tacaswell

import matplotlib.pyplot as plt


def zoom_factory(ax,base_scale = 2.):
    def zoom_fun(event):
        # get the current x and y limits
        cur_xlim = ax.get_xlim()
        cur_ylim = ax.get_ylim()
        # set the range
        cur_xrange = (cur_xlim[1] - cur_xlim[0])*.5

tacaswell

import itertools
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.scale as mscale


def factory(break_point, slope_ratio):
    def forward(values):
        out = np.array(values, copy=True)
        out -= break_point

tacaswell

from types import SimpleNamespace
import matplotlib.image as mimage
from matplotlib.patches import Rectangle
from matplotlib.backends.backend_agg import RendererAgg
import matplotlib.transforms as mtransforms
import matplotlib.path as mpath
import matplotlib.colors as mcolors
import numpy as np

tacaswell

Hot-fixing and extending conda environments

Introduction

We deploy root-owned conda environments which are the basis of the data collection and analysis environments. On one hand because these are owned by root they are write-protected and ensure that users can not accidentally break the environment, on the other hand because they are write-protected they can not be upgraded or extended. While we want to run with a stable, standard, well understood software environment, we do need this

tacaswell

import numpy as np
import matplotlib
import matplotlib.lines
from matplotlib.artist import allow_rasterization
import matplotlib.pyplot as plt


class MatplotlibException(Exception):
    ...

tacaswell

#! /usr/bin/env python3


_facilities = {
    "SLAC/ALS": "America/Los_Angeles",
    "APS": "America/Chicago",
    "NSLS-II": "America/New_York",
    "DLS": "Europe/London",
    "MAXIV": "Europe/Paris",
    "SLS":  "Europe/Paris",

tacaswell

import string
from itertools import cycle
from six.moves import zip

def label_axes(fig, labels=None, loc=None, **kwargs):
    """
    Walks through axes and labels each.

    kwargs are collected and passed to `annotate`

tacaswell

Collection of notebooks an files for playing with gpcam data

1. explore_pgcam_data.ipynb -> demo of how to pulll reduced data out of the msgpack databroker
2. extract.py -> code run on xpd against raw data broker to sort out what raw runs we are interested in
3. reprocess.py -> code run to re-process the raw data and produce the reduced outputs

tacaswell

def format_frac(fr):
    '''Convert a/b to latex'''
    sp = str(fr).split('/')
    if len(sp) == 1:
        return sp[0]
    else:
        return r'$\frac{%s}{%s}$' % tuple(sp)
        
frac_size = 4

tacaswell

from event_model import compose_run
import datetime
import dateutil.parser
import itertools
from io import StringIO
import subprocess
import numpy as np
import functools

def extract_event(ts, batch):