Mascope SDK

Python SDK for the Mascope mass spectrometry data analysis platform. Designed for researchers who want to load and analyze data from a Mascope server in Jupyter notebooks or Python scripts, or export it to use in other environments.

New to Python? The user docs include a step-by-step getting started guide that walks you from installing an editor to running your first tutorial notebook. It is also available on any Mascope instance under /docs/sdk/getting-started/.

Contents

• Installation
• Tutorial Notebooks
• Quick Start
• Configuration
• High-Level Loaders
• Peak Assignments
• Caching
• API Reference
• Examples
• For Developers

Installation

Prerequisites

• Python 3.10+: python.org/downloads
• An IDE that supports Jupyter notebooks: VS Code with Jupyter and Data Wrangler extensions is recommended

Set up a virtual environment

python -m venv .venv

Activate it (Windows):

.venv\Scripts\activate

Or on macOS/Linux:

source .venv/bin/activate

Install the SDK

pip install mascope_sdk

Or with uv:

uv add mascope_sdk

To run the bundled tutorial notebooks, install with the examples extra instead - it adds the plotting and analysis libraries the notebooks use (plotly + nbformat, matplotlib, numpy, scipy, ipykernel):

pip install "mascope_sdk[examples]"   # or: uv add "mascope_sdk[examples]"

Tutorial Notebooks

The best way to learn the SDK is to walk through the bundled example notebooks. They cover everything from basic setup to advanced analysis workflows.

The notebooks need the examples extra (see Installation): pip install "mascope_sdk[examples]".

Copy them to your project directory:

import mascope_sdk

mascope_sdk.copy_examples("./tutorials")

This creates a tutorials/ folder with the following notebooks:

Open them in VS Code (or any Jupyter-compatible IDE) and run the cells. Each notebook is self-contained, just make sure your .env credentials are set up first (see Configuration).

Existing files are never overwritten, so you can safely re-run copy_examples after an SDK update to get new notebooks.

Quick Start

1. Configure credentials

Create a .env file in your working directory (or any parent directory):

MASCOPE_URL=https://example.mascope.app
MASCOPE_ACCESS_TOKEN=your-api-token

Tip: Generate the API token in your Mascope instance's user settings.

2. Use the SDK

from mascope_sdk import MascopeClient

# Auto-loads credentials from .env
mascope = MascopeClient(workspace="My Workspace")

# List datasets (returns a DataFrame)
datasets = mascope.datasets.list()

# List batches by dataset name
batches = mascope.batches.list("My Dataset")

# List samples from a single batch (raises if ambiguous)
samples = mascope.samples.list(batch="My Batch")

# List samples from all matching batches (containing the given keyword)
samples = mascope.samples.list(batches="Uronium")

# Load peaks across all samples in matching batches
peaks = mascope.load_peaks(dataset="My Dataset", batches="Uronium")

# Load peaks across a subset of samples, within matching batches
peaks = mascope.load_peaks(dataset="My Dataset", batches="Uronium", samples="12:")

# Plot a sample spectrum (based on sample id)
spectrum = mascope.samples.get_spectrum(sample_id=samples.iloc[0]["sample_item_id"])

import matplotlib.pyplot as plt

plt.scatter(spectrum["mz"], spectrum["intensity"])
plt.xlabel("m/z")
plt.ylabel("Intensity")
plt.show()

See more examples below.

Configuration

The MascopeClient can be configured in three ways (in override priority order):

1. Constructor parameters (highest priority):

   Initialize client with parameters:

   mascope = MascopeClient(
       url="https://example.mascope.app",
       access_token="your-token",
       workspace="My Workspace",
   )
   

2. Environment variables:

   Set environment variables:

   export MASCOPE_URL=https://example.mascope.app
   export MASCOPE_ACCESS_TOKEN=your-token
   

   Initialize client (parameters are read from the environment unless overridden):

   mascope = MascopeClient()
   

3. .env file (recommended for notebooks):

   Create .env file in the project directory:

   MASCOPE_URL=https://example.mascope.app
   MASCOPE_ACCESS_TOKEN=your-token
   

   Initialize client (parameters are read from the .env file unless overridden):

   mascope = MascopeClient()
   

Workspace Selection

The workspace parameter selects which workspace to operate on. It accepts a name, substring, or ID:

# Explicit workspace selection
mascope = MascopeClient(workspace="My Workspace")

If omitted and your account belongs to exactly one workspace, it is auto-selected. If you belong to multiple workspaces, a ConfigurationError is raised listing the available options.

High-Level Loaders

The SDK provides four convenience loaders that handle dataset/batch/sample resolution, concurrent requests, and progress bars automatically. These are the recommended way to load data for analysis.

load_peaks: Peaks across batches

Load averaged peaks ("sum spectrum") for all samples across one or more batches, returned as a single DataFrame enriched with batch and sample metadata.

The batches / samples filters are case-insensitive literal substrings, so one filter can select several batches at once. Pass exact=True to match a single batch by its full name, or a compiled re.Pattern for a regex.

import re

# All peaks from every batch whose name contains "Uronium"
peaks = mascope.load_peaks(dataset="My Dataset", batches="Uronium")

# Exactly one batch, by full name
peaks = mascope.load_peaks(dataset="My Dataset", batches="Uronium 2026-01", exact=True)

# Regex: batches from 2025 or 2026
peaks = mascope.load_peaks(
    dataset="My Dataset", batches=re.compile("2025|2026", re.IGNORECASE)
)

# Filter by sample name
peaks = mascope.load_peaks(dataset="My Dataset", samples="blank")

# All peaks from every batch (skip confirmation prompt)
peaks = mascope.load_peaks(dataset="My Dataset", confirm_above=None)

# Without match data, areas only
peaks = mascope.load_peaks(dataset="My Dataset", matches=False, heights=False)

load_peak_timeseries: Intra-sample timeseries

Load per-scan intensity timeseries for peaks matching a compound, ion, or isotope across batches. Provide exactly one of compound, ion, or isotope — the value can be a formula or compound name. Pass a list to load multiple targets in a single call (peaks are discovered once per sample).

# Timeseries for all peaks matched to Urea (by name or formula)
ts = mascope.load_peak_timeseries(
    dataset="My Dataset",
    batches="Uronium",
    compound="Urea",  # or compound="CH4N2O"
)

# Multiple compounds in one call
ts = mascope.load_peak_timeseries(
    dataset="My Dataset",
    compound=["Urea", "Lactic acid"],
)

# Plot per-sample timeseries
import matplotlib.pyplot as plt

for name, group in ts.groupby("sample_item_name"):
    plt.plot(group["time"], group["height"], label=name)
plt.legend()
plt.show()

load_peaks_by_stage: Stage-based peak loading

Load averaged peaks for distinct time-range stages of a single sample. Useful when a measurement has phases (e.g. blank, sample introduction, wash).

stages = [
    (0, 30, "blank"),
    (30, 120, "sample"),
    (120, 180, "wash"),
]

peaks = mascope.load_peaks_by_stage(sample="My Sample", stages=stages)

# Compare areas between stages
peaks.groupby("stage_name")["area"].sum()

The sample parameter accepts a sample name or ID. Stage tuples can be (t_min, t_max) or (t_min, t_max, name).

Key columns: stage, stage_name, t_min, t_max, plus all columns from get_peaks.

Peaks left out of a time range

A stage, like any get_peaks(..., t_min=, t_max=), is averaged from each peak's per-scan timeseries, and the server computes a timeseries only when it is first requested. Matching requests the peaks it matches, so the ones still without are mostly the unmatched peaks - and a time-ranged read leaves those out. The frame is then shorter than the same read without a time range, and nothing in its rows shows it.

The server says so in a warning, which the SDK logs and attaches to the frame:

peaks = mascope.load_peaks_by_stage(sample=sample_id, stages=stages)
# WARNING | API warning: 212 peak(s) were excluded because their timeseries
#           have not been computed yet. ... SDK:
#           samples.compute_peak_timeseries('...') computes them.

peaks.attrs["warnings"]  # [] when nothing was left out

When every peak is left out - a sample that was detected and never matched has no timeseries at all - the result is an empty frame that carries the warning, not None. None still means that nothing was found and nothing was warned about.

compute_peak_timeseries requests the timeseries of every peak that has none, which computes and stores them; a time-ranged read made afterwards includes all peaks:

if peaks is not None and peaks.attrs["warnings"]:
    mascope.samples.compute_peak_timeseries(sample_id)
    peaks = mascope.load_peaks_by_stage(sample=sample_id, stages=stages)

It is slow where many peaks are missing: the API computes one peak per request, and the server reads the sample's data file back for each, so a sample that was never matched can take thousands of requests. Give it mz_min / mz_max, or peak_ids=[...], to compute only what the analysis needs. Peaks that already have a timeseries are skipped, so it can be interrupted and run again, and it goes on where it stopped.

Older servers. An empty attrs["warnings"] means "nothing was left out" from a server newer than 1.10.1. Servers up to 1.10.1 warn only while the read still lists some peaks: one that left out every peak is answered exactly as a range that holds none. The SDK cannot tell the two apart there, so an empty time-ranged read from such a server carries a warning of the SDK's own that says it may be either. Their warning also still ends "Re-run peak detection", which does not bring the peaks back; the SDK's hint that follows it in the log is the one to act on.

Combining loads. pd.concat keeps attrs only when every frame's are equal, and the warning carries a count - so two samples that are both short differ, and pd.concat drops their warnings and their provenance. Combine loads with mascope.concat, which keeps both:

peaks = mascope.concat(
    mascope.load_peaks_by_stage(sample=sample_id, stages=stages)
    for sample_id in sample_ids
)

peaks.attrs["warnings"]  # what any of the loads warned, each once
peaks.attrs["provenance"]  # kept when every load came from the same build

attrs["warnings"] is on the frames get_peaks, load_peaks and load_peaks_by_stage return, with the caveats every attrs entry has (see Provenance): read it from the frame the loader returned, or from mascope.concat of several.

load_batch_ledger: The batch ledger

Load a batch's batch ledger — the batch-primary record of peak assignment: one batch peak per species across the batch's samples, with the consensus formula and tier the samples' assignments vote for, and one member per sample the species was seen in, carrying that sample's own reading of the peak. Every processed sample folds in as it arrives, so the ledger is complete without a per-sample run in sight.

# One row per member: the whole ledger of every matching batch, flat
ledger = mascope.load_batch_ledger(dataset="My Dataset", batches="Uronium")
ledger.to_csv("ledger.csv", index=False)  # a short way to any format

# The species table rides along: one row per batch peak
species = ledger.attrs["batch_peaks"]
species.groupby("sample_batch_name")["consensus_tier"].value_counts()

# Or the species table alone
species = mascope.load_batch_ledger(dataset="My Dataset", members=False)

Key columns of a member row: the anchor's batch_peak_id, batch_mz, consensus_formula, consensus_tier, support_fraction, n_present, curated (pinned by hand for the whole batch), beside the member's sample_item_name, sample_peak_id, mz, intensity, assigned_formula, source, tier, role, fit_score — so a sample that dissents from the batch reads as one row saying both. Per batch, the same reads are mascope.batch_peaks.list(batch_id), .members(batch_id, sample_id=...) and .verdicts(batch_id) (the batch-level verdicts recorded on its species). The app's Batch peaks pane exports the same rows as a CSV from its view menu.

load_assignments: Peak assignments across batches

Load the persisted peak-assignment results (see Peak assignments) of every sample across one or more batches, concatenated into a single DataFrame enriched with batch and sample metadata — the peak-assignment counterpart of load_peaks.

Read-only: each sample contributes its latest completed assignment run; samples without one are skipped (and logged), not assigned on the fly.

# Assignments of every sample in matching batches
assignments = mascope.load_assignments(dataset="My Dataset", batches="Uronium")

# Confidently assigned peaks that came from the untargeted stage
assignments = mascope.load_assignments(
    dataset="My Dataset", tier="assigned", source="untargeted"
)

# Tier breakdown per sample
assignments.groupby(["sample_item_name", "tier"]).size()

Key columns: sample_batch_name, sample_item_name, datetime_utc, plus all columns from peak_assignments.get (one row per observed peak; core rows only — fetch alternatives/provenance per assignment with peak_assignments.detail).

Confirmation prompt

load_peaks, load_peak_timeseries, and load_assignments show an interactive confirmation prompt when the number of samples exceeds confirm_above. Defaults are 100 for load_peaks and load_assignments, and 20 for load_peak_timeseries. This prevents accidentally launching hundreds of concurrent requests from a notebook cell. Set confirm_above=None to disable.

Provenance

Every frame a high-level loader returns carries the provenance of the server that served it, on df.attrs["provenance"]: the deployment's id, the Mascope version it runs, the match-score and peak-assignment engine versions in force, and when this client first heard from that build. Keep it with what you derive from the frame, and a published figure can name the build its numbers came from.

peaks = mascope.load_peaks(dataset="My Dataset", batches="Uronium")

peaks.attrs["provenance"]
# {'provenance_version': 1,
#  'generated_utc': '2026-10-01T12:00:00Z',
#  'deployment_id': 'k3J9xQ2mP0aB7cD1',
#  'produced_with': {'mascope_version': 'vX.Y.Z',
#                    'match_score_version': 1,
#                    'peak_assignment_engine_version': '0.5.0'}}

# The same block, asked directly
mascope.provenance()

The versions describe the server as it runs when asked. A result it stored earlier was computed by whichever build ran then; where a record carries provenance of its own, as a peak-assignment run does on df.attrs["run"], that record is the authority for it.

Frames loaded from one deployment and build carry the same block, so pd.concat of several loads keeps it. Frames from different builds - the server was updated between two loads - carry different blocks, and pd.concat drops the attribute: no single build produced the result. pd.concat compares the whole of attrs, so the same happens when two peak loads differ in attrs["warnings"]. That is not only a load short of peaks beside a whole one: the warning carries a count, so any two loads that are both short differ, which is the usual case for a loop over samples. mascope.concat combines loads without that: it keeps every load's warnings, and the provenance when every load carries the same block.

The attribute is best effort, in two ways. A server that predates it (GET /api/provenance answers 404) returns its frames without the attribute, and the load succeeds as before; so does any other failure to ask, which holds the frame back for about 15 seconds at most. And pandas does not reliably carry attrs through other operations - merge, copy and the like - so read it from the frames the loaders returned, or from their concatenation: the same caveat attrs["run"] and attrs["batch_peaks"] live with.

Keeping it when you save a frame

attrs live only on the DataFrame: saving it as CSV or Excel drops them without a word. Write the block beside the file instead:

import json

peaks.to_csv("peaks.csv", index=False)
with open("peaks.provenance.json", "w", encoding="utf-8") as f:
    json.dump(peaks.attrs["provenance"], f, indent=2)

Parquet keeps attrs inside the file - peaks.to_parquet("peaks.parquet"), and pd.read_parquet brings them back - as long as pandas is 2.1 or newer and pyarrow is installed, neither of which the SDK itself requires. A load_batch_ledger frame is the exception: its attrs also hold the species table, a DataFrame that Parquet cannot keep there, so to_parquet raises. Save the species table as a file of its own first:

ledger = mascope.load_batch_ledger(dataset="My Dataset")

species = ledger.attrs.pop("batch_peaks")
species.to_parquet("species.parquet")
ledger.to_parquet("ledger.parquet")  # keeps attrs["provenance"]

Peak Assignments

Mascope's peak-centric assignment engine assigns a composition to every observed peak of a sample — database-known targets first (Stage A), then untargeted composition search (Stage B) — arbitrates a single owner per peak, and files each assignment into a confidence tier (assigned | candidate | below_assignability | unassigned). The tier is read off the row's evidence — its fit_score weighted by the chemical plausibility of the assigned formula — under the run's tier_bands. fit_score is served alongside it, unchanged, as the pure fit measurement. Runs are launched from the Mascope app and persisted; the SDK reads the results (it does not trigger runs).

The top tier used to be called identified. The API still accepts that spelling wherever a tier is sent and normalises it to assigned, so scripts written against the old vocabulary keep working.

This coexists with targeted matching (mascope.matching, get_peaks match columns): a database-sourced assignment is the targeted result, anchored on the peak.

# Run history of a sample, newest first
runs = mascope.peak_assignments.list_runs(sample_id)

# The whole ledger of the latest completed run: one row per observed peak.
# Pages through the API internally; run metadata rides on df.attrs["run"].
assignments = mascope.peak_assignments.get(sample_id)
assignments.attrs["run"]["engine_version"]
assignments["tier"].value_counts()

# Server-side filters (a bad value raises ValidationError naming the accepted set)
assigned = mascope.peak_assignments.get(sample_id, tier="assigned")
stage_b = mascope.peak_assignments.get(sample_id, source="untargeted")
curated = mascope.peak_assignments.get(sample_id, source="manual")

# A specific (e.g. older) run
old = mascope.peak_assignments.get(
    sample_id, run_id=runs.iloc[-1]["peak_assignment_run_id"]
)

# Full detail of one assignment: alternatives considered + scoring provenance
full = mascope.peak_assignments.detail(
    sample_id, assignments.iloc[0]["peak_assignment_id"]
)

Key get() columns: sample_peak_mz, sample_peak_intensity, role (M0 | iso_child | reagent | artifact | unassigned), assigned_formula, ion_formula, isotope_formula, source (database | untargeted | manual), fit_score, evidence, mz_error_ppm, tier, p_correct, and target_compound_id/target_ion_id for database-sourced assignments.

Hand-curated rows (source: manual)

A person can overrule the engine on a single row from the app's peak inspector — promote a close alternative, or commit a re-search hit. That row is persisted with source manual, so it leaves database/untargeted rather than joining them:

# Wrong: these two no longer sum to the run
stage_a = mascope.peak_assignments.get(sample_id, source="database")
stage_b = mascope.peak_assignments.get(sample_id, source="untargeted")

# Right: read the run once and split it locally, so nothing can fall out
assignments = mascope.peak_assignments.get(sample_id)
assignments.groupby("source", dropna=False).size()

Peaks nothing explained carry no source at all (None), which is why the groupby above passes dropna=False. A manual row is not necessarily an assigned one either: when an override displaces a compound, the isotopologues of that compound are stripped and end up source: manual, tier: unassigned, with no formula.

peak_assignments.detail() on a curated row returns a provenance.manual block — action, user_id, at, and previous (the displaced winner, kept verbatim), plus manual.demoted, the archive of the isotopologues the override stripped so committing that compound back restores them.

Which engine produced a run

A run's ledger does not say who computed it — the run does. list_runs (and df.attrs["run"]) carries:

This matters because reads default to the latest completed run whatever its engine, so a published run is what you get unless you ask for another. It is also what makes two engines comparable on one sample — read each run by id and join on sample_peak_id:

runs = mascope.peak_assignments.list_runs(sample_id)
runs[["engine", "engine_version", "tier_bands", "status"]]

mine = mascope.peak_assignments.get(sample_id, run_id=<a mascope run id>)
theirs = mascope.peak_assignments.get(sample_id, run_id=<an imported run id>)
side_by_side = mine.merge(theirs, on="sample_peak_id", suffixes=("_mascope", "_ext"))

Compare tiers only against each run's own tier_bands: the same word means different confidence under different thresholds. An imported run's p_correct is always empty — that column is Mascope's own calibrated judgement and an import may not write it.

For cross-sample analysis use the load_assignments loader; for a guided walk-through see tutorial notebook 10_peak_assignment.ipynb.

Caching

Dataset, batch, sample, and ionization mechanism listings are cached (in volatile memory) automatically after the first call. This speeds up repeated name resolution and avoids redundant API calls. When data on the server changes (e.g. new batch created), the cache needs to be cleared to reload the data on the next call. The cache is not persisted on disk, so restarting the kernel always clears the cache.

# Clear the cache when server data changes
mascope.clear_cache()

API Reference

MascopeClient

from mascope_sdk import MascopeClient

mascope = MascopeClient(workspace="My Workspace")

Resources

All list() methods accept names (or substrings) instead of IDs and return pd.DataFrame | None. A plain string filters case-insensitively as a literal substring — regex metacharacters carry no special meaning, so a name like "Sample (A)" matches as-is. To filter with a regular expression, pass a compiled pattern; case comes from its flags. For example, batches=re.compile("2025|2026") matches batch names containing "2025" or "2026".

mascope.datasets

mascope.batches

mascope.samples

A get_peaks over a time range (t_min / t_max) leaves out the peaks whose timeseries has not been computed yet; see Peaks left out of a time range.

list accepts exactly one of batch (must match a single batch; raises if ambiguous) or batches (returns samples from all matching batches, with an added sample_batch_name column).

mascope.sample_files

A sample names the file it was cut from in sample_file_id (mascope.samples.get(sample_id)["sample_file_id"]). get_scans returns one row per scan of that file with the scan's statistics (ScanNumber, StartTime in minutes, TIC, ScanType, MsType) and its trailer under the instrument's own labels, colon included ("FT Resolution:", "Conversion Parameter B:"). Scans of every MS order are in it and none is left out; filter on MsType for the MS1 scans alone. No peak is read for it, so it stays under a megabyte for a file whose centroids run to hundreds.

file_id = mascope.samples.get(sample_id)["sample_file_id"]
scans = mascope.sample_files.get_scans(
    file_id,
    fields=[
        "Conversion Parameter B:",
        "Conversion Parameter C:",
        "FT Resolution:",
        "MsType",
    ],
    first_scan=1,
    last_scan=100,
)

fields keeps the named columns (ScanNumber always comes with them); first_scan / last_scan, t_min / t_max (seconds) and polarity narrow the scans. The number of scans in the file and the file's own statistics ride on df.attrs["num_of_scans"] and df.attrs["stats_per_file"]. Only a file whose raw Orbitrap data is stored has a scan table; any other is refused with a ValidationError.

mascope.matching

mascope.peak_assignments

get reads the latest completed run unless run_id is given, and pages through the API internally — the whole run comes back as one DataFrame. See Peak Assignments.

mascope.ionization

Columns: ionization_mechanism_id, ionization_mechanism (the mechanism in the standard adduct notation: [M+H]+, [M-H]-, [M+Br]-, [M]+. for electron transfer), ionization_mechanism_polarity, shipped (whether Mascope ships the mechanism, so that every server holds it from its first start).

mascope.cheminfo

Examples

Load peaks and plot by compound

from mascope_sdk import MascopeClient

mascope = MascopeClient(workspace="My Workspace")

peaks = mascope.load_peaks(dataset="My Dataset", batches="Uronium")

# Filter to matched peaks and summarise by compound
matched = peaks[peaks["target_compound_formula"].notna()]
summary = matched.groupby("target_compound_formula")["area"].mean()
summary.sort_values(ascending=False).head(10).plot.barh()

Intra-sample timeseries

import matplotlib.pyplot as plt
from mascope_sdk import MascopeClient

mascope = MascopeClient(workspace="My Workspace")

ts = mascope.load_peak_timeseries(
    dataset="My Dataset",
    compound="Urea",
)

# Plot per-isotope timeseries for one sample
sample = ts[ts["sample_item_name"] == ts["sample_item_name"].iloc[0]]
for isotope, group in sample.groupby("target_isotope_formula"):
    plt.plot(group["time"], group["height"], label=isotope)
plt.xlabel("Time (s)")
plt.ylabel("Intensity")
plt.legend()
plt.show()

Compare stages within a sample

from mascope_sdk import MascopeClient

mascope = MascopeClient(workspace="My Workspace")

# First load samples so the name is cached for resolution
mascope.samples.list(batch="My Batch")

stages = [
    (0, 30, "blank"),
    (30, 120, "sample"),
    (120, 180, "wash"),
]

peaks = mascope.load_peaks_by_stage(sample="My Sample", stages=stages)
peaks.groupby("stage_name")[["area", "height"]].mean()

Low-level peak timeseries

import matplotlib.pyplot as plt
from mascope_sdk import MascopeClient

mascope = MascopeClient(workspace="My Workspace")

ts = mascope.samples.get_peak_timeseries(
    sample_id="sample-123",
    mz=180.063,
    mz_tolerance_ppm=5.0,
)

if ts is not None:
    plt.plot(ts["time"], ts["height"])
    plt.xlabel("Time (s)")
    plt.ylabel("Intensity")
    plt.title(f"Peak at m/z {ts['mz'].iloc[0]:.3f}")
    plt.show()

For Developers

Logging

The SDK logs operational info (batch resolution, request counts, etc.) via loguru. The default level is INFO.

Set the MASCOPE_SDK_LOG_LEVEL environment variable to change it:

MASCOPE_SDK_LOG_LEVEL=DEBUG    # verbose (HTTP requests, cache hits, etc.)
MASCOPE_SDK_LOG_LEVEL=WARNING  # quiet (only warnings and errors)

Or in Python before importing the SDK:

import os

os.environ["MASCOPE_SDK_LOG_LEVEL"] = "DEBUG"

from mascope_sdk import MascopeClient

SSL Verification

By default the SDK verifies SSL certificates. To disable verification (e.g. for local development with a self-signed certificate), set:

MASCOPE_SDK_VERIFY_SSL=false

Or pass it to the constructor:

mascope = MascopeClient(verify_ssl=False)

Error Handling

from mascope_sdk import MascopeClient
from mascope_sdk.exceptions import (
    AuthenticationError,
    NotFoundError,
    ConfigurationError,
)

try:
    mascope = MascopeClient()
    sample = mascope.samples.get("invalid-id")
except ConfigurationError:
    print("Missing MASCOPE_URL or MASCOPE_ACCESS_TOKEN")
except AuthenticationError:
    print("Invalid API token")
except NotFoundError:
    print("Sample not found")

Exception Hierarchy

• MascopeError — Base exception
  • ConfigurationError — Missing/invalid configuration
  • MascopeConnectionError — Network/connection issues
    • MascopeTimeoutError — Request timeout
  • MascopeAPIError — API errors (includes status_code, message, url)
    • AuthenticationError — 401/403 responses
    • NotFoundError — 404 responses
    • ValidationError — 422 responses
    • ServerError — 5xx responses

Project Structure

mascope_sdk/
├── __init__.py          # Public exports (MascopeClient, exceptions)
├── client.py            # MascopeClient: main entry point and high-level loader methods
├── exceptions.py        # Exception hierarchy
├── _http.py             # Low-level HTTP session (requests wrapper)
├── _resolve.py          # Name-to-ID resolution helpers
├── _loaders.py          # High-level loaders (load_peaks, load_peak_timeseries, load_peaks_by_stage, load_assignments)
├── _concurrent.py       # ThreadPoolExecutor wrapper with progress bars and cancellation
├── _agents.py           # Internal HTTP helpers for Mascope agents (file-agent)
├── resources/
│   ├── _base.py         # BaseResource: shared HTTP helpers and datetime coercion
│   ├── batches.py       # BatchesResource
│   ├── cheminfo.py      # CheminfoResource (m/z queries)
│   ├── datasets.py      # DatasetsResource
│   ├── ionization.py    # IonizationResource
│   ├── matching.py      # MatchingResource (compound matching)
│   ├── peak_assignments.py  # PeakAssignmentsResource (read persisted assignment runs)
│   ├── sample_files.py  # SampleFilesResource (a file's scan table)
│   ├── samples.py       # SamplesResource (peaks, spectra, timeseries)
│   └── workspaces.py    # WorkspacesResource
└── examples/            # Jupyter notebook examples

Key patterns:

• client.py owns the public API. High-level loaders (load_peaks, etc.) are thin wrappers that delegate to _loaders.py.
• resources/ contains one class per API domain. Each resource inherits BaseResource which provides _get() / _post() helpers and automatic datetime column coercion.
• _concurrent.py centralises ThreadPoolExecutor usage with run_concurrent(), which handles progress bars (tqdm), None-filtering, future cancellation on error, and the max_workers <= 8 guard.
• _resolve.py handles name matching (literal substring for strings, regex for compiled patterns) and name -> ID resolution (used by resources and loaders).
• Underscore-prefixed modules (_http, _loaders, _concurrent, _resolve, _agents) are internal — not part of the public API.