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z>AOBPRSXr4DIc`{h03R5-FSF?cdBf3XWc+RK!B>yog>Dz7%+5Wk8ocg7K>XSj3>(Y z-Tv;;dA-X_x=#(7j-~rk3W_C9=JM|kj_*p+qrAXN{FqZ;$7>SJP+)|CeR|)-KHl0I z`2}A9@{JRZk?a$8*Brlf+|6QAZQq;Do<2*Sd!fS;bo}2j0ED|}jhg#iBI(?}fJE#G1(N%n#Z zW8m#0x?lTbKHTT$oaQ<3YEC#(@+-PO6hg=h8n3_*kOQ^%$v)!y?Z05+$-)epew%mf zE8F}~uw`ckIh1w(ukAlX^JK}(%&XKhZVtQ4@{MV}aw<@`b20wPGZawCBv02d_v{a} z{KiY?{;gCq5Fb@|?)&!NCNEx(@4*#wHGLnOqNg1?*M1JJ`PRRc@-t=GO>gZlnIC)P z%T#%5T^jysPVm)S=n{9^Piw_L`L|N|!~CLwr>8Jif9zzRX|9B++{Ci9>U7S~#ryRP zxmR7pa6&ga!+*|PIpXmOiKvP_nwcAfSNvYPww zzOL(k3bf-lIk*y>-v&zgMR`EGu@iE~S)}%*>R0;yQwUjMV0m|&Lw>h;f+WQbVrOhX zPl29f`6vA!BNbqla1JecFY?+kwQ5Gejo4LQg6olJKkDTFQ$m=$WMHYg{VM{zjN)<;24;@ttT9P%Wl zvom4wZ%7$I@hsW^gd1;QbeM z4H-PRe2iC-CGGl!a$$L!LGcNl2_u<^9ksIC^S6=F-|ZJa5Mr1itn#6`fdepk4>ud~ z3y_gWWd>FmqtVOM#`fjr4s}c5*07&W5%013@l)nn3oB1t?Bjw5n`%)j=6<(Z z{`5Q2;&&umy~Xc#xQ)hqXXRkf?u7r@?>3%qJl~V5l8I(c@D3`0.05) and small in magnitude (D<0.3). (c) Determines the structural precision of a snapshot model (`snapshot_{state}_{time}.ChiSquare.pdf`). RMSD clustering is performed at 1 Å intervals until the clustered population (% clustered) is greater than 80%, and either the χ2 p-value is greater than 0.05 or Cramer’s V is less than 0.1. The sampling precision is indicated by the dashed black line. (d) Populations from sample 1 and sample 2 are shown for each cluster (`snapshot_{state}_{time}.Cluster_Population.pdf`). +Plots for determining the sampling precision are shown below for a single snapshot model, 1_2min. (a) Tests the convergence of the lowest scoring model (`snapshot_{state}_{time}.Top_Score_Conv.pdf`). Error bars represent standard deviations of the best scores, estimated by selecting different subsets of models 10 times. The light-blue line indicates a lower bound reference on the total score. (b) Tests that the scores of two independently sampled models come from the same distribution (`snapshot_{state}_{time}.Score_Dist.pdf`). The difference between the two distributions, as measured by the KS test statistic (D) and KS test p-value (p) indicates that the difference is both statistically insignificant (p>0.05) and small in magnitude (D<0.3). (c) Determines the structural precision of a snapshot model (`snapshot_{state}_{time}.ChiSquare.pdf`). RMSD clustering is performed at 1 Å intervals until the clustered population (% clustered) is greater than 80%, and either the χ2 p-value is greater than 0.05 or Cramer’s V is less than 0.1. The sampling precision is indicated by the dashed black line. (d) Populations from sample 1 and sample 2 are shown for each cluster (`snapshot_{state}_{time}.Cluster_Population.pdf`). \image html Snapshot_Exhaust.png width=1200px Further structural analysis can be calculated by using the `cluster.*` files. The `cluster.*.{sample}.txt` files contain the model number for the models in that cluster, where `{sample}` indicates which round of sampling the models came from. The `cluster.*` folder contains an RMF for centroid model of that cluster, along with the localization densities for each protein. The localization densities of each protein from each independent sampling can be compared to ensure independent samplings produce the same results. -Ideally, each of these plots should be checked for each snapshot. As a way to summarize the output of these checks, we can gather the results of the KS test and the sampling precision test for all snapshots. This is done by running `extract_exhaust_data` and `save_exhaust_data_as_png`, which write `KS_sampling_precision_output.txt` and `KS_sampling_precision_output.png`, respectively. +Ideally, each of these plots should be checked for each snapshot model. As a way to summarize the output of these checks, we can gather the results of the KS test and the sampling precision test for all snapshot models. This is done by running `extract_exhaust_data` and `save_exhaust_data_as_png`, which write `KS_sampling_precision_output.txt` and `KS_sampling_precision_output.png`, respectively. \code{.py} # 8 calling extract_exhaust_data @@ -327,4 +327,4 @@ Here, we plotted each centroid model (A - blue, B - orange, and C - purple) from \image html static_snapshots_noCC.png width=600px -Finally, now that snapshot models have been assessed, we can perform [Modeling of trajectories.] (@ref trajectories) +Finally, now that snapshot models have been assessed, we can perform [Modeling of a trajectory.] (@ref trajectories) diff --git a/doc/trajectory.md b/doc/trajectory.md index e21cb0548..1468fd260 100644 --- a/doc/trajectory.md +++ b/doc/trajectory.md @@ -1,4 +1,4 @@ -Modeling of Trajectories {#trajectories} +Modeling of a Trajectory {#trajectories} ==================================== Here, we describe the final modeling problem in our composite workflow, how to build models of trajectory models using IMP. @@ -13,33 +13,33 @@ Heterogeneity models inform the possible compositional states at each time point # Trajectory modeling step 2: representation, scoring function, and search process {#trajectories2} -Trajectory modeling connects alternative snapshot models at adjacent time points, followed by scoring the trajectories based on their fit to the input information, as described in full [here](https://www.biorxiv.org/content/10.1101/2024.08.06.606842v1.abstract). +Trajectory modeling connects alternative snapshot models at adjacent time points, followed by scoring the trajectory models based on their fit to the input information, as described in full [here](https://www.biorxiv.org/content/10.1101/2024.08.06.606842v1.abstract). ## Background behind integrative spatiotemporal modeling ### Representing the model {#trajectory_representation} -We choose to represent dynamic processes as a trajectory of snapshot models, with one snapshot at each time point. In this case, we modeled snapshots at 3 time points (0, 1, and 2 minutes), so a single trajectory model will consist of 3 snapshots, one at each 0, 1, and 2 minutes. The modeling procedure described here will produce a set of scored trajectory models, which can be displayed as a directed acyclic graph, where nodes in the graph represent the snapshot model and edges represent connections between snapshots at neighboring time points. +We choose to represent dynamic processes as a trajectory of snapshot models, with one snapshot model at each time point. In this case, we computed snapshot models at 3 time points (0, 1, and 2 minutes), so a single trajectory model will consist of 3 snapshot models, one at each 0, 1, and 2 minutes. The modeling procedure described here will produce a set of scored trajectory models, which can be displayed as a directed acyclic graph, where nodes in the graph represent the snapshot model and edges represent connections between snapshot models at neighboring time points. ### Scoring the model {#trajectory_scoring} -To score trajectories, we incorporate both the scores of individual snapshots, as well as the scores of transitions between them. Under the assumption that the process is Markovian (*i.e.* memoryless), the weight of a trajectory takes the form: +To score trajectory models, we incorporate both the scores of individual snapshot models, as well as the scores of transitions between them. Under the assumption that the process is Markovian (*i.e.* memoryless), the weight of a trajectory model takes the form: \f[ W(\chi) \propto \displaystyle\prod^{T}_{t=0} P( X_{N,t}, N_{t} | D_{t}) \cdot \displaystyle\prod^{T-1}_{t=0} W(X_{N,t+1},N_{t+1} | X_{N,t},N_{t}, D_{t,t+1}), \f] -where \f$t\f$ indexes times from 0 until the final modeled snapshot (\f$T\f$); \f$P(X_{N,t}, N_{t} | D_{t})\f$ is the snapshot model score; and \f$W(X_{N,t+1},N_{t+1} | X_{N,t},N_{t}, D_{t,t+1})\f$ is the transition score. Trajectory weights (\f$W(\chi)\f$) are normalized so that the sum of all trajectory weights is 1.0. Transition scores are currently based on a simple metric that either allows or disallows a transition. Transitions are only allowed if all proteins in the first snapshot model are included in the second snapshot model. In the future, we hope to include more detailed transition scoring terms, which may take into account experimental information or physical models of macromolecular dynamics. +where \f$t\f$ indexes times from 0 until the final modeled snapshot (\f$T\f$); \f$P(X_{N,t}, N_{t} | D_{t})\f$ is the snapshot model score; and \f$W(X_{N,t+1},N_{t+1} | X_{N,t},N_{t}, D_{t,t+1})\f$ is the transition score. Trajectory model weights (\f$W(\chi)\f$) are normalized so that the sum of all trajectory models' weights is 1.0. Transition scores are currently based on a simple metric that either allows or disallows a transition. Transitions are only allowed if all proteins in the first snapshot model are included in the second snapshot model. In the future, we hope to include more detailed transition scoring terms, which may take into account experimental information or physical models of macromolecular dynamics. ### Searching for good scoring models {#trajectory_searching} -Trajectories are constructed by enumerating all connections between adjacent snapshots and scoring these trajectories according to the equation above. This procedure results in a set of weighted trajectories. +Trajectory models are constructed by enumerating all connections between adjacent snapshot models and scoring these trajectory models according to the equation above. This procedure results in a set of weighted trajectory models. ## Code for integrative spatiotemporal modeling {#trajectory_example} -Navigate to `Trajectories/Trajectories_Modeling`. The `create_trajectories.py` script runs the above steps to create a spatiotemporal integrative model from the previously sampled snapshots. +Navigate to `Trajectories/Trajectories_Modeling`. The `create_trajectories.py` script runs the above steps to create a spatiotemporal integrative model from the previously sampled snapshot models. -First, we copy all relevant files to a new directory, `data`. These files are (i) `{state}_{time}.config` files, which include the subcomplexes that are in each state, (ii) `{state}_{time}_scores.log`, which is a list of all scores of all models in that snapshot, and (iii) `exp_comp{prot}.csv`, which is the experimental copy number for each protein (`{prot}`) as a function of time. +First, we copy all relevant files to a new directory, `data`. These files are (i) `{state}_{time}.config` files, which include the subcomplexes that are in each state, (ii) `{state}_{time}_scores.log`, which is a list of all scores of all structural models in that snapshot model, and (iii) `exp_comp{prot}.csv`, which is the experimental copy number for each protein (`{prot}`) as a function of time. \code{.py} # copy all the relevant files for create_DAG @@ -68,7 +68,7 @@ nodes, graph, graph_prob, graph_scores = IMP.spatiotemporal.create_DAG(state_dic \endcode The inputs we included are: -- state_dict (dict): a dictionary that defines the spatiotemporal model. Keys are strings for each time point in the spatiotemporal process and values are integers corresponding to the number of snapshots modeled at that time point +- state_dict (dict): a dictionary that defines the spatiotemporal model. Keys are strings for each time point in the spatiotemporal process and values are integers corresponding to the number of snapshot models computed at that time point - out_pdf (bool): whether to write the probability distribution function (pdf). - npaths (int): Number of states two write to a file (path*.txt). - input_dir (str): directory with the input information. @@ -76,18 +76,18 @@ The inputs we included are: - output_dir (str): directory to which model will be written. Will be created if it does not exist. - spatio_temporal_rule (bool): whether to include our transition scoring term, which enforces that all proteins in the first snapshot model are included in the second snapshot model. - expected_subcomplexes (list): list of string objects, which is the subcomplexes to look when enforcing the spatiotemporal rule. Strings should be substrings of those in `{state}_{time}.config` files. -- score_comp (bool): whether to score the composition of each snapshot. +- score_comp (bool): whether to score the composition of each snapshot model. - exp_comp_map (dictionary): key is a string with the name of each protein that will undergo composition scoring, value is the `.csv` file with the copy number data for that protein. - draw_dag (bool): whether to write out an image of the directed acyclic graph. After running `spatiotemporal.create_DAG`, a variety of outputs are written: -- `cdf.txt`: the cumulative distribution function for the set of trajectories. -- `pdf.txt`: the probability distribution function for the set of trajectories. -- `labeled_pdf.txt`: Each row has 2 columns and represents a different trajectory model. The first column labels a single trajectory as a series of snapshots, where each snapshot is written as `{state}_{time}|` in sequential order. The second column is the probability distribution function corresponding to that trajectory. +- `cdf.txt`: the cumulative distribution function for the set of trajectory models. +- `pdf.txt`: the probability distribution function for the set of trajectory models. +- `labeled_pdf.txt`: Each row has 2 columns and represents a different trajectory model. The first column labels a single trajectory model as a series of snapshot models, where each snapshot model is written as `{state}_{time}|` in sequential order. The second column is the probability distribution function corresponding to that trajectory model. - `dag_heatmap.eps` and `dag_heatmap`: image of the directed acyclic graph from the set of models. - `path*.txt`: files where each row includes a `{state}_{time}` string, so that rows correspond to the states visited over that trajectory model. Files are numbered from the most likely path to the least likely path. -Now that we have a trajectory model, we can plot the directed acyclic graph (left) and the series of centroid models from each snapshot along the most likely trajectory (right). Each row corresponds to a different time point in the assembly process (0 min, 1 min, and 2 min). Each node is shaded according to its weight in the final model (\f$W(X_{N,t}N_{t})\f$). Proteins are colored as A - blue, B - orange, and C - purple. +Now that we have a trajectory model, we can plot the directed acyclic graph (left) and the series of centroid models from each snapshot model along the most likely trajectory model (right). Each row corresponds to a different time point in the assembly process (0 min, 1 min, and 2 min). Each node is shaded according to its weight in the final model (\f$W(X_{N,t}N_{t})\f$). Proteins are colored as A - blue, B - orange, and C - purple. \image html Spatiotemporal_Model.png width=600px @@ -162,7 +162,7 @@ Temporal precision between ../outputA/labeled_pdf.txt and ../outputB/labeled_pdf ## Model precision {#trajectory_precision} -Next, we calculate the precision of the model, using `analysis.precision`. Here, the model precision calculates the number of trajectories with high weights. The precision ranges from 1.0 to 1/d, where d is the number of trajectories. Values approaching 1.0 indicate the model set can be described by a single trajectory, while values close to 1/d indicate that all trajectories have similar weights. +Next, we calculate the precision of the model, using `analysis.precision`. Here, the model precision calculates the number of trajectory models with high weights. The precision ranges from 1.0 to 1/d, where d is the number of trajectory models. Values approaching 1.0 indicate the model set can be described by a single trajectory model, while values close to 1/d indicate that all trajectory models have similar weights. \code{.py} ## 2 - calculation of precision of the model @@ -178,7 +178,7 @@ print("") print("") \endcode -The `analysis.precision` function reads in the `labeled_pdf` of the complete model, and writes the output file to `analysis_output_precision/precision.txt`, shown below. The value close to 1.0 indicates that the set of models can be sufficiently represented by a single trajectory. +The `analysis.precision` function reads in the `labeled_pdf` of the complete model, and writes the output file to `analysis_output_precision/precision.txt`, shown below. The value close to 1.0 indicates that the set of models can be sufficiently represented by a single trajectory model. \code{.txt} Precision of ../Trajectories_Modeling/output/labeled_pdf.txt: @@ -187,7 +187,7 @@ Precision of ../Trajectories_Modeling/output/labeled_pdf.txt: ## Comparison against data used in model construction {#trajectory_comparison} -We then evaluate the model against data used in model construction. First, we calculate the cross-correlation between the original EM map and the forward density projected from each snapshot model. We wrote the `ccEM` function to perform this comparison for all snapshots. +We then evaluate the model against data used in model construction. First, we calculate the cross-correlation between the original EM map and the forward density projected from each snapshot model. We wrote the `ccEM` function to perform this comparison for all snapshot models. \code{.py} # 3a - comparison of the model to data used in modeling (EM) @@ -198,7 +198,7 @@ print("") print("") \endcode -The output of `ccEM` is written in `ccEM_output/`. It contains forward densities for each snapshot model (`MRC_{state}_{time}.mrc`) and `ccEM_calculations.txt`, which contains the cross-correlation to the experimental EM profile for each snapshot. +The output of `ccEM` is written in `ccEM_output/`. It contains forward densities for each snapshot model (`MRC_{state}_{time}.mrc`) and `ccEM_calculations.txt`, which contains the cross-correlation to the experimental EM profile for each snapshot model. After comparing the model to EM data, we aimed to compare the model to copy number data, and wrote the `forward_model_copy_number` function to evaluate the copy numbers from our set of trajectory models. @@ -213,13 +213,13 @@ print("") The output of `forward_model_copy_number` is written in `forward_model_copy_number/`. The folder contains `CN_prot_{prot}.txt` files for each protein, which have the mean and standard deviation of protein copy number at each time point. -Here, we plot the comparison between the experimental data used in model construction and the set of trajectory models. This analysis includes the cross-correlation coefficient between the experimental EM density and the forward density of the set of sufficiently good scoring modeled structures in the highest weighted trajectory (a), as well as comparisons between experimental and modeled protein copy numbers for proteins A (b), B (c), and C (d). Here, we see the model is in good agreement with the data used to construct it. +Here, we plot the comparison between the experimental data used in model construction and the set of trajectory models. This analysis includes the cross-correlation coefficient between the experimental EM density and the forward density of the set of sufficiently good scoring modeled structures in the highest weighted trajectory model (a), as well as comparisons between experimental and modeled protein copy numbers for proteins A (b), B (c), and C (d). Here, we see the model is in good agreement with the data used to construct it. \image html Spatiotemporal_Assessment_Included.png width=1200px ## Validation against data not used in model construction {#trajectory_validation} -Finally, we aim to compare the model to data not used in model construction. Specifically, we reserved SAXS data for model validation. We aimed to compare the forward scattering profile from the centroid model of each snapshot to the experimental profile. To make this comparison, we wrote functions that converted each centroid RMF to a PDB (`convert_rmfs`), copied the experimental SAXS profiles to the appropriate folder (`copy_SAXS_dat_files`), and ran [FoXS](https://integrativemodeling.org/tutorials/foxs/foxs.html) on each PDB to evaluate its agreement to the experimental profile (`process_foxs`). +Finally, we aim to compare the model to data not used in model construction. Specifically, we reserved SAXS data for model validation. We aimed to compare the forward scattering profile from the centroid structural model of each snapshot model to the experimental profile. To make this comparison, we wrote functions that converted each centroid RMF to a PDB (`convert_rmfs`), copied the experimental SAXS profiles to the appropriate folder (`copy_SAXS_dat_files`), and ran [FoXS](https://integrativemodeling.org/tutorials/foxs/foxs.html) on each PDB to evaluate its agreement to the experimental profile (`process_foxs`). \code{.py} ## 4 - comparison of the model to data used in modeling (SAXS, native pdb of final complex) @@ -236,7 +236,7 @@ print("") print("") \endcode -The output of this analysis is written to `SAXS_comparison`. Standard FoXS outputs are available for each snapshot (`snapshot{state}_{time}.*`). In particular, the `.fit` files include the forward and experimental profiles side by side, with the \f$\chi^2\f$ for the fit. Further, the `{time}_FoXS.*` files include the information for all snapshots at that time point, including plots of each profile in comparison to the experimental profile (`{time}_FoXS.png`). +The output of this analysis is written to `SAXS_comparison`. Standard FoXS outputs are available for each snapshot model (`snapshot{state}_{time}.*`). In particular, the `.fit` files include the forward and experimental profiles side by side, with the \f$\chi^2\f$ for the fit. Further, the `{time}_FoXS.*` files include the information for all snapshot models at that time point, including plots of each profile in comparison to the experimental profile (`{time}_FoXS.png`). As our model was generated from synthetic data, the ground truth structure is known at each time point. In addition to validating the model by assessing its comparison to SAXS data, we could approximate the model accuracy by comparing the snapshot model to the PDB structure, although this comparison is not perfect as the PDB structure was used to inform the structure of *rigid bodies* in the snapshot model. To do so, we wrote a function (`RMSD`) that calculates the RMSD between each structural model and the orignal PDB. @@ -250,13 +250,13 @@ print("") print("") \endcode -The output of this function is written in `RMSD_calculation_output`. The function outputs `rmsd_{state}_{time}.png` files, which plots the RMSD for each model within each snapshot. This data is then summarized in `RMSD_analysis.txt`, which includes the minimum RMSD, average RMSD, and number of snapshots in each snapshot model. +The output of this function is written in `RMSD_calculation_output`. The function outputs `rmsd_{state}_{time}.png` files, which plots the RMSD for each structural model within each snapshot model. This data is then summarized in `RMSD_analysis.txt`, which includes the minimum RMSD, average RMSD, and number of structural models in each snapshot model. -Here, we plot the results for assessing the spatiotemporal model with data not used to construct it. Comparisons are made between the centroid structure of the most populated cluster in each snapshot at each time point and the experimental SAXS profile for 0 (a), 1 (b), and 2 (c) minutes. Further, we plot both the sampling precision (dark red) and the RMSD to the PDB structure (light red) for each snapshot in the highest trajectory model (d). +Here, we plot the results for assessing the spatiotemporal model with data not used to construct it. Comparisons are made between the centroid structure of the most populated cluster in each snapshot model at each time point and the experimental SAXS profile for 0 (a), 1 (b), and 2 (c) minutes. Further, we plot both the sampling precision (dark red) and the RMSD to the PDB structure (light red) for each snapshot model in the highest trajectory model (d). \image html Spatiotemporal_Assessment_Unused.png width=1200px -To quantitatively compare the model to SAXS data, we used the \f$\chi^2\f$ to compare each snapshot model to the experimental profile. We note that the \f$\chi^2\f$ are substantially lower for the models along the highest trajectory (1_0min, 1_1min, and 1_2min) than for other models, indicating that the highest weighted trajectory is in better agreement with the experimental SAXS data than other possible trajectories. +To quantitatively compare the model to SAXS data, we used the \f$\chi^2\f$ to compare each snapshot model to the experimental profile. We note that the \f$\chi^2\f$ are substantially lower for the models along the highest trajectory model (1_0min, 1_1min, and 1_2min) than for other models, indicating that the highest weighted trajectory model is in better agreement with the experimental SAXS data than other possible trajectory models. \image html Chi2_Table.png width=600px diff --git a/modeling/Trajectories/Trajectories_Assessment/trajectories_assessment.py b/modeling/Trajectories/Trajectories_Assessment/trajectories_assessment.py index 527c549d2..5caf3d46f 100644 --- a/modeling/Trajectories/Trajectories_Assessment/trajectories_assessment.py +++ b/modeling/Trajectories/Trajectories_Assessment/trajectories_assessment.py @@ -33,24 +33,29 @@ -analysis (from IMP.spatiotemporal) """ # 1 - copy_files_for_data (copy all relevant files into 'data' directory) -def copy_files_for_data(state_dict, custom_source_dir1 = None, custom_source_dir2 = None): +def copy_files_for_data(state_dict, custom_source_dir1 = None, custom_source_dir2 = None, custom_source_dir3 = None): """ - Copies three types of files important to generate two independent trajectory models (A and B): + Copies three types of files important to generate trajectory models: -.config files created with start_sim.py in Snapshot_Modeling (source_dir1) -time-dependent stoichiometry data for each timepoint. Data should be presented in .csv file. With this function all csv file in source_dir2 will be copied. These .csv files will be used in the exp_comp dictionary in create_DAG function -scoresA and scoresB for each snapshot created with imp sampcon exhaust - (source_dir1 + snapshot + good_scoring_models) + (source_dir1 + snapshot + good_scoring_models) are merged into total score .txt using merge_scores helper function. + All copied files are gathered in newly created './data/' directory, where everything is prepared for create_DAG + function. + :param state_dict (dict): state_dict: dictionary that defines the spatiotemporal model. The keys are strings that correspond to each time point in the stepwise temporal process. Keys should be ordered according to the steps in the spatiotemporal process. The values are integers that correspond to the number of possible states at that timepoint. - :param custom_source_dir1 (optional - str): Custom path to snapshot modeling dir (start_sim.py), to copy .config - files and scoresA/scoresB (custom_source_dir1 + snapshot{state}_{time} + 'good_scoring_models') + :param custom_source_dir1 (optional - str): Custom path to heterogeneity modeling dir (heterogeneity_modeling.py), + to copy .config files :param custom_source_dir2 (optional - str): Custom path to stoichiometry data dir + :param custom_source_dir3 (optional - str): Custom path to snapshot modeling dir (start_sim.py), to copy .config + files and to access scoresA/scoresB (custom_source_dir3 + snapshot{state}_{time} + 'good_scoring_models') """ # Create the destination directory for all the data copied in this function destination_dir = './data/' @@ -60,7 +65,7 @@ def copy_files_for_data(state_dict, custom_source_dir1 = None, custom_source_dir if custom_source_dir1: source_dir1 = custom_source_dir1 else: - source_dir1 = '../../Snapshots/Snapshots_Modeling/' + source_dir1 = '../../Heterogeneity/Heterogeneity_Modeling/' # path to stoichiometry data dir if custom_source_dir2: @@ -68,6 +73,12 @@ def copy_files_for_data(state_dict, custom_source_dir1 = None, custom_source_dir else: source_dir2 = '../../Input_Information/gen_FCS/' + # path to snapshot modeling dir + if custom_source_dir3: + source_dir3 = custom_source_dir3 + else: + source_dir3 = '../../Snapshots/Snapshots_Modeling/' + # Copy all .config files from the first source directory to the destination directory try: for file_name in os.listdir(source_dir1): @@ -94,7 +105,7 @@ def copy_files_for_data(state_dict, custom_source_dir1 = None, custom_source_dir try: for time in state_dict.keys(): for state in range(1, state_dict[time] + 1): - snapshot_dir = os.path.join(source_dir1, f'snapshot{state}_{time}') + snapshot_dir = os.path.join(source_dir3, f'snapshot{state}_{time}') good_scoring_models_dir = os.path.join(snapshot_dir, 'good_scoring_models') if os.path.isdir(good_scoring_models_dir): for score_file in ['scoresA.txt', 'scoresB.txt']: diff --git a/modeling/Trajectories/Trajectories_Modeling/create_trajectories.py b/modeling/Trajectories/Trajectories_Modeling/create_trajectories.py index 7fca8a603..088aa5725 100644 --- a/modeling/Trajectories/Trajectories_Modeling/create_trajectories.py +++ b/modeling/Trajectories/Trajectories_Modeling/create_trajectories.py @@ -34,7 +34,7 @@ def merge_scores(fileA, fileB, outputFile): with open(outputFile, 'w') as output: output.writelines(merged_data) -def create_data_and_copy_files(state_dict, custom_source_dir1 = None, custom_source_dir2 = None): +def create_data_and_copy_files(state_dict, custom_source_dir1 = None, custom_source_dir2 = None, custom_source_dir3 = None): """ Copies three types of files important to generate trajectory models: -.config files created with start_sim.py in Snapshot_Modeling (source_dir1) @@ -52,20 +52,22 @@ def create_data_and_copy_files(state_dict, custom_source_dir1 = None, custom_sou stepwise temporal process. Keys should be ordered according to the steps in the spatiotemporal process. The values are integers that correspond to the number of possible states at that timepoint. - :param custom_source_dir1 (optional - str): Custom path to snapshot modeling dir (start_sim.py), to copy .config - files and to access scoresA/scoresB (custom_source_dir1 + snapshot{state}_{time} + 'good_scoring_models') + :param custom_source_dir1 (optional - str): Custom path to heterogeneity modeling dir (heterogeneity_modeling.py), + to copy .config files :param custom_source_dir2 (optional - str): Custom path to stoichiometry data dir + :param custom_source_dir3 (optional - str): Custom path to snapshot modeling dir (start_sim.py), to copy .config + files and to access scoresA/scoresB (custom_source_dir3 + snapshot{state}_{time} + 'good_scoring_models') """ # Create the destination directory if it does not exist (./data/). Here all the destination_dir = './data/' os.makedirs(destination_dir, exist_ok=True) - # Path to snapshot modeling dir + # Path to heterogeneity modeling dir if custom_source_dir1: source_dir1 = custom_source_dir1 else: - source_dir1 = '../../Snapshots/Snapshots_Modeling/' + source_dir1 = '../../Heterogeneity/Heterogeneity_Modeling/' # Path to stoichiometry data dir if custom_source_dir2: @@ -73,6 +75,12 @@ def create_data_and_copy_files(state_dict, custom_source_dir1 = None, custom_sou else: source_dir2 = '../../Input_Information/gen_FCS/' + # Path to snapshot modeling dir + if custom_source_dir3: + source_dir3 = custom_source_dir3 + else: + source_dir3 = '../../Snapshots/Snapshots_Modeling/' + # Copy all .config files from the first source directory to the destination directory try: for file_name in os.listdir(source_dir1): @@ -100,8 +108,8 @@ def create_data_and_copy_files(state_dict, custom_source_dir1 = None, custom_sou for state in range(1, state_dict[time] + 1): dir_name = f"snapshot{state}_{time}" good_scoring_path = "good_scoring_models" - file_a = os.path.join(source_dir1, dir_name, good_scoring_path, "scoresA.txt") - file_b = os.path.join(source_dir1, dir_name, good_scoring_path, "scoresB.txt") + file_a = os.path.join(source_dir3, dir_name, good_scoring_path, "scoresA.txt") + file_b = os.path.join(source_dir3, dir_name, good_scoring_path, "scoresB.txt") output_file = os.path.join(destination_dir, f"{state}_{time}_scores.log") # name of the output file try: @@ -110,7 +118,7 @@ def create_data_and_copy_files(state_dict, custom_source_dir1 = None, custom_sou merge_scores(file_a, file_b, output_file) # call helper function to merge files print(f"Scores for snapshot{state}_{time} have been merged and saved") else: # many things can go wrong here, so it is good to know where is the problem - print(f"Path doesn't exist: {source_dir1}") + print(f"Path doesn't exist: {source_dir3}") print(f"Files not found in directory: {dir_name}") print(f"Files not found in directory: {file_a}") print(f"Files not found in directory: {file_b}") diff --git a/modeling_empty/Trajectories/Trajectories_Modeling/create_trajectories.py b/modeling_empty/Trajectories/Trajectories_Modeling/create_trajectories.py index 7fca8a603..088aa5725 100644 --- a/modeling_empty/Trajectories/Trajectories_Modeling/create_trajectories.py +++ b/modeling_empty/Trajectories/Trajectories_Modeling/create_trajectories.py @@ -34,7 +34,7 @@ def merge_scores(fileA, fileB, outputFile): with open(outputFile, 'w') as output: output.writelines(merged_data) -def create_data_and_copy_files(state_dict, custom_source_dir1 = None, custom_source_dir2 = None): +def create_data_and_copy_files(state_dict, custom_source_dir1 = None, custom_source_dir2 = None, custom_source_dir3 = None): """ Copies three types of files important to generate trajectory models: -.config files created with start_sim.py in Snapshot_Modeling (source_dir1) @@ -52,20 +52,22 @@ def create_data_and_copy_files(state_dict, custom_source_dir1 = None, custom_sou stepwise temporal process. Keys should be ordered according to the steps in the spatiotemporal process. The values are integers that correspond to the number of possible states at that timepoint. - :param custom_source_dir1 (optional - str): Custom path to snapshot modeling dir (start_sim.py), to copy .config - files and to access scoresA/scoresB (custom_source_dir1 + snapshot{state}_{time} + 'good_scoring_models') + :param custom_source_dir1 (optional - str): Custom path to heterogeneity modeling dir (heterogeneity_modeling.py), + to copy .config files :param custom_source_dir2 (optional - str): Custom path to stoichiometry data dir + :param custom_source_dir3 (optional - str): Custom path to snapshot modeling dir (start_sim.py), to copy .config + files and to access scoresA/scoresB (custom_source_dir3 + snapshot{state}_{time} + 'good_scoring_models') """ # Create the destination directory if it does not exist (./data/). Here all the destination_dir = './data/' os.makedirs(destination_dir, exist_ok=True) - # Path to snapshot modeling dir + # Path to heterogeneity modeling dir if custom_source_dir1: source_dir1 = custom_source_dir1 else: - source_dir1 = '../../Snapshots/Snapshots_Modeling/' + source_dir1 = '../../Heterogeneity/Heterogeneity_Modeling/' # Path to stoichiometry data dir if custom_source_dir2: @@ -73,6 +75,12 @@ def create_data_and_copy_files(state_dict, custom_source_dir1 = None, custom_sou else: source_dir2 = '../../Input_Information/gen_FCS/' + # Path to snapshot modeling dir + if custom_source_dir3: + source_dir3 = custom_source_dir3 + else: + source_dir3 = '../../Snapshots/Snapshots_Modeling/' + # Copy all .config files from the first source directory to the destination directory try: for file_name in os.listdir(source_dir1): @@ -100,8 +108,8 @@ def create_data_and_copy_files(state_dict, custom_source_dir1 = None, custom_sou for state in range(1, state_dict[time] + 1): dir_name = f"snapshot{state}_{time}" good_scoring_path = "good_scoring_models" - file_a = os.path.join(source_dir1, dir_name, good_scoring_path, "scoresA.txt") - file_b = os.path.join(source_dir1, dir_name, good_scoring_path, "scoresB.txt") + file_a = os.path.join(source_dir3, dir_name, good_scoring_path, "scoresA.txt") + file_b = os.path.join(source_dir3, dir_name, good_scoring_path, "scoresB.txt") output_file = os.path.join(destination_dir, f"{state}_{time}_scores.log") # name of the output file try: @@ -110,7 +118,7 @@ def create_data_and_copy_files(state_dict, custom_source_dir1 = None, custom_sou merge_scores(file_a, file_b, output_file) # call helper function to merge files print(f"Scores for snapshot{state}_{time} have been merged and saved") else: # many things can go wrong here, so it is good to know where is the problem - print(f"Path doesn't exist: {source_dir1}") + print(f"Path doesn't exist: {source_dir3}") print(f"Files not found in directory: {dir_name}") print(f"Files not found in directory: {file_a}") print(f"Files not found in directory: {file_b}")