj_type=proj_type) ax.set_xticks([]) ax.set_yticks([]) ax.set_zticks([]) - plt.axis('off') + ax.axis('off') ax.set_xlabel('x') ax.set_ylabel('y') ax.set_zlabel('z') diff --git a/visualize/utils.py b/visualize/utils.py index cc805d57e..992f38805 100644 --- a/visualize/utils.py +++ b/visualize/utils.py @@ -65,8 +65,8 @@ def write_video_from_images( Returns: None. Writes the video to the specified path. ''' - try: - subprocess.check_output(["module load", "ffmpeg"]) + try: + subprocess.check_output(["module load", "ffmpeg"], shell = True) except Exception as e: raise EnvironmentError("Could not load ffmpeg") from e