From 0e34de967abba3342006a0274928a04f39281370 Mon Sep 17 00:00:00 2001 From: Michael Zingale Date: Sun, 1 Sep 2024 18:29:44 -0400 Subject: [PATCH] disable verbosity by default when directly using Pyro() (#231) Now in a notebook, we don't get the "pyro..." message or the message from the initial conditions. This can still be overridden by passing a verbosity into Pyro() via the parameters dictionary. --- examples/examples.ipynb | 210 ++++++------------ pyro/advection/problems/smooth.py | 4 +- pyro/advection/problems/tophat.py | 4 +- pyro/advection_nonuniform/problems/slotted.py | 4 +- pyro/burgers/problems/converge.py | 4 +- pyro/burgers/problems/test.py | 4 +- pyro/burgers/problems/tophat.py | 4 +- pyro/compressible/problems/acoustic_pulse.py | 3 +- pyro/compressible/problems/advect.py | 3 +- pyro/compressible/problems/bubble.py | 3 +- pyro/compressible/problems/gresho.py | 3 +- pyro/compressible/problems/hse.py | 3 +- pyro/compressible/problems/kh.py | 3 +- pyro/compressible/problems/logo.py | 3 +- pyro/compressible/problems/quad.py | 3 +- pyro/compressible/problems/ramp.py | 3 +- pyro/compressible/problems/rt.py | 3 +- pyro/compressible/problems/rt2.py | 4 +- pyro/compressible/problems/sedov.py | 3 +- pyro/compressible/problems/sod.py | 3 +- pyro/diffusion/problems/gaussian.py | 3 +- pyro/incompressible/problems/converge.py | 4 +- pyro/incompressible/problems/shear.py | 3 +- .../incompressible_viscous/problems/cavity.py | 4 +- .../problems/converge.py | 4 +- pyro/incompressible_viscous/problems/shear.py | 3 +- pyro/lm_atm/problems/bubble.py | 3 +- pyro/lm_atm/problems/gresho.py | 3 +- pyro/pyro_sim.py | 10 +- pyro/swe/problems/acoustic_pulse.py | 3 +- pyro/swe/problems/advect.py | 3 +- pyro/swe/problems/dam.py | 3 +- pyro/swe/problems/kh.py | 3 +- pyro/swe/problems/logo.py | 4 +- pyro/swe/problems/quad.py | 3 +- 35 files changed, 146 insertions(+), 184 deletions(-) diff --git a/examples/examples.ipynb b/examples/examples.ipynb index dbda17221..55d525bb7 100644 --- a/examples/examples.ipynb +++ b/examples/examples.ipynb @@ -47,16 +47,7 @@ "cell_type": "code", "execution_count": 3, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\u001b[1mpyro ...\u001b[0m\n", - "\u001b[1minitializing the smooth advection problem...\u001b[0m\n" - ] - } - ], + "outputs": [], "source": [ "pyro_sim = Pyro(solver)\n", "pyro_sim.initialize_problem(problem_name, param_file)" @@ -94,7 +85,7 @@ "driver.max_dt_change = 1e+33\n", "driver.max_steps = 500\n", "driver.tmax = 1.0\n", - "driver.verbose = 1.0\n", + "driver.verbose = 0\n", "io.basename = smooth_\n", "io.do_io = 1\n", "io.dt_out = 0.2\n", @@ -141,9 +132,9 @@ "outputs": [ { "data": { - "image/png": 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", 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", "text/plain": [ - "
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" ] }, "metadata": {}, @@ -152,7 +143,7 @@ { "data": { "text/plain": [ - "
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" ] }, "metadata": {}, @@ -178,15 +169,7 @@ "nbval-ignore-output" ] }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " 1 0.02500 0.02500\n" - ] - } - ], + "outputs": [], "source": [ "pyro_sim.single_step()" ] @@ -199,15 +182,7 @@ "nbval-ignore-output" ] }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " 2 0.05000 0.02500\n" - ] - } - ], + "outputs": [], "source": [ "pyro_sim.single_step()" ] @@ -223,51 +198,14 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 9, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\u001b[1minitializing the smooth advection problem...\u001b[0m\n", - " 1 0.10000 0.10000\n", - " 2 0.20000 0.10000\n", - "\u001b[33moutputting...\u001b[0m\n", - " 3 0.30000 0.10000\n", - " 4 0.40000 0.10000\n", - "\u001b[33moutputting...\u001b[0m\n", - " 5 0.50000 0.10000\n", - " 6 0.60000 0.10000\n", - " 7 0.70000 0.10000\n", - "\u001b[33moutputting...\u001b[0m\n", - " 8 0.80000 0.10000\n", - " 9 0.90000 0.10000\n", - "\u001b[33moutputting...\u001b[0m\n", - " 10 1.00000 0.10000\n", - " 11 1.00000 0.00000\n", - "\u001b[33moutputting...\u001b[0m\n", - "\u001b[33moutputting...\u001b[0m\n", - "\u001b[33mparameter vis.store_images never used\u001b[0m\n", - "main: 0.02791619300842285\n" - ] - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# NBVAL_IGNORE_OUTPUT \n", "extra_parameters = {'vis.dovis': False, 'mesh.nx': 8, 'mesh.ny':8, 'particles.do_particles': False}\n", "pyro_sim.initialize_problem(problem_name, param_file, inputs_dict=extra_parameters)\n", - "pyro_sim.run_sim()" + "pyro_sim.run_sim;" ] }, { @@ -279,7 +217,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 10, "metadata": { "tags": [ "nbval-ignore-output" @@ -288,9 +226,9 @@ "outputs": [ { "data": { - "image/png": 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", + "image/png": 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", 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" 0.99836213 1.00032876 1.0019269 1.00172162 1.00034788 1.00025512\n", - " 1.00085108 0.99958517 0.99836213 1.00032876]\n", - " [1.0034205 1.00146403 1.00015378 1.00085108 1.00036435 0.99369049\n", - " 0.99065988 1.00056788 1.0034205 1.00146403 1.00015378 1.00085108\n", - " 1.00036435 0.99369049 0.99065988 1.00056788]\n", - " [1.03779893 1.00815036 1.00017897 0.99958517 0.99369049 0.97697494\n", - " 0.9889869 1.02461354 1.03779893 1.00815036 1.00017897 0.99958517\n", - " 0.99369049 0.97697494 0.9889869 1.02461354]\n", - " [1.19356852 1.05467319 1.00141073 0.99836213 0.99065988 0.9889869\n", - " 1.09690099 1.19801589 1.19356852 1.05467319 1.00141073 0.99836213\n", - " 0.99065988 0.9889869 1.09690099 1.19801589]\n", - " [1.36368295 1.14089365 1.01213761 1.00032876 1.00056788 1.02461354\n", - " 1.19801589 1.36305079 1.36368295 1.14089365 1.01213761 1.00032876\n", - " 1.00056788 1.02461354 1.19801589 1.36305079]\n", - " [1.36456254 1.1670318 1.01964861 1.0019269 1.0034205 1.03779893\n", - " 1.19356852 1.36368295 1.36456254 1.1670318 1.01964861 1.0019269\n", - " 1.0034205 1.03779893 1.19356852 1.36368295]\n", - " [1.1670318 1.1010689 1.01794261 1.00172162 1.00146403 1.00815036\n", - " 1.05467319 1.14089365 1.1670318 1.1010689 1.01794261 1.00172162\n", - " 1.00146403 1.00815036 1.05467319 1.14089365]\n", - " [1.01964861 1.01794261 1.00386665 1.00034788 1.00015378 1.00017897\n", - " 1.00141073 1.01213761 1.01964861 1.01794261 1.00386665 1.00034788\n", - " 1.00015378 1.00017897 1.00141073 1.01213761]\n", - " [1.0019269 1.00172162 1.00034788 1.00025512 1.00085108 0.99958517\n", - " 0.99836213 1.00032876 1.0019269 1.00172162 1.00034788 1.00025512\n", - " 1.00085108 0.99958517 0.99836213 1.00032876]\n", - " [1.0034205 1.00146403 1.00015378 1.00085108 1.00036435 0.99369049\n", - " 0.99065988 1.00056788 1.0034205 1.00146403 1.00015378 1.00085108\n", - " 1.00036435 0.99369049 0.99065988 1.00056788]\n", - " [1.03779893 1.00815036 1.00017897 0.99958517 0.99369049 0.97697494\n", - " 0.9889869 1.02461354 1.03779893 1.00815036 1.00017897 0.99958517\n", - " 0.99369049 0.97697494 0.9889869 1.02461354]\n", - " [1.19356852 1.05467319 1.00141073 0.99836213 0.99065988 0.9889869\n", - " 1.09690099 1.19801589 1.19356852 1.05467319 1.00141073 0.99836213\n", - " 0.99065988 0.9889869 1.09690099 1.19801589]\n", - " [1.36368295 1.14089365 1.01213761 1.00032876 1.00056788 1.02461354\n", - " 1.19801589 1.36305079 1.36368295 1.14089365 1.01213761 1.00032876\n", - " 1.00056788 1.02461354 1.19801589 1.36305079]]\n" + "[[1. 1. 1. 1. 1. 1.\n", + " 1. 1. 1. 1. 1. 1.\n", + " 1. 1. 1. 1. ]\n", + " [1. 1. 1. 1. 1. 1.\n", + " 1. 1. 1. 1. 1. 1.\n", + " 1. 1. 1. 1. ]\n", + " [1. 1. 1. 1. 1. 1.\n", + " 1. 1. 1. 1. 1. 1.\n", + " 1. 1. 1. 1. ]\n", + " [1. 1. 1. 1. 1. 1.\n", + " 1. 1. 1. 1. 1. 1.\n", + " 1. 1. 1. 1. ]\n", + " [1. 1. 1. 1. 1. 1.00000003\n", + " 1.00000125 1.00000814 1.00000814 1.00000125 1.00000003 1.\n", + " 1. 1. 1. 1. ]\n", + " [1. 1. 1. 1. 1.00000003 1.00000814\n", + " 1.00034611 1.00225693 1.00225693 1.00034611 1.00000814 1.00000003\n", + " 1. 1. 1. 1. ]\n", + " [1. 1. 1. 1. 1.00000125 1.00034611\n", + " 1.01471703 1.09596709 1.09596709 1.01471703 1.00034611 1.00000125\n", + " 1. 1. 1. 1. ]\n", + " [1. 1. 1. 1. 1.00000814 1.00225693\n", + " 1.09596709 1.62578401 1.62578401 1.09596709 1.00225693 1.00000814\n", + " 1. 1. 1. 1. ]\n", + " [1. 1. 1. 1. 1.00000814 1.00225693\n", + " 1.09596709 1.62578401 1.62578401 1.09596709 1.00225693 1.00000814\n", + " 1. 1. 1. 1. ]\n", + " [1. 1. 1. 1. 1.00000125 1.00034611\n", + " 1.01471703 1.09596709 1.09596709 1.01471703 1.00034611 1.00000125\n", + " 1. 1. 1. 1. ]\n", + " [1. 1. 1. 1. 1.00000003 1.00000814\n", + " 1.00034611 1.00225693 1.00225693 1.00034611 1.00000814 1.00000003\n", + " 1. 1. 1. 1. ]\n", + " [1. 1. 1. 1. 1. 1.00000003\n", + " 1.00000125 1.00000814 1.00000814 1.00000125 1.00000003 1.\n", + " 1. 1. 1. 1. ]\n", + " [1. 1. 1. 1. 1. 1.\n", + " 1. 1. 1. 1. 1. 1.\n", + " 1. 1. 1. 1. ]\n", + " [1. 1. 1. 1. 1. 1.\n", + " 1. 1. 1. 1. 1. 1.\n", + " 1. 1. 1. 1. ]\n", + " [1. 1. 1. 1. 1. 1.\n", + " 1. 1. 1. 1. 1. 1.\n", + " 1. 1. 1. 1. ]\n", + " [1. 1. 1. 1. 1. 1.\n", + " 1. 1. 1. 1. 1. 1.\n", + " 1. 1. 1. 1. ]]\n" ] } ], @@ -402,21 +340,21 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - " 1.0009 0.99959 0.99836 1.0003 1.0019 1.0017 1.0003 1.0003 \n", - " 1.0002 1.0002 1.0014 1.0121 1.0196 1.0179 1.0039 1.0003 \n", - " 1.0015 1.0082 1.0547 1.1409 1.167 1.1011 1.0179 1.0017 \n", - " 1.0034 1.0378 1.1936 1.3637 1.3646 1.167 1.0196 1.0019 \n", - " 1.0006 1.0246 1.198 1.3631 1.3637 1.1409 1.0121 1.0003 \n", - " 0.99066 0.98899 1.0969 1.198 1.1936 1.0547 1.0014 0.99836 \n", - " 0.99369 0.97697 0.98899 1.0246 1.0378 1.0082 1.0002 0.99959 \n", - " 1.0004 0.99369 0.99066 1.0006 1.0034 1.0015 1.0002 1.0009 \n", + " 1 1 1 1 1 1 1 1 \n", + " 1 1 1.0003 1.0023 1.0023 1.0003 1 1 \n", + " 1 1.0003 1.0147 1.096 1.096 1.0147 1.0003 1 \n", + " 1 1.0023 1.096 1.6258 1.6258 1.096 1.0023 1 \n", + " 1 1.0023 1.096 1.6258 1.6258 1.096 1.0023 1 \n", + " 1 1.0003 1.0147 1.096 1.096 1.0147 1.0003 1 \n", + " 1 1 1.0003 1.0023 1.0023 1.0003 1 1 \n", + " 1 1 1 1 1 1 1 1 \n", "\n", " ^ y\n", " |\n", @@ -446,7 +384,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.12.4" + "version": "3.12.5" } }, "nbformat": 4, diff --git a/pyro/advection/problems/smooth.py b/pyro/advection/problems/smooth.py index 1adc8acda..75dd33e0b 100644 --- a/pyro/advection/problems/smooth.py +++ b/pyro/advection/problems/smooth.py @@ -10,9 +10,9 @@ def init_data(my_data, rp): """ initialize the smooth advection problem """ - del rp # this problem doesn't use runtime params - msg.bold("initializing the smooth advection problem...") + if rp.get_param("driver.verbose"): + msg.bold("initializing the smooth advection problem...") # make sure that we are passed a valid patch object if not isinstance(my_data, patch.CellCenterData2d): diff --git a/pyro/advection/problems/tophat.py b/pyro/advection/problems/tophat.py index 79e8ca747..d89a1c725 100644 --- a/pyro/advection/problems/tophat.py +++ b/pyro/advection/problems/tophat.py @@ -8,9 +8,9 @@ def init_data(myd, rp): """ initialize the tophat advection problem """ - del rp # this problem doesn't use runtime params - msg.bold("initializing the tophat advection problem...") + if rp.get_param("driver.verbose"): + msg.bold("initializing the tophat advection problem...") # make sure that we are passed a valid patch object if not isinstance(myd, patch.CellCenterData2d): diff --git a/pyro/advection_nonuniform/problems/slotted.py b/pyro/advection_nonuniform/problems/slotted.py index 6c38d978f..f148d19b5 100755 --- a/pyro/advection_nonuniform/problems/slotted.py +++ b/pyro/advection_nonuniform/problems/slotted.py @@ -8,7 +8,9 @@ def init_data(my_data, rp): """ initialize the slotted advection problem """ - msg.bold("initializing the slotted advection problem...") + + if rp.get_param("driver.verbose"): + msg.bold("initializing the slotted advection problem...") # make sure that we are passed a valid patch object if not isinstance(my_data, patch.CellCenterData2d): diff --git a/pyro/burgers/problems/converge.py b/pyro/burgers/problems/converge.py index 6644c800f..eae07e997 100644 --- a/pyro/burgers/problems/converge.py +++ b/pyro/burgers/problems/converge.py @@ -10,9 +10,9 @@ def init_data(my_data, rp): """ initialize the smooth burgers convergence problem """ - del rp # this problem doesn't use runtime params - msg.bold("initializing the smooth burgers convergence problem...") + if rp.get_param("driver.verbose"): + msg.bold("initializing the smooth burgers convergence problem...") # make sure that we are passed a valid patch object if not isinstance(my_data, patch.CellCenterData2d): diff --git a/pyro/burgers/problems/test.py b/pyro/burgers/problems/test.py index 560b99972..bec70d88e 100644 --- a/pyro/burgers/problems/test.py +++ b/pyro/burgers/problems/test.py @@ -8,9 +8,9 @@ def init_data(myd, rp): """ initialize the burgers test problem """ - del rp # this problem doesn't use runtime params - msg.bold("initializing the burgers test problem...") + if rp.get_param("driver.verbose"): + msg.bold("initializing the burgers test problem...") # make sure that we are passed a valid patch object if not isinstance(myd, patch.CellCenterData2d): diff --git a/pyro/burgers/problems/tophat.py b/pyro/burgers/problems/tophat.py index 111d61f0f..d3371e17a 100644 --- a/pyro/burgers/problems/tophat.py +++ b/pyro/burgers/problems/tophat.py @@ -8,9 +8,9 @@ def init_data(myd, rp): """ initialize the tophat burgers problem """ - del rp # this problem doesn't use runtime params - msg.bold("initializing the tophat burgers problem...") + if rp.get_param("driver.verbose"): + msg.bold("initializing the tophat burgers problem...") # make sure that we are passed a valid patch object if not isinstance(myd, patch.CellCenterData2d): diff --git a/pyro/compressible/problems/acoustic_pulse.py b/pyro/compressible/problems/acoustic_pulse.py index 253688f56..fb818473a 100644 --- a/pyro/compressible/problems/acoustic_pulse.py +++ b/pyro/compressible/problems/acoustic_pulse.py @@ -12,7 +12,8 @@ def init_data(myd, rp): """initialize the acoustic_pulse problem. This comes from McCourquodale & Coella 2011""" - msg.bold("initializing the acoustic pulse problem...") + if rp.get_param("driver.verbose"): + msg.bold("initializing the acoustic pulse problem...") # make sure that we are passed a valid patch object if not isinstance(myd, (patch.CellCenterData2d, fv.FV2d)): diff --git a/pyro/compressible/problems/advect.py b/pyro/compressible/problems/advect.py index 88c65eece..0e358d95c 100644 --- a/pyro/compressible/problems/advect.py +++ b/pyro/compressible/problems/advect.py @@ -11,7 +11,8 @@ def init_data(my_data, rp): """ initialize a smooth advection problem for testing convergence """ - msg.bold("initializing the advect problem...") + if rp.get_param("driver.verbose"): + msg.bold("initializing the advect problem...") # make sure that we are passed a valid patch object if not isinstance(my_data, patch.CellCenterData2d): diff --git a/pyro/compressible/problems/bubble.py b/pyro/compressible/problems/bubble.py index 43d694b4d..ef78486d9 100644 --- a/pyro/compressible/problems/bubble.py +++ b/pyro/compressible/problems/bubble.py @@ -11,7 +11,8 @@ def init_data(my_data, rp): """ initialize the bubble problem """ - msg.bold("initializing the bubble problem...") + if rp.get_param("driver.verbose"): + msg.bold("initializing the bubble problem...") # make sure that we are passed a valid patch object if not isinstance(my_data, patch.CellCenterData2d): diff --git a/pyro/compressible/problems/gresho.py b/pyro/compressible/problems/gresho.py index a98dd36cc..9d2acb54a 100644 --- a/pyro/compressible/problems/gresho.py +++ b/pyro/compressible/problems/gresho.py @@ -11,7 +11,8 @@ def init_data(my_data, rp): """ initialize the Gresho vortex problem """ - msg.bold("initializing the Gresho vortex problem...") + if rp.get_param("driver.verbose"): + msg.bold("initializing the Gresho vortex problem...") # make sure that we are passed a valid patch object if not isinstance(my_data, patch.CellCenterData2d): diff --git a/pyro/compressible/problems/hse.py b/pyro/compressible/problems/hse.py index 6853f2b4a..a654a7953 100644 --- a/pyro/compressible/problems/hse.py +++ b/pyro/compressible/problems/hse.py @@ -11,7 +11,8 @@ def init_data(my_data, rp): """ initialize the HSE problem """ - msg.bold("initializing the HSE problem...") + if rp.get_param("driver.verbose"): + msg.bold("initializing the HSE problem...") # make sure that we are passed a valid patch object if not isinstance(my_data, patch.CellCenterData2d): diff --git a/pyro/compressible/problems/kh.py b/pyro/compressible/problems/kh.py index b3a0e29d8..775914a8e 100644 --- a/pyro/compressible/problems/kh.py +++ b/pyro/compressible/problems/kh.py @@ -9,7 +9,8 @@ def init_data(my_data, rp): """ initialize the Kelvin-Helmholtz problem """ - msg.bold("initializing the Kelvin-Helmholtz problem...") + if rp.get_param("driver.verbose"): + msg.bold("initializing the Kelvin-Helmholtz problem...") # make sure that we are passed a valid patch object if not isinstance(my_data, patch.CellCenterData2d): diff --git a/pyro/compressible/problems/logo.py b/pyro/compressible/problems/logo.py index 78b20ed76..85c3894f4 100644 --- a/pyro/compressible/problems/logo.py +++ b/pyro/compressible/problems/logo.py @@ -12,7 +12,8 @@ def init_data(my_data, rp): """ initialize the logo problem """ - msg.bold("initializing the logo problem...") + if rp.get_param("driver.verbose"): + msg.bold("initializing the logo problem...") # make sure that we are passed a valid patch object if not isinstance(my_data, patch.CellCenterData2d): diff --git a/pyro/compressible/problems/quad.py b/pyro/compressible/problems/quad.py index 5a3e5b405..28b08f188 100644 --- a/pyro/compressible/problems/quad.py +++ b/pyro/compressible/problems/quad.py @@ -11,7 +11,8 @@ def init_data(my_data, rp): """ initialize the quadrant problem """ - msg.bold("initializing the quadrant problem...") + if rp.get_param("driver.verbose"): + msg.bold("initializing the quadrant problem...") # make sure that we are passed a valid patch object if not isinstance(my_data, patch.CellCenterData2d): diff --git a/pyro/compressible/problems/ramp.py b/pyro/compressible/problems/ramp.py index dea8925f7..dbc84c8fe 100644 --- a/pyro/compressible/problems/ramp.py +++ b/pyro/compressible/problems/ramp.py @@ -12,7 +12,8 @@ def init_data(my_data, rp): """ initialize the double Mach reflection problem """ - msg.bold("initializing the double Mach reflection problem...") + if rp.get_param("driver.verbose"): + msg.bold("initializing the double Mach reflection problem...") # make sure that we are passed a valid patch object if not isinstance(my_data, patch.CellCenterData2d): diff --git a/pyro/compressible/problems/rt.py b/pyro/compressible/problems/rt.py index 2dda4085e..b1b11516f 100644 --- a/pyro/compressible/problems/rt.py +++ b/pyro/compressible/problems/rt.py @@ -11,7 +11,8 @@ def init_data(my_data, rp): """ initialize the rt problem """ - msg.bold("initializing the rt problem...") + if rp.get_param("driver.verbose"): + msg.bold("initializing the rt problem...") # make sure that we are passed a valid patch object if not isinstance(my_data, patch.CellCenterData2d): diff --git a/pyro/compressible/problems/rt2.py b/pyro/compressible/problems/rt2.py index 22e526913..a6947b5ba 100644 --- a/pyro/compressible/problems/rt2.py +++ b/pyro/compressible/problems/rt2.py @@ -15,10 +15,10 @@ def init_data(my_data, rp): - """ initialize the rt problem """ - msg.bold("initializing the rt problem...") + if rp.get_param("driver.verbose"): + msg.bold("initializing the rt problem...") # make sure that we are passed a valid patch object if not isinstance(my_data, patch.CellCenterData2d): diff --git a/pyro/compressible/problems/sedov.py b/pyro/compressible/problems/sedov.py index 352754bea..19d689678 100644 --- a/pyro/compressible/problems/sedov.py +++ b/pyro/compressible/problems/sedov.py @@ -12,7 +12,8 @@ def init_data(my_data, rp): """ initialize the sedov problem """ - msg.bold("initializing the sedov problem...") + if rp.get_param("driver.verbose"): + msg.bold("initializing the sedov problem...") # make sure that we are passed a valid patch object if not isinstance(my_data, patch.CellCenterData2d): diff --git a/pyro/compressible/problems/sod.py b/pyro/compressible/problems/sod.py index faef05653..c6545b782 100644 --- a/pyro/compressible/problems/sod.py +++ b/pyro/compressible/problems/sod.py @@ -9,7 +9,8 @@ def init_data(my_data, rp): """ initialize the sod problem """ - msg.bold("initializing the sod problem...") + if rp.get_param("driver.verbose"): + msg.bold("initializing the sod problem...") # make sure that we are passed a valid patch object if not isinstance(my_data, patch.CellCenterData2d): diff --git a/pyro/diffusion/problems/gaussian.py b/pyro/diffusion/problems/gaussian.py index e0c5e37d1..fea7eff32 100644 --- a/pyro/diffusion/problems/gaussian.py +++ b/pyro/diffusion/problems/gaussian.py @@ -18,7 +18,8 @@ def phi_analytic(dist, t, t_0, k, phi_1, phi_2): def init_data(my_data, rp): """ initialize the Gaussian diffusion problem """ - msg.bold("initializing the Gaussian diffusion problem...") + if rp.get_param("driver.verbose"): + msg.bold("initializing the Gaussian diffusion problem...") # make sure that we are passed a valid patch object if not isinstance(my_data, patch.CellCenterData2d): diff --git a/pyro/incompressible/problems/converge.py b/pyro/incompressible/problems/converge.py index 0bd93af8e..23f4b8b8d 100644 --- a/pyro/incompressible/problems/converge.py +++ b/pyro/incompressible/problems/converge.py @@ -36,9 +36,9 @@ def init_data(my_data, rp): """ initialize the incompressible converge problem """ - del rp # this problem doesn't use runtime params - msg.bold("initializing the incompressible converge problem...") + if rp.get_param("driver.verbose"): + msg.bold("initializing the incompressible converge problem...") # make sure that we are passed a valid patch object if not isinstance(my_data, patch.CellCenterData2d): diff --git a/pyro/incompressible/problems/shear.py b/pyro/incompressible/problems/shear.py index 828d42b03..95eebc685 100644 --- a/pyro/incompressible/problems/shear.py +++ b/pyro/incompressible/problems/shear.py @@ -27,7 +27,8 @@ def init_data(my_data, rp): """ initialize the incompressible shear problem """ - msg.bold("initializing the incompressible shear problem...") + if rp.get_param("driver.verbose"): + msg.bold("initializing the incompressible shear problem...") # make sure that we are passed a valid patch object if not isinstance(my_data, patch.CellCenterData2d): diff --git a/pyro/incompressible_viscous/problems/cavity.py b/pyro/incompressible_viscous/problems/cavity.py index 77e8a0dbe..b8b6b6d8a 100644 --- a/pyro/incompressible_viscous/problems/cavity.py +++ b/pyro/incompressible_viscous/problems/cavity.py @@ -18,9 +18,9 @@ def init_data(my_data, rp): """ initialize the lid-driven cavity """ - del rp # this problem doesn't use runtime params - msg.bold("initializing the lid-driven cavity problem...") + if rp.get_param("driver.verbose"): + msg.bold("initializing the lid-driven cavity problem...") # make sure that we are passed a valid patch object if not isinstance(my_data, patch.CellCenterData2d): diff --git a/pyro/incompressible_viscous/problems/converge.py b/pyro/incompressible_viscous/problems/converge.py index b19b05728..8f1aab3f2 100644 --- a/pyro/incompressible_viscous/problems/converge.py +++ b/pyro/incompressible_viscous/problems/converge.py @@ -35,9 +35,9 @@ def init_data(my_data, rp): """ initialize the incompressible viscous converge problem """ - del rp # this problem doesn't use runtime params - msg.bold("initializing the incompressible viscous converge problem...") + if rp.get_param("driver.verbose"): + msg.bold("initializing the incompressible viscous converge problem...") # make sure that we are passed a valid patch object if not isinstance(my_data, patch.CellCenterData2d): diff --git a/pyro/incompressible_viscous/problems/shear.py b/pyro/incompressible_viscous/problems/shear.py index 828d42b03..95eebc685 100644 --- a/pyro/incompressible_viscous/problems/shear.py +++ b/pyro/incompressible_viscous/problems/shear.py @@ -27,7 +27,8 @@ def init_data(my_data, rp): """ initialize the incompressible shear problem """ - msg.bold("initializing the incompressible shear problem...") + if rp.get_param("driver.verbose"): + msg.bold("initializing the incompressible shear problem...") # make sure that we are passed a valid patch object if not isinstance(my_data, patch.CellCenterData2d): diff --git a/pyro/lm_atm/problems/bubble.py b/pyro/lm_atm/problems/bubble.py index c8317181b..91502fdf4 100644 --- a/pyro/lm_atm/problems/bubble.py +++ b/pyro/lm_atm/problems/bubble.py @@ -11,7 +11,8 @@ def init_data(my_data, base, rp): """ initialize the bubble problem """ - msg.bold("initializing the bubble problem...") + if rp.get_param("driver.verbose"): + msg.bold("initializing the bubble problem...") # make sure that we are passed a valid patch object if not isinstance(my_data, patch.CellCenterData2d): diff --git a/pyro/lm_atm/problems/gresho.py b/pyro/lm_atm/problems/gresho.py index 5ad74d97c..5cfef1faf 100644 --- a/pyro/lm_atm/problems/gresho.py +++ b/pyro/lm_atm/problems/gresho.py @@ -11,7 +11,8 @@ def init_data(my_data, base, rp): """ initialize the Gresho vortex problem """ - msg.bold("initializing the Gresho vortex problem...") + if rp.get_param("driver.verbose"): + msg.bold("initializing the Gresho vortex problem...") # make sure that we are passed a valid patch object if not isinstance(my_data, patch.CellCenterData2d): diff --git a/pyro/pyro_sim.py b/pyro/pyro_sim.py index 14e396f68..8a9fda411 100755 --- a/pyro/pyro_sim.py +++ b/pyro/pyro_sim.py @@ -49,7 +49,8 @@ def __init__(self, solver_name, from_commandline=False): runtime vis by default. """ - msg.bold('pyro ...') + if from_commandline: + msg.bold('pyro ...') if solver_name not in valid_solvers: msg.fail(f"ERROR: {solver_name} is not a valid solver") @@ -118,12 +119,11 @@ def initialize_problem(self, problem_name, inputs_file=None, inputs_dict=None, self.rp.load_params(inputs_file, no_new=1) - # manually override the dovis default + # manually override the dovis and verbose defaults # for Jupyter, we want runtime vis disabled by default - if self.from_commandline: - self.rp.set_param("vis.dovis", 1) - else: + if not self.from_commandline: self.rp.set_param("vis.dovis", 0) + self.rp.set_param("driver.verbose", 0) if inputs_dict is not None: for k, v in inputs_dict.items(): diff --git a/pyro/swe/problems/acoustic_pulse.py b/pyro/swe/problems/acoustic_pulse.py index 980b7a351..36120969c 100644 --- a/pyro/swe/problems/acoustic_pulse.py +++ b/pyro/swe/problems/acoustic_pulse.py @@ -12,7 +12,8 @@ def init_data(myd, rp): """initialize the acoustic_pulse problem. This comes from McCourquodale & Coella 2011""" - msg.bold("initializing the acoustic pulse problem...") + if rp.get_param("driver.verbose"): + msg.bold("initializing the acoustic pulse problem...") # make sure that we are passed a valid patch object if not isinstance(myd, patch.CellCenterData2d): diff --git a/pyro/swe/problems/advect.py b/pyro/swe/problems/advect.py index c309f1ea6..5ce400e3d 100644 --- a/pyro/swe/problems/advect.py +++ b/pyro/swe/problems/advect.py @@ -11,7 +11,8 @@ def init_data(my_data, rp): """ initialize a smooth advection problem for testing convergence """ - msg.bold("initializing the advect problem...") + if rp.get_param("driver.verbose"): + msg.bold("initializing the advect problem...") # make sure that we are passed a valid patch object if not isinstance(my_data, patch.CellCenterData2d): diff --git a/pyro/swe/problems/dam.py b/pyro/swe/problems/dam.py index 8c88e5869..f4081f17a 100644 --- a/pyro/swe/problems/dam.py +++ b/pyro/swe/problems/dam.py @@ -9,7 +9,8 @@ def init_data(my_data, rp): """ initialize the dam problem """ - msg.bold("initializing the dam problem...") + if rp.get_param("driver.verbose"): + msg.bold("initializing the dam problem...") # make sure that we are passed a valid patch object if not isinstance(my_data, patch.CellCenterData2d): diff --git a/pyro/swe/problems/kh.py b/pyro/swe/problems/kh.py index 8c8ba55f8..98ee00ae3 100644 --- a/pyro/swe/problems/kh.py +++ b/pyro/swe/problems/kh.py @@ -9,7 +9,8 @@ def init_data(my_data, rp): """ initialize the Kelvin-Helmholtz problem """ - msg.bold("initializing the Kelvin-Helmholtz problem...") + if rp.get_param("driver.verbose"): + msg.bold("initializing the Kelvin-Helmholtz problem...") # make sure that we are passed a valid patch object if not isinstance(my_data, patch.CellCenterData2d): diff --git a/pyro/swe/problems/logo.py b/pyro/swe/problems/logo.py index 54500e77c..dbd42f7be 100644 --- a/pyro/swe/problems/logo.py +++ b/pyro/swe/problems/logo.py @@ -11,9 +11,9 @@ def init_data(my_data, rp): """ initialize the sedov problem """ - del rp # this problem doesn't use runtime params - msg.bold("initializing the logo problem...") + if rp.get_param("driver.verbose"): + msg.bold("initializing the logo problem...") # make sure that we are passed a valid patch object if not isinstance(my_data, patch.CellCenterData2d): diff --git a/pyro/swe/problems/quad.py b/pyro/swe/problems/quad.py index 3616c6717..a8fa0eb0e 100644 --- a/pyro/swe/problems/quad.py +++ b/pyro/swe/problems/quad.py @@ -11,7 +11,8 @@ def init_data(my_data, rp): """ initialize the quadrant problem """ - msg.bold("initializing the quadrant problem...") + if rp.get_param("driver.verbose"): + msg.bold("initializing the quadrant problem...") # make sure that we are passed a valid patch object if not isinstance(my_data, patch.CellCenterData2d):