{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Estimating Fluxes at INST state" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To estimate metabolic fluxes at the INST state, FreeFlux uses a nonlinear optimization approach that takes into account both metabolite concentrations and MDVs from multiple timepoints. The problem can be defined mathematically as follows:\n", "\n", "![Optimization problem ><](images/inst_eqn.gif)\n", "\n", "Here, ${\\bf{x}}_{sim}$ represents the simulated MDVs at all timepoints, and ${\\bf{v}}_{sim}$ represents the simulated fluxes. Both are functions of the independent variable vector $\\bf{p}$, which includes free fluxes $\\bf{u}$ and concentrations $\\bf{c}$. ${\\bf{x}}_{exp}$ and ${\\bf{v}}_{exp}$ are the measurements of MDV and flux, respectively, and ${\\bf{\\Sigma }}_{{{\\bf{x}}_{exp}}}^{ - 1}$ and ${\\bf{\\Sigma }}_{{{\\bf{v}}_{exp}}}^{ - 1}$ are the inverses of the covariance matrix of measurements. $\\bf{N}$ is the null space of stoichiometric matrix of the network reactions, and $\\bf{T}$ denotes the matrix that transforms total fluxes to net fluxes." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Solving both the Fluxes and Concentrations" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Similar to before, we start by building a metabolic model by reading the reactions from a [file](https://github.com/Chaowu88/freeflux/tree/main/models/toy)." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "from freeflux import Model\n", "\n", "MODEL_FILE = 'path/to/reactions.tsv'\n", "\n", "model = Model('demo')\n", "model.read_from_file(MODEL_FILE)\n", "\n", "ifit = model.fitter('inst')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Next, we specify the labeling strategy for the labeled substrates:" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "ifit.set_labeling_strategy(\n", " 'AcCoA', \n", " labeling_pattern = ['01', '11'], \n", " percentage = [0.25, 0.25], \n", " purity = [1, 1]\n", ") # call this method for each labeled substrate" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The measured metabolite-derived isotopomer fractions (MDVs) and fluxes can be loaded one by one using `set_measured_MDV` and `set_measured_flux`, or all at once from [files](https://github.com/Chaowu88/freeflux/tree/main/models/toy) using `set_measured_MDVs_from_file` and `set_measured_fluxes_from_file`:" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "MEASURED_MDV_FILE = 'path/to/measured_inst_MDVs.tsv'\n", "MEASURED_FLUX_FILE = 'path/to/measured_fluxes.tsv'\n", "\n", "ifit.set_measured_MDVs_from_file(MEASURED_MDV_FILE)\n", "ifit.set_measured_fluxes_from_file(MEASURED_FLUX_FILE)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Next, we set the lower and upper bounds for the net fluxes and concentrations using `set_flux_bounds` and `set_concentration_bounds`, respectively:" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "ifit.set_flux_bounds('all', bounds = [-100, 100])\n", "ifit.set_concentration_bounds('all', bounds = [0.001, 10])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "
\n", "\n", "Note:

1. The upper bound should be great than the lower bound. Use `set_measured_flux` or `set_measured_fluxes_from_file` for equality assignment; 2. Bounds specified by `set_concentration_bounds` are not used as constraints in the optimization, it just defines the sampling range of the initial guess of concentrations; 3. By default, fluxes, concentrations and timepoints have the unit of $\\mu$mol gCDW$^{-1}$ s$^{-1}$, $\\mu$mol gCDW$^{-1}$, and s, respectively.\n", "\n", "
" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Finally, we solve for the fluxes and concentrations:" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "INST fitting [elapsed: 0:00:56]\n", "\n", "True\n" ] } ], "source": [ "ifit.prepare(n_jobs = 3)\n", "res = ifit.solve(\n", " solver = 'ralg', \n", " ini_fluxes = None, \n", " fit_measured_fluxes = True\n", ")\n", "print(res.optimization_successful)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The \"fit_measured_fluxes\" parameter in the `solve` method determines whether the measured fluxes are included in the optimization or not." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Currently, two methods are available to solve the fitting problem: the sequential least squares programming (solver=\"slsqp\") and the r-algorithm with adaptive space dilation (solver=\"ralg\", OpenOpt required). The two methods may have different performance on different models and input data. To use the r-algorithm solver, OpenOpt is required, and you can find more details on how to install it [here](https://freeflux.readthedocs.io/en/latest/installation.html)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The \"ini_fluxes\" argument is used to enter an initial guess of the fluxes, which can be derived from the last successful optimization. This argument takes a Pandas [Series](https://pandas.pydata.org/docs/reference/api/pandas.Series.html) object or a .tsv/.xlsx file. A good initial guess can help with convergence. If None, the solve method will use a random initial guess sampled from the feasible region of fluxes estimated by flux variability analysis [(FVA)](https://freeflux.readthedocs.io/en/latest/fba.html)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The \"fit_measured_fluxes\" argument determines whether the second flux item is included in the objective function of the above-formulated problem. Set this argument to *True* if exchange fluxes (e.g., substrate uptake rate, product secretion rate, or the specific cell growth) are measured and provided by `set_measured_flux` or `set_measured_fluxes_from_file`. In this case, the estimated fluxes have the same unit as the measured ones, which is $\\mu$mol gCDW$^{-1}$ s$^{-1}$ by default." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Additional arguments can be used to control the optimization process. For example, the \"tol\" and \"max_iters\" arguments can be used to set the tolerance level and maximum number of iterations for the solver, respectively. The \"show_progress\" argument can be set to *True* to display the time elapsed during the optimization." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The results of the optimization are stored in an InstFitResults object. The optimal objective value can be accessed through the `opt_objective` attribute of the object." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.018" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "res.opt_objective" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The optimal fluxes can be accessed by:" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "v1: 10.001\n", "v2: 10.001\n", "v3: 4.972\n", "v4: 5.029\n", "v5: 5.029\n", "v6: 5.029\n", "v7: 4.972\n" ] } ], "source": [ "print(res.opt_net_fluxes) # opt_total_fluxes for total fluxes" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "And the optimal concentration can be accessed by:" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "AKG: 0.281\n", "Cit: 4.98\n", "Fum: 0.141\n", "Glu: 0.514\n", "OAA: 2.246\n", "Suc: 0.049\n" ] } ], "source": [ "print(res.opt_concentrations)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can also use `plot_normal_probability` to check the goodness of fit:" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "res.plot_normal_probability(show_fig = True, output_dir = None)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Comparison of the simulated and experimental measurements can be made by:" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ " res.plot_simulated_vs_measured_MDVs(show_fig = True, output_dir = None)" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "res.plot_simulated_vs_measured_fluxes(show_fig = True, output_dir = None)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Symmetric confidence intervals of fluxes at convergence can be obtained using the `estimate_confidence_intervals` method:" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "v1: [9.125, 10.876]\n", "v2: [9.125, 10.876]\n", "v3: [4.219, 5.725]\n", "v4: [3.845, 6.213]\n", "v5: [3.845, 6.213]\n", "v6: [3.845, 6.213]\n", "v7: [4.219, 5.725]\n" ] } ], "source": [ "print(res.estimate_confidence_intervals(\n", " which = 'net', # which = \"total\" for total fluxes\n", " confidence_level = 0.95\n", ")) " ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "AKG: [0.0, 3.603]\n", "Cit: [3.013, 6.946]\n", "Fum: [0.0, 1138.422]\n", "Glu: [0.0, 2.857]\n", "OAA: [0.0, 972.684]\n", "Suc: [0.0, 181.646]\n" ] } ], "source": [ "print(res.estimate_confidence_intervals(which = 'conc', confidence_level = 0.95))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can evaluate the contribution of measurement variances (including all timepoints) to the uncertainty of estimated fluxes by calculating the contribution matrix:" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " Glu_12345_m0_0.1 Glu_12345_m1_0.1 Glu_12345_m2_0.1 Glu_12345_m3_0.1 \\\n", "v1 0.006657 0.001562 0.002262 0.000016 \n", "v2 0.006657 0.001562 0.002262 0.000016 \n", "v3 0.012128 0.002408 0.003313 0.000010 \n", "v4 0.000094 0.000004 0.000002 0.000024 \n", "v5 0.000094 0.000004 0.000002 0.000024 \n", "v6 0.000094 0.000004 0.000002 0.000024 \n", "v7 0.012128 0.002408 0.003313 0.000010 \n", "\n", " Glu_12345_m4_0.1 Glu_12345_m5_0.1 Glu_12345_m0_0.2 Glu_12345_m1_0.2 \\\n", "v1 2.239544e-06 3.003782e-10 0.049795 0.015252 \n", "v2 2.239544e-06 3.003782e-10 0.049795 0.015252 \n", "v3 1.395488e-07 1.694144e-11 0.084663 0.015899 \n", "v4 1.805789e-06 2.380560e-10 0.000403 0.000123 \n", "v5 1.805789e-06 2.380560e-10 0.000403 0.000123 \n", "v6 1.805789e-06 2.380560e-10 0.000403 0.000123 \n", "v7 1.395488e-07 1.694144e-11 0.084663 0.015899 \n", "\n", " Glu_12345_m2_0.2 Glu_12345_m3_0.2 ... Glu_12345_m3_1.0 \\\n", "v1 0.023665 0.001707 ... 0.147273 \n", "v2 0.023665 0.001707 ... 0.147273 \n", "v3 0.021487 0.000233 ... 0.000035 \n", "v4 0.000420 0.001620 ... 0.082639 \n", "v5 0.000420 0.001620 ... 0.082639 \n", "v6 0.000420 0.001620 ... 0.082639 \n", "v7 0.021487 0.000233 ... 0.000035 \n", "\n", " Glu_12345_m4_1.0 Glu_12345_m5_1.0 Glu_12345_m0_2.0 Glu_12345_m1_2.0 \\\n", "v1 0.020384 0.000204 0.142737 0.043837 \n", "v2 0.020384 0.000204 0.142737 0.043837 \n", "v3 0.000023 0.000002 0.407109 0.004165 \n", "v4 0.011793 0.000132 0.469267 0.038342 \n", "v5 0.011793 0.000132 0.469267 0.038342 \n", "v6 0.011793 0.000132 0.469267 0.038342 \n", "v7 0.000023 0.000002 0.407109 0.004165 \n", "\n", " Glu_12345_m2_2.0 Glu_12345_m3_2.0 Glu_12345_m4_2.0 Glu_12345_m5_2.0 \\\n", "v1 0.017978 0.005100 0.000345 0.000342 \n", "v2 0.017978 0.005100 0.000345 0.000342 \n", "v3 0.007589 0.123542 0.015118 0.000143 \n", "v4 0.023875 0.076347 0.004156 0.000037 \n", "v5 0.023875 0.076347 0.004156 0.000037 \n", "v6 0.023875 0.076347 0.004156 0.000037 \n", "v7 0.007589 0.123542 0.015118 0.000143 \n", "\n", " v1 \n", "v1 0.170514 \n", "v2 0.170514 \n", "v3 0.000466 \n", "v4 0.101747 \n", "v5 0.101747 \n", "v6 0.101747 \n", "v7 0.000466 \n", "\n", "[7 rows x 31 columns]\n" ] } ], "source": [ "print(res.estimate_contribution_matrix(which = 'net')) # which = \"total\" for total fluxes" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The sensitivities of estimated fluxes with respect to measurement perturbations (including all timepoints) can be obtained by:" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " Glu_12345_m0_0.1 Glu_12345_m1_0.1 Glu_12345_m2_0.1 Glu_12345_m3_0.1 \\\n", "v1 -3.369194 1.632079 1.963988 -0.164362 \n", "v2 -3.369194 1.632079 1.963988 -0.164362 \n", "v3 -3.912126 1.743226 2.044739 0.110744 \n", "v4 0.542932 -0.111147 -0.080751 -0.275106 \n", "v5 0.542932 -0.111147 -0.080751 -0.275106 \n", "v6 0.542932 -0.111147 -0.080751 -0.275106 \n", "v7 -3.912126 1.743226 2.044739 0.110744 \n", "\n", " Glu_12345_m4_0.1 Glu_12345_m5_0.1 Glu_12345_m0_0.2 Glu_12345_m1_0.2 \\\n", "v1 -0.061796 -0.000716 -9.214526 5.099624 \n", "v2 -0.061796 -0.000716 -9.214526 5.099624 \n", "v3 0.013270 0.000146 -10.336268 4.479272 \n", "v4 -0.075066 -0.000862 1.121742 0.620352 \n", "v5 -0.075066 -0.000862 1.121742 0.620352 \n", "v6 -0.075066 -0.000862 1.121742 0.620352 \n", "v7 0.013270 0.000146 -10.336268 4.479272 \n", "\n", " Glu_12345_m2_0.2 Glu_12345_m3_0.2 ... Glu_12345_m3_1.0 \\\n", "v1 6.352281 -1.705824 ... -15.846819 \n", "v2 6.352281 -1.705824 ... -15.846819 \n", "v3 5.207194 0.542625 ... 0.211619 \n", "v4 1.145087 -2.248449 ... -16.058438 \n", "v5 1.145087 -2.248449 ... -16.058438 \n", "v6 1.145087 -2.248449 ... -16.058438 \n", "v7 5.207194 0.542625 ... 0.211619 \n", "\n", " Glu_12345_m4_1.0 Glu_12345_m5_1.0 Glu_12345_m0_2.0 Glu_12345_m1_2.0 \\\n", "v1 -5.895495 -0.590468 -15.600880 8.645712 \n", "v2 -5.895495 -0.590468 -15.600880 8.645712 \n", "v3 0.170868 0.051010 22.665844 -2.292551 \n", "v4 -6.066363 -0.641478 -38.266723 10.938263 \n", "v5 -6.066363 -0.641478 -38.266723 10.938263 \n", "v6 -6.066363 -0.641478 -38.266723 10.938263 \n", "v7 0.170868 0.051010 22.665844 -2.292551 \n", "\n", " Glu_12345_m2_2.0 Glu_12345_m3_2.0 Glu_12345_m4_2.0 Glu_12345_m5_2.0 \\\n", "v1 5.536727 2.948962 -0.766712 -0.763809 \n", "v2 5.536727 2.948962 -0.766712 -0.763809 \n", "v3 -3.094699 -12.486040 -4.367789 -0.424765 \n", "v4 8.631426 15.435002 3.601078 -0.339044 \n", "v5 8.631426 15.435002 3.601078 -0.339044 \n", "v6 8.631426 15.435002 3.601078 -0.339044 \n", "v7 -3.094699 -12.486040 -4.367789 -0.424765 \n", "\n", " v1 \n", "v1 0.170514 \n", "v2 0.170514 \n", "v3 -0.007671 \n", "v4 0.178185 \n", "v5 0.178185 \n", "v6 0.178185 \n", "v7 -0.007671 \n", "\n", "[7 rows x 31 columns]\n" ] } ], "source": [ "print(res.estimate_sensitivity(which = 'net')) # which = \"total\" for total fluxes" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Similarly, the `with` statement can also be used to perform flux estimation:\n", "\n", "```\n", "with model.fitter('inst') as ifit:\n", " ifit.set_labeling_strategy(\n", " 'AcCoA', \n", " labeling_pattern = ['01', '11'], \n", " percentage = [0.25, 0.25], \n", " purity = [1,1]\n", " )\n", " ifit.set_measured_MDVs_from_file(MEASURED_MDVS)\n", " ifit.set_measured_fluxes_from_file(MEASURED_FLUXES)\n", " ifit.set_flux_bounds(\n", " 'all', \n", " bounds = [-100, 100]\n", " )\n", " ifit.set_concentration_bounds(\n", " 'all', \n", " bounds = [0.001, 10]\n", " )\n", " ifit.prepare(n_jobs = 3)\n", " res = ifit.solve(solver = 'ralg')\n", "```" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "A more complex example of flux estimation at INST state using an *Synechocystis* model is provided in the script \"[tutorial_synechocystis_inst_estimation.py](https://github.com/Chaowu88/freeflux/tree/main/tutorials)\"" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Dilution Parameter" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Transient labeling of metabolites can be impeded by dilution effect. To account for it, we can add several special reactions to the network:" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "v1: 0A(abc) $\\rightarrow$ As(abc)\\\n", "v2: 0Au(abc) $\\rightarrow$ As(abc)\\\n", "v3: As(abc) $\\rightarrow$ sink" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "where A is the metabolite involved in metabolic reactions, Au is the metabolically inactive (unlabeled) fraction, and As is the pseudo-metabolite denoting the metabolites measured in samples. The zero coefficient in v1 and v2 guarantees that the additional metabolites will have no effect on the mass balance of network reactions. The flux values of the reactions could be arbitrary, but the ratio v2/(v1+v2) is determined by the labeling patterns of A, Au, and As, which is estimated as the dilution parameter." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Then, we need to tell the decomposer the unlabeled dilution source(s), i.e., \"Au\" in the above example by setting the argument \"dilution_from = 'Au'\" in the `prepare` method." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Solving the Fluxes with Confidence Intervals" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Uncertainties of fluxes and concentrations can be estimated using the Monte Carlo method. The following code demonstrates how to do this:" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "INST fitting with CIs [elapsed: 0:07:49]\n", "\n" ] } ], "source": [ "model = Model('demo')\n", "model.read_from_file(MODEL_FILE)\n", "\n", "with model.fitter('inst') as ifit:\n", " ifit.set_labeling_strategy(\n", " 'AcCoA', \n", " labeling_pattern = ['01', '11'], \n", " percentage = [0.25, 0.25], \n", " purity = [1,1]\n", " )\n", " ifit.set_measured_MDVs_from_file(MEASURED_MDV_FILE)\n", " ifit.set_measured_fluxes_from_file(MEASURED_FLUX_FILE)\n", " ifit.set_flux_bounds(\n", " 'all', \n", " bounds = [-100, 100]\n", " )\n", " ifit.set_concentration_bounds(\n", " 'all', \n", " bounds = [0.001, 10]\n", " )\n", " ifit.prepare(n_jobs = 30)\n", " res = ifit.solve_with_confidence_intervals(\n", " n_runs = 30, \n", " n_jobs = 3\n", " )" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "
\n", "\n", "Note:

For the estimation of CIs for large-sized models, we strongly recommend running with parallel jobs. One can achieve this by setting the 'n_jobs' argument of the `solve_with_confidence_intervals` method to a value greater than 1.\n", "\n", "
" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "v1: [8.078, 10.78]\n", "v2: [8.078, 10.78]\n", "v3: [2.102, 5.724]\n", "v4: [3.605, 8.557]\n", "v5: [3.605, 8.557]\n", "v6: [3.605, 8.557]\n", "v7: [2.102, 5.724]\n" ] } ], "source": [ "print(res.estimate_confidence_intervals(\n", " which = 'net', # which = \"total\" for total fluxes\n", " confidence_level = 0.95\n", ")) " ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "AKG: [0.0, 1.277]\n", "Cit: [0.001, 5.554]\n", "Fum: [0.0, 1.23]\n", "Glu: [0.0, 3.375]\n", "OAA: [0.001, 2.916]\n", "Suc: [0.0, 2.489]\n" ] } ], "source": [ "print(res.estimate_confidence_intervals(which = 'conc', confidence_level = 0.95))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For a more complex example of flux estimation with CIs of a *Synechocystis* model, please refer to the script \"[tutorial_synechocystis_inst_estimation.py](https://github.com/Chaowu88/freeflux/tree/main/tutorials)\"" ] } ], "metadata": { "kernelspec": { "display_name": "Python [conda env:freeflux-py37]", "language": "python", "name": "conda-env-freeflux-py37-py" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.0" } }, "nbformat": 4, "nbformat_minor": 4 }