Scientist Python SDK Example Script

[nh916]

This is the script that Sarah Av-Ron, one of the brilliant MIT scientists, used to upload her data into the CRIPT platform, using CRIPT Python SDK 2.1.0

import json
import os
import time
import uuid
from glob import glob
from pathlib import Path

import cript as c
import pandas as pd

if __name__ == "__main__":
    cript_host = "https://api.criptapp.org/"

    api_token = os.getenv("CRIPT_Token")

    storage_token = os.getenv("CRIPT_STORAGE_TOKEN")

    wells = ["A1", "A2", "A3", "A4", "B1", "B2", "B3", "B4", "C1", "C2", "C3", "C4"]

    with c.API(
        host=cript_host, api_token=api_token, storage_token=storage_token
    ) as api:
        project_paginator = api.search(
            node_type=c.Project,
            search_mode=c.SearchModes.EXACT_NAME,
            value_to_search="Polyester clear zone biodegradability",
        )

        project_api = project_paginator.current_page_results[0]

        project = c.load_nodes_from_json(json.dumps(project_api))

        print(project.json)
        print("\nOr more pretty\n")
        print(project.get_json(indent=2).json)

        collection = project.collection[0]

        polymer_path_1 = "/Volumes/Lab Member Files/Sarah/Biodegradation testing/"
        polymer_path_2 = (
            "/Volumes/Lab Member Files/Sarah/PNAS_biodegradation_paper/data_transfer/"
        )

        numbering = pd.read_csv(
            "/.../Documents/PhD/Manuscript_biodegradation/reaction_PNAS_numbering.csv",
            header=0,
        ).values

        directories = pd.read_csv(
            "/.../Documents/PhD/Manuscript_biodegradation/codes/all_reactions_directory.csv",
            index_col=0,
        )

        states = pd.read_csv(
            "/.../Documents/PhD/Manuscript_biodegradation/codes/all_physical_state.csv",
            index_col=0,
        )

        rates = pd.read_csv(
            "/.../Documents/PhD/Manuscript_biodegradation/codes/all_rates.csv",
            index_col=0,
        )

        monomers = pd.read_csv(
            "/.../Documents/PhD/Manuscript_biodegradation/codes/all_reactions_monomers.csv",
            index_col=0,
        )

        init = 640
        stop = -1

        for i, row in enumerate(numbering[init:stop]):
            reaction, number = row

            print("\n")
            print("\n")
            print("\n")
            print("############################################################")
            print("############################################################")
            print(f"Working on {i + init}, reaction {reaction}, polymer {number}")
            print("############################################################")
            print("############################################################")
            print("\n")
            print("\n")
            print("\n")

            ###############################################################
            ###############################################################
            ###############################################################
            # creating experiment
            # Material creation and properties

            polymer_name = f"PNAS polymer {number}"

            polymer_dir_name = directories["directory"][reaction]

            experiment = c.Experiment(
                name=f"Synthesis and clear zone biodegradation testing of {polymer_name}"
            )
            collection.experiment = [experiment]

            polymerprop = []

            rate = rates["rate"][reaction]
            if rate != 0:
                biodegradability = 1
            else:
                biodegradability = 0
            polymerprop.append(
                c.Property(
                    key="biodegradable", value=biodegradability, type="none", unit=None
                )
            )

            state = states["state"][reaction]
            if state == "v":
                phase = "liquide"
            else:
                phase = "solid"
            polymerprop.append(
                c.Property(key="phase", value=phase, type="none", unit=None)
            )

            monomer1 = monomers["monomer1"][reaction]
            monomer2 = monomers["monomer2"][reaction]
            bigSMILES = "{[][<]" + monomer1 + "[<],[>]" + monomer2 + "[>][]}"

            mat_poly = c.Material(
                name=polymer_name,
                identifiers=[{"bigsmiles": bigSMILES}],
                property=polymerprop,
                keyword=["polyesters"],
            )

            project.material = [mat_poly]

            ###############################################################
            ###############################################################
            ###############################################################
            # clear zone sampel preparation process
            process = c.Process(
                name=f"Clear zone biodegrataion test sample preparation",
                type="multistep",
                description="40 mg of polymer was added to a 5.54 mL (1 dram) vial with 0.8 mL of dichloromethane (DCM). The vial is left on a shaker for at least 45 min before making the sample. Double concentrated agar solution was prepared by adding 40 g of agar to 500 mL MilliQ and autoclaving. Basal medium was prepared by adding 2.56 g K2HPO4, 2.08 g KH2PO4, 1.0 g NH4Cl and 0.247 g anhydrous MgSO4 in 500 mL MilliQ water. The solution's pH was adjusted to 6.8 using NaOH and then filter sterilized. An 80 mL sterile autoclave vial was filled with 16 mL of the double concentrated growth basal medium and heated to 60 °C in a water bath. 20 mL of double concentrated agar at 60 °C was added to the bottle. 4 mL of double concentrated basal medium was added to the polymer vial. The vial was vigorously shaken to mix the aqueous solution and DCM, and the content was quickly poured into the autoclave bottle set at 60 °C. After waiting for 30 sec allowing most of the DCM to bubble off to prevent overflowing during homogenizing, the solution was mixed using a Scilogexe D500 homogenizer with a with S20F/ER20 probe head for 15 sec at 10 000 rpm. After 15 s, the solution was mixed again for another 15 sec. 10 mL of this mixed solution was added to a sterile conical tube containing 50 μL of solution A and 150 μL of solution B, where solution A was prepared by adding 1.0 g of ferric ammonium citrate and 0.1 g CaCl2 to 100 mL MilliQ water and then filter-sterilized, and solution B is a 1.0 M succinic acid solution adjusted to pH 6 with NaOH and filter sterilized. The conical tube was then turned upside down a few times to gently mix the solution without generating any bubbles. Finally, 0.8 mL was sterilely pipetted into 8 wells of a sterile 12- well plate (wells A1, A2, A3, A4, B1, B4, C1, and C4). The remaining 4 wells were filled with the same solution not containing any polymer as controls (wells B2, B3, C2, and C3). Paucimonas lemoignei was cultured in growth medium for about 30 hours in an incubator shaker set to 30 °C and 180 rpm, reaching an OD between 0.5 and 0.8. Wells in rows A and C of the well plate were inoculated with 1 μL of the grown bacteria. The drop was deposited at the center of the well. Plates were incubated for a total of 13 days at 30 °C with a humidity source within the chamber to prevent the agar from drying. The transmission of each well was monitored by imaging the wells at regular time intervals using an Opentron 1st generation robot with a 2.8-12mm Varifocal USB Webcam Mini Camera with adjustable focus and a 8x11 in Kaiser Slimlite Plano light tablet. Before each plate measurement, the lid of the 12 well plate was passed over a flame in a sterile environment to remove any water condensation which would interfere with imaging. For the first 72 hours, all samples were monitored every 8 hours. For the following 3 days (until the 6th day after inoculation), all samples were monitored every 24 hours, with the exception of samples where a clear zone could be detected with the naked eye, in which cases monitoring every 8 hours was maintained. After that the 6th day, all samples were monitored on days 8, 10 and 13.",
            )
            experiment.process += [process]

            ## load data node of measurement pictures
            picture_files = []
            measurements = sorted(
                glob(f"{polymer_path_1}{polymer_dir_name}/{polymer_dir_name}'_M*")
            )
            # print(measurements)
            for m, measurement in enumerate(measurements[:1]):
                m_time = measurement.split("M")[-1]
                for w, well in enumerate(wells):
                    # does_file_exist("{measurement}/ImgSam_{well}.png")
                    pic_file = c.File(
                        name=f"clear zone biodegradation testing picture - well {well} - {m_time} min",
                        source=f"{measurement}/ImgSam_{well}.png",
                        type="data",
                        extension=".png",
                    )
                    picture_files.append(pic_file)
            picture_data = c.Data(
                name="raw pictures from clear zone biodegradation testing",
                type="biodegradation_data",
                file=picture_files,
                sample_preparation=process,
            )

            experiment.data += [picture_data]

            ###############################################################
            ###############################################################
            ###############################################################
            ## load data node of colony binary image and centers
            process = c.Process(
                name=f"Clear zone data analysis - step 1 - center detection of wells",
                type="multistep",
                description="The last measurement image is used to determine the colony center and size for the full series of measurements of a given well. A median filter of kernel size 7 is applied to the image 10 times to increase contrast between the darker colony and the rest of the agar plate. All well images being cropped to be centered on the well, a mask is applied to the image by setting all pixels outside of a centered disk of radius 300 pixels to 100, to hide the well plate around the agar. The resulting image is then displayed, and the user must click on the colony, aiming for darker colored pixels. Say the color of the clicked pixel is c, all the pixels in the image that are of color below c+5 are set to 1, all others to 0. Using a function defined in the cv2 library, all pixels connected to the clicked pixel with a value of 1 are kept at 1, all others set to zero. The pixels set to 1 correspond to those part of the colony, giving the binary image of the colony. The center of the colony is set as the center of mass of the image, computed using the function center_of_mass from the scipy.ndimage module. The size of the colony is set as the maximum distance of two pixels part of the colony.",
            )
            experiment.process += [process]

            process = project.collection[0].experiment[0].process[1]

            data_files = []
            for w, well in enumerate(wells):
                data_file = c.File(
                    name=f"clear zone biodegradation colony binary image - well {well}",
                    source=f"{polymer_path_1}{polymer_dir_name}/center_colony_binary_{well}.npy",
                    type="data",
                    extension=".npy",
                )
                data_files.append(data_file)
            data_file = c.File(
                name=f"clear zone biodegradation center coordinates",
                source=f"{polymer_path_1}{polymer_dir_name}/centers.csv",
                type="data",
                extension=".csv",
            )
            data_files.append(data_file)

            data_file = c.File(
                name=f"clear zone biodegradation colony sizes",
                source=f"{polymer_path_1}{polymer_dir_name}/colonysize.csv",
                type="data",
                extension=".csv",
            )
            data_files.append(data_file)

            data_files_cript = c.Data(
                name="Clear zone data analysis - step 1 - center detection coordinates",
                type="biodegradation_data",
                file=data_files,
                sample_preparation=process,
            )

            experiment.data += [data_files_cript]

            ###############################################################
            ###############################################################
            ###############################################################
            ## Load mask images of polymers for shading curve extraction
            process = c.Process(
                name=f"Clear zone data analysis - step 2 - sample defect masking",
                type="multistep",
                description="The last measurement image is used to find defects in the sample (such as surface scratches, dust or other sample impurities). If there are any visible defects, the binary image containes zones of zero pixel value overlapping the defects, with ones everywhere else.",
            )

            experiment.process += [process]

            data_files = []
            for w, well in enumerate(wells):
                data_file = c.File(
                    name=f"clear zone biodegradation defect mask binary image - well {well}",
                    source=f"{polymer_path_1}{polymer_dir_name}/img_mask_{well}.npy",
                    type="data",
                    extension=".npy",
                )
                data_files.append(data_file)
            data_files_cript = c.Data(
                name="Clear zone data analysis - step 2 - sample defect masks",
                type="biodegradation_data",
                file=data_files,
                sample_preparation=process,
            )

            experiment.data += [data_files_cript]

            collection.experiment = [experiment]
            project.validate()
            print(project.get_json(indent=2).json)
            api.save(project)

            ###############################################################
            ###############################################################
            ###############################################################
            ## colony size calculation
            process = c.Process(
                name=f"Clear zone data analysis - step 3 - shading curve extraction",
                type="multistep",
                description="For each well and time measurement, a shading curve is generated. Starting from the detected center in the step 1, the shading curve has the value of the average color of a donut of 3-pixel thickness and of given radius. To have proper overlap of the curves, the two uninoculated controls containing no polymer (wells B2 and B3) are used to determine the y-axis displacement that each time measurement needed to overlap perfectly with the time zero curve; this gives the time-measurement displacement values.",
            )

            experiment.process += [process]

            process = project.collection[0].experiment[0].process[1]

            data_files = []
            for w, well in enumerate(wells):
                data_file = c.File(
                    name=f"clear zone biodegradation shading curves - well {well}",
                    source=f"{polymer_path_1}{polymer_dir_name}/rshade_all_time_{well}.csv",
                    type="data",
                    extension=".csv",
                )
                data_files.append(data_file)
            data_file = c.File(
                name=f"clear zone biodegradation shading curves time-measurement adjustement values",
                source=f"{polymer_path_1}{polymer_dir_name}/dy_sub_adjustment.csv",
                type="data",
                extension=".csv",
            )
            data_files.append(data_file)

            data_files_cript = c.Data(
                name="Clear zone data analysis - step 3 - shading curve and adjustement",
                type="biodegradation_data",
                file=data_files,
                sample_preparation=process,
            )

            experiment.data += [data_files_cript]

            ###############################################################
            ###############################################################
            ###############################################################
            ## cleaning out bad time measurments
            process = c.Process(
                name=f"Clear zone data analysis - step 4 - time measurements clean-out",
                type="multistep",
                description="To account for measurement errors such as improper placement of the plate in the holder or improper blackout of the measurement robot, the user is asked to delete time-specific shading curves when they fail to properly overlap with the other time measurements. For each well, defective time points are eliminated from future analysis steps.",
            )

            experiment.process += [process]

            process = project.collection[0].experiment[0].process[1]

            data_files = []
            for w, well in enumerate(wells):
                data_file = c.File(
                    name=f"clear zone biodegradation cleaned times - well {well}",
                    source=f"{polymer_path_2}{polymer_dir_name}/cleaned_times_{well}.npy",
                    type="data",
                    extension=".npy",
                )
                data_files.append(data_file)
            data_files_cript = c.Data(
                name="Clear zone data analysis - step 4 - cleaned time measurments",
                type="biodegradation_data",
                file=data_files,
                sample_preparation=process,
            )

            experiment.data += [data_files_cript]

            ###############################################################
            ###############################################################
            ###############################################################
            ## cleaning out bad time measurments
            process = c.Process(
                name=f"Clear zone data analysis - step 5 - biodegradation determination",
                type="multistep",
                description=' The shading curves are transformed into normalized OD curves by first adding the computed displacement, and then subtracting the time zero curve to all other shading curves for a given well before applying the log. The integration of each OD curve between distance to center of 3.7 and 7.0 is computed, and all the replicates\' integration values are plotted according to measurement time. The range of time points to include in the degradation "rate" calculation are selected where ther is the clearest linear trend, or selecting all the data if there is no trend. The sample is determined as biodegradable if the average slope of the replicates is more than that of the no polymer inoculated controls (wells C2 and C3), with a difference of at least 1 standard deviation. If the average slope changes signs when 1.5 standard deviation is added or subtracted, the sample is marked as non-degradable. The shape of the OD curves indicate the biodegradation behavior of the sample.',
            )

            experiment.process += [process]

            process = project.collection[0].experiment[0].process[1]

            data_files = []
            for w, well in enumerate(wells):
                data_file = c.File(
                    name=f"clear zone biodegradation intergration values - well {well}",
                    source=f"{polymer_path_2}{polymer_dir_name}/integration_ODdivOD0_{well}.npy",
                    type="data",
                    extension=".npy",
                )
                data_files.append(data_file)
            data_file = c.File(
                name=f"clear zone biodegradation intergration limites",
                source=f"{polymer_path_2}{polymer_dir_name}/intergration_coordinate_limits.npy",
                type="data",
                extension=".npy",
            )
            data_files.append(data_file)

            data_file = c.File(
                name=f"clear zone biodegradation intergration values",
                source=f"{polymer_path_2}{polymer_dir_name}/intergration_rates_fixed_limits.csv",
                type="data",
                extension=".csv",
            )
            data_files.append(data_file)

            data_file = c.File(
                name=f"clear zone biodegradation normalized OD and rate plot",
                source=f"{polymer_path_2}{polymer_dir_name}/plot_integration_ODdivOD0_with_rate.png",
                type="data",
                extension=".png",
            )
            data_files.append(data_file)

            data_file = c.File(
                name=f"clear zone biodegradation behavior",
                source=f"{polymer_path_2}{polymer_dir_name}/biodegradation_classification.npy",
                type="data",
                extension=".npy",
            )
            data_files.append(data_file)

            data_files_cript = c.Data(
                name="Clear zone data analysis - step 5 - biodegradabilty determination data",
                type="biodegradation_data",
                file=data_files,
                sample_preparation=process,
            )

            experiment.data += [data_files_cript]

            collection.experiment = [experiment]
            project.validate()
            print(project.get_json(indent=2).json)
            api.save(project)

[nh916]

This script is provided as a reference to spark ideas and review past implementations. While it serves as an example, it hasn't been fully certified. Please use it with caution. If you encounter any issues, kindly report them to us. Your feedback is valuable!