def main(self, run_path):
    "\n        run_path: path to the run folder (don't forget this is a subfolder of job)\n        "
    run = RunResult(run_path)
    name = run.run_folder.parent.name
    prefix = f'{name}__'
    tell(f'Prefixing saved files with {prefix}')
    tell('Saving sig.npy')
    np.save(f'{prefix}sig.npy', run.sigproc_v2.sig())
    tell('Saving noi.npy')
    np.save(f'{prefix}noi.npy', run.sigproc_v2.noi())
    tell('Saving df.csv')
    run.sigproc_v2.fields__n_peaks__peaks__radmat().to_csv(f'{prefix}df.csv')
    ims = []
    for fl_i in range(run.sigproc_v2.n_fields):
        tell(f'Loading align field {fl_i} of {run.sigproc_v2.n_fields}')
        ims += [run.sigproc_v2.aln_unfilt_chcy_ims(fl_i)]
    tell('Saving aln_ims.npy')
    np.save(f'{prefix}aln_ims.npy', np.stack(ims))
    tell('Saving example.py')
    utils.save(f'{prefix}example.py', (((f'''import numpy as np
''' + f'''import pandas as pd

''') + f'prefix = "{prefix}"') + utils.smart_wrap('\n                sig = np.load(f"{prefix}sig.npy")\n                noi = np.load(f"{prefix}noi.npy")\n                df = pd.read_csv(f"{prefix}df.csv")\n                ims = np.load(f"{prefix}aln_ims.npy", mmap_mode="r")\n                n_peaks = sig.shape[0]\n                n_fields, n_channels, n_cycles, im_mea, _ = ims.shape\n\n                # Examine some peak\n                peak_i = 123  # 0 <= peak_i < n_peaks\n                ch_i = 0  # 0 <= ch_i < n_channels\n                cy_i = 0  # 0 <= cy_i < n_cycles\n                y, x, fl_i = df[df.peak_i == peak_i][["aln_y", "aln_x", "field_i"]].drop_duplicates().values.flatten().astype(int)\n                peak_radius = 10\n                peak_im = ims[fl_i, ch_i, cy_i, y-peak_radius:y+peak_radius, x-peak_radius:x+peak_radius]\n                # Now peak_im is a centered sub-image of that peak with shape=(peak_radius, peak_radius)\n            ', width=200, assert_if_exceeds_width=True)))
    tell('\n\nThe following commands may be useful:')
    tell(f'  aws s3 cp {prefix}sig.npy s3://erisyon-public')
    tell(f'  aws s3 cp {prefix}noi.npy s3://erisyon-public')
    tell(f'  aws s3 cp {prefix}df.csv s3://erisyon-public')
    tell(f'  aws s3 cp {prefix}aln_ims.npy s3://erisyon-public')
    tell(f'  aws s3 cp {prefix}example.py s3://erisyon-public')
run_path: path to the run folder (don't forget this is a subfolder of job)
def main(self, run_path):
    "\n        \n        "
    run = RunResult(run_path)
    name = run.run_folder.parent.name
    prefix = f'{name}__'
    tell(f'Prefixing saved files with {prefix}')
    tell('Saving sig.npy')
    np.save(f'{prefix}sig.npy', run.sigproc_v2.sig())
    tell('Saving noi.npy')
    np.save(f'{prefix}noi.npy', run.sigproc_v2.noi())
    tell('Saving df.csv')
    run.sigproc_v2.fields__n_peaks__peaks__radmat().to_csv(f'{prefix}df.csv')
    ims = []
    for fl_i in range(run.sigproc_v2.n_fields):
        tell(f'Loading align field {fl_i} of {run.sigproc_v2.n_fields}')
        ims += [run.sigproc_v2.aln_unfilt_chcy_ims(fl_i)]
    tell('Saving aln_ims.npy')
    np.save(f'{prefix}aln_ims.npy', np.stack(ims))
    tell('Saving example.py')
    utils.save(f'{prefix}example.py', (((f'import numpy as np
' + f'import pandas as pd

') + f'prefix = "{prefix}"') + utils.smart_wrap('\n                sig = np.load(f"{prefix}sig.npy")\n                noi = np.load(f"{prefix}noi.npy")\n                df = pd.read_csv(f"{prefix}df.csv")\n                ims = np.load(f"{prefix}aln_ims.npy", mmap_mode="r")\n                n_peaks = sig.shape[0]\n                n_fields, n_channels, n_cycles, im_mea, _ = ims.shape\n\n                # Examine some peak\n                peak_i = 123  # 0 <= peak_i < n_peaks\n                ch_i = 0  # 0 <= ch_i < n_channels\n                cy_i = 0  # 0 <= cy_i < n_cycles\n                y, x, fl_i = df[df.peak_i == peak_i][["aln_y", "aln_x", "field_i"]].drop_duplicates().values.flatten().astype(int)\n                peak_radius = 10\n                peak_im = ims[fl_i, ch_i, cy_i, y-peak_radius:y+peak_radius, x-peak_radius:x+peak_radius]\n                # Now peak_im is a centered sub-image of that peak with shape=(peak_radius, peak_radius)\n            ', width=200, assert_if_exceeds_width=True)))
    tell('\n\nThe following commands may be useful:')
    tell(f'  aws s3 cp {prefix}sig.npy s3://erisyon-public')
    tell(f'  aws s3 cp {prefix}noi.npy s3://erisyon-public')
    tell(f'  aws s3 cp {prefix}df.csv s3://erisyon-public')
    tell(f'  aws s3 cp {prefix}aln_ims.npy s3://erisyon-public')
    tell(f'  aws s3 cp {prefix}example.py s3://erisyon-public')