add loop components to experiments folder (#7)

Author: ryan-gillis

src/np_workflows/experiments/openscope_loop/__init__.py

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+from .main_loop_pilot import new_experiment, Hab, Ephys, validate_selected_workflow
+from .loop_workflow_widget import loop_workflow_widget

src/np_workflows/experiments/openscope_loop/camstim_scripts/barcode_mapping_script.py

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+"""
+May '23 OpenScope: Barcode stimuli
+"""
+
+import argparse
+import json
+import logging
+import os
+import time
+
+import numpy as np
+from psychopy import visual
+from camstim import Foraging
+from camstim import Stimulus_v2
+from camstim import SweepStim_v2
+from camstim import Warp, Window
+
+
+# get params ------------------------------------------------------------------
+# stored in json file -
+# path to json supplied by camstim via command line arg when this script is called
+
+parser = argparse.ArgumentParser()
+parser.add_argument(
+    "params_path",
+    nargs="?",
+    type=str,
+    default="",
+)
+args, _ = parser.parse_known_args()
+
+with open(args.params_path, "r") as f:
+    json_params = json.load(f)
+
+# Create display window
+# ----------------------------------------------------------------------------
+window = Window(
+    fullscr=True,
+    monitor=json_params["monitor"],
+    screen=0,
+    warp=Warp.Spherical,
+)
+
+# patch the Stimulus_v2 class to allow for serializing without large arrays
+# ----------------------------------------------------------------------------
+class Stimulus_v2_MinusFrameArrays(Stimulus_v2):
+
+    def __init__(self, *args, **kwargs):
+        super(Stimulus_v2_MinusFrameArrays, self).__init__(*args, **kwargs)
+
+    def package(self):
+        """
+        Package for serializing - minus large arrays of frame timing/order.
+        """
+        if not self.save_sweep_table:
+            self.sweep_table = None
+            self.sweep_params = self.sweep_params.keys()
+        self_dict = self.__dict__
+        del self_dict['sweep_frames']
+        del self_dict['sweep_order']
+        self_dict['stim'] = str(self_dict['stim'])
+        return wecanpicklethat(self_dict)
+    
+# ----------------------------------------------------------------------------
+# setup mapping stim
+"""from mapping_script_v2.py"""
+
+mapping_stimuli = []
+
+# load common stimuli
+gabor_path = json_params["gabor_path"]
+flash_path = json_params["flash_path"]
+gabor = Stimulus_v2_MinusFrameArrays.from_file(gabor_path, window)
+flash = Stimulus_v2_MinusFrameArrays.from_file(flash_path, window)
+
+gabor_duration_sec = json_params["default_gabor_duration_seconds"]
+flash_duration_sec = json_params["default_flash_duration_seconds"]
+
+original_duration_sec = gabor_duration_sec + flash_duration_sec
+
+# if max total duration is set, and less than original movie length, cut down display sequence:
+max_mapping_duation_minutes = json_params[
+    "max_total_duration_minutes"
+]  # can be zero, in which case we use the full movie length
+max_mapping_duration_sec = max_mapping_duation_minutes * 60
+if 0 < max_mapping_duration_sec < original_duration_sec:
+    logging.info("Mapping duration capped at %s minutes", max_mapping_duation_minutes)
+
+    logging.info("original gabor duration: %s sec", gabor_duration_sec)
+    logging.info("original flash duration: %s sec", flash_duration_sec)
+    logging.info("max mapping duration: %s sec", max_mapping_duration_sec)
+
+    gabor_duration_sec = (
+        max_mapping_duration_sec * gabor_duration_sec
+    ) / original_duration_sec
+    flash_duration_sec = (
+        max_mapping_duration_sec * flash_duration_sec
+    ) / original_duration_sec
+
+    logging.info("modified gabor duration: %s sec", gabor_duration_sec)
+    logging.info("modified flash duration: %s sec", flash_duration_sec)
+
+# setup timing
+mapping_sequence_start_sec = 0  # if stims are daisy-chained within one script, this should be the end of the prev stim
+gabor.set_display_sequence([(mapping_sequence_start_sec, gabor_duration_sec)])
+flash.set_display_sequence(
+    [(gabor_duration_sec, gabor_duration_sec + flash_duration_sec)]
+)
+
+mapping_stimuli = [gabor, flash]
+
+mapping_sequence_end_sec = (
+    gabor_duration_sec + flash_duration_sec
+)  # if daisy-chained, the next stim in this script should start at this time
+
+# create SweepStim_v2 instance for main stimulus
+ss = SweepStim_v2(
+    window,
+    stimuli=mapping_stimuli,
+    pre_blank_sec=json_params["pre_blank_screen_sec"],
+    post_blank_sec=json_params["post_blank_screen_sec"],
+    params=json_params["sweepstim"],
+)
+
+# add in foraging so we can track wheel, potentially give rewards, etc
+f = Foraging(
+    window=window,
+    auto_update=False,
+    params=json_params["sweepstim"],
+    nidaq_tasks={
+        "digital_input": ss.di,
+        "digital_output": ss.do,
+    },
+)  # share di and do with SS
+
+ss.add_item(f, "foraging")
+
+ss.run()

src/np_workflows/experiments/openscope_loop/camstim_scripts/barcode_opto_script.py

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+# -*- coding: utf-8 -*-
+"""
+optotagging.py
+
+runs optotagging code for ecephys pipeline experiments
+
+by [email protected]
+
+(c) 2018 Allen Institute for Brain Science
+
+"""
+import camstim  # ensures "magic" gets setup properly by importing first
+import logging  # must occur after camstim import for "magic"
+from camstim.zro import agent
+
+import numpy as np
+from toolbox.IO.nidaq import AnalogOutput
+from toolbox.IO.nidaq import DigitalOutput
+
+import datetime
+import numpy as np
+import time
+import pickle as pkl
+
+
+# %%
+
+
+def run_optotagging(levels, conditions, waveforms, isis, sampleRate=10000.):
+
+    from toolbox.IO.nidaq import AnalogOutput
+    from toolbox.IO.nidaq import DigitalOutput
+
+    sweep_on = np.array([0, 0, 1, 0, 0, 0, 0, 0], dtype=np.uint8)
+    stim_on = np.array([0, 0, 1, 1, 0, 0, 0, 0], dtype=np.uint8)
+    stim_off = np.array([0, 0, 1, 0, 0, 0, 0, 0], dtype=np.uint8)
+    sweep_off = np.array([0, 0, 0, 0, 0, 0, 0, 0], dtype=np.uint8)
+
+    ao = AnalogOutput('Dev1', channels=[1])
+    ao.cfg_sample_clock(sampleRate)
+
+    do = DigitalOutput('Dev1', 2)
+
+    do.start()
+    ao.start()
+
+    do.write(sweep_on)
+    time.sleep(5)
+
+    for i, level in enumerate(levels):
+
+        print(level)
+
+        data = waveforms[conditions[i]]
+
+        do.write(stim_on)
+        ao.write(data * level)
+        do.write(stim_off)
+        time.sleep(isis[i])
+
+    do.write(sweep_off)
+    do.clear()
+    ao.clear()
+
+# %%
+
+
+def generatePulseTrain(pulseWidth, pulseInterval, numRepeats, riseTime, sampleRate=10000.):
+
+    data = np.zeros((int(sampleRate),), dtype=np.float64)
+   # rise_samples =
+
+    rise_and_fall = (
+        ((1 - np.cos(np.arange(sampleRate*riseTime/1000., dtype=np.float64)*2*np.pi/10))+1)-1)/2
+    half_length = rise_and_fall.size / 2
+    rise = rise_and_fall[:half_length]
+    fall = rise_and_fall[half_length:]
+
+    peak_samples = int(sampleRate*(pulseWidth-riseTime*2)/1000)
+    peak = np.ones((peak_samples,))
+
+    pulse = np.concatenate((rise,
+                            peak,
+                            fall))
+
+    interval = int(pulseInterval*sampleRate/1000.)
+
+    for i in range(0, numRepeats):
+        data[i*interval:i*interval+pulse.size] = pulse
+
+    return data
+
+
+# %% create waveforms
+
+def optotagging(mouseID, operation_mode='experiment', level_list=[1.15, 1.28, 1.345], genotype=None):
+
+    sampleRate = 10000
+
+    # 1 s cosine ramp:
+    data_cosine = (((1 - np.cos(np.arange(sampleRate, dtype=np.float64)
+                                * 2*np.pi/sampleRate)) + 1) - 1)/2  # create raised cosine waveform
+
+    # 1 ms cosine ramp:
+    rise_and_fall = (
+        ((1 - np.cos(np.arange(sampleRate*0.001, dtype=np.float64)*2*np.pi/10))+1)-1)/2
+    half_length = rise_and_fall.size / 2
+
+    # pulses with cosine ramp:
+    pulse_2ms = np.concatenate((rise_and_fall[:half_length], np.ones(
+        (int(sampleRate*0.001),)), rise_and_fall[half_length:]))
+    pulse_5ms = np.concatenate((rise_and_fall[:half_length], np.ones(
+        (int(sampleRate*0.004),)), rise_and_fall[half_length:]))
+    pulse_10ms = np.concatenate((rise_and_fall[:half_length], np.ones(
+        (int(sampleRate*0.009),)), rise_and_fall[half_length:]))
+
+    data_2ms_10Hz = np.zeros((sampleRate,), dtype=np.float64)
+
+    for i in range(0, 10):
+        interval = sampleRate / 10
+        data_2ms_10Hz[i*interval:i*interval+pulse_2ms.size] = pulse_2ms
+
+    data_5ms = np.zeros((sampleRate,), dtype=np.float64)
+    data_5ms[:pulse_5ms.size] = pulse_5ms
+
+    data_10ms = np.zeros((sampleRate,), dtype=np.float64)
+    data_10ms[:pulse_10ms.size] = pulse_10ms
+
+    data_10s = np.zeros((sampleRate*10,), dtype=np.float64)
+    data_10s[:-2] = 1
+
+    # %% for experiment
+
+    isi = 1.5
+    isi_rand = 0.5
+    numRepeats = 50
+
+    condition_list = [2, 3]
+    waveforms = [data_2ms_10Hz, data_5ms, data_10ms, data_cosine]
+
+    opto_levels = np.array(level_list*numRepeats*len(condition_list))  # BLUE
+    opto_conditions = condition_list*numRepeats*len(level_list)
+    opto_conditions = np.sort(opto_conditions)
+    opto_isis = np.random.random(opto_levels.shape) * isi_rand + isi
+
+    p = np.random.permutation(len(opto_levels))
+
+    # implement shuffle?
+    opto_levels = opto_levels[p]
+    opto_conditions = opto_conditions[p]
+
+    # %% for testing
+
+    if operation_mode == 'test_levels':
+        isi = 2.0
+        isi_rand = 0.0
+
+        numRepeats = 2
+
+        condition_list = [0]
+        waveforms = [data_10s, data_10s]
+
+        opto_levels = np.array(level_list*numRepeats *
+                               len(condition_list))  # BLUE
+        opto_conditions = condition_list*numRepeats*len(level_list)
+        opto_conditions = np.sort(opto_conditions)
+        opto_isis = np.random.random(opto_levels.shape) * isi_rand + isi
+
+    elif operation_mode == 'pretest':
+        numRepeats = 1
+
+        condition_list = [0]
+        data_2s = data_10s[-sampleRate*2:]
+        waveforms = [data_2s]
+
+        opto_levels = np.array(level_list*numRepeats *
+                               len(condition_list))  # BLUE
+        opto_conditions = condition_list*numRepeats*len(level_list)
+        opto_conditions = np.sort(opto_conditions)
+        opto_isis = [1]*len(opto_conditions)
+    # %%
+
+    outputDirectory = agent.OUTPUT_DIR
+    fileDate = str(datetime.datetime.now()).replace(':', '').replace(
+        '.', '').replace('-', '').replace(' ', '')[2:14]
+    fileName = outputDirectory  + "/" + fileDate + '_'+mouseID + '.opto.pkl'
+
+    print('saving info to: ' + fileName)
+    fl = open(fileName, 'wb')
+    output = {}
+
+    output['opto_levels'] = opto_levels
+    output['opto_conditions'] = opto_conditions
+    output['opto_ISIs'] = opto_isis
+    output['opto_waveforms'] = waveforms
+
+    pkl.dump(output, fl)
+    fl.close()
+    print('saved.')
+
+    # %%
+    run_optotagging(opto_levels, opto_conditions,
+                    waveforms, opto_isis, float(sampleRate))
+
+
+# %%
+if __name__ == "__main__":
+    import json
+    import argparse
+
+    parser = argparse.ArgumentParser()
+    parser.add_argument('json_params', type=str, )
+    args, _ = parser.parse_known_args()
+
+    with open(args.json_params, 'r', ) as f:
+        json_params = json.load(f)
+
+    logging.info('Optotagging with params: %s' % json_params)
+    optotagging(**json_params)