diff --git a/examples/adv/SWinvertWorkflow.ipynb b/examples/adv/SWinvertWorkflow.ipynb index d76027e..994b0a1 100644 --- a/examples/adv/SWinvertWorkflow.ipynb +++ b/examples/adv/SWinvertWorkflow.ipynb @@ -44,7 +44,7 @@ "\n", "### An important note\n", "\n", - "__This notebook is intended as a tool to expedite surface wave inversion, however it is of paramount importance that the user have some working knowledge of surface wave inversion to understand what they are doing. We strongly recommend that this notebook not be used as \"black-box\" software. At a minimum we recommend the user to read Vantassel and Cox (2020), citation above, to familarize themselves with the basics of surface wave inversion and the specific recommendations presented therein__.\n", + "__This notebook is intended as a tool to expedite surface wave inversion, however it is of paramount importance that the user have some working knowledge of surface wave inversion to understand what they are doing. We strongly recommend that this notebook not be used as \"black-box\" software. At a minimum we recommend the user to read Vantassel and Cox (2020), citation above, to familiarize themselves with the basics of surface wave inversion and the specific recommendations presented therein__.\n", "\n", "All the best \n", "\n", @@ -141,7 +141,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "e09ac312b344467b8d219e2fe1eb75fd", + "model_id": "f39367cee90647aaa24c79e859f82c04", "version_major": 2, "version_minor": 0 }, @@ -155,7 +155,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "7200498087bd4a7fb461714a48a399e1", + "model_id": "5ae65fa408cd41469c274a12e912957f", "version_major": 2, "version_minor": 0 }, @@ -169,7 +169,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "38d58d5535bb4a5f890bf87f85853bde", + "model_id": "3f86caaba43443ae9cbd20ec8a90ebd9", "version_major": 2, "version_minor": 0 }, @@ -211,10 +211,10 @@ "source": [ "### Resampling the Experimental Disperison\n", "\n", - "1. Select the domain in which you wish to resample. _wavelength is recommended._\n", - "2. Select whether you want to resample in log or linear space. _log is recommended._\n", - "3. Select the `minimum`, `maximum`, and `number` of points. _20-30 points are recommended._\n", - "4. Select the `tarname` and `version` of Geopsy used to define the output `.target` file.\n", + "1. Select the `domain` in which you wish to resample. _wavelength is recommended._\n", + "2. Select the `resample_type` either log or linear. _log is recommended._\n", + "3. Select the minimum (`pmin`), maximum (`pmax`), and number of points (`pn`) after resampling. Note that `pmin` and `pmax` are in terms of the selected `domain` (i.e., either frequency or wavelength). _20-30 points are recommended._\n", + "4. Select the `target_name` and `version` of Geopsy used to define the output `.target` file.\n", "5. Review the figure to ensure your data has been resampled correctly, then proceed to the next cell.\n", "\n", "[Back to top](#License-Information)" @@ -239,21 +239,21 @@ } ], "source": [ - "domain = 'wavelength' # 'frequency' or 'wavelength'\n", - "res_type = 'log' # 'log' or 'linear'\n", - "pmin = 2 # minimum value\n", - "pmax = 150 # maximum value\n", - "pn = 25 # number of samples\n", - "tarname = \"Tar5\" # Name of target file (without the .target suffix)\n", - "version = \"2\" # Major version of Geopsy \"2\" or \"3\"\n", + "domain = 'wavelength' # 'frequency' or 'wavelength'\n", + "resample_type = 'log' # 'log' or 'linear'\n", + "pmin = 2 # Minimum value after resampling in units of domain\n", + "pmax = 150 # Maximum value after resampling in units of domain\n", + "pn = 25 # Number of samples\n", + "target_name = \"Tar5\" # Name of target file (without the .target suffix)\n", + "version = \"2\" # Major version of Geopsy \"2\" or \"3\"\n", "\n", "# Resample\n", - "tar.resample(pmin=pmin, pmax=pmax, pn=pn, res_type=res_type, domain=domain, inplace=True)\n", + "tar.resample(pmin=pmin, pmax=pmax, pn=pn, res_type=resample_type, domain=domain, inplace=True)\n", "\n", "# Save to Disk\n", "if os.path.isdir(\"0_targets/\")==False:\n", " os.mkdir(\"0_targets/\")\n", - "tar.to_target(f\"0_targets/{tarname}\", version=version)\n", + "tar.to_target(f\"0_targets/{target_name}\", version=version)\n", "plotter(tar)" ] }, @@ -282,7 +282,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "4fae7146b157419c8581e85152991cf0", + "model_id": "17487627a71c4f959d7613be15b75663", "version_major": 2, "version_minor": 0 }, @@ -387,7 +387,7 @@ "\n", "There are two ways to run your inversion(s):\n", "\n", - "1. Locally using the `.target` and `.param` files which have been written in the previous sections. (Not Recommended)\n", + "1. Locally using the `.target` and `.param` files which have been written in the previous sections. (Not Recommended for reasons provided below)\n", "2. Remotely using the DesignSafe-CI application `SWbatch`. (Recommended)\n", "\n", "See the appropriate section below for instructions.\n", @@ -404,7 +404,7 @@ "_Note: Running locally is generally not recommended as the DesignSafe-CI application `SWbatch` has been specifically designed to integrate with the inputs generated by this notebook and generate the outputs expected from this notebook. However, as some will undoubtedly still want to run their inversion's locally instructions are provided below._\n", "\n", "1. Load the `.target` and `.param` files into Dinver. The `.target` and `.param` files are located in the `0_targets` and `1_parameters` directories created by this notebook.\n", - "2. Setup the inversion's tuning parameters. Full details are provided in Vantassel and Cox (2020, however for completeness a brief summary is provided here. Number of independnety runs (i.e., Ntrial) should be greater than 3, It*Ns > 50,000 (e.g., It=200, Ns=250), Nr ~= 100, Ns0>Nr (e.g., Ns0=10000).\n", + "2. Setup the inversion's tuning parameters. Full details are provided in Vantassel and Cox (2020, however for completeness a brief summary is provided here. Number of independent runs (i.e., Ntrial) should be greater than 3, It*Ns > 50,000 (e.g., It=200, Ns=250), Nr ~= 100, Ns0>Nr (e.g., Ns0=10000).\n", "4. After completing your inversions export the desired number of ground models and dispersion curves to text format, using the Geopsy command line interface. Refer to the provided sample outputs in the `3_text` directory for the naming conventions assumed by this notebook.\n" ] }, @@ -419,7 +419,7 @@ "1. Read through the cell below and select your inversion tuning parameters.\n", "2. When done, run the cell and inspect the output.\n", "3. If there is an issue edit the cell and run it again.\n", - "4. Finally, run the following cell to launch your inverison on Stampede2. To monitor the progress of your inversion go to `Research Workbench>Workspace>Job Status`.\n" + "4. Finally, run the following cell to launch your inversion on Stampede2. To monitor the progress of your inversion go to `Research Workbench>Workspace>Job Status`.\n" ] }, { @@ -440,7 +440,7 @@ "It = \"20\" # Number of iterations, a minimum of 200 is recommended.\n", "Ns = \"25\" # Number of samples per iteration, a minimum of 250 is recommended.\n", "Nr = \"100\" # Number of models to consider when resampling, 100 is recommended.\n", - "Ns0 = \"100\" # Number of intial samples, any value greater than Nr is recommended.\n", + "Ns0 = \"100\" # Number of initial samples, any value greater than Nr is recommended.\n", "\n", "# Outputs\n", "nprofile = \"3\" # Number of ground models and dispersion curves to export\n", @@ -503,6 +503,12 @@ "source": [ "### Importing the Data\n", "\n", + "In order for the data to import correctly you must provide a relative or full path to the `3_text` directory.\n", + "\n", + "1. For those running this as a tutorial, no changes are necessary here.\n", + "2. For those running this locally, it is recommended you follow the same directory structure provided in the example, and therefore no changes are necessary.\n", + "3. For those running this remotely on DesignSafe-CI, you will need to replace the `full_path` variable in the cell below with the full path to the `3_text` directory containing your results. For your convenience, an incomplete `full_path` variable is provided below and commented out. To complete the path you will need to replace `` with the actual path. The easiest way to find the full path to your data is by using the Job Status viewer by selecting `Research Workbench>Job Status>Your Desired Job>View` which will bring you to your job results. Alternatively, you can move the `3_text` directory form the job archive into the current directory, in which no changes to `full_path` are necessary.\n", + "\n", "[Back to top](#License-Information)" ] }, @@ -513,7 +519,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -530,7 +536,9 @@ "nlove = 0 # Number of love modes, may use \"all\"\n", "ngm = 1 # Number of ground models, may use \"all\"\n", "\n", - "fnames = glob.glob('3_text/*_DC.txt')\n", + "full_path = \"./3_text/\"\n", + "# full_path = \"/home/jupyter/MyData/archive//3_text/\"\n", + "fnames = glob.glob(full_path + \"*_DC.txt\")\n", "fnames.sort(key=lambda x: int(re.findall(r\".*[\\\\/].*_.*[LF][TRN][IL]?(\\d+)_Tr\\d+_DC.txt$\",x)[0]))\n", "\n", "dcs, gms = {}, {}\n", @@ -559,12 +567,13 @@ " dcs[partype][parnumber].update({seed:swprepost.DispersionSuite.from_geopsy(fname=fname, nsets=ndc, \n", " nrayleigh=nrayleigh, nlove=nlove)})\n", " gms[partype][parnumber].update({seed:swprepost.GroundModelSuite.from_geopsy(fname=fname[:-6]+\"GM.txt\", nmodels=ngm)})\n", - "\n", " \n", "ncols = len(list(dcs.keys()))\n", "fig, axs = plt.subplots(nrows=1, ncols=ncols, sharey=True, figsize=(3*ncols,3), dpi=150)\n", "axs = [axs] if type(axs) != np.ndarray else axs\n", "bestseed = {}\n", + "blabel = \"Each Trial\"\n", + "fiter = True\n", "for ax, partype in zip(axs, dcs):\n", " bestseed.update({partype:{}})\n", " for parnumber in dcs[partype]:\n", @@ -572,9 +581,12 @@ " for seed in dcs[partype][parnumber].keys():\n", " seeds.append(seed)\n", " misfits.append(dcs[partype][parnumber][seed].misfits[0])\n", - " ax.plot(parnumber, misfits[-1], 'bo')\n", + " ax.plot(parnumber, misfits[-1], 'bo', label=blabel, alpha=0.2)\n", + " blabel = None\n", " bestseed[partype].update({parnumber:seeds[misfits.index(min(misfits))]})\n", - " ax.plot(parnumber, min(misfits),'ro')\n", + " if fiter:\n", + " fiter = False\n", + " ax.legend()\n", " ax.set_title(\"Parameterization Type: \"+partype)\n", "axs[0].set_ylabel(\"Dispersion Misfit, \"+\"$m_{dc}$\")\n", "plt.show()" @@ -586,6 +598,8 @@ "source": [ "### General Settings\n", "\n", + "_Note: If you are considering more than six parameterizations, you must provide additional colors in the list below._\n", + "\n", "[Back to top](#License-Information)" ] },