diff --git a/data/fake_results/all_ps_DEMO.npy b/data/fake_results/all_ps_DEMO.npy deleted file mode 100644 index 5400d8113..000000000 Binary files a/data/fake_results/all_ps_DEMO.npy and /dev/null differ diff --git a/data/fake_results/all_times_DEMO.txt b/data/fake_results/all_times_DEMO.txt deleted file mode 100644 index 01e54d6b6..000000000 --- a/data/fake_results/all_times_DEMO.txt +++ /dev/null @@ -1 +0,0 @@ -57130.19921875,57130.2109375,57130.21875,57131.19921875,57131.2109375,57131.21875,57132.19921875,57132.2109375,57132.21875,57133.19921875 diff --git a/data/fake_results/config_DEMO.yml b/data/fake_results/config_DEMO.yml new file mode 100644 index 000000000..dae979456 --- /dev/null +++ b/data/fake_results/config_DEMO.yml @@ -0,0 +1,84 @@ +ang_arr: +- 0.5 +- 0.5 +- 11 +average_angle: 0.0 +center_thresh: 0.0 +chunk_size: 1000000 +clip_negative: true +cluster_function: DBSCAN +cluster_type: position +debug: false +do_clustering: true +do_mask: true +do_stamp_filter: true +encode_num_bytes: -1 +eps: 0.03 +flag_keys: +- BAD +- EDGE +- NO_DATA +- SUSPECT +- UNMASKEDNAN +gpu_filter: true +im_filepath: ../data/demo +ind_output_files: true +known_obj_jpl: false +known_obj_obs: 3 +known_obj_thresh: null +lh_level: 10.0 +mask_bit_vector: null +mask_bits_dict: + BAD: 0 + CLIPPED: 9 + CR: 3 + CROSSTALK: 10 + DETECTED: 5 + DETECTED_NEGATIVE: 6 + EDGE: 4 + INEXACT_PSF: 11 + INTRP: 2 + NOT_DEBLENDED: 12 + NO_DATA: 8 + REJECTED: 13 + SAT: 1 + SENSOR_EDGE: 14 + SUSPECT: 7 + UNMASKEDNAN: 15 +mask_grow: 10 +mask_num_images: 10 +mask_threshold: null +max_lh: 1000.0 +mjd_lims: null +mom_lims: +- 37.5 +- 37.5 +- 1.5 +- 1.0 +- 1.0 +num_cores: 1 +num_obs: 7 +output_suffix: DEMO +peak_offset: +- 3.0 +- 3.0 +psf_file: null +psf_val: 1.4 +repeated_flag_keys: [] +res_filepath: ./fake_results +result_filename: ./fake_results/results.ecsv +save_all_stamps: true +sigmaG_lims: +- 15 +- 60 +stamp_radius: 10 +stamp_type: cpp_median +time_file: null +v_arr: +- 0 +- 20 +- 21 +x_pixel_bounds: null +x_pixel_buffer: null +y_pixel_bounds: null +y_pixel_buffer: null diff --git a/data/fake_results/filtered_likes_DEMO.txt b/data/fake_results/filtered_likes_DEMO.txt deleted file mode 100644 index b4d2898de..000000000 --- a/data/fake_results/filtered_likes_DEMO.txt +++ /dev/null @@ -1 +0,0 @@ -243.3549 diff --git a/data/fake_results/lc_DEMO.txt b/data/fake_results/lc_DEMO.txt deleted file mode 100644 index 2eb2c80fd..000000000 --- a/data/fake_results/lc_DEMO.txt +++ /dev/null @@ -1 +0,0 @@ -860.3051629964147,863.1902270605538,839.5871716813997,870.71170711359,849.6934252172084,821.1530260203929,705.1772349111621,674.4296558241741,624.8854843640303,693.4307555929325 diff --git a/data/fake_results/lc_index_DEMO.txt b/data/fake_results/lc_index_DEMO.txt deleted file mode 100644 index eb905544e..000000000 --- a/data/fake_results/lc_index_DEMO.txt +++ /dev/null @@ -1 +0,0 @@ -0,1,2,3,4,5,6,7,8,9 diff --git a/data/fake_results/phi_DEMO.txt b/data/fake_results/phi_DEMO.txt deleted file mode 100644 index bc1aba983..000000000 --- a/data/fake_results/phi_DEMO.txt +++ /dev/null @@ -1 +0,0 @@ -0.009727592580020428,0.009727592580020428,0.009727592580020428,0.009727592580020428,0.009727592580020428,0.009727592580020428,0.009727592580020428,0.009727592580020428,0.009727592580020428,0.009727592580020428 diff --git a/data/fake_results/ps_DEMO.txt b/data/fake_results/ps_DEMO.txt deleted file mode 100644 index 08ea32efe..000000000 --- a/data/fake_results/ps_DEMO.txt +++ /dev/null @@ -1 +0,0 @@ -0.2052 -0.2591 -0.2753 -2.1057 -0.1538 -0.5112 -1.7340 0.1474 -0.2684 0.2818 -0.4580 0.0688 -1.2922 -1.4527 0.1163 0.1975 -0.5159 -0.2592 -1.2637 -0.5878 1.0495 -1.1612 -0.1046 -0.2153 0.5004 0.4318 0.3046 -0.2367 -0.8995 0.0426 -0.8715 1.9661 0.2736 -1.3369 -0.0050 0.2563 -0.3109 -0.0333 -0.3263 -0.4411 0.6756 0.6689 1.3694 0.1782 -1.0197 -0.9226 -0.9170 1.0303 0.7771 0.0747 -1.3740 0.3777 0.6471 1.3930 -1.1005 0.1458 -0.6741 -0.4435 1.0270 -0.4198 -0.0059 0.3433 -1.3974 0.1357 -0.4449 -1.5004 0.7318 0.2104 0.0012 -1.0066 -0.0616 0.9050 -1.1719 0.6480 -0.9066 0.2306 0.8373 -0.1948 0.1455 -0.6911 0.7658 -1.5705 -0.8298 0.7320 0.5242 -0.5068 0.5434 0.4023 0.2811 -1.0626 -0.5512 1.1685 0.3296 0.9059 -0.0630 1.4629 -0.2855 0.2503 1.7976 -1.1828 0.3156 -0.1747 -0.5214 -1.2013 -0.2817 1.6566 0.7123 -0.0285 1.3507 -0.2857 -0.2447 -1.0454 -0.5191 0.0108 0.4764 -0.7758 0.0096 0.2494 1.0997 0.8838 -0.0398 -0.5247 0.0928 1.0096 -0.0984 -1.2346 -1.7986 1.1975 -1.8537 0.7713 0.4848 0.9241 1.3976 -0.9410 0.4105 0.0406 1.4023 0.0156 0.2285 -0.3065 -1.3644 -0.3451 0.2326 -0.4678 0.5225 0.1855 -0.7546 0.6427 1.0144 -0.5652 1.2343 -0.9667 -0.4722 -0.1448 -0.5469 1.8377 1.5350 1.1660 -0.0903 0.3687 -0.0280 0.1248 0.1231 -1.3071 -0.5213 -0.4905 0.2297 -0.1877 1.2500 0.7330 0.2091 -0.2985 -0.2425 1.2108 0.1652 1.5368 -0.4264 -0.1167 0.1467 -1.6512 0.1521 0.0220 -0.2960 -0.7601 0.9479 -1.0298 -0.5362 -0.0550 -0.4640 -0.0054 1.1465 0.2338 -1.2322 0.2031 0.2854 0.1235 -0.5874 -0.8953 0.9730 0.7947 0.9920 -0.7822 -0.8524 0.6467 0.6345 -0.3604 0.2441 0.0005 1.1333 -0.0763 -0.0935 -0.3608 1.1306 -0.3524 0.4220 -0.0789 0.3687 -0.6785 -0.2501 -0.2619 11.9763 47.9223 12.1300 1.5043 -0.3277 1.3010 -1.1680 0.6349 -0.6217 -0.4306 0.4634 -0.3508 -0.4165 -0.7895 -0.1505 -0.0333 -0.6152 1.3554 -0.2695 0.4254 2.0886 52.3379 196.7265 53.8318 2.3544 1.1335 0.4885 -0.6615 -1.7157 -0.6701 -0.6932 -0.3294 1.0295 0.1032 0.9272 -0.3645 -0.2231 -0.6303 0.0169 -0.1073 -1.4757 0.8203 11.2423 46.1871 14.1803 1.8055 0.4560 0.5930 0.0352 0.1134 0.5576 -0.4808 0.1931 1.0132 -0.4663 0.7389 -0.8354 0.1338 -0.5876 -0.1226 0.3687 0.6973 -0.1744 -1.1913 -0.8561 1.8925 0.5275 1.0304 0.3020 1.1005 1.1479 -0.6565 0.1331 0.8499 -0.2110 -0.2580 -0.4242 -1.1644 -0.1362 1.0111 0.8025 -0.2351 -0.3594 -0.1489 0.7744 -0.1853 0.7315 0.5234 -0.1186 1.6780 -1.2181 0.3847 0.6379 0.4817 -1.3124 0.2157 0.6653 0.2786 1.0965 0.6362 0.5687 -0.0933 1.3312 0.7428 0.4186 0.4245 -0.6080 0.5771 -0.3074 -0.6256 -0.3209 -0.5071 0.6802 1.0152 -0.5343 0.4986 -0.2177 0.5708 -0.1687 0.0246 0.7658 0.3838 0.2215 1.5374 0.1177 -1.0368 0.0961 1.0588 0.5299 0.1728 -0.9002 -0.5939 -0.3423 0.7535 -1.1930 -0.0165 -0.3495 -0.3450 -0.0753 -0.2716 0.4030 1.0146 -1.1325 -0.6709 -0.0805 -0.3088 0.0208 -0.7467 0.7845 0.5269 0.0422 -0.9752 0.2442 0.6234 -1.3073 -1.7417 -0.6487 0.3862 -0.0601 -1.3159 0.4051 0.1282 0.4502 -0.6129 0.9213 0.9633 0.9149 0.0546 1.2305 0.8865 -0.6920 0.6546 -0.5310 -0.2037 0.3844 -0.3945 0.3208 -0.4283 0.8817 -0.6900 -0.3742 0.5580 1.1402 -0.9679 -0.3780 0.0582 -0.3956 0.0563 -0.2088 -1.3710 -1.1154 -0.5613 -0.4624 -0.0677 -0.2987 -1.2624 -0.8460 0.0093 -0.0323 -1.3281 -1.1082 0.5503 0.5646 -0.8901 -0.1862 -0.7135 0.8412 0.1299 -0.2897 0.1595 0.2652 -1.7709 -1.2705 -0.1774 0.8188 -0.8324 0.6022 -0.0976 0.8570 -0.6922 -0.3674 diff --git a/data/fake_results/psi_DEMO.txt b/data/fake_results/psi_DEMO.txt deleted file mode 100644 index 8a628c9c4..000000000 --- a/data/fake_results/psi_DEMO.txt +++ /dev/null @@ -1 +0,0 @@ -8.368698120117188,8.39676284790039,8.16716194152832,8.469928741455078,8.265471458435059,7.987842082977295,6.859676837921143,6.560576915740967,6.078631401062012,6.7454118728637695 diff --git a/data/fake_results/results.ecsv b/data/fake_results/results.ecsv index ee41fa575..5e092460a 100644 --- a/data/fake_results/results.ecsv +++ b/data/fake_results/results.ecsv @@ -1,21 +1,18 @@ # %ECSV 1.0 # --- # datatype: -# - {name: trajectory_x, datatype: int64} -# - {name: trajectory_y, datatype: int64} -# - {name: trajectory_vx, datatype: float64} -# - {name: trajectory_vy, datatype: float64} -# - {name: obs_count, datatype: int64} -# - {name: flux, datatype: float64} +# - {name: x, datatype: int64} +# - {name: y, datatype: int64} +# - {name: vx, datatype: float64} +# - {name: vy, datatype: float64} # - {name: likelihood, datatype: float64} -# - {name: stamp, datatype: string, subtype: 'float32[21,21]'} -# - {name: valid_indices, datatype: string, subtype: 'int64[9]'} +# - {name: flux, datatype: float64} +# - {name: obs_count, datatype: int64} # - {name: psi_curve, datatype: string, subtype: 'float64[10]'} # - {name: phi_curve, datatype: string, subtype: 'float64[10]'} +# - {name: obs_valid, datatype: string, subtype: 'bool[10]'} +# - {name: stamp, datatype: string, subtype: 'float32[21,21]'} # - {name: all_stamps, datatype: string, subtype: 'float32[10,21,21]'} -# - {name: pred_ra, datatype: string, subtype: json} -# - {name: pred_dec, datatype: string, subtype: json} -# - {name: all_times, datatype: string, subtype: 'float64[10]'} # schema: astropy-2.0 -trajectory_x trajectory_y trajectory_vx trajectory_vy obs_count flux likelihood stamp valid_indices psi_curve phi_curve all_stamps pred_ra pred_dec all_times -106 44 10.465370178222656 0.4760266840457916 9 822.6404418945312 243.40751873206497 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diff --git a/data/fake_results/results_DEMO.txt b/data/fake_results/results_DEMO.txt index 6c410b423..067c1e9fb 100644 --- a/data/fake_results/results_DEMO.txt +++ b/data/fake_results/results_DEMO.txt @@ -1 +1 @@ -lh: 243.354874 flux: 780.256409 x: 106 y: 44 x_v: 9.513972 y_v: -0.432751 obs_count: 10 +lh: 227.512299 flux: 729.461182 x: 106 y: 44 vx: 9.513972 vy: 0.432751 obs_count: 10 valid: 1 diff --git a/data/fake_results/times_DEMO.txt b/data/fake_results/times_DEMO.txt deleted file mode 100644 index 01e54d6b6..000000000 --- a/data/fake_results/times_DEMO.txt +++ /dev/null @@ -1 +0,0 @@ -57130.19921875,57130.2109375,57130.21875,57131.19921875,57131.2109375,57131.21875,57132.19921875,57132.2109375,57132.21875,57133.19921875 diff --git a/data/fake_results_noisy/all_times_DEMO.txt b/data/fake_results_noisy/all_times_DEMO.txt deleted file mode 100644 index 01e54d6b6..000000000 --- a/data/fake_results_noisy/all_times_DEMO.txt +++ /dev/null @@ -1 +0,0 @@ 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a/data/fake_results_noisy/lc_DEMO.txt b/data/fake_results_noisy/lc_DEMO.txt deleted file mode 100644 index ce810f46d..000000000 --- a/data/fake_results_noisy/lc_DEMO.txt +++ /dev/null @@ -1,63 +0,0 @@ -343.8707054135296,333.8963620890264,325.5001129256769,342.18650956067364,323.4951120271542,324.7434551695594,308.5732289266278,300.53633827650043,299.4074545116321,341.05174351213503 -359.6922592919067,315.7391494413123,312.84374236171965,312.9959464516876,254.35149000141024,221.48883778271988,457.2865266694642,439.57212743454403,373.5854780355968,109.62162574104184 -350.9577297984868,341.26968212211807,329.2732057479026,329.26310782760197,320.4663241653385,323.8596175385879,291.0789006295212,312.739282807542,316.1753207754619,289.33700937766605 -348.7559420016787,321.681285347915,306.0848513675017,319.7040047111884,342.57581870158356,319.49618853102123,318.0113530755564,303.6326498529477,281.85104361228895,280.4915498180312 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--0.012335652485489845,-0.01990439184010029,-0.006282346788793802,1.9651007652282715,2.208970308303833,2.358959674835205,-0.03227132931351662,0.0,0.0,0.0 -0.10709918290376663,-0.038327086716890335,0.1674918532371521,1.560570240020752,1.6474334001541138,1.7195106744766235,0.05698171257972717,0.0,0.0,0.0 diff --git a/data/fake_results_noisy/times_DEMO.txt b/data/fake_results_noisy/times_DEMO.txt deleted file mode 100644 index 55d9db841..000000000 --- a/data/fake_results_noisy/times_DEMO.txt +++ /dev/null @@ -1,63 +0,0 @@ -57130.19921875,57130.2109375,57130.21875,57131.19921875,57131.2109375,57131.21875,57132.19921875,57132.2109375,57132.21875,57133.19921875 -57130.19921875,57130.2109375,57130.21875,57131.19921875,57131.2109375,57131.21875,57132.19921875,57132.2109375,57132.21875 -57130.19921875,57130.2109375,57130.21875,57131.19921875,57131.2109375,57131.21875,57132.19921875,57132.2109375,57132.21875,57133.19921875 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-57130.19921875,57130.2109375,57130.21875,57131.19921875,57131.2109375,57131.21875,57132.19921875,57132.2109375,57132.21875,57133.19921875 -57131.19921875,57131.2109375,57131.21875 -57130.19921875,57130.2109375,57130.21875,57131.19921875,57131.2109375,57131.21875,57132.19921875 diff --git a/notebooks/KBMOD_Demo.ipynb b/notebooks/KBMOD_Demo.ipynb index 240908adb..97f395012 100644 --- a/notebooks/KBMOD_Demo.ipynb +++ b/notebooks/KBMOD_Demo.ipynb @@ -172,7 +172,7 @@ "source": [ "We then check that results were written to an output directory. The configuration parameters above specify that KBMOD should write two types of output files:\n", "\n", - "1. A combined serialized ``ResultList`` saved as a .ecsv file (``\"result_filename\": \"./fake_results/results.ecsv\")``.\n", + "1. A combined serialized ``Results`` saved as a .ecsv file (``\"result_filename\": \"./fake_results/results.ecsv\")``.\n", "2. (Legacy format) A series of individual output files (``\"res_filepath\": res_filepath``).\n", "\n", "Users can shut off these outputs but passing ``None`` to the configuration options." @@ -195,7 +195,7 @@ "source": [ "# Analyzing Results\n", "\n", - "The run function we used returns a `ResultList` containing the individual results of the run. We can perform basic actions on this data structure such as sorting it, extracting individual results, or performing additional filtering." + "The run function we used returns a `Results` object containing the individual results of the run. We can perform basic actions on this data structure such as sorting it, extracting individual results, or performing additional filtering." ] }, { @@ -204,14 +204,8 @@ "metadata": {}, "outputs": [], "source": [ - "print(f\"Search found {results.num_results()} results.\")\n", - "\n", - "# Sorting one result doesn't do anything.\n", - "results.sort()\n", - "\n", - "# Output the trajectory.\n", - "best_result = results.results[0]\n", - "print(best_result.trajectory)" + "print(f\"Search found {len(results)} results.\")\n", + "print(results)" ] }, { @@ -229,15 +223,12 @@ "source": [ "import matplotlib.pyplot as plt\n", "\n", - "fig, axs = plt.subplots(3, 1)\n", - "axs[0].plot(results.all_times, best_result.psi_curve)\n", + "fig, axs = plt.subplots(2, 1)\n", + "axs[0].plot(results[\"psi_curve\"][0])\n", "axs[0].set_title(\"Psi\")\n", "\n", - "axs[1].plot(results.all_times, best_result.phi_curve)\n", - "axs[1].set_title(\"Psi\")\n", - "\n", - "axs[2].plot(results.all_times, best_result.likelihood_curve)\n", - "axs[2].set_title(\"Likelihood\")" + "axs[1].plot(results[\"phi_curve\"][0])\n", + "axs[1].set_title(\"Psi\")" ] }, { @@ -271,7 +262,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.12.1" + "version": "3.12.2" } }, "nbformat": 4, diff --git a/notebooks/TrajectoryExplorer.ipynb b/notebooks/TrajectoryExplorer.ipynb index 68dd72c91..027c7d413 100644 --- a/notebooks/TrajectoryExplorer.ipynb +++ b/notebooks/TrajectoryExplorer.ipynb @@ -97,7 +97,7 @@ "source": [ "## Using TrajectoryExplorer\n", "\n", - "`TrajectoryExplorer` is constructed from the `ImageStack` and an optional configuration. Once it is instantiated, we can use the `evaluate_linear_trajectory` function to query specific trajectories. The function returns a `ResultRow` with a variety of information. The most important is a `Trajectory` object that stores the raw statistics coming from the GPU search." + "`TrajectoryExplorer` is constructed from the `ImageStack` and an optional configuration. Once it is instantiated, we can use the `evaluate_linear_trajectory` function to query specific trajectories. The function returns a `Results` object with a single row and a variety of columns, including the raw statistics from the GPU search." ] }, { @@ -109,7 +109,28 @@ "explorer = TrajectoryExplorer(fake_ds.stack)\n", "\n", "result = explorer.evaluate_linear_trajectory(50, 60, 5.0, -2.0)\n", - "print(result.trajectory)" + "print(result)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can access individual statistics about the results by using the column name and row index of 0." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "best_row = result[0]\n", + "\n", + "print(\n", + " f\"The trajectory starts at ({best_row['x']}, {best_row['y']}) and moves as ({best_row['vx']}, {best_row['vy']})\"\n", + ")\n", + "print(f\"It has {best_row['obs_count']} observations and a likelihood of {best_row['likelihood']}.\")" ] }, { @@ -125,15 +146,12 @@ "metadata": {}, "outputs": [], "source": [ - "fig, axs = plt.subplots(3, 1)\n", - "axs[0].plot(fake_times, result.psi_curve)\n", + "fig, axs = plt.subplots(2, 1)\n", + "axs[0].plot(fake_times, best_row[\"psi_curve\"])\n", "axs[0].set_title(\"Psi\")\n", "\n", - "axs[1].plot(fake_times, result.phi_curve)\n", - "axs[1].set_title(\"Psi\")\n", - "\n", - "axs[2].plot(fake_times, result.likelihood_curve)\n", - "axs[2].set_title(\"Likelihood\")" + "axs[1].plot(fake_times, best_row[\"phi_curve\"])\n", + "axs[1].set_title(\"Psi\")" ] }, { @@ -157,7 +175,7 @@ " for j in range(w):\n", " ind = w * i + j\n", " if ind < num_images:\n", - " axs[i, j].imshow(result.all_stamps[ind], cmap=\"gray\")" + " axs[i, j].imshow(result[\"all_stamps\"][0][ind], cmap=\"gray\")" ] }, { @@ -196,7 +214,7 @@ "print(f\"Object starts at ({ra0}, {dec0}) with velocity ({v_ra}, {v_dec}).\")\n", "\n", "result = explorer.evaluate_angle_trajectory(ra0, dec0, v_ra, v_dec, my_wcs)\n", - "print(result.trajectory)" + "print(result)" ] }, { @@ -205,7 +223,7 @@ "source": [ "## Filtering\n", "\n", - "A key component of KBMOD is the clipped Sigma-G filtering. `TrajectoryExplorer` does not do any filtering be default so that users can see all the information for the given trajectory. However, it provides the ability to apply the filtering manually. The unfiltered time stamps are stored in `valid_indices`." + "A key component of KBMOD is the clipped Sigma-G filtering. `TrajectoryExplorer` does not do any filtering be default so that users can see all the information for the given trajectory. However, it provides the ability to apply the filtering manually. The mask of time step validity is stored in the column `obs_valid`." ] }, { @@ -215,7 +233,9 @@ "outputs": [], "source": [ "explorer.apply_sigma_g(result)\n", - "print(f\"Valid time steps={result.valid_indices}\")" + "\n", + "for i in range(len(fake_times)):\n", + " print(f\"Time {i} is valid = {result['obs_valid'][0][i]}.\")" ] }, { diff --git a/notebooks/kbmod_analysis_demo.ipynb b/notebooks/kbmod_analysis_demo.ipynb index 8c1d847e1..a3aa8794f 100644 --- a/notebooks/kbmod_analysis_demo.ipynb +++ b/notebooks/kbmod_analysis_demo.ipynb @@ -18,7 +18,7 @@ "import numpy as np\n", "\n", "from kbmod.analysis.plot_results import *\n", - "from kbmod.result_list import *\n", + "from kbmod.results import Results\n", "\n", "%matplotlib inline" ] @@ -27,7 +27,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "To run an analysis of KBMOD's output you need results in the form of either a `ResultList` or saved files. For the purposes of this notebook you can use that fake result data (corresponding to the `data/demo` images) included in `data/fake_results/results.ecsv`. These results were generated by the KBMOD_Demo notebook." + "To run an analysis of KBMOD's output you need results in the form of either a `Results` object or saved files. For the purposes of this notebook you can use that fake result data (corresponding to the `data/demo` images) included in `data/fake_results/results.ecsv`. These results were generated by the KBMOD_Demo notebook." ] }, { @@ -38,7 +38,7 @@ "source": [ "## Loading Results\n", "\n", - "We load all of the result data as a `ResultList` using the class's `read_table` function. We extract the first row to use for later examples." + "We load all of the result data as a `Results` object using the class's `read_table` function. We extract the first row to use for later examples." ] }, { @@ -47,22 +47,20 @@ "metadata": {}, "outputs": [], "source": [ - "results = ResultList.read_table(\"../data/fake_results/results.ecsv\")\n", - "print(f\"Loaded {results.num_results()} results.\")\n", + "results = Results.read_table(\"../data/fake_results/results.ecsv\")\n", + "print(f\"Loaded {len(results)} results.\")\n", "\n", - "row0 = results.results[0]" + "row0 = results[0]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "Note that in previous versions (<=1.1) KBMOD saved output was also saved in a series of individual files such as `all_ps_DEMO.npy` and `phi_DEMO.txt`. You can still load these using the `load_result_list_from_files` function:\n", + "Note that in previous versions (<=1.1) KBMOD saved output was also saved in a series of individual files such as `results_DEMO.txt` for the a file of the Trajectory objects. You can still load these using the `from_trajectory_file` function:\n", "\n", "```\n", - "results_dir = \"../data/fake_results/\"\n", - "results_suffix = \"DEMO\"\n", - "results = load_result_list_from_files(results_dir, results_suffix)\n", + "results = Results.from_trajectory_file(\"../data/fake_results/results_DEMO.txt\")\n", "```" ] }, @@ -72,7 +70,7 @@ "source": [ "## Plotting the Results\n", "\n", - "We can use the `ResultsVisualizer` class to plot the different aspects of the `ResultList`.\n", + "We can use the `ResultsVisualizer` class to plot the different aspects of the `Results` object.\n", "\n", "We start by plotting the coadded stamp for the top result." ] @@ -83,7 +81,7 @@ "metadata": {}, "outputs": [], "source": [ - "ResultsVisualizer.plot_single_stamp(row0.stamp)" + "ResultsVisualizer.plot_single_stamp(row0[\"stamp\"])" ] }, { @@ -99,7 +97,7 @@ "metadata": {}, "outputs": [], "source": [ - "ResultsVisualizer.plot_stamps(row0.all_stamps, columns=5)" + "ResultsVisualizer.plot_stamps(row0[\"all_stamps\"], columns=5)" ] }, { @@ -115,49 +113,37 @@ "metadata": {}, "outputs": [], "source": [ + "all_times = [\n", + " 57130.19921875,\n", + " 57130.2109375,\n", + " 57130.21875,\n", + " 57131.19921875,\n", + " 57131.2109375,\n", + " 57131.21875,\n", + " 57132.19921875,\n", + " 57132.2109375,\n", + " 57132.21875,\n", + " 57133.19921875,\n", + "]\n", + "\n", "fig1 = plt.figure(figsize=(10, 15))\n", "(ax1, ax2, ax3) = fig1.subplots(3, 1)\n", "\n", "ResultsVisualizer.plot_time_series(\n", - " row0.light_curve, times=results.all_times, axes=ax1, title=\"Lightcurve with time spacing\"\n", + " row0[\"psi_curve\"], times=all_times, axes=ax1, title=\"Phi curve with time spacing\"\n", ")\n", "\n", - "ResultsVisualizer.plot_time_series(row0.light_curve, axes=ax2, title=\"Lightcurve with equal spacing\")\n", + "ResultsVisualizer.plot_time_series(row0[\"psi_curve\"], axes=ax2, title=\"Phi curve with equal spacing\")\n", "\n", "ResultsVisualizer.plot_time_series(\n", - " row0.light_curve,\n", - " times=results.all_times,\n", + " row0[\"psi_curve\"],\n", + " times=all_times,\n", " indices=[0, 1, 2, 4, 5, 7, 8],\n", " axes=ax3,\n", - " title=\"Lightcurve with time spacing and invalid indices\",\n", + " title=\"phi curve with time spacing and invalid indices\",\n", ")" ] }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Or we can put it altogether with the `plot_result_row` function." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "ResultsVisualizer.plot_result_row(row0)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Histograms of Results\n", - "\n", - "We can plot the histograms of individual attributes of the results. First we need to load a result set with more than one entry. Then we plot the histogram of number of observations in the trajectory." - ] - }, { "cell_type": "code", "execution_count": null, @@ -234,7 +220,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.12.1" + "version": "3.12.2" } }, "nbformat": 4, diff --git a/notebooks/kbmod_results_and_filtering.ipynb b/notebooks/kbmod_results_and_filtering.ipynb index 0de5b460c..0bddbf41b 100644 --- a/notebooks/kbmod_results_and_filtering.ipynb +++ b/notebooks/kbmod_results_and_filtering.ipynb @@ -6,7 +6,7 @@ "source": [ "# KBMOD Results and Filtering \n", " \n", - "This notebook demonstrates the basic functionality for loading and filtering results. KBMOD provides the ability to load results into a ``ResultList`` data structure and then apply a sequence of filters to those results. New filters can be defined by inheriting from the ``Filter`` class." + "This notebook demonstrates the basic functionality for loading and filtering results. KBMOD provides the ability to load results into a ``Results`` data structure and then apply a sequence of filters to those results." ] }, { @@ -24,8 +24,7 @@ "outputs": [], "source": [ "# everything we will need for this demo\n", - "from kbmod.filters.stats_filters import LHFilter, NumObsFilter\n", - "from kbmod.result_list import load_result_list_from_files, ResultList\n", + "from kbmod.results import Results\n", "import matplotlib.pyplot as plt\n", "import numpy as np" ] @@ -36,7 +35,9 @@ "source": [ "# Load the results\n", "\n", - "We use the fake result data provided in ``data/fake_results_noisy`` which is generated from 256 x 256 images with multiple fake objects inserted. KBMOD is run with wider than normal filter parameters so as to produce a noisy set of results." + "We use the fake result data provided in ``data/fake_results_noisy`` which is generated from 256 x 256 images with multiple fake objects inserted. KBMOD is run with wider than normal filter parameters so as to produce a noisy set of results.\n", + "\n", + "The `Results` object behaves like an astropy Table with some additional book keeping to help with filtering." ] }, { @@ -45,53 +46,21 @@ "metadata": {}, "outputs": [], "source": [ - "results = load_result_list_from_files(\"../data/fake_results_noisy/\", \"DEMO\")\n", - "print(f\"Loaded {results.num_results()} results.\")\n", + "results = Results.from_trajectory_file(\"../data/fake_results_noisy/results_DEMO.txt\")\n", + "print(f\"Loaded {len(results)} results with columns {results.colnames}\")\n", "\n", "# Turn on filtered result tracking.\n", "results.track_filtered = True\n", "\n", - "# Show the first five results.\n", - "for i in range(5):\n", - " print(results.results[i].trajectory)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Sorting Results\n", - "\n", - "We can sort the results by any of the attributes of a ``ResultRow`` in either increasing or decreasing order." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "results.sort(key=\"obs_count\")\n", - "print(f\"Top 5 by observation count:\")\n", - "for i in range(5):\n", - " print(results.results[i].trajectory)\n", - "\n", - "print(f\"\\nBottom 5 by Flux:\")\n", - "results.sort(key=\"flux\", reverse=False)\n", - "for i in range(5):\n", - " print(results.results[i].trajectory)\n", - "\n", - "# Return to sorted by decreasing likelihood.\n", - "results.sort(key=\"final_likelihood\")" + "# Show the top 5 rows\n", + "print(results[0:5])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "# Extracting Individual Attributes\n", - "\n", - "The `ResultList`class provides a helper function `get_result_values()` that allows a user to extract all of the values for a given attribute of the results. For example we could extract all of the flux values and create a histogram." + "We can access individual rows and columns using the `[]` notation:" ] }, { @@ -100,77 +69,16 @@ "metadata": {}, "outputs": [], "source": [ - "flux_values = results.get_result_values(\"trajectory.flux\")\n", - "plt.hist(flux_values)" + "results[\"likelihood\"][0]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "# Filtering\n", - "\n", - "First we create a filter based on the observations' likelihood and apply it to the result set." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Filter out all results that have a likelihood < 40.0.\n", - "filter1 = LHFilter(40.0, None)\n", - "print(f\"Applying {filter1.get_filter_name()}\")\n", - "results.apply_filter(filter1)\n", - "print(f\"{results.num_results()} results remaining.\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can look at the rows that passed the filter. These are stored in the ``ResultList``'s ``results`` list. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "for i in range(5):\n", - " print(results.results[i].trajectory)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Because we set ``results.track_filtered = True`` above, the ``ResultList`` also keeps each row that was rejected by one of the filters. These rows are indexed by the filter name, allowing the user to determine which rows were removed during which filtering stage. \n", - "\n", - "We can use the ``get_filtered`` function to retrieve all the filtered rows for a given filter name:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Extract the rows that did not pass filter1.\n", - "filtered_list = results.get_filtered(filter1.get_filter_name())\n", - "for i in range(5):\n", - " print(filtered_list[i].trajectory)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can apply multiple filters to the ``ResultList`` to progressively rule out more and more candidate trajectories. We can even apply the same filter with different parameters.\n", + "# Sorting Results\n", "\n", - "Next we apply the ``NumObsFilter`` to filter out anything with fewer than 10 observations:" + "We can sort the results by any of the cols of a ``Results`` in either increasing or decreasing order by operating directly on its table object." ] }, { @@ -179,45 +87,25 @@ "metadata": {}, "outputs": [], "source": [ - "# Filter out all results with fewer than 10 observations.\n", - "filter2 = NumObsFilter(10)\n", - "print(f\"Applying {filter2.get_filter_name()}\")\n", - "results.apply_filter(filter2)\n", - "print(f\"{results.num_results()} results remaining.\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "To visualize the effect of this filter, we can plot one of the unfiltered stamps and one of the filtered stamps. Note that we retrieve the rows filtered by the ``LHFilter`` using the ``get_filtered`` with the filter’s name." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "fig, axs = plt.subplots(1, 2)\n", + "results.table.sort(keys=\"obs_count\")\n", + "print(f\"Top 5 by observation count:\")\n", + "print(results[0:5])\n", "\n", - "unfiltered_stamp = np.array(results.results[0].stamp).reshape([21, 21])\n", - "axs[0].imshow(unfiltered_stamp, cmap=\"gray\")\n", - "axs[0].set_title(\"Unfiltered Stamp\")\n", + "print(f\"\\nBottom 5 by Flux:\")\n", + "results.table.sort(keys=[\"flux\"], reverse=False)\n", + "print(results[0:5])\n", "\n", - "filtered_list2 = results.get_filtered(filter1.get_filter_name())\n", - "filtered_stamp = np.array(filtered_list2[0].stamp).reshape([21, 21])\n", - "axs[1].imshow(filtered_stamp, cmap=\"gray\")\n", - "axs[1].set_title(\"Filtered Stamp\")" + "# Return to sorted by decreasing likelihood.\n", + "results.table.sort(keys=[\"likelihood\"])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "### Reverting filters\n", + "# Extracting Individual Attributes\n", "\n", - "As long as we have ``track_filtered`` turned on, we can undo any of the filtering steps. This appends the previously filtered results to the end of the list (and thus does not preserve ordering). However we can always re-sort if needed." + "Since the `Results`class stores data as a table, the user can easily extract all of the values for a given attribute of the results. For example we could extract all of the flux values and create a histogram." ] }, { @@ -226,21 +114,16 @@ "metadata": {}, "outputs": [], "source": [ - "results.revert_filter(filter1.get_filter_name())\n", - "print(f\"{results.num_results()} results remaining.\")" + "plt.hist(results[\"flux\"])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "# Outputing Results\n", - "\n", - "In addition to the \"many files\" format provided in the original KBMOD (and used to load the demo files), we can output the results data to a single YAML string or an AstroPy Table.\n", - "\n", - "### YAML String\n", + "# Filtering\n", "\n", - "When serializing to a YAML string we can either save the entire ``ResultList`` (including the filtered rows) or just the unfiltered rows. To save space, the default is to serialize just the unfiltered rows." + "Using the `filter_rows()` method, you can filter out individual rows based on either their indices or a Boolean mask. In addition to the indices/mask, the `filter_rows()` method allows you to specify and optional label for later analysis." ] }, { @@ -249,20 +132,17 @@ "metadata": {}, "outputs": [], "source": [ - "# Serialize the unfiltered results\n", - "yaml_str_a = results.to_yaml()\n", - "print(f\"Unfiltered is serialized to a string of length {len(yaml_str_a)}\")\n", - "\n", - "# Serialize the entire data structure\n", - "yaml_str_b = results.to_yaml(serialize_filtered=True)\n", - "print(f\"Full data structure is serialized to a string of length {len(yaml_str_b)}\")" + "# Filter out all results that have a likelihood < 40.0.\n", + "mask = results[\"likelihood\"] > 40.0\n", + "results.filter_rows(mask, \"likelihood\")\n", + "print(f\"{len(results)} results remaining.\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "We can send this YAML string to another machine or save it for later analysis. We can reload the ``ResultList`` directly from the YAML string." + "We can look at the rows that passed the filter." ] }, { @@ -271,19 +151,16 @@ "metadata": {}, "outputs": [], "source": [ - "loaded_results = ResultList.from_yaml(yaml_str_b)\n", - "print(\"Results loaded:\")\n", - "print(f\" * {loaded_results.num_results()} unfiltered rows.\")\n", - "print(f\" * {len(loaded_results.get_filtered())} filtered rows.\")" + "print(results)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "### AstroPy Tables\n", + "Because we set ``results.track_filtered = True`` above, the ``Results`` object also keeps each row that was rejected by one of the filters. These rows are indexed by the filter name, allowing the user to determine which rows were removed during which filtering stage. \n", "\n", - "Users may want to interact with the results data in a more familiar Table format. We support exporting the ``ResultList`` as an astropy ``Table``. Note that the table format will not enforce consistency across columns. For example changing the psi and phi curves in a table will not update the likelihoods. For this reason, it is recommended that you only export the table when you have completed the per-row operations." + "We can use the ``get_filtered`` function to retrieve all the filtered rows for a given filter name:" ] }, { @@ -292,36 +169,18 @@ "metadata": {}, "outputs": [], "source": [ - "tbl = results.to_table()\n", - "tbl" + "# Extract the rows that did not pass filter1.\n", + "filtered = results.get_filtered(\"likelihood\")\n", + "print(filtered)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "Depending on what columns are set in the result table, we can visualize results directly. For example we can look at the co-added stamps for the first few results by accessing them directly from the `stamp` column." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "fig, axs = plt.subplots(1, 3)\n", + "We can apply multiple filters to the ``Results`` object to progressively rule out more and more candidate trajectories. We can even apply the same filter with different parameters.\n", "\n", - "for i in range(3):\n", - " stamp = tbl[\"stamp\"][i].reshape([21, 21])\n", - " axs[i].imshow(stamp, cmap=\"gray\")\n", - " axs[i].set_title(f\"Codded stamp {i}\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can use the AstroPy table to do filtering and then propagate the results back to the result list via the `index` column. For example if we want to filter on trajectories starting in a box of x=[100, 200] and y=[200,300] we would use a mask on the astropy table:" + "Next we filter out anything with fewer than 10 observations:" ] }, { @@ -330,21 +189,18 @@ "metadata": {}, "outputs": [], "source": [ - "mask = (\n", - " (tbl[\"trajectory_x\"] >= 100)\n", - " & (tbl[\"trajectory_x\"] <= 200)\n", - " & (tbl[\"trajectory_y\"] >= 200)\n", - " & (tbl[\"trajectory_x\"] <= 300)\n", - ")\n", - "tbl = tbl[mask]\n", - "tbl" + "# Filter out all results with fewer than 10 observations.\n", + "results.filter_rows(results[\"obs_count\"] >= 10, \"obscount=10\")\n", + "print(f\"{len(results)} results remaining.\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "We can then propagate the results back to the result list by using the `sync_table_indices()` function. This function will update *both* the `ResultList`'s entries and the table's index column so they are consistent. This is a short term work around until we rewrite `ResultList` as a full table itself." + "### Reverting filters\n", + "\n", + "As long as we have ``track_filtered`` turned on, we can undo any of the filtering steps. This appends the previously filtered results to the end of the list (and thus does not preserve ordering). However we can always re-sort if needed." ] }, { @@ -353,17 +209,9 @@ "metadata": {}, "outputs": [], "source": [ - "results.sync_table_indices(tbl)\n", - "for res in results.results:\n", - " print(res.trajectory)" + "results.revert_filter(\"likelihood\")\n", + "print(f\"{len(results)} results remaining.\")" ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { diff --git a/src/kbmod/analysis/plot_results.py b/src/kbmod/analysis/plot_results.py index 5bd97dd8b..4cb7cec72 100644 --- a/src/kbmod/analysis/plot_results.py +++ b/src/kbmod/analysis/plot_results.py @@ -117,72 +117,3 @@ def plot_time_series(values, times=None, axes=None, indices=None, title=None): if title is not None: axes.set_title(title) - - @staticmethod - def plot_result_row(row, times=None, title=None, fig=None): - """Plot a time series on the graph. - - Parameters - ---------- - row : ResultRow - The ResultRow to plot. - times : a list or np.array of floats - The array of the time stamps. If None then uses equally spaced points. - title : string - The title string to use. - fig : matplotlib.figure - The figure to use or None to create a new figure. - """ - if fig is None: - fig = plt.figure() - - # Create subfigures on the top and bottom. - (fig_top, fig_bot) = fig.subfigures(2, 1) - - # In the top subfigure plot the coadded stamp on the left and - # the light curve on the right. - (ax_stamp, ax_lc) = fig_top.subplots(1, 2) - if row.stamp is not None: - ResultsVisualizer.plot_single_stamp(row.stamp, axes=ax_stamp) - ax_stamp.set_title("Coadded Stamp") - else: - ax_stamp.text(0.5, 0.5, "No Stamp") - - if row.light_curve is not None: - ResultsVisualizer.plot_time_series( - row.light_curve, - times=times, - indices=row.valid_indices, - axes=ax_lc, - title="Lightcurve", - ) - else: - ax_lc.text(0.5, 0.5, "No Lightcurve") - - # If there are all_stamps, plot those. - if row.all_stamps is not None: - ResultsVisualizer.plot_stamps(row.all_stamps, fig=fig_bot, columns=5) - else: - ax = fig_bot.add_axes([0, 0, 1, 1]) - ax.text(0.5, 0.5, "No Individual Stamps") - - @staticmethod - def plot_starting_pixel_histogram(results, height, width): - """Plot a histogram of the starting pixels of each found trajectory. - - Parameters - ---------- - results : `ResultList` - The results to analyze. - height : `int` - The image height in pixels - width : `int` - The image width in pixels - """ - fig, ax = plt.subplots() - - x_vals = results.get_result_values("trajectory.x") - y_vals = results.get_result_values("trajectory.y") - _, _, _, img = ax.hist2d(x_vals, y_vals, bins=[height, width]) - fig.colorbar(img, ax=ax) - ax.set_title("Histogram of Starting Pixel") diff --git a/src/kbmod/filters/sigma_g_filter.py b/src/kbmod/filters/sigma_g_filter.py index 2895c15c7..78e046881 100644 --- a/src/kbmod/filters/sigma_g_filter.py +++ b/src/kbmod/filters/sigma_g_filter.py @@ -159,6 +159,7 @@ def compute_clipped_sigma_g_matrix(self, lh): index_valid = np.isfinite(lh) & (lh < upper_bnd) & (lh > lower_bnd) return index_valid + def apply_clipped_sigma_g(clipper, result_data): """This function applies a clipped median filter to the results of a KBMOD search using sigmaG as a robust estimater of standard deviation.