| @@ -1,29 +1,12 | |||
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1 | Prerequisites: | |
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2 | ||
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3 | Core: | |
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4 | -numpy 1.8.0 | |
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5 | -scipy | |
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6 | -math | |
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7 | -matplotlib | |
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8 | -h5py | |
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9 | -ftplib | |
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10 | -paramiko (optional for SendTFilesToServer) | |
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11 | -stuffr (optional for jroIO_hf) | |
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12 | -pyfits (Fits data) | |
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13 | ||
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14 | GUI: | |
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15 | -PyQt4 | |
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16 | -wxPython | |
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17 | ||
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18 | 1 | Signal Chain Installation: |
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19 | 2 | |
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20 | 1. Install numpy, matplotlib, TKAgg | |
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3 | 1. Install system dependencies: python-pip python-dev gfortran libpng-dev freetype* libblas-dev liblapack-dev libatlas-base-dev python-qt4 | |
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21 | 4 | 2. Install digital_rf_hdf5 module (developed by Haystack Observatory) |
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22 | 5 | if you want to use USRP data |
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23 | 6 | 3. untar schainpy-x.x.x.tar.gz |
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24 | 7 | 4. cd schainpy-x.x.x |
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25 | 8 | 5. execute: |
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26 |
[hostname]$ sudo p |
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9 | [hostname]$ sudo pip install ./ | |
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27 | 10 | 6. testing gui: |
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28 | 11 | [hostname]$ schainGUI (enter) |
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29 | 12 | |
| @@ -343,7 +343,7 class OperationConf(): | |||
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343 | 343 | |
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344 | 344 | self.parmConfObjList = [] |
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345 | 345 | |
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346 |
parmElementList = opElement. |
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346 | parmElementList = opElement.iter(ParameterConf().getElementName()) | |
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347 | 347 | |
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348 | 348 | for parmElement in parmElementList: |
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349 | 349 | parmConfObj = ParameterConf() |
| @@ -568,7 +568,7 class ProcUnitConf(): | |||
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568 | 568 | |
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569 | 569 | self.opConfObjList = [] |
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570 | 570 | |
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571 |
opElementList = upElement. |
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571 | opElementList = upElement.iter(OperationConf().getElementName()) | |
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572 | 572 | |
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573 | 573 | for opElement in opElementList: |
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574 | 574 | opConfObj = OperationConf() |
| @@ -798,7 +798,7 class ReadUnitConf(ProcUnitConf): | |||
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798 | 798 | |
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799 | 799 | self.opConfObjList = [] |
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800 | 800 | |
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801 |
opElementList = upElement. |
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801 | opElementList = upElement.iter(OperationConf().getElementName()) | |
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802 | 802 | |
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803 | 803 | for opElement in opElementList: |
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804 | 804 | opConfObj = OperationConf() |
| @@ -1026,7 +1026,7 class Project(): | |||
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1026 | 1026 | self.name = self.projectElement.get('name') |
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1027 | 1027 | self.description = self.projectElement.get('description') |
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1028 | 1028 | |
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1029 |
readUnitElementList = self.projectElement. |
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1029 | readUnitElementList = self.projectElement.iter(ReadUnitConf().getElementName()) | |
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1030 | 1030 | |
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1031 | 1031 | for readUnitElement in readUnitElementList: |
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1032 | 1032 | readUnitConfObj = ReadUnitConf() |
| @@ -1037,7 +1037,7 class Project(): | |||
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1037 | 1037 | |
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1038 | 1038 | self.procUnitConfObjDict[readUnitConfObj.getId()] = readUnitConfObj |
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1039 | 1039 | |
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1040 |
procUnitElementList = self.projectElement. |
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1040 | procUnitElementList = self.projectElement.iter(ProcUnitConf().getElementName()) | |
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1041 | 1041 | |
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1042 | 1042 | for procUnitElement in procUnitElementList: |
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1043 | 1043 | procUnitConfObj = ProcUnitConf() |
| @@ -312,7 +312,7 class Axes: | |||
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312 | 312 | decimationy = None |
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313 | 313 | |
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314 | 314 | __MAXNUMX = 200 |
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315 |
__MAXNUMY = |
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315 | __MAXNUMY = 100 | |
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316 | 316 | |
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317 | 317 | __MAXNUMTIME = 500 |
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318 | 318 | |
| @@ -501,13 +501,15 class Axes: | |||
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501 | 501 | xlen = len(x) |
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502 | 502 | ylen = len(y) |
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503 | 503 | |
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504 | decimationx = numpy.floor(xlen/self.__MAXNUMX) - 1 if numpy.floor(xlen/self.__MAXNUMX)>1 else 1 | |
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505 | decimationy = numpy.floor(ylen/self.__MAXNUMY) + 1 | |
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504 | decimationx = int(xlen/self.__MAXNUMX)+1 \ | |
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505 | if int(xlen/self.__MAXNUMX)>1 else 1 | |
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506 | decimationy = int(ylen/self.__MAXNUMY) \ | |
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507 | if int(ylen/self.__MAXNUMY)>1 else 1 | |
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506 | 508 |
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509 | x_buffer = x#[::decimationx] | |
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510 | y_buffer = y#[::decimationy] | |
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511 | z_buffer = z#[::decimationx, ::decimationy] | |
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507 | 512 | |
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508 | x_buffer = x[::decimationx] | |
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509 | y_buffer = y[::decimationy] | |
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510 | z_buffer = z[::decimationx, ::decimationy] | |
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511 | 513 | #=================================================== |
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512 | 514 | |
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513 | 515 | if self.__firsttime: |
| @@ -604,12 +606,14 class Axes: | |||
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604 | 606 | xlen = len(self.x_buffer) |
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605 | 607 | ylen = len(y) |
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606 | 608 | |
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607 |
decimationx = |
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608 | decimationy = numpy.floor(ylen/maxNumY) + 1 | |
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609 | decimationx = int(xlen/self.__MAXNUMX) \ | |
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610 | if int(xlen/self.__MAXNUMX)>1 else 1 | |
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611 | decimationy = int(ylen/self.__MAXNUMY) \ | |
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612 | if int(ylen/self.__MAXNUMY)>1 else 1 | |
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609 | 613 | |
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610 | x_buffer = self.x_buffer[::decimationx] | |
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611 | y_buffer = y[::decimationy] | |
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612 | z_buffer = z_buffer[::decimationx, ::decimationy] | |
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614 | x_buffer = self.x_buffer#[::decimationx] | |
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615 | y_buffer = y#[::decimationy] | |
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616 | z_buffer = z_buffer#[::decimationx, ::decimationy] | |
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613 | 617 | #=================================================== |
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614 | 618 | |
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615 | 619 | x_buffer, y_buffer, z_buffer = self.__fillGaps(x_buffer, y_buffer, z_buffer) |
| @@ -5,6 +5,8 import numpy | |||
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5 | 5 | from figure import Figure, isRealtime, isTimeInHourRange |
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6 | 6 | from plotting_codes import * |
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7 | 7 | |
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8 | import matplotlib.pyplot as plt | |
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9 | ||
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8 | 10 | class MomentsPlot(Figure): |
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9 | 11 | |
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10 | 12 | isConfig = None |
| @@ -446,11 +448,10 class WindProfilerPlot(Figure): | |||
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446 | 448 | # tmax = None |
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447 | 449 | |
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448 | 450 | x = dataOut.getTimeRange1(dataOut.outputInterval) |
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449 | # y = dataOut.heightList | |
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450 | 451 | y = dataOut.heightList |
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451 | ||
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452 | 452 | z = dataOut.data_output.copy() |
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453 | 453 | nplots = z.shape[0] #Number of wind dimensions estimated |
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454 | ||
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454 | 455 | nplotsw = nplots |
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455 | 456 | |
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456 | 457 | #If there is a SNR function defined |
| @@ -471,7 +472,8 class WindProfilerPlot(Figure): | |||
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471 | 472 | |
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472 | 473 | # showprofile = False |
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473 | 474 | # thisDatetime = dataOut.datatime |
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474 | thisDatetime = datetime.datetime.utcfromtimestamp(dataOut.ltctime) | |
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475 | #thisDatetime = datetime.datetime.utcfromtimestamp(dataOut.ltctime) | |
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476 | thisDatetime = datetime.datetime.now() | |
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475 | 477 | title = wintitle + "Wind" |
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476 | 478 | xlabel = "" |
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477 | 479 | ylabel = "Height (km)" |
| @@ -490,8 +492,8 class WindProfilerPlot(Figure): | |||
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490 | 492 | |
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491 | 493 | self.xmin, self.xmax = self.getTimeLim(x, xmin, xmax, timerange) |
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492 | 494 | |
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493 | if ymin == None: ymin = numpy.nanmin(y) | |
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494 | if ymax == None: ymax = numpy.nanmax(y) | |
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495 | #if ymin == None: ymin = numpy.nanmin(y) | |
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496 | #if ymax == None: ymax = numpy.nanmax(y) | |
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495 | 497 | |
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496 | 498 | if zmax == None: zmax = numpy.nanmax(abs(z[range(2),:])) |
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497 | 499 | #if numpy.isnan(zmax): zmax = 50 |
| @@ -501,9 +503,9 class WindProfilerPlot(Figure): | |||
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501 | 503 | if zmax_ver == None: zmax_ver = numpy.nanmax(abs(z[2,:])) |
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502 | 504 | if zmin_ver == None: zmin_ver = -zmax_ver |
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503 | 505 | |
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504 | if dataOut.data_SNR is not None: | |
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505 | if SNRmin == None: SNRmin = numpy.nanmin(SNRavgdB) | |
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506 | if SNRmax == None: SNRmax = numpy.nanmax(SNRavgdB) | |
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506 | # if dataOut.data_SNR is not None: | |
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507 | # if SNRmin == None: SNRmin = numpy.nanmin(SNRavgdB) | |
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508 | # if SNRmax == None: SNRmax = numpy.nanmax(SNRavgdB) | |
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507 | 509 | |
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508 | 510 | |
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509 | 511 | self.FTP_WEI = ftp_wei |
| @@ -518,8 +520,8 class WindProfilerPlot(Figure): | |||
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518 | 520 | |
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519 | 521 | self.setWinTitle(title) |
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520 | 522 | |
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521 | if ((self.xmax - x[1]) < (x[1]-x[0])): | |
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522 | x[1] = self.xmax | |
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523 | #if ((self.xmax - x[1]) < (x[1]-x[0])): | |
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524 | # x[1] = self.xmax | |
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523 | 525 | |
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524 | 526 | strWind = ['Zonal', 'Meridional', 'Vertical'] |
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525 | 527 | strCb = ['Velocity (m/s)','Velocity (m/s)','Velocity (cm/s)'] |
| @@ -561,7 +563,7 class WindProfilerPlot(Figure): | |||
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561 | 563 | thisDatetime=thisDatetime, |
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562 | 564 | update_figfile=update_figfile) |
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563 | 565 | |
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564 | if dataOut.ltctime + dataOut.outputInterval >= self.xmax: | |
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566 | if False and dataOut.ltctime + dataOut.outputInterval >= self.xmax: | |
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565 | 567 | self.counter_imagwr = wr_period |
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566 | 568 | self.isConfig = False |
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567 | 569 | update_figfile = True |
| @@ -778,7 +780,7 class ParametersPlot(Figure): | |||
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778 | 780 | |
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779 | 781 | |
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780 | 782 | |
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781 |
class Parameters |
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783 | class ParametersPlot(Figure): | |
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782 | 784 | |
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783 | 785 | __isConfig = None |
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784 | 786 | __nsubplots = None |
| @@ -86,7 +86,7 class SpectraPlot(Figure): | |||
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86 | 86 | save=False, figpath='./', figfile=None, show=True, ftp=False, wr_period=1, |
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87 | 87 | server=None, folder=None, username=None, password=None, |
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88 | 88 | ftp_wei=0, exp_code=0, sub_exp_code=0, plot_pos=0, realtime=False, |
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89 |
xaxis=" |
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89 | xaxis="velocity", **kwargs): | |
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90 | 90 | |
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91 | 91 | """ |
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92 | 92 | |
| @@ -104,6 +104,8 class SpectraPlot(Figure): | |||
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104 | 104 | zmax : None |
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105 | 105 | """ |
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106 | 106 | |
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107 | colormap = kwargs.get('colormap','jet') | |
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108 | ||
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107 | 109 | if realtime: |
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108 | 110 | if not(isRealtime(utcdatatime = dataOut.utctime)): |
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109 | 111 | print 'Skipping this plot function' |
| @@ -514,10 +516,10 class RTIPlot(Figure): | |||
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514 | 516 | |
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515 | 517 | def run(self, dataOut, id, wintitle="", channelList=None, showprofile='True', |
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516 | 518 | xmin=None, xmax=None, ymin=None, ymax=None, zmin=None, zmax=None, |
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517 |
timerange=None, |
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519 | timerange=None, | |
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518 | 520 | save=False, figpath='./', lastone=0,figfile=None, ftp=False, wr_period=1, show=True, |
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519 | 521 | server=None, folder=None, username=None, password=None, |
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520 | ftp_wei=0, exp_code=0, sub_exp_code=0, plot_pos=0): | |
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522 | ftp_wei=0, exp_code=0, sub_exp_code=0, plot_pos=0, **kwargs): | |
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521 | 523 | |
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522 | 524 | """ |
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523 | 525 | |
| @@ -535,6 +537,7 class RTIPlot(Figure): | |||
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535 | 537 | zmax : None |
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536 | 538 | """ |
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537 | 539 | |
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540 | colormap = kwargs.get('colormap', 'jet') | |
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538 | 541 | if not isTimeInHourRange(dataOut.datatime, xmin, xmax): |
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539 | 542 | return |
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540 | 543 | |
| @@ -18,6 +18,8 from matplotlib.ticker import FuncFormatter, LinearLocator | |||
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18 | 18 | #Actualizacion de las funciones del driver |
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19 | 19 | ########################################### |
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20 | 20 | |
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21 | # create jro colormap | |
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22 | ||
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21 | 23 | jet_values = matplotlib.pyplot.get_cmap("jet", 100)(numpy.arange(100))[10:90] |
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22 | 24 | blu_values = matplotlib.pyplot.get_cmap("seismic_r", 20)(numpy.arange(20))[10:15] |
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23 | 25 | ncmap = matplotlib.colors.LinearSegmentedColormap.from_list("jro", numpy.vstack((blu_values, jet_values))) |
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1 | NO CONTENT: modified file |
| @@ -399,70 +399,7 class ParamReader(ProcessingUnit): | |||
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399 | 399 | self.listData = listdata |
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400 | 400 | return |
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401 | 401 | |
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402 | def __setDataArray(self, dataset, shapes): | |
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403 | 402 | |
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404 | nDims = shapes[0] | |
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405 | ||
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406 | nDim2 = shapes[1] #Dimension 0 | |
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407 | ||
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408 | nDim1 = shapes[2] #Dimension 1, number of Points or Parameters | |
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409 | ||
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410 | nDim0 = shapes[3] #Dimension 2, number of samples or ranges | |
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411 | ||
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412 | mode = shapes[4] #Mode of storing | |
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413 | ||
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414 | blockList = self.blockList | |
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415 | ||
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416 | blocksPerFile = self.blocksPerFile | |
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417 | ||
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418 | #Depending on what mode the data was stored | |
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419 | if mode == 0: #Divided in channels | |
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420 | arrayData = dataset.value.astype(numpy.float)[0][blockList] | |
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421 | if mode == 1: #Divided in parameter | |
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422 | strds = 'table' | |
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423 | nDatas = nDim1 | |
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424 | newShapes = (blocksPerFile,nDim2,nDim0) | |
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425 | elif mode==2: #Concatenated in a table | |
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426 | strds = 'table0' | |
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427 | arrayData = dataset[strds].value | |
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428 | #Selecting part of the dataset | |
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429 | utctime = arrayData[:,0] | |
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430 | u, indices = numpy.unique(utctime, return_index=True) | |
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431 | ||
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432 | if blockList.size != indices.size: | |
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433 | indMin = indices[blockList[0]] | |
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434 | if blockList[-1] + 1 >= indices.size: | |
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435 | arrayData = arrayData[indMin:,:] | |
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436 | else: | |
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437 | indMax = indices[blockList[-1] + 1] | |
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438 | arrayData = arrayData[indMin:indMax,:] | |
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439 | return arrayData | |
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440 | ||
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441 | #------- One dimension --------------- | |
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442 | if nDims == 0: | |
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443 | arrayData = dataset.value.astype(numpy.float)[0][blockList] | |
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444 | ||
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445 | #------- Two dimensions ----------- | |
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446 | elif nDims == 2: | |
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447 | arrayData = numpy.zeros((blocksPerFile,nDim1,nDim0)) | |
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448 | newShapes = (blocksPerFile,nDim0) | |
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449 | nDatas = nDim1 | |
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450 | ||
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451 | for i in range(nDatas): | |
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452 | data = dataset[strds + str(i)].value | |
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453 | arrayData[:,i,:] = data[blockList,:] | |
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454 | ||
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455 | #------- Three dimensions --------- | |
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456 | else: | |
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457 | arrayData = numpy.zeros((blocksPerFile,nDim2,nDim1,nDim0)) | |
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458 | for i in range(nDatas): | |
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459 | ||
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460 | data = dataset[strds + str(i)].value | |
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461 | ||
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462 | for b in range(blockList.size): | |
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463 | arrayData[b,:,i,:] = data[:,:,blockList[b]] | |
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464 | ||
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465 | return arrayData | |
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466 | 403 | |
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467 | 404 | def __setDataOut(self): |
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468 | 405 | listMeta = self.listMeta |
| @@ -571,7 +571,9 class SpectraWriter(JRODataWriter, Operation): | |||
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571 | 571 | |
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572 | 572 | if self.dataOut.flagDiscontinuousBlock: |
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573 | 573 | self.data_spc.fill(0) |
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574 | if self.dataOut.data_cspc is not None: | |
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574 | 575 | self.data_cspc.fill(0) |
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576 | if self.dataOut.data_dc is not None: | |
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575 | 577 | self.data_dc.fill(0) |
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576 | 578 | self.setNextFile() |
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577 | 579 | |
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1 | NO CONTENT: modified file |
| @@ -33,6 +33,8 class SpectraHeisProc(ProcessingUnit): | |||
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33 | 33 | self.dataOut.nCode = self.dataIn.nCode |
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34 | 34 | self.dataOut.code = self.dataIn.code |
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35 | 35 | # self.dataOut.nProfiles = 1 |
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36 | self.dataOut.ippFactor = 1 | |
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37 | self.dataOut.noise_estimation = None | |
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36 | 38 | # self.dataOut.nProfiles = self.dataOut.nFFTPoints |
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37 | 39 | self.dataOut.nFFTPoints = self.dataIn.nHeights |
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38 | 40 | # self.dataOut.channelIndexList = self.dataIn.channelIndexList |
| @@ -5,4 +5,4 $Id: Processor.py 1 2012-11-12 18:56:07Z murco $ | |||
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5 | 5 | ''' |
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6 | 6 | |
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7 | 7 | from jroutils_ftp import * |
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8 |
from jroutils_publish import |
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8 | from jroutils_publish import * | |
| @@ -9,11 +9,11 import paho.mqtt.client as mqtt | |||
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9 | 9 | |
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10 | 10 | from schainpy.model.proc.jroproc_base import Operation |
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11 | 11 | |
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12 | ||
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13 | 12 | class PrettyFloat(float): |
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14 | 13 | def __repr__(self): |
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15 | 14 | return '%.2f' % self |
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16 | 15 | |
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16 | ||
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17 | 17 | def pretty_floats(obj): |
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18 | 18 | if isinstance(obj, float): |
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19 | 19 | return PrettyFloat(obj) |
| @@ -23,80 +23,130 def pretty_floats(obj): | |||
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23 | 23 |
return map(pretty_floats, obj) |
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24 | 24 | return obj |
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25 | 25 | |
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26 | ||
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26 | 27 | class PublishData(Operation): |
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28 | """Clase publish.""" | |
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27 | 29 | |
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28 | 30 | __MAXNUMX = 80 |
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29 | 31 | __MAXNUMY = 80 |
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30 | 32 | |
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31 | 33 | def __init__(self): |
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32 | ||
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34 | """Inicio.""" | |
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33 | 35 | Operation.__init__(self) |
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34 | ||
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35 | 36 | self.isConfig = False |
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36 | 37 |
self.client = None |
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37 | 38 | |
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38 | @staticmethod | |
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39 | def on_disconnect(client, userdata, rc): | |
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39 | def on_disconnect(self, client, userdata, rc): | |
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40 | 40 | if rc != 0: |
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41 | 41 |
print("Unexpected disconnection.") |
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42 | self.connect() | |
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42 | 43 | |
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43 | def setup(self, host, port=1883, username=None, password=None, **kwargs): | |
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44 | ||
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45 | self.client = mqtt.Client() | |
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44 | def connect(self): | |
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45 | print 'trying to connect' | |
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46 | 46 | try: |
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47 | self.client.connect(host=host, port=port, keepalive=60*10, bind_address='') | |
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47 | self.client.connect( | |
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48 | host=self.host, | |
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49 | port=self.port, | |
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50 | keepalive=60*10, | |
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51 | bind_address='') | |
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52 | print "connected" | |
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53 | self.client.loop_start() | |
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54 | # self.client.publish( | |
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55 | # self.topic + 'SETUP', | |
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56 | # json.dumps(setup), | |
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57 | # retain=True | |
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58 | # ) | |
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48 | 59 | except: |
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60 | print "MQTT Conection error." | |
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49 | 61 | self.client = False |
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62 | ||
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63 | def setup(self, host, port=1883, username=None, password=None, **kwargs): | |
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64 | ||
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50 | 65 | self.topic = kwargs.get('topic', 'schain') |
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51 | 66 | self.delay = kwargs.get('delay', 0) |
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67 | self.plottype = kwargs.get('plottype', 'spectra') | |
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52 | 68 | self.host = host |
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53 | 69 | self.port = port |
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54 | 70 | self.cnt = 0 |
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55 | ||
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56 | def run(self, dataOut, host, datatype='data_spc', **kwargs): | |
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57 | ||
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58 | if not self.isConfig: | |
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59 | self.setup(host, **kwargs) | |
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60 | self.isConfig = True | |
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61 | ||
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62 | data = getattr(dataOut, datatype) | |
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63 | ||
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64 | z = data/dataOut.normFactor | |
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71 | setup = [] | |
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72 | for plot in self.plottype: | |
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73 | setup.append({ | |
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74 | 'plot': plot, | |
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75 | 'topic': self.topic + plot, | |
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76 | 'title': getattr(self, plot + '_' + 'title', False), | |
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77 | 'xlabel': getattr(self, plot + '_' + 'xlabel', False), | |
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78 | 'ylabel': getattr(self, plot + '_' + 'ylabel', False), | |
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79 | 'xrange': getattr(self, plot + '_' + 'xrange', False), | |
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80 | 'yrange': getattr(self, plot + '_' + 'yrange', False), | |
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81 | 'zrange': getattr(self, plot + '_' + 'zrange', False), | |
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82 | }) | |
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83 | self.client = mqtt.Client( | |
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84 | client_id='jc'+self.topic + 'SCHAIN', | |
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85 | clean_session=True) | |
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86 | self.client.on_disconnect = self.on_disconnect | |
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87 | self.connect() | |
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88 | ||
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89 | def publish_data(self, plottype): | |
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90 | data = getattr(self.dataOut, 'data_spc') | |
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91 | if plottype == 'spectra': | |
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92 | z = data/self.dataOut.normFactor | |
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65 | 93 | zdB = 10*numpy.log10(z) |
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66 | avg = numpy.average(z, axis=1) | |
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67 | avgdB = 10*numpy.log10(avg) | |
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68 | ||
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69 | 94 |
xlen |
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70 | ||
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71 | ||
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72 | 95 | dx = numpy.floor(xlen/self.__MAXNUMX) + 1 |
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73 | 96 | dy = numpy.floor(ylen/self.__MAXNUMY) + 1 |
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74 | ||
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75 |
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76 | AVG = [0 for i in dataOut.channelList] | |
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77 | ||
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78 | for i in dataOut.channelList: | |
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97 | Z = [0 for i in self.dataOut.channelList] | |
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98 | for i in self.dataOut.channelList: | |
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79 | 99 | Z[i] = zdB[i][::dx, ::dy].tolist() |
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80 | AVG[i] = avgdB[i][::dy].tolist() | |
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81 | ||
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82 | payload = {'timestamp':dataOut.utctime, | |
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100 | payload = { | |
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101 | 'timestamp': self.dataOut.utctime, | |
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83 | 102 |
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84 | 'data_profile':pretty_floats(AVG), | |
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85 | 'channels': ['Ch %s' % ch for ch in dataOut.channelList], | |
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86 | 'interval': dataOut.getTimeInterval(), | |
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103 | 'channels': ['Ch %s' % ch for ch in self.dataOut.channelList], | |
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104 | 'interval': self.dataOut.getTimeInterval(), | |
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105 | 'xRange': [0, 80] | |
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87 | 106 |
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88 | 107 | |
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108 | elif plottype in ('rti', 'power'): | |
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109 | z = data/self.dataOut.normFactor | |
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110 | avg = numpy.average(z, axis=1) | |
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111 | avgdB = 10*numpy.log10(avg) | |
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112 | xlen, ylen = z[0].shape | |
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113 | dy = numpy.floor(ylen/self.__MAXNUMY) + 1 | |
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114 | AVG = [0 for i in self.dataOut.channelList] | |
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115 | for i in self.dataOut.channelList: | |
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116 | AVG[i] = avgdB[i][::dy].tolist() | |
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117 | payload = { | |
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118 | 'timestamp': self.dataOut.utctime, | |
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119 | 'data': pretty_floats(AVG), | |
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120 | 'channels': ['Ch %s' % ch for ch in self.dataOut.channelList], | |
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121 | 'interval': self.dataOut.getTimeInterval(), | |
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122 | 'xRange': [0, 80] | |
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123 | } | |
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124 | elif plottype == 'noise': | |
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125 | noise = self.dataOut.getNoise()/self.dataOut.normFactor | |
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126 | noisedB = 10*numpy.log10(noise) | |
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127 | payload = { | |
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128 | 'timestamp': self.dataOut.utctime, | |
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129 | 'data': pretty_floats(noisedB.reshape(-1, 1).tolist()), | |
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130 | 'channels': ['Ch %s' % ch for ch in self.dataOut.channelList], | |
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131 | 'interval': self.dataOut.getTimeInterval(), | |
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132 | 'xRange': [0, 80] | |
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133 | } | |
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89 | 134 | |
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90 | #if self.cnt==self.interval and self.client: | |
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91 | 135 |
print 'Publishing data to {}'.format(self.host) |
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92 | self.client.publish(self.topic, json.dumps(payload), qos=0) | |
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93 | time.sleep(self.delay) | |
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94 | #self.cnt = 0 | |
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95 | #else: | |
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96 | # self.cnt += 1 | |
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136 | print '*************************' | |
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137 | self.client.publish(self.topic + plottype, json.dumps(payload), qos=0) | |
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97 | 138 | |
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98 | 139 | |
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99 | def close(self): | |
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140 | def run(self, dataOut, host, **kwargs): | |
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141 | self.dataOut = dataOut | |
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142 | if not self.isConfig: | |
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143 | self.setup(host, **kwargs) | |
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144 | self.isConfig = True | |
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100 | 145 | |
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146 | map(self.publish_data, self.plottype) | |
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147 | time.sleep(self.delay) | |
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148 | ||
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149 | def close(self): | |
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101 | 150 |
if self.client: |
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151 | self.client.loop_stop() | |
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102 | 152 | self.client.disconnect() |
| @@ -33,8 +33,12 setup(name="schainpy", | |||
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33 | 33 | include_package_data=False, |
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34 | 34 | scripts =['schainpy/gui/schainGUI', |
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35 | 35 | 'schainpy/scripts/schain'], |
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36 |
install_requires=[ |
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36 | install_requires=[ | |
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37 | 37 | "scipy >= 0.9.0", |
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38 | "h5py >= 2.0.1", | |
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38 | 39 | "matplotlib >= 1.0.0", |
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40 | "pyfits >= 2.0.0", | |
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41 | "numpy >= 1.6.0", | |
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42 | "paramiko", | |
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39 | 43 | ], |
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40 | 44 | ) No newline at end of file |
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