generate_random_gateList(3, 1, 1)
({'circ_type': array([[3, 3]], dtype=int32),
  'gate_type': array([[[2, 1, 0],
          [3, 0, 1],
          [4, 1, 0]]], dtype=int32),
  'gate_param': array([[0.7726073, 2.6923583, 0.       ]], dtype=float32)},
 {'gate_map': {'h': 1, 'ry': 2, 'rz': 3, 'cx': 4, 'measure': 5},
  'num_cx': 1,
  'num_qubit': 3,
  'num_gate': 3,
  'num_circ': 1})
generate_random()
saving data as hdf5: /pscratch/sd/g/gzquse/qgear/nbs/circ/rcirc_68df59.gate_list.h5
h5-write : circ_type (1, 2) int32
h5-write : gate_type (1, 12, 3) int32
h5-write : gate_param (1, 12) float32
h5-write : meta.JSON as string (1,) object
closed  hdf5: /pscratch/sd/g/gzquse/qgear/nbs/circ/rcirc_68df59.gate_list.h5  size=0.01 MB, elaT=0.0 sec
{'gate_map': {'cx': 4, 'h': 1, 'measure': 5, 'ry': 2, 'rz': 3},
 'hash': '68df59',
 'num_circ': 1,
 'num_cx': 4,
 'num_gate': 12,
 'num_qubit': 5,
 'short_name': 'rcirc_68df59'}
'/pscratch/sd/g/gzquse/qgear/nbs/circ/rcirc_68df59.gate_list.h5'
from qgear.runner import run_gate_job
run_gate_job(
    exp="rcirc_0a89fd",
    backend="nvidia",
    numshots=1000
)
read data from hdf5: /pscratch/sd/g/gzquse/qgear/nbs/circ/rcirc_0a89fd.gate_list.h5
read obj: circ_type (1, 2) int32
read obj: gate_param (1, 12) float32
read obj: gate_type (1, 12, 3) int32
read str: meta.JSON 1 <class 'numpy.ndarray'>
 done h5, num rec:3  elaT=0.0 sec
  closed  yaml: /pscratch/sd/g/gzquse/qgear/nbs/meas/rcirc_0a89fd_adj-gpu_fp32.yaml  size=0.3 kB   elaT=0.0 sec
M:done rcirc_0a89fd elaT=1755461516.8 sec
{'date': '20250817_131156_PDT',
 'elapsed_time': 1755461516.8493607,
 'gate_map': {'cx': 4, 'h': 1, 'measure': 5, 'ry': 2, 'rz': 3},
 'hash': '0a89fd',
 'num_circ': 1,
 'num_cx': 4,
 'num_gate': 12,
 'num_meas_strings': [4],
 'num_qpus': 1,
 'num_qubit': 5,
 'num_shots': 1000,
 'short_name': 'rcirc_0a89fd',
 'target': 'nvidia',
 'target2': 'adj-gpu'}
{'gate_map': {'h': 1, 'ry': 2, 'rz': 3, 'cx': 4, 'measure': 5},
 'num_cx': 4,
 'num_qubit': 5,
 'num_gate': 12,
 'num_circ': 1,
 'hash': '0a89fd',
 'short_name': 'rcirc_0a89fd',
 'num_qpus': 1,
 'elapsed_time': 1755461516.8493607,
 'target': 'nvidia',
 'date': '20250817_131156_PDT',
 'num_meas_strings': [4],
 'target2': 'adj-gpu',
 'num_shots': 1000}
from qgear.metrics_plotter import metrics_plot

# Only specify the date folder(s)
metrics_plot()
  read  yaml: /qgear/meas/rcirc_fdba43_adj-gpu_fp32.yaml
PlotterBackbone: Running in Jupyter → using inline backend
MetricsPlotter : Graphics started
Graphics saving to  out/metrics_f2.png

from qgear.image import create_img
from qgear.runner import run_qcrank
from qgear.plotter import process_qcrank_experiment

# Step 1: Create input
create_img(tag="b2", inp_path=None, out_path="out")

# Step 2: Run simulation
run_qcrank("canImg_b2_32_32", inp_path="out", out_path="out", exp_name="canImg_b2_32_32")

# Step 3: Post-process and plot
process_qcrank_experiment("canImg_b2_32_32", inp_path="out", out_path="out", show_plots="abc")
sh: /pscratch/sd/g/gzquse/cudaq/lib/libtinfo.so.6: no version information available (required by /lib64/libreadline.so.7)
sh: /pscratch/sd/g/gzquse/cudaq/lib/libtinfo.so.6: no version information available (required by /lib64/libreadline.so.7)
Loading built-in image 'high-heels_x32_y32.png' from qgear/data/
Metadata: {'image_name': 'high-heels_x32_y32', 'image_shape_xy': [32, 32], 'image_pixels': 1024, 'canned_type': 'gray_image'}
Normalized image min/max: -1.0, 1.0
Flattened image shape: (1024,), reshaped: (2, 512)
saving data as hdf5: out/canImg_b2_32_32.qcrank_inp.h5
h5-write : phys_image (32, 32) uint8
h5-write : image_name as string (1,) object
h5-write : norm_image (32, 32) float32
h5-write : inp_udata (1, 2, 512) float32
h5-write : inp_fdata (512, 2, 1) float32
h5-write : meta.JSON as string (1,) object
closed  hdf5: out/canImg_b2_32_32.qcrank_inp.h5  size=0.02 MB, elaT=0.3 sec
Saved QCrank input: out/canImg_b2_32_32.qcrank_inp.h5
read data from hdf5: out/canImg_b2_32_32.qcrank_inp.h5
read str: image_name 1 <class 'numpy.ndarray'>
read obj: inp_fdata (512, 2, 1) float32
read obj: inp_udata (1, 2, 512) float32
read str: meta.JSON 1 <class 'numpy.ndarray'>
read obj: norm_image (32, 32) float32
read obj: phys_image (32, 32) uint8
 done h5, num rec:5  elaT=0.0 sec
{'canned': {'canned_type': 'gray_image',
            'image_name': 'high-heels_x32_y32',
            'image_pixels': 1024,
            'image_shape_xy': [32, 32]},
 'payload': {'nq_addr': 9,
             'nq_fdata': 2,
             'num_clbit': 11,
             'num_sample': 1,
             'qcrank_max_fval': 3.141592653589793,
             'seq_len': 512},
 'short_name': 'canImg_b2_32_32',
 'submit': {'num_shots': 204800}}
M: using backend=nvidia, total shots=204800
M: circuit has 11 qubits
circ_orig depth_aziz: {'cx': 512, '2q': 512, '3q': 0, '4q+': 2, '1q': 514} , ops: {'ry': 1024, 'cx': 1024, 'measure': 11, 'h': 9, 'barrier': 2, 'qubits': 11}
circ_transpMeta:
{'1q_gate_count': 1033,
 '2q_gate_count': 1024,
 '2q_gate_depth': 512,
 'num_qubit': 11,
 'phys_qubits': [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10],
 'transpile_backend': 'nvidia'}
M: execution-ready 1 circuits on 11 qubits on nvidia
qiskit_to_gateList: nGate 2070
saving data as hdf5: out/canImg_b2_32_32.gate_list.h5
h5-write : circ_type (1, 2) int32
h5-write : gate_type (1, 2070, 3) int32
h5-write : gate_param (1, 2070) float32
h5-write : meta.JSON as string (1,) object
closed  hdf5: out/canImg_b2_32_32.gate_list.h5  size=0.04 MB, elaT=0.1 sec
read data from hdf5: out/canImg_b2_32_32.gate_list.h5
read obj: circ_type (1, 2) int32
read obj: gate_param (1, 2070) float32
read obj: gate_type (1, 2070, 3) int32
read str: meta.JSON 1 <class 'numpy.ndarray'>
 done h5, num rec:3  elaT=0.0 sec
M: job canImg_b2_32_32 started, nCirc=1, nq=11, shots/circ=400, target=nvidia
RCQ: done 903 nvidia, elapsed 2.28s
Job QA: {'status': 'JobStatus.DONE', 'num_circ': 1, 'num_clbits': 11, 'device': 'GPU', 'method': 'statevector', 'noise': 'ideal', 'shots': 204800, 'time_taken': 2.2765326499938965}
saving data as hdf5: out/canImg_b2_32_32.h5
h5-write : image_name (1,) object
h5-write : inp_fdata (512, 2, 1) float32
h5-write : inp_udata (1, 2, 512) float32
h5-write : norm_image (32, 32) float32
h5-write : phys_image (32, 32) uint8
h5-write : raw_nkey (1,) int32
h5-write : raw_ikey (1, 903) int32
h5-write : raw_mshot (1, 903) int32
h5-write : meta.JSON as string (1,) object
closed  hdf5: out/canImg_b2_32_32.h5  size=0.03 MB, elaT=0.1 sec
read data from hdf5: out/canImg_b2_32_32.h5
read str: image_name 1 <class 'numpy.ndarray'>
read obj: inp_fdata (512, 2, 1) float32
read obj: inp_udata (1, 2, 512) float32
read str: meta.JSON 1 <class 'numpy.ndarray'>
read obj: norm_image (32, 32) float32
read obj: phys_image (32, 32) uint8
read obj: raw_ikey (1, 903) int32
read obj: raw_mshot (1, 903) int32
read obj: raw_nkey (1,) int32
 done h5, num rec:8  elaT=0.0 sec
rec_udata: (1, 2, 512) addrBitsL: [2, 3, 4, 5, 6, 7, 8, 9, 10]
ic=0 marginal ibit=0 done, elaT=0.0 min
ic=0 marginal ibit=1 done, elaT=0.0 min
saving data as hdf5: out/canImg_b2_32_32.post.h5
h5-write : image_name (1,) object
h5-write : inp_fdata (512, 2, 1) float32
h5-write : inp_udata (1, 2, 512) float32
h5-write : norm_image (32, 32) float32
h5-write : phys_image (32, 32) uint8
h5-write : raw_ikey (1, 903) int32
h5-write : raw_mshot (1, 903) int32
h5-write : raw_nkey (1,) int32
h5-write : rec_udata (1, 2, 512) float64
h5-write : rec_norm_image (32, 32) float64
h5-write : meta.JSON as string (1,) object
closed  hdf5: out/canImg_b2_32_32.post.h5  size=0.05 MB, elaT=0.0 sec
PlotterBackbone: Running in Jupyter → using inline backend
Plotter : Graphics started
plotted  high-heels_x32_y32
Graphics saving to  out/canImg_b2_32_32_f1.png
Graphics saving to  out/canImg_b2_32_32_f2.png
Graphics saving to  out/canImg_b2_32_32_f3.png

({'image_name': array([b'high-heels_x32_y32'], dtype=object),
  'inp_fdata': array([[[0.   ],
          [0.   ]],
  
         [[0.   ],
          [0.   ]],
  
         [[0.   ],
          [0.   ]],
  
         ...,
  
         [[0.   ],
          [2.11 ]],
  
         [[0.   ],
          [0.857]],
  
         [[0.   ],
          [0.   ]]], shape=(512, 2, 1), dtype=float32),
  'inp_udata': array([[[ 1.   ,  1.   ,  1.   , ..., -0.514,  0.655,  1.   ],
          [ 1.   ,  1.   ,  1.   , ...,  1.   ,  1.   ,  1.   ]]],
        shape=(1, 2, 512), dtype=float32),
  'norm_image': array([[1.   , 1.   , 1.   , ..., 1.   , 1.   , 1.   ],
         [1.   , 1.   , 1.   , ..., 1.   , 1.   , 1.   ],
         [1.   , 1.   , 1.   , ..., 1.   , 1.   , 1.   ],
         ...,
         [1.   , 1.   , 0.953, ..., 1.   , 1.   , 1.   ],
         [1.   , 1.   , 1.   , ..., 0.992, 1.   , 1.   ],
         [1.   , 1.   , 1.   , ..., 1.   , 1.   , 1.   ]],
        shape=(32, 32), dtype=float32),
  'phys_image': array([[255, 255, 255, ..., 255, 255, 255],
         [255, 255, 255, ..., 255, 255, 255],
         [255, 255, 255, ..., 255, 255, 255],
         ...,
         [255, 255, 249, ..., 255, 255, 255],
         [255, 255, 255, ..., 254, 255, 255],
         [255, 255, 255, ..., 255, 255, 255]], shape=(32, 32), dtype=uint8),
  'raw_ikey': array([[ 480, 1732,  760, 1350,  544, 1968,  264, 1148,  214, 1964,  768,
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           504, 1860, 1700,  846,    8, 1864,  740,  156,  312, 1976, 1222,
          1342,  144, 1992,  572, 1832, 1704,  900, 1036, 1616, 1960,   24,
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          1440, 1436,  730,  216,  582,  883, 1749, 1316, 1100, 1452, 2041,
          1880, 1196, 1138, 1356, 1610,  706,  478,  720, 1418,  814, 1527,
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          1494,  910, 1534,  609,  737, 1989, 1237, 1485,  365, 1021,  747,
          1515]], dtype=int32),
  'raw_mshot': array([[458, 456, 442, 441, 440, 439, 438, 438, 437, 435, 433, 432, 428,
          428, 428, 427, 426, 426, 425, 425, 425, 425, 424, 424, 424, 423,
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          231, 231, 231, 231, 229, 229, 228, 228, 228, 225, 221, 221, 220,
          220, 219, 216, 216, 215, 213, 211, 210, 209, 208, 206, 206, 205,
          205, 205, 205, 204, 200, 199, 197, 195, 194, 194, 192, 190, 189,
          188, 188, 187, 187, 184, 184, 183, 183, 183, 178, 178, 177, 177,
          176, 176, 174, 173, 173, 171, 170, 170, 170, 169, 167, 167, 167,
          166, 165, 163, 163, 163, 161, 161, 159, 158, 158, 158, 157, 157,
          155, 155, 155, 154, 152, 152, 151, 150, 150, 149, 149, 148, 147,
          146, 145, 142, 141, 141, 141, 140, 140, 140, 136, 135, 135, 134,
          134, 134, 133, 133, 132, 130, 129, 129, 129, 129, 129, 128, 128,
          128, 127, 127, 126, 126, 125, 125, 125, 125, 124, 123, 123, 123,
          122, 121, 120, 120, 120, 118, 118, 117, 117, 117, 117, 116, 114,
          114, 113, 112, 112, 112, 110, 110, 109, 108, 108, 108, 107, 107,
          107, 106, 106, 106, 105, 104, 103, 102, 101, 101, 100, 100,  98,
           98,  98,  97,  97,  96,  95,  94,  94,  93,  92,  92,  91,  91,
           90,  90,  89,  89,  88,  88,  88,  87,  87,  87,  85,  85,  85,
           84,  84,  83,  82,  81,  81,  81,  81,  80,  79,  79,  78,  78,
           77,  77,  76,  76,  76,  76,  75,  75,  75,  73,  73,  73,  73,
           73,  70,  70,  70,  70,  70,  70,  69,  69,  68,  68,  67,  66,
           65,  65,  65,  64,  64,  63,  62,  60,  60,  59,  59,  58,  57,
           57,  57,  57,  56,  56,  56,  55,  55,  55,  55,  54,  54,  54,
           54,  53,  52,  51,  51,  50,  50,  50,  49,  49,  49,  47,  47,
           47,  46,  46,  45,  45,  45,  43,  42,  41,  40,  39,  38,  37,
           37,  37,  37,  37,  36,  34,  34,  32,  32,  32,  31,  31,  29,
           29,  29,  28,  28,  28,  27,  27,  26,  26,  26,  25,  25,  23,
           22,  22,  22,  21,  21,  20,  19,  18,  18,  17,  16,  15,  15,
           14,  13,  12,  11,  11,   9,   9,   8,   8,   8,   8,   8,   7,
            7,   7,   7,   6,   6,   6,   6,   6,   6,   6,   6,   5,   5,
            5,   5,   5,   5,   5,   5,   5,   5,   5,   4,   4,   4,   4,
            4,   4,   4,   4,   4,   4,   4,   4,   4,   4,   4,   4,   4,
            4,   3,   3,   3,   3,   3,   3,   3,   3,   3,   3,   3,   3,
            3,   3,   3,   3,   3,   3,   3,   2,   2,   2,   2,   2,   2,
            2,   2,   2,   2,   2,   2,   2,   2,   2,   2,   2,   2,   2,
            1,   1,   1,   1,   1,   1,   1,   1,   1,   1,   1,   1,   1,
            1,   1,   1,   1,   1,   1]], dtype=int32),
  'raw_nkey': array([903], dtype=int32),
  'rec_udata': array([[[ 1.   ,  1.   ,  1.   , ..., -0.509,  0.619,  1.   ],
          [ 1.   ,  1.   ,  1.   , ...,  1.   ,  1.   ,  1.   ]]],
        shape=(1, 2, 512)),
  'rec_norm_image': array([[1.   , 1.   , 1.   , ..., 1.   , 1.   , 1.   ],
         [1.   , 1.   , 1.   , ..., 1.   , 1.   , 1.   ],
         [1.   , 1.   , 1.   , ..., 1.   , 1.   , 1.   ],
         ...,
         [1.   , 1.   , 0.933, ..., 1.   , 1.   , 1.   ],
         [1.   , 1.   , 1.   , ..., 1.   , 1.   , 1.   ],
         [1.   , 1.   , 1.   , ..., 1.   , 1.   , 1.   ]], shape=(32, 32)),
  'meta.JSON': '{"payload": {"num_sample": 1, "nq_addr": 9, "seq_len": 512, "nq_fdata": 2, "num_clbit": 11, "qcrank_max_fval": 3.141592653589793, "num_qubit": 11}, "canned": {"image_name": "high-heels_x32_y32", "image_shape_xy": [32, 32], "image_pixels": 1024, "canned_type": "gray_image"}, "short_name": "canImg_b2_32_32", "submit": {"num_shots": 204800, "backend": "nvidia", "date": "20250817_185540_PDT", "unix_time": 1755482140}, "transpile": {"num_qubit": 11, "phys_qubits": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10], "transpile_backend": "nvidia", "2q_gate_depth": 512, "1q_gate_count": 1033, "2q_gate_count": 1024}, "hash": "72266a", "job_qa": {"status": "JobStatus.DONE", "num_circ": 1, "num_clbits": 11, "device": "GPU", "method": "statevector", "noise": "ideal", "shots": 204800, "time_taken": 2.2765326499938965}}'},
 {'payload': {'num_sample': 1,
   'nq_addr': 9,
   'seq_len': 512,
   'nq_fdata': 2,
   'num_clbit': 11,
   'qcrank_max_fval': 3.141592653589793,
   'num_qubit': 11},
  'canned': {'image_name': 'high-heels_x32_y32',
   'image_shape_xy': [32, 32],
   'image_pixels': 1024,
   'canned_type': 'gray_image'},
  'short_name': 'canImg_b2_32_32',
  'submit': {'num_shots': 204800,
   'backend': 'nvidia',
   'date': '20250817_185540_PDT',
   'unix_time': 1755482140},
  'transpile': {'num_qubit': 11,
   'phys_qubits': [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10],
   'transpile_backend': 'nvidia',
   '2q_gate_depth': 512,
   '1q_gate_count': 1033,
   '2q_gate_count': 1024},
  'hash': '72266a',
  'job_qa': {'status': 'JobStatus.DONE',
   'num_circ': 1,
   'num_clbits': 11,
   'device': 'GPU',
   'method': 'statevector',
   'noise': 'ideal',
   'shots': 204800,
   'time_taken': 2.2765326499938965},
  'plot': {'resid_max_range': 0.4}})