def custom_default_params_list(sorter_name, check=False):
"""
Get a dictionary of params for a sorter.
if check if True just return default.
"""
default_params = ss.get_default_params(sorter_name)
if check:
default_params = default_params
elif sorter_name == "klusta":
default_params["detect_sign"] = 1
default_params["extract_s_before"] = 10
default_params["extract_s_after"] = 40
default_params["num_starting_clusters"] = 50
default_params["threshold_strong_std_factor"] = 4.5
elif sorter_name == "spykingcircus":
default_params["detect_sign"] = 1
default_params["adjacency_radius"] = 0.2
default_params["detect_threshold"] = 4.5
default_params["template_width_ms"] = 3
default_params["filter"] = False
default_params["num_workers"] = 8
elif sorter_name == "herdingspikes":
default_params["filter"] = False
return default_params
reference='median')
##############################################################################
# Now you are ready to spikesort using the :code:`sorters` module!
# Let's first check which sorters are implemented and which are installed
print('Available sorters', ss.available_sorters())
print('Installed sorters', ss.installed_sorter_list)
##############################################################################
# The :code:`ss.installed_sorter_list` will list the sorters installed in the machine. Each spike sorter
# is implemented as a class. We can see we have Klusta and Mountainsort4 installed.
# Spike sorters come with a set of parameters that users can change. The available parameters are dictionaries and
# can be accessed with:
print(ss.get_default_params('mountainsort4'))
print(ss.get_default_params('klusta'))
##############################################################################
# Let's run mountainsort4 and change one of the parameter, the detection_threshold:
sorting_MS4 = ss.run_mountainsort4(recording=recording_cmr, detect_threshold=6)
##############################################################################
# Alternatively we can pass full dictionary containing the parameters:
ms4_params = ss.get_default_params('mountainsort4')
ms4_params['detect_threshold'] = 4
ms4_params['curation'] = False
# parameters set by params dictionary
print(recording_preprocessed)
##############################################################################
# Now you are ready to spike sort using the :code:`sorters` module!
# Let's first check which sorters are implemented and which are installed
print('Available sorters', ss.available_sorters())
print('Installed sorters', ss.installed_sorters())
##############################################################################
# The :code:`ss.installed_sorters()` will list the sorters installed in the machine.
# We can see we have HerdingSpikes and Tridesclous installed.
# Spike sorters come with a set of parameters that users can change.
# The available parameters are dictionaries and can be accessed with:
print(ss.get_default_params('herdingspikes'))
print(ss.get_default_params('tridesclous'))
##############################################################################
# Let's run herdingspikes and change one of the parameter, say, the detect_threshold:
sorting_HS = ss.run_herdingspikes(recording=recording_preprocessed, detect_threshold=4)
print(sorting_HS)
##############################################################################
# Alternatively we can pass full dictionary containing the parameters:
other_params = ss.get_default_params('herdingspikes')
other_params['detect_threshold'] = 5
# parameters set by params dictionary
